# note that 'readxl' is in the list of packages despite being part of tidyverse: as readxl is not a core tidyverse package, it needs to be loaded explicitly
pacman::p_load(plyr, sf, sfdep, tmap, tidyverse, readxl, kableExtra, zoo, plotly, Kendall)Take-home Exercise 2
1.0 Overview
1.1 Background
The COVID-19 vaccination in Indonesia is an ongoing mass immunisation in response to the COVID-19 pandemic in Indonesia. On 13 January 2021, the program commenced when President Joko Widodo was vaccinated at the presidential palace. In terms of total doses given, Indonesia ranks third in Asia and fifth in the world.
According to wikipedia, as of 5 February 2023 at 18:00 WIB (UTC+7), 204,266,655 people had received the first dose of the vaccine and 175,131,893 people had been fully vaccinated; 69,597,474 of them had been inoculated with the booster or the third dose, while 1,585,164 had received the fourth dose. Jakarta has the highest percentage of population fully vaccinated with 103.46%, followed by Bali and Special Region of Yogyakarta with 85.45% and 83.02% respectively.
Despite its compactness, the cumulative vaccination rate are not evenly distributed within DKI Jakarta. The question is where are the sub-districts with relatively higher number of vaccination rate and how they changed over time.
1.2 Objectives
Exploratory Spatial Data Analysis (ESDA) hold tremendous potential to address complex problems facing society. In this study, you are tasked to apply appropriate Local Indicators of Spatial Association (LISA) and Emerging Hot Spot Analysis (EHSA) to uncover the spatio-temporal trends of COVID-19 vaccination in DKI Jakarta.
2.0 Installing R Packages & Importing Data
2.1 Packages Used
sf –> a relatively new R package specially designed to import, manage and process vector-based geospatial data in R.
tidyverse –> used for wrangling and visualisations
funModelling –> for EDA and visualisations
spatstat –> has a wide range of useful functions for point pattern analysis. Used to perform 1st- and 2nd-order spatial point patterns analysis and derive kernel density estimation (KDE) layer.
raster –> reads, writes, manipulates, analyses and model of gridded spatial data (i.e. raster)
tmap –> provides functions for plotting cartographic quality static point patterns maps or interactive maps by using leaflet API.
maptools –> provides a set of tools for manipulating geographic data. To convert Spatial objects into ppp format of spatstat.
sfdep –> simple feature spatial dependence
plyr –> to load function ldply()
zoo –> to check if space-time cube exists
plotly –> to generate interactive maps for Man-Kendall Test Graphs
Kendall –> to calculate values for Man-Kendall Test
2.2 Installing R Packages
3.0 Importing Data
3.1 Importing Geospatial Data
st_read() will make the data into sf format
jakarta <- st_read(dsn = "data/geospatial",
layer = "BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA")Reading layer `BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA' from data source
`/Users/yashica/Desktop/xtc0/IS415-GAA/Take-home_Ex/Take_home_Ex02/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 269 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 106.3831 ymin: -6.370815 xmax: 106.9728 ymax: -5.184322
Geodetic CRS: WGS 843.1.1 Pre-processing Geospatial Data
3.1.1.1 Invalid Geometries
Let’s check for invalid geometries!
length(which(st_is_valid(jakarta) == FALSE))[1] 03.1.1.2 Missing Values
Now, let us check for missing values!
#the rowSums(is.na(jakarta))!=0 checks every row if there are NA values, returning TRUE or FALSE
# the jakarta 'wrapper' prints said rows that contain NA values
jakarta[rowSums(is.na(jakarta))!=0,]Simple feature collection with 2 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 106.8412 ymin: -6.154036 xmax: 106.8612 ymax: -6.144973
Geodetic CRS: WGS 84
OBJECT_ID KODE_DESA DESA KODE PROVINSI KAB_KOTA KECAMATAN
243 25645 31888888 DANAU SUNTER 318888 DKI JAKARTA <NA> <NA>
244 25646 31888888 DANAU SUNTER DLL 318888 DKI JAKARTA <NA> <NA>
DESA_KELUR JUMLAH_PEN JUMLAH_KK LUAS_WILAY KEPADATAN PERPINDAHA JUMLAH_MEN
243 <NA> 0 0 0 0 0 0
244 <NA> 0 0 0 0 0 0
PERUBAHAN WAJIB_KTP SILAM KRISTEN KHATOLIK HINDU BUDHA KONGHUCU KEPERCAYAA
243 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0
PRIA WANITA BELUM_KAWI KAWIN CERAI_HIDU CERAI_MATI U0 U5 U10 U15 U20 U25
243 0 0 0 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0 0 0 0
U30 U35 U40 U45 U50 U55 U60 U65 U70 U75 TIDAK_BELU BELUM_TAMA TAMAT_SD SLTP
243 0 0 0 0 0 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0 0 0 0 0 0
SLTA DIPLOMA_I DIPLOMA_II DIPLOMA_IV STRATA_II STRATA_III BELUM_TIDA
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
APARATUR_P TENAGA_PEN WIRASWASTA PERTANIAN NELAYAN AGAMA_DAN PELAJAR_MA
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
TENAGA_KES PENSIUNAN LAINNYA GENERATED KODE_DES_1 BELUM_ MENGUR_ PELAJAR_
243 0 0 0 <NA> <NA> 0 0 0
244 0 0 0 <NA> <NA> 0 0 0
PENSIUNA_1 PEGAWAI_ TENTARA KEPOLISIAN PERDAG_ PETANI PETERN_ NELAYAN_1
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
INDUSTR_ KONSTR_ TRANSP_ KARYAW_ KARYAW1 KARYAW1_1 KARYAW1_12 BURUH BURUH_
243 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0
BURUH1 BURUH1_1 PEMBANT_ TUKANG TUKANG_1 TUKANG_12 TUKANG__13 TUKANG__14
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
TUKANG__15 TUKANG__16 TUKANG__17 PENATA PENATA_ PENATA1_1 MEKANIK SENIMAN_
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
TABIB PARAJI_ PERANCA_ PENTER_ IMAM_M PENDETA PASTOR WARTAWAN USTADZ JURU_M
243 0 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0 0
PROMOT ANGGOTA_ ANGGOTA1 ANGGOTA1_1 PRESIDEN WAKIL_PRES ANGGOTA1_2
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
ANGGOTA1_3 DUTA_B GUBERNUR WAKIL_GUBE BUPATI WAKIL_BUPA WALIKOTA WAKIL_WALI
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
ANGGOTA1_4 ANGGOTA1_5 DOSEN GURU PILOT PENGACARA_ NOTARIS ARSITEK AKUNTA_
243 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0
KONSUL_ DOKTER BIDAN PERAWAT APOTEK_ PSIKIATER PENYIA_ PENYIA1 PELAUT
243 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0
PENELITI SOPIR PIALAN PARANORMAL PEDAGA_ PERANG_ KEPALA_ BIARAW_ WIRASWAST_
243 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0
LAINNYA_12 LUAS_DESA KODE_DES_3 DESA_KEL_1 KODE_12
243 0 0 <NA> <NA> 0
244 0 0 <NA> <NA> 0
geometry
243 MULTIPOLYGON (((106.8612 -6...
244 MULTIPOLYGON (((106.8504 -6...2 rows have NA values! DESA_KEUR, a sub_district contains NA values so we should remove the rows for that column. It could affect our analysis.
jakarta <- na.omit(jakarta,c("DESA_KELUR"))We can see that the sf object is in geographic coordinate system. We would need to convert this to a projected coordinate system so that we can do distance-related measurements.
Looking at the Geodetic CRS, we see that the CRS code WGS 84 is being used. We should transform this sf object by using DGN 95 instead.
jakarta <- st_transform(jakarta,
crs = 23837)
jakartaSimple feature collection with 267 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -814909.7 ymin: 787584.9 xmax: -747172.2 ymax: 919446.7
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
First 10 features:
OBJECT_ID KODE_DESA DESA KODE PROVINSI KAB_KOTA
1 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT
2 25478 3173031007 GLODOK 317303 DKI JAKARTA JAKARTA BARAT
3 25397 3171031003 HARAPAN MULIA 317103 DKI JAKARTA JAKARTA PUSAT
4 25400 3171031006 CEMPAKA BARU 317103 DKI JAKARTA JAKARTA PUSAT
5 25378 3101011001 PULAU PANGGANG 310101 DKI JAKARTA KEPULAUAN SERIBU
6 25379 3101011002 PULAU KELAPA 310101 DKI JAKARTA KEPULAUAN SERIBU
7 25390 3171021001 PASAR BARU 317102 DKI JAKARTA JAKARTA PUSAT
8 25382 3101021002 PULAU TIDUNG 310102 DKI JAKARTA KEPULAUAN SERIBU
9 25391 3171021002 KARANG ANYAR 317102 DKI JAKARTA JAKARTA PUSAT
10 25394 3171021005 MANGGA DUA SELATAN 317102 DKI JAKARTA JAKARTA PUSAT
KECAMATAN DESA_KELUR JUMLAH_PEN JUMLAH_KK LUAS_WILAY
1 TAMAN SARI KEAGUNGAN 21609 7255 0.36
2 TAMAN SARI GLODOK 9069 3273 0.37
3 KEMAYORAN HARAPAN MULIA 29085 9217 0.53
4 KEMAYORAN CEMPAKA BARU 41913 13766 0.97
5 KEPULAUAN SERIBU UTARA PULAU PANGGANG 6947 2026 0.93
6 KEPULAUAN SERIBU UTARA PULAU KELAPA 7059 2056 0.95
7 SAWAH BESAR PASAR BARU 15793 5599 1.76
8 KEPULAUAN SERIBU SELATAN. PULAU TIDUNG 5891 1658 1.14
9 SAWAH BESAR KARANG ANYAR 33383 11276 0.47
10 SAWAH BESAR MANGGA DUA SELATAN 35906 12817 1.31
KEPADATAN PERPINDAHA JUMLAH_MEN PERUBAHAN WAJIB_KTP SILAM KRISTEN KHATOLIK
1 60504 102 68 20464 16027 15735 2042 927
2 24527 25 52 8724 7375 1842 2041 1460
3 54465 131 104 27497 20926 26328 1710 531
4 42993 170 151 38323 30264 36813 3392 1082
5 7497 17 14 6853 4775 6941 6 0
6 7401 26 32 6993 4812 7057 0 0
7 8971 58 36 15006 12559 7401 3696 1602
8 5156 13 10 5807 3989 5891 0 0
9 71628 113 60 31014 24784 23057 4058 2100
10 27463 178 92 33021 26730 23424 5130 2575
HINDU BUDHA KONGHUCU KEPERCAYAA PRIA WANITA BELUM_KAWI KAWIN CERAI_HIDU
1 15 2888 2 0 11049 10560 10193 10652 255
2 9 3716 1 0 4404 4665 4240 4364 136
3 42 469 5 0 14696 14389 14022 13450 430
4 127 495 1 3 21063 20850 20336 19487 523
5 0 0 0 0 3547 3400 3366 3224 101
6 0 2 0 0 3551 3508 3334 3404 80
7 622 2462 10 0 7833 7960 7578 7321 217
8 0 0 0 0 2954 2937 2836 2791 44
9 25 4134 9 0 16887 16496 15860 15945 381
10 27 4740 10 0 18338 17568 17239 17198 476
CERAI_MATI U0 U5 U10 U15 U20 U25 U30 U35 U40 U45 U50 U55 U60
1 509 1572 1751 1703 1493 1542 1665 1819 1932 1828 1600 1408 1146 836
2 329 438 545 524 521 543 628 691 782 675 607 619 602 614
3 1183 2232 2515 2461 2318 2113 2170 2363 2595 2371 2250 1779 1379 1054
4 1567 3092 3657 3501 3486 3098 3024 3188 3662 3507 3391 2696 1909 1397
5 256 640 645 620 669 619 639 564 590 480 421 346 252 197
6 241 648 684 630 671 609 582 592 572 486 457 369 318 211
7 677 802 995 1016 1106 1081 1002 1236 1422 1200 1163 1099 979 880
8 220 585 588 513 548 491 523 478 504 397 365 288 235 162
9 1197 2220 2687 2653 2549 2313 2446 2735 3034 2689 2470 2129 1843 1386
10 993 2399 2953 2754 2666 2515 2725 3122 3385 3037 2597 2282 1930 1394
U65 U70 U75 TIDAK_BELU BELUM_TAMA TAMAT_SD SLTP SLTA DIPLOMA_I DIPLOMA_II
1 587 312 415 3426 1964 2265 3660 8463 81 428
2 555 311 414 1200 481 655 1414 3734 23 273
3 654 411 420 4935 2610 2346 3167 12172 84 1121
4 970 631 704 7328 3763 2950 5138 16320 179 1718
5 122 69 74 1306 730 1518 906 2040 22 101
6 114 55 61 1318 676 2054 1357 1380 15 59
7 747 488 577 2121 1278 1169 2236 5993 43 573
8 111 65 38 973 732 1266 852 1570 36 97
9 958 554 717 5075 3241 4424 5858 12448 85 604
10 932 573 642 6089 3184 3620 6159 14080 83 740
DIPLOMA_IV STRATA_II STRATA_III BELUM_TIDA APARATUR_P TENAGA_PEN WIRASWASTA
1 1244 74 4 3927 81 70 8974
2 1241 46 2 1388 10 43 3832
3 2477 166 7 5335 513 288 10662
4 4181 315 21 8105 931 402 14925
5 314 10 0 1788 246 130 788
6 191 8 1 1627 75 93 728
7 2199 168 13 2676 156 81 6145
8 357 8 0 1129 160 123 819
9 1582 63 3 5985 132 123 12968
10 1850 92 9 6820 79 73 14714
PERTANIAN NELAYAN AGAMA_DAN PELAJAR_MA TENAGA_KES PENSIUNAN LAINNYA
1 1 0 6 4018 28 57 4447
2 0 0 6 1701 29 50 2010
3 1 2 5 6214 80 276 5709
4 3 0 40 9068 142 498 7799
5 2 960 0 1342 34 20 1637
6 2 1126 0 1576 26 7 1799
7 1 1 49 3135 60 59 3430
8 3 761 2 1501 11 14 1368
9 2 1 10 6823 48 56 7235
10 5 2 11 6866 55 75 7206
GENERATED KODE_DES_1 BELUM_ MENGUR_ PELAJAR_ PENSIUNA_1 PEGAWAI_ TENTARA
1 30 Juni 2019 3173031006 3099 4447 3254 80 48 4
2 30 Juni 2019 3173031007 1032 2026 1506 65 5 0
3 30 Juni 2019 3171031003 4830 5692 6429 322 366 41
4 30 Juni 2019 3171031006 7355 7692 8957 603 612 57
5 30 Juni 2019 3101011001 1663 1576 1476 24 223 3
6 30 Juni 2019 3101011002 1704 1731 1469 8 72 0
7 30 Juni 2019 3171021001 2390 3500 3185 70 65 74
8 30 Juni 2019 3101021002 1213 1323 1223 20 143 1
9 30 Juni 2019 3171021002 5330 7306 6993 75 73 20
10 30 Juni 2019 3171021005 5605 7042 6858 97 48 12
KEPOLISIAN PERDAG_ PETANI PETERN_ NELAYAN_1 INDUSTR_ KONSTR_ TRANSP_ KARYAW_
1 10 31 0 0 1 7 3 2 6735
2 1 5 0 0 0 3 0 0 3034
3 16 1 1 0 1 4 2 7 7347
4 42 3 2 0 0 3 6 4 10185
5 11 6 0 0 914 1 3 0 237
6 8 1 1 0 1071 3 8 0 264
7 2 2 1 0 0 0 1 0 4319
8 9 4 0 1 794 0 6 0 184
9 17 3 1 0 0 1 1 0 9405
10 7 1 1 0 1 7 5 3 10844
KARYAW1 KARYAW1_1 KARYAW1_12 BURUH BURUH_ BURUH1 BURUH1_1 PEMBANT_ TUKANG
1 9 0 23 515 1 0 0 1 0
2 2 0 4 155 0 0 0 1 0
3 74 5 25 971 0 1 0 4 0
4 231 15 35 636 0 0 0 1 0
5 4 0 141 63 2 1 0 1 0
6 0 0 50 218 1 25 0 0 0
7 16 0 16 265 1 0 0 7 0
8 1 1 157 55 0 2 0 0 0
9 13 0 6 1085 0 0 0 5 0
10 10 1 9 652 1 1 0 1 1
TUKANG_1 TUKANG_12 TUKANG__13 TUKANG__14 TUKANG__15 TUKANG__16 TUKANG__17
1 1 0 1 0 1 7 1
2 0 0 1 0 0 4 0
3 0 0 0 0 0 10 0
4 0 0 1 0 1 14 0
5 0 0 0 0 0 0 0
6 0 0 1 0 0 0 0
7 0 0 0 0 0 2 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 7 0
10 0 0 1 0 1 8 1
PENATA PENATA_ PENATA1_1 MEKANIK SENIMAN_ TABIB PARAJI_ PERANCA_ PENTER_
1 0 0 0 11 4 1 0 0 1
2 0 0 0 1 0 0 0 0 0
3 0 0 0 10 12 0 0 0 0
4 0 0 1 8 28 0 0 0 0
5 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0 0
7 1 0 0 4 2 1 0 0 1
8 0 0 0 0 0 0 0 0 0
9 0 0 0 7 3 0 0 0 0
10 0 0 1 8 4 1 0 0 0
IMAM_M PENDETA PASTOR WARTAWAN USTADZ JURU_M PROMOT ANGGOTA_ ANGGOTA1
1 0 2 0 7 6 0 0 0 0
2 0 4 1 1 1 0 0 1 0
3 0 5 0 16 1 0 0 0 0
4 0 33 1 27 5 0 0 0 0
5 0 0 0 0 1 0 0 0 0
6 0 0 0 0 0 0 0 0 0
7 0 20 8 4 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0
9 0 10 0 8 0 0 0 1 0
10 0 8 0 6 1 0 0 0 0
ANGGOTA1_1 PRESIDEN WAKIL_PRES ANGGOTA1_2 ANGGOTA1_3 DUTA_B GUBERNUR
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
WAKIL_GUBE BUPATI WAKIL_BUPA WALIKOTA WAKIL_WALI ANGGOTA1_4 ANGGOTA1_5 DOSEN
1 0 0 0 0 0 0 0 3
2 0 0 0 0 0 0 0 2
3 0 0 0 0 0 0 0 23
4 0 0 0 0 0 0 0 36
5 0 0 0 0 0 0 0 1
6 0 0 0 0 0 0 0 2
7 0 0 0 0 0 0 0 11
8 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 3
10 0 0 0 0 0 0 0 5
GURU PILOT PENGACARA_ NOTARIS ARSITEK AKUNTA_ KONSUL_ DOKTER BIDAN PERAWAT
1 72 1 4 0 1 1 1 16 3 7
2 40 0 1 0 0 0 0 32 1 0
3 272 2 8 3 2 0 2 35 9 25
4 378 3 22 5 3 0 11 68 18 44
5 118 0 0 0 0 0 0 0 12 12
6 72 0 0 0 0 0 0 1 8 10
7 69 0 5 4 2 0 4 63 1 3
8 116 0 0 0 0 0 0 0 3 6
9 126 0 5 0 0 0 0 27 3 12
10 71 0 4 0 0 0 0 32 3 20
APOTEK_ PSIKIATER PENYIA_ PENYIA1 PELAUT PENELITI SOPIR PIALAN PARANORMAL
1 0 0 0 0 0 0 65 0 0
2 0 0 0 0 0 1 3 0 0
3 2 1 0 0 6 0 94 0 0
4 3 0 0 0 16 0 123 0 0
5 1 0 0 0 1 0 0 0 0
6 0 0 0 0 1 0 1 0 0
7 0 0 0 0 0 1 61 0 0
8 0 0 0 0 14 0 0 0 0
9 1 0 0 0 2 0 76 0 0
10 2 0 0 0 4 0 79 0 1
PEDAGA_ PERANG_ KEPALA_ BIARAW_ WIRASWAST_ LAINNYA_12 LUAS_DESA KODE_DES_3
1 379 0 0 0 1370 94 25476 3173031006
2 126 0 0 1 611 57 25477 3173031007
3 321 0 0 0 1723 82 25396 3171031003
4 562 0 0 0 3099 122 25399 3171031006
5 11 0 0 0 131 12 25377 3101011001
6 10 0 0 0 119 10 25378 3101011002
7 412 0 0 22 1128 41 25389 3171021001
8 15 0 0 0 259 6 25381 3101021002
9 202 0 0 3 2321 89 25390 3171021002
10 225 0 0 0 2677 158 25393 3171021005
DESA_KEL_1 KODE_12 geometry
1 KEAGUNGAN 317303 MULTIPOLYGON (((-764674.9 8...
2 GLODOK 317303 MULTIPOLYGON (((-764859.9 8...
3 HARAPAN MULIA 317103 MULTIPOLYGON (((-760021.3 8...
4 CEMPAKA BARU 317103 MULTIPOLYGON (((-759415 810...
5 PULAU PANGGANG 310101 MULTIPOLYGON (((-796698.3 8...
6 PULAU KELAPA 310101 MULTIPOLYGON (((-795419 870...
7 PASAR BARU 317102 MULTIPOLYGON (((-762362.7 8...
8 PULAU TIDUNG 310102 MULTIPOLYGON (((-801734.4 8...
9 KARANG ANYAR 317102 MULTIPOLYGON (((-762950.3 8...
10 MANGGA DUA SELATAN 317102 MULTIPOLYGON (((-762822.4 8...Now, the output changed from Geodetic CRS: WGS 84 to Projected CRS: DGN95. This shows jakarta has now been successfully converted from geographic coordinate system to projected coordinate system!
Let’s check if the CRS has been properly assigned!
st_crs(jakarta)Coordinate Reference System:
User input: EPSG:23837
wkt:
PROJCRS["DGN95 / Indonesia TM-3 zone 50.1",
BASEGEOGCRS["DGN95",
DATUM["Datum Geodesi Nasional 1995",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4755]],
CONVERSION["Indonesia TM-3 zone 50.1",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",0,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",115.5,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9999,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",200000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",1500000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["easting (X)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["northing (Y)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre."],
AREA["Indonesia - onshore between 114°E and 117°E."],
BBOX[-9.15,114,4.37,117.01]],
ID["EPSG",23837]]We can see that the current dataset has 269 rows and 162 columns! However, for this analysis, we only require the first 9 columns. As such, we should only select those 9 columns, with the 9th field being JUMLAH_PEN (Total Population).
jakartaNGA <- jakarta[, 0:9]3.1.1.3 Removal of Outer Islands
We only want jakartaNGA to contain information about the mainland and exclude outer islands. To do so, let’s first visualise the data…
# plots the geometry only
plot(st_geometry(jakartaNGA))
As we can see, jakartaNGA includes both the mainland and the outer islands. As per the assignment requirements, since the outer islands aren’t relevant to our analysis, we’ll have to remove them.
There are a few ways we can do this - but the best way would be to explore our data. When looking at the jakartaNGA data, we’re drawn to to KAB_KOTA (City), KECAMATAN (District) and DESA_KELUR (Village). Of these three, KAB_KOTA would be the logical choice: it’s the coarsest-grained level of distinction. Let’s check for its unique values:
unique(jakartaNGA$"KAB_KOTA")[1] "JAKARTA BARAT" "JAKARTA PUSAT" "KEPULAUAN SERIBU" "JAKARTA UTARA"
[5] "JAKARTA TIMUR" "JAKARTA SELATAN" As we can see, all cities within Jakarta have a JAKARTA prefix, while KEPULAUAN SERIBU (translated to ‘Thousand Islands’) refers to the outer islands. Just to check, we can visualise KAB_KOTA:
# with jakartaNGA as the input data (setting the 'base')
# draw the KAB_KOTA (city) polygons
# essentially shades the map according to the city divisions
tm_shape(jakartaNGA) +
tm_polygons("KAB_KOTA")
Now that we know how to identify the outer islands, it’s time to remove them! To only get the mainland, for the column KAB_KOTA, we’d have to exclude the value “KEPULAUAN SERIBU”.
jakartaNGA <- jakartaNGA %>% filter(jakartaNGA$KAB_KOTA != "KEPULAUAN SERIBU")
jakartaNGASimple feature collection with 261 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -779259.9 ymin: 787584.9 xmax: -747172.2 ymax: 819039
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
First 10 features:
OBJECT_ID KODE_DESA DESA KODE PROVINSI KAB_KOTA
1 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT
2 25478 3173031007 GLODOK 317303 DKI JAKARTA JAKARTA BARAT
3 25397 3171031003 HARAPAN MULIA 317103 DKI JAKARTA JAKARTA PUSAT
4 25400 3171031006 CEMPAKA BARU 317103 DKI JAKARTA JAKARTA PUSAT
5 25390 3171021001 PASAR BARU 317102 DKI JAKARTA JAKARTA PUSAT
6 25391 3171021002 KARANG ANYAR 317102 DKI JAKARTA JAKARTA PUSAT
7 25394 3171021005 MANGGA DUA SELATAN 317102 DKI JAKARTA JAKARTA PUSAT
8 25386 3171011003 PETOJO UTARA 317101 DKI JAKARTA JAKARTA PUSAT
9 25403 3171041001 SENEN 317104 DKI JAKARTA JAKARTA PUSAT
10 25408 3171041006 BUNGUR 317104 DKI JAKARTA JAKARTA PUSAT
KECAMATAN DESA_KELUR JUMLAH_PEN geometry
1 TAMAN SARI KEAGUNGAN 21609 MULTIPOLYGON (((-764674.9 8...
2 TAMAN SARI GLODOK 9069 MULTIPOLYGON (((-764859.9 8...
3 KEMAYORAN HARAPAN MULIA 29085 MULTIPOLYGON (((-760021.3 8...
4 KEMAYORAN CEMPAKA BARU 41913 MULTIPOLYGON (((-759415 810...
5 SAWAH BESAR PASAR BARU 15793 MULTIPOLYGON (((-762362.7 8...
6 SAWAH BESAR KARANG ANYAR 33383 MULTIPOLYGON (((-762950.3 8...
7 SAWAH BESAR MANGGA DUA SELATAN 35906 MULTIPOLYGON (((-762822.4 8...
8 GAMBIR PETOJO UTARA 21828 MULTIPOLYGON (((-764144 810...
9 SENEN SENEN 8643 MULTIPOLYGON (((-761646.2 8...
10 SENEN BUNGUR 23001 MULTIPOLYGON (((-761014.1 8...3.1.1.4 Renaming Columns in jakartaNGA
What do the remaining columns mean in English?
- “KODE_DESA” == village code
- “DESA” == village
- “KODE” == code
- “PROVINSI” == province
- “KAB_KOTA” == city district
- “KECEMATAN” == sub-district
Lastly, let’s translate the column names of jakartaNGA into English for ease of comprehension.
# with reference to: https://www.codegrepper.com/code-examples/r/rename+column+name+in+r
# renames the columns in the style New_Name = OLD_NAME
jakarta_renamed <- jakartaNGA %>%
dplyr::rename(
Object_ID=OBJECT_ID,
Village_Code=KODE_DESA,
Village=DESA,
Code=KODE,
Province=PROVINSI,
City=KAB_KOTA,
District=KECAMATAN,
Village_Code=KODE_DESA,
Sub_District=DESA_KELUR,
Total_Population=JUMLAH_PEN
)jakarta_renamedSimple feature collection with 261 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -779259.9 ymin: 787584.9 xmax: -747172.2 ymax: 819039
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
First 10 features:
Object_ID Village_Code Village Code Province City
1 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT
2 25478 3173031007 GLODOK 317303 DKI JAKARTA JAKARTA BARAT
3 25397 3171031003 HARAPAN MULIA 317103 DKI JAKARTA JAKARTA PUSAT
4 25400 3171031006 CEMPAKA BARU 317103 DKI JAKARTA JAKARTA PUSAT
5 25390 3171021001 PASAR BARU 317102 DKI JAKARTA JAKARTA PUSAT
6 25391 3171021002 KARANG ANYAR 317102 DKI JAKARTA JAKARTA PUSAT
7 25394 3171021005 MANGGA DUA SELATAN 317102 DKI JAKARTA JAKARTA PUSAT
8 25386 3171011003 PETOJO UTARA 317101 DKI JAKARTA JAKARTA PUSAT
9 25403 3171041001 SENEN 317104 DKI JAKARTA JAKARTA PUSAT
10 25408 3171041006 BUNGUR 317104 DKI JAKARTA JAKARTA PUSAT
District Sub_District Total_Population
1 TAMAN SARI KEAGUNGAN 21609
2 TAMAN SARI GLODOK 9069
3 KEMAYORAN HARAPAN MULIA 29085
4 KEMAYORAN CEMPAKA BARU 41913
5 SAWAH BESAR PASAR BARU 15793
6 SAWAH BESAR KARANG ANYAR 33383
7 SAWAH BESAR MANGGA DUA SELATAN 35906
8 GAMBIR PETOJO UTARA 21828
9 SENEN SENEN 8643
10 SENEN BUNGUR 23001
geometry
1 MULTIPOLYGON (((-764674.9 8...
2 MULTIPOLYGON (((-764859.9 8...
3 MULTIPOLYGON (((-760021.3 8...
4 MULTIPOLYGON (((-759415 810...
5 MULTIPOLYGON (((-762362.7 8...
6 MULTIPOLYGON (((-762950.3 8...
7 MULTIPOLYGON (((-762822.4 8...
8 MULTIPOLYGON (((-764144 810...
9 MULTIPOLYGON (((-761646.2 8...
10 MULTIPOLYGON (((-761014.1 8...3.1.2 EDA Geospatial
Before we move on into importing the aspatial data and into the meat of our geovisualiastion, it’s important to get a feel for the data that we’re working with, especially on the sub-district level. As such, let’s take a quick glimpse:
# reveals the data type of all fields + some values
glimpse(jakarta_renamed)Rows: 261
Columns: 10
$ Object_ID <dbl> 25477, 25478, 25397, 25400, 25390, 25391, 25394, 2538…
$ Village_Code <chr> "3173031006", "3173031007", "3171031003", "3171031006…
$ Village <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA BARU…
$ Code <dbl> 317303, 317303, 317103, 317103, 317102, 317102, 31710…
$ Province <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI JAK…
$ City <chr> "JAKARTA BARAT", "JAKARTA BARAT", "JAKARTA PUSAT", "J…
$ District <chr> "TAMAN SARI", "TAMAN SARI", "KEMAYORAN", "KEMAYORAN",…
$ Sub_District <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA BARU…
$ Total_Population <dbl> 21609, 9069, 29085, 41913, 15793, 33383, 35906, 21828…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-764674.9 8..., MULTIPOL…length(unique(jakarta_renamed$"Sub_District"))[1] 261length(unique(jakarta_renamed$"District"))[1] 42Note that there are 261 unique sub-districts, and 42 unique distrcits. The max number of categories for mapping with tmap is 30 - and even though we can adjust the max.categories in tmap_options, 261 segmented sections (and even 42 sections) on a single map is too fragmented and minute for human eyes. As such, the only level which is human-readable is the ‘City’ level.
# shades the map according to the city divisions
tm_shape(jakarta_renamed) +
tm_polygons("City")
3.2 Importing Aspatial Data
Now, let’s import the 12 .xlsx files. To do so, we’d need to install, import via the pacman function and use the read_xlsx function from tidyverse’s readxl package. After which, the read_excel() function from readxl can be used.
jul2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Juli 2021).xlsx")
glimpse(jul2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 4441501, 12333, 13875, 18…
$ `JUMLAH\r\nDOSIS 1` <dbl> 4499710, 11614, 15506, 10…
$ `JUMLAH\r\nDOSIS 2` <dbl> 1663218, 4181, 4798, 3658…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 6162928, 15795, 20304, 14…
$ `LANSIA\r\nDOSIS 1` <dbl> 502579, 1230, 2012, 865, …
$ `LANSIA\r\nDOSIS 2` <dbl> 440910, 1069, 1729, 701, …
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 943489, 2299, 3741, 1566,…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1052883, 3333, 2586, 2837…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 666009, 2158, 1374, 1761,…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1718892, 5491, 3960, 4598…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 56660, 78, 122, 174, 71, …
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 38496, 51, 84, 106, 57, 7…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 95156, 129, 206, 280, 128…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 76397, 101, 90, 215, 73, …
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 67484, 91, 82, 192, 67, 3…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 143881, 192, 172, 407, 14…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 2279398, 5506, 9012, 5408…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 446028, 789, 1519, 897, 4…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2725426, 6295, 10531, 630…
$ `REMAJA\r\nDOSIS 1` <dbl> 531793, 1366, 1684, 1261,…
$ `REMAJA\r\nDOSIS 2` <dbl> 4291, 23, 10, 1, 1, 8, 6,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 536084, 1389, 1694, 1262,…Here’s July 2021’s data with 268 records and 27 columns.
The glimpse() function can reveal to us if there’s a duplicated column. There are no duplicate columns for July 2021.
Now, let’s import the other 11 files and check for duplicate columns.
aug2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Agustus 2021).xlsx")
glimpse(aug2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 3277484, 9191, 10400, 125…
$ `JUMLAH\r\nDOSIS 1` <dbl> 5663727, 14756, 18981, 16…
$ `JUMLAH\r\nDOSIS 2` <dbl> 3412906, 8935, 10470, 776…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 9076633, 23691, 29451, 24…
$ `LANSIA\r\nDOSIS 1` <dbl> 535001, 1300, 2104, 1043,…
$ `LANSIA\r\nDOSIS 2` <dbl> 468678, 1140, 1849, 780, …
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1003679, 2440, 3953, 1823…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1393352, 4194, 3643, 4293…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1007921, 3135, 2519, 2548…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2401273, 7329, 6162, 6841…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 65340, 89, 137, 188, 80, …
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 53995, 77, 119, 163, 71, …
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 119335, 166, 256, 351, 15…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 79502, 106, 92, 229, 78, …
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 72588, 96, 83, 203, 74, 3…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 152090, 202, 175, 432, 15…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 2941837, 7385, 11033, 872…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 1377349, 3277, 4541, 3010…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 4319186, 10662, 15574, 11…
$ `REMAJA\r\nDOSIS 1` <dbl> 648695, 1682, 1972, 2090,…
$ `REMAJA\r\nDOSIS 2` <dbl> 432375, 1210, 1359, 1062,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1081070, 2892, 3331, 3152…Here’s August 2021’s data with 268 records and 27 columns.
sep2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (30 September 2021).xlsx")
glimpse(sep2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 2235772, 6688, 7581, 8708…
$ `JUMLAH\r\nDOSIS 1` <dbl> 6705439, 17259, 21800, 20…
$ `JUMLAH\r\nDOSIS 2` <dbl> 5171697, 13376, 16438, 14…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 11877136, 30635, 38238, 3…
$ `LANSIA\r\nDOSIS 1` <dbl> 587215, 1417, 2270, 1263,…
$ `LANSIA\r\nDOSIS 2` <dbl> 518944, 1263, 2033, 988, …
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1106159, 2680, 4303, 2251…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1468382, 3938, 3883, 4540…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1305200, 3454, 3356, 3903…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2773582, 7392, 7239, 8443…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 84049, 158, 173, 248, 100…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 75657, 148, 157, 229, 91,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 159706, 306, 330, 477, 19…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 112296, 140, 135, 329, 11…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 104381, 124, 125, 300, 11…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 216677, 264, 260, 629, 23…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 3677943, 9564, 12969, 114…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 2548057, 6788, 8944, 7023…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 6226000, 16352, 21913, 18…
$ `REMAJA\r\nDOSIS 1` <dbl> 775554, 2042, 2370, 2510,…
$ `REMAJA\r\nDOSIS 2` <dbl> 619458, 1599, 1823, 1969,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1395012, 3641, 4193, 4479…Here’s September 2021’s data with 268 records and 27 columns.
oct2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Oktober 2021).xlsx")
glimpse(oct2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1880524, 5991, 6557, 7586…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7060687, 17956, 22824, 21…
$ `JUMLAH\r\nDOSIS 2` <dbl> 5729001, 14504, 18185, 16…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 12789688, 32460, 41009, 3…
$ `LANSIA\r\nDOSIS 1` <dbl> 608940, 1447, 2336, 1322,…
$ `LANSIA\r\nDOSIS 2` <dbl> 543483, 1296, 2104, 1104,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1152423, 2743, 4440, 2426…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1484292, 3972, 3917, 4595…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1349105, 3555, 3465, 4072…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2833397, 7527, 7382, 8667…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 86323, 165, 175, 259, 101…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 81721, 160, 168, 245, 96,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 168044, 325, 343, 504, 19…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 113911, 140, 136, 338, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 107383, 128, 128, 310, 12…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 221294, 268, 264, 648, 24…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 3948804, 10101, 13744, 12…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 2949023, 7567, 10266, 849…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 6897827, 17668, 24010, 20…
$ `REMAJA\r\nDOSIS 1` <dbl> 818417, 2131, 2516, 2672,…
$ `REMAJA\r\nDOSIS 2` <dbl> 698286, 1798, 2054, 2220,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1516703, 3929, 4570, 4892…Here’s October 2021’s data with 268 records and 27 columns.
nov2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (30 November 2021).xlsx")
glimpse(nov2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1723821, 5527, 5986, 6802…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7217390, 18420, 23395, 22…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6172636, 15466, 19404, 18…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 13390026, 33886, 42799, 4…
$ `LANSIA\r\nDOSIS 1` <dbl> 624751, 1473, 2391, 1385,…
$ `LANSIA\r\nDOSIS 2` <dbl> 571830, 1351, 2192, 1224,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1196581, 2824, 4583, 2609…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1487961, 3980, 3926, 4614…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1369705, 3601, 3516, 4146…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2857666, 7581, 7442, 8760…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 86710, 169, 176, 259, 101…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 83506, 163, 172, 252, 98,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 170216, 332, 348, 511, 19…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 114292, 140, 135, 341, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 109221, 128, 128, 323, 12…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 223513, 268, 263, 664, 24…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4069550, 10473, 14182, 12…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3304266, 8329, 11215, 978…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 7373816, 18802, 25397, 22…
$ `REMAJA\r\nDOSIS 1` <dbl> 834126, 2185, 2585, 2733,…
$ `REMAJA\r\nDOSIS 2` <dbl> 734108, 1894, 2181, 2355,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1568234, 4079, 4766, 5088…Here’s November 2021’s data with 268 records and 27 columns.
dec2021 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Desember 2021).xlsx")
glimpse(dec2021)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1623736, 5062, 5626, 6335…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7317475, 18885, 23755, 22…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6370175, 15996, 20026, 18…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 13687650, 34881, 43781, 4…
$ `LANSIA\r\nDOSIS 1` <dbl> 634516, 1520, 2427, 1418,…
$ `LANSIA\r\nDOSIS 2` <dbl> 586624, 1375, 2247, 1294,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1221140, 2895, 4674, 2712…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1485857, 3981, 3922, 4603…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1372180, 3607, 3521, 4153…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2858037, 7588, 7443, 8756…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 86905, 169, 176, 260, 101…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 83995, 164, 174, 253, 99,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 170900, 333, 350, 513, 20…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 114612, 140, 136, 345, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 110119, 128, 129, 327, 12…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 224731, 268, 265, 672, 24…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4150113, 10841, 14450, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3467714, 8782, 11715, 104…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 7617827, 19623, 26165, 23…
$ `REMAJA\r\nDOSIS 1` <dbl> 845472, 2234, 2644, 2783,…
$ `REMAJA\r\nDOSIS 2` <dbl> 749543, 1940, 2240, 2401,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1595015, 4174, 4884, 5184…Here’s December 2021’s data with 268 records and 27 columns.
jan2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Januari 2022).xlsx")
glimpse(jan2022)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1538221, 4647, 5388, 5967…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7402990, 19300, 23993, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6516678, 16477, 20463, 19…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 13919668, 35777, 44456, 4…
$ `LANSIA\r\nDOSIS 1` <dbl> 644280, 1564, 2459, 1446,…
$ `LANSIA\r\nDOSIS 2` <dbl> 598309, 1399, 2291, 1327,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1242589, 2963, 4750, 2773…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1478564, 3971, 3900, 4592…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1369268, 3604, 3506, 4158…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2847832, 7575, 7406, 8750…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88073, 177, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 85942, 171, 179, 260, 99,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 174015, 348, 357, 522, 20…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115123, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 111364, 130, 130, 331, 12…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 226487, 270, 265, 679, 24…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4215232, 11158, 14620, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3579348, 9173, 12024, 109…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 7794580, 20331, 26644, 24…
$ `REMAJA\r\nDOSIS 1` <dbl> 861718, 2290, 2701, 2840,…
$ `REMAJA\r\nDOSIS 2` <dbl> 772447, 2000, 2333, 2488,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1634165, 4290, 5034, 5328…Here’s January 2022’s data with 268 records and 27 columns.
feb2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (27 Februari 2022).xlsx")
glimpse(feb2022)Rows: 268
Columns: 27
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1517196, 4592, 5319, 5903…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7424015, 19355, 24062, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6590380, 16687, 20738, 19…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 14014395, 36042, 44800, 4…
$ `LANSIA\r\nDOSIS 1` <dbl> 646481, 1567, 2465, 1451,…
$ `LANSIA\r\nDOSIS 2` <dbl> 604751, 1418, 2336, 1348,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1251232, 2985, 4801, 2799…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1478545, 3971, 3899, 4590…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1371190, 3614, 3512, 4161…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 2849735, 7585, 7411, 8751…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88088, 178, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 86046, 171, 179, 260, 99,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 174134, 349, 357, 522, 20…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115186, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 111623, 130, 130, 331, 12…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 226809, 270, 265, 679, 24…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4232389, 11200, 14670, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3638187, 9327, 12227, 111…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 7870576, 20527, 26897, 24…
$ `REMAJA\r\nDOSIS 1` <dbl> 863326, 2299, 2715, 2845,…
$ `REMAJA\r\nDOSIS 2` <dbl> 778583, 2027, 2354, 2512,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1641909, 4326, 5069, 5357…Here’s February 2022’s data with 268 records and 27 columns.
mar2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Maret 2022).xlsx")
glimpse(mar2022)Rows: 268
Columns: 34
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1482471, 4522, 5186, 5780…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7458740, 19425, 24195, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6682911, 16909, 21000, 20…
$ `JUMLAH\r\nDOSIS 3` <dbl> 1836511, 3934, 6122, 4124…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 15978162, 40268, 51317, 4…
$ `LANSIA\r\nDOSIS 1` <dbl> 649601, 1574, 2475, 1457,…
$ `LANSIA\r\nDOSIS 2` <dbl> 610754, 1433, 2350, 1366,…
$ `LANSIA\r\nDOSIS 3` <dbl> 610754, 1433, 2350, 1366,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1533150, 3545, 6052, 3283…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1481237, 3980, 3910, 4604…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1375686, 3634, 3523, 4175…
$ `PELAYAN PUBLIK\r\nDOSIS 3` <dbl> 200536, 579, 660, 453, 24…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 3057459, 8193, 8093, 9232…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88150, 178, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 86122, 173, 179, 260, 99,…
$ `GOTONG ROYONG\r\nDOSIS 3` <dbl> 19460, 22, 53, 57, 19, 41…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 193732, 373, 410, 579, 22…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115527, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 112027, 130, 130, 331, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 3` <dbl> 84640, 103, 94, 239, 83, …
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 312194, 373, 359, 918, 32…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4258776, 11250, 14773, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3715052, 9502, 12436, 114…
$ `TAHAPAN 3\r\nDOSIS 3` <dbl> 1248211, 2671, 4048, 2891…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 9222039, 23423, 31257, 28…
$ `REMAJA\r\nDOSIS 1` <dbl> 865449, 2303, 2724, 2851,…
$ `REMAJA\r\nDOSIS 2` <dbl> 783270, 2037, 2382, 2541,…
$ `REMAJA\r\nDOSIS 3` <dbl> 10869, 21, 40, 24, 7, 28,…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1659588, 4361, 5146, 5416…Here’s March 2022’s data with 268 records and 34 columns.
apr2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (30 April 2022).xlsx")
glimpse(apr2022)Rows: 268
Columns: 34
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1453423, 4449, 5101, 5699…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7487788, 19498, 24280, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6727002, 17027, 21134, 20…
$ `JUMLAH\r\nDOSIS 3` <dbl> 2720796, 6568, 8915, 6491…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 16935586, 43093, 54329, 5…
$ `LANSIA\r\nDOSIS 1` <dbl> 651696, 1579, 2481, 1458,…
$ `LANSIA\r\nDOSIS 2` <dbl> 613044, 1441, 2360, 1376,…
$ `LANSIA\r\nDOSIS 3` <dbl> 613044, 1441, 2360, 1376,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1599248, 3750, 6301, 3425…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1483630, 3983, 3920, 4611…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1378338, 3640, 3529, 4187…
$ `PELAYAN PUBLIK\r\nDOSIS 3` <dbl> 366145, 1099, 1096, 941, …
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 3228113, 8722, 8545, 9739…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88200, 178, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 86184, 173, 179, 260, 99,…
$ `GOTONG ROYONG\r\nDOSIS 3` <dbl> 38179, 71, 95, 120, 41, 7…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 212563, 422, 452, 642, 24…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115623, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 112253, 130, 130, 333, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 3` <dbl> 89811, 109, 105, 259, 91,…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 317687, 379, 370, 940, 33…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4281576, 11308, 14842, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3750893, 9596, 12545, 116…
$ `TAHAPAN 3\r\nDOSIS 3` <dbl> 1866526, 4503, 6084, 4519…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 9898995, 25407, 33471, 29…
$ `REMAJA\r\nDOSIS 1` <dbl> 867063, 2310, 2724, 2858,…
$ `REMAJA\r\nDOSIS 2` <dbl> 786290, 2047, 2391, 2557,…
$ `REMAJA\r\nDOSIS 3` <dbl> 25627, 56, 75, 61, 19, 71…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1678980, 4413, 5190, 5476…Here’s April 2022’s data with 268 records and 34 columns.
may2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (31 Mei 2022).xlsx")
glimpse(may2022)Rows: 268
Columns: 34
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1445540, 4440, 5084, 5676…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7495671, 19507, 24297, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6743764, 17077, 21182, 20…
$ `JUMLAH\r\nDOSIS 3` <dbl> 2885301, 7022, 9484, 7030…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 17124736, 43606, 54963, 5…
$ `LANSIA\r\nDOSIS 1` <dbl> 652411, 1580, 2482, 1461,…
$ `LANSIA\r\nDOSIS 2` <dbl> 614259, 1442, 2367, 1378,…
$ `LANSIA\r\nDOSIS 3` <dbl> 614259, 1442, 2367, 1378,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1615382, 3804, 6385, 3468…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1483896, 3982, 3920, 4612…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1379577, 3645, 3534, 4192…
$ `PELAYAN PUBLIK\r\nDOSIS 3` <dbl> 395504, 1185, 1185, 1033,…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 3258977, 8812, 8639, 9837…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88234, 179, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 86232, 173, 179, 260, 99,…
$ `GOTONG ROYONG\r\nDOSIS 3` <dbl> 43402, 100, 111, 132, 53,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 217868, 452, 468, 654, 25…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115658, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 112327, 130, 131, 333, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 3` <dbl> 91061, 110, 108, 262, 93,…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 319046, 380, 374, 943, 33…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4287820, 11318, 14850, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3763773, 9632, 12577, 116…
$ `TAHAPAN 3\r\nDOSIS 3` <dbl> 1975879, 4777, 6455, 4893…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 10027472, 25727, 33882, 3…
$ `REMAJA\r\nDOSIS 1` <dbl> 867652, 2308, 2732, 2858,…
$ `REMAJA\r\nDOSIS 2` <dbl> 787596, 2055, 2394, 2562,…
$ `REMAJA\r\nDOSIS 3` <dbl> 30743, 68, 89, 81, 26, 80…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1685991, 4431, 5215, 5501…Here’s May 2022’s data with 268 records and 34 columns.
jun2022 <- read_xlsx("data/aspatial/Data Vaksinasi Berbasis Kelurahan (30 Juni 2022).xlsx")
glimpse(jun2022)Rows: 268
Columns: 34
$ `KODE KELURAHAN` <chr> NA, "3172051003", "317304…
$ `WILAYAH KOTA` <chr> NA, "JAKARTA UTARA", "JAK…
$ KECAMATAN <chr> NA, "PADEMANGAN", "TAMBOR…
$ KELURAHAN <chr> "TOTAL", "ANCOL", "ANGKE"…
$ SASARAN <dbl> 8941211, 23947, 29381, 29…
$ `BELUM VAKSIN` <dbl> 1431393, 4402, 5041, 5632…
$ `JUMLAH\r\nDOSIS 1` <dbl> 7509818, 19545, 24340, 23…
$ `JUMLAH\r\nDOSIS 2` <dbl> 6756584, 17106, 21213, 20…
$ `JUMLAH\r\nDOSIS 3` <dbl> 3031594, 7369, 10086, 739…
$ `TOTAL VAKSIN\r\nDIBERIKAN` <dbl> 17297996, 44020, 55639, 5…
$ `LANSIA\r\nDOSIS 1` <dbl> 653401, 1582, 2483, 1466,…
$ `LANSIA\r\nDOSIS 2` <dbl> 615341, 1447, 2368, 1382,…
$ `LANSIA\r\nDOSIS 3` <dbl> 615341, 1447, 2368, 1382,…
$ `LANSIA TOTAL \r\nVAKSIN DIBERIKAN` <dbl> 1630553, 3848, 6464, 3495…
$ `PELAYAN PUBLIK\r\nDOSIS 1` <dbl> 1484892, 3982, 3924, 4613…
$ `PELAYAN PUBLIK\r\nDOSIS 2` <dbl> 1380501, 3646, 3536, 4195…
$ `PELAYAN PUBLIK\r\nDOSIS 3` <dbl> 420795, 1244, 1265, 1104,…
$ `PELAYAN PUBLIK TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 3286188, 8872, 8725, 9912…
$ `GOTONG ROYONG\r\nDOSIS 1` <dbl> 88277, 180, 178, 262, 102…
$ `GOTONG ROYONG\r\nDOSIS 2` <dbl> 86277, 174, 179, 260, 99,…
$ `GOTONG ROYONG\r\nDOSIS 3` <dbl> 45143, 104, 115, 135, 56,…
$ `GOTONG ROYONG TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 219697, 458, 472, 657, 25…
$ `TENAGA KESEHATAN\r\nDOSIS 1` <dbl> 115697, 140, 135, 348, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 2` <dbl> 112383, 130, 131, 333, 12…
$ `TENAGA KESEHATAN\r\nDOSIS 3` <dbl> 91999, 113, 108, 266, 95,…
$ `TENAGA KESEHATAN TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 320079, 383, 374, 947, 33…
$ `TAHAPAN 3\r\nDOSIS 1` <dbl> 4298906, 11352, 14884, 13…
$ `TAHAPAN 3\r\nDOSIS 2` <dbl> 3773713, 9652, 12601, 116…
$ `TAHAPAN 3\r\nDOSIS 3` <dbl> 2075349, 5009, 6872, 5151…
$ `TAHAPAN 3 TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 10147968, 26013, 34357, 3…
$ `REMAJA\r\nDOSIS 1` <dbl> 868645, 2309, 2736, 2862,…
$ `REMAJA\r\nDOSIS 2` <dbl> 788369, 2057, 2398, 2564,…
$ `REMAJA\r\nDOSIS 3` <dbl> 36497, 80, 113, 95, 29, 1…
$ `REMAJA TOTAL\r\nVAKSIN DIBERIKAN` <dbl> 1693511, 4446, 5247, 5521…Here’s June 2022’s data with 268 records and 34 columns.
After importing the aspatial data, we can tell that from July 2021 to February 2022, there are 27 columns while from March 2022 to June 2022, there are 34 columns.
Upon closer analysis, in 2022, for the months March, April, May and June, there are more columns as more vaccines are being administered and tracked. For the months before March 2022, only 2 vaccination doses were administered and tracked.
3.2.1 Processing Aspatial Data
Since we have 12 separate aspatial objects, to make analysis easier, we should combine these objects into 1. This is so that we can easily merge with the geospatial object, jakartaNGA later on.
Here’s a quick outline of how we’d be processing the aspatial data: - Determine the common and necessary columns to extract from each of the 12 aspatial objects. - we need to create an extra Date column that has the month and year of the observation, which was originally in the file name - Combine the 12 cleaned aspatial objects into 1.
3.2.1.1 Creating an Aspatial Data Pre-processing Function
From our task, we’re supposed to compute the monthly vaccination rate from July 2021 to June 2022 at sub-district level.
From the aspatial data columns, we should be focusing on SASARAN (target number of people to be vaccinated) and BELUM VAKSIN (people who have yet to be vaccinated) instead of some other population-based columns eg TOTAL VAKSIN DIBERIKAN.
TOTAL VAKSIN DIBERIKAN does not actually reflect the whole population’s vaccination rate as young people aged 14 and below are excluded from the vaccination.
Here are the columns of interest: - KELURAHAN (Sub-District) - SASARAN (Target) - BELUM VAKSIN (Unvaccinated) - KODE KELURAHAN (Sub-District Code)
Now that we know our requirements, we can do this step-by-step: importing all the files into one df, retaining the necessary columns (and deleting duplicate columns), then adding the date column. Alternatively, we can combine all of these into a function!
# takes in an aspatial data filepath and returns a processed output
aspatial_preprocess <- function(filepath){
# the .name_repair="minimal" is to indicate not to replace duplicate column names with 'NAME...n' like we saw above!
# reference: https://readxl.tidyverse.org/articles/articles/column-names.html
result_file <- read_xlsx(filepath)[-1,]
# Create the Date Column
# the format of our files is: Standar Kelurahan Data Corona (DD Month YYYY Pukul Ti.me)
# while the start is technically "(", "(" is part of a regular expression and leads to a warning message, so we'll use "Corona" instead. The [[1]] refers to the first element in the list.
# we're loading it as DD-Month-YYYY format
# the end is 1 space before "Pukul", which means we have to -2 spaces (one for P, one for space)
# as such, the most relevant functions are substr (returns a substring) and either str_locate (returns location of substring as an integer matrix) or gregexpr (returns a list of locations of substring)
# reference https://stackoverflow.com/questions/14249562/find-the-location-of-a-character-in-string
startpoint <- gregexpr(pattern="Kelurahan", filepath)[[1]] + 11
endpoint <- nchar(filepath) - 6
result_file$Date <- substr(filepath, startpoint, endpoint)
# Retain the Relevant Columns
result_file <- result_file %>%
select("Date","KODE KELURAHAN", "KELURAHAN", "SASARAN", "BELUM VAKSIN")
return(result_file)
}Firstly, let’s grab the 12 aspatial objects to combine.
# in the folder 'data/aspatial', find files with the extension '.xlsx' and add it to our fileslist
# the full.names=TRUE prepends the directory path to the file names, giving a relative file path - otherwise, only the file names (not the paths) would be returned
# reference: https://stat.ethz.ch/R-manual/R-devel/library/base/html/list.files.html
fileslist <-list.files(path = "data/aspatial", pattern = "*.xlsx", full.names=TRUE)
# afterwards, for every element in fileslist, apply aspatial_process function
dflist <- lapply(seq_along(fileslist), function(x) aspatial_preprocess(fileslist[x]))Lastly, we’ll need to convert the dflist into an actual dataframe with ldply(), like so:
jakarta_vac_points <- ldply(dflist, data.frame)glimpse(jakarta_vac_points)Rows: 3,204
Columns: 5
$ Date <chr> "27 Februari 2022", "27 Februari 2022", "27 Februari 20…
$ KODE.KELURAHAN <chr> "3172051003", "3173041007", "3175041005", "3175031003",…
$ KELURAHAN <chr> "ANCOL", "ANGKE", "BALE KAMBANG", "BALI MESTER", "BAMBU…
$ SASARAN <dbl> 23947, 29381, 29074, 9752, 26285, 21566, 23886, 47898, …
$ BELUM.VAKSIN <dbl> 4592, 5319, 5903, 1649, 4030, 3950, 3344, 9382, 3772, 7…3.2.1.2 Formatting Date Column
# parses the 'Date' column into Month(Full Name)-YYYY datetime objects
# locale="ind" means that the locale has been set as Indonesia
#Sys.setlocale(locale="ind")#holder = jakarta_vac_points#https://stackoverflow.com/questions/13871614/replacing-values-from-a-column-using-a-condition-in-r
jakarta_vac_points$Date[jakarta_vac_points$Date == "27 Februari 2022"] <- "27 February 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "30 April 2022"] <- "30 April 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "30 Juni 2022"] <- "30 June 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "30 November 2021"] <- "30 November 2021"
jakarta_vac_points$Date[jakarta_vac_points$Date == "30 September 2021"] <- "30 September 2021"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Agustus 2021"] <- "31 August 2021"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Desember 2021"] <- "31 December 2021"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Januari 2022"] <- "31 January 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Juli 2021"] <- "31 July 2021"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Maret 2022"] <- "31 March 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Mei 2022"] <- "31 May 2022"
jakarta_vac_points$Date[jakarta_vac_points$Date == "31 Oktober 2021"] <- "31 October 2021"jakarta_vac_points Date KODE.KELURAHAN KELURAHAN SASARAN
1 27 February 2022 3172051003 ANCOL 23947
2 27 February 2022 3173041007 ANGKE 29381
3 27 February 2022 3175041005 BALE KAMBANG 29074
4 27 February 2022 3175031003 BALI MESTER 9752
5 27 February 2022 3175101006 BAMBU APUS 26285
6 27 February 2022 3174031002 BANGKA 21566
7 27 February 2022 3175051002 BARU 23886
8 27 February 2022 3175041004 BATU AMPAR 47898
9 27 February 2022 3171071002 BENDUNGAN HILIR 21494
10 27 February 2022 3175031002 BIDARA CINA 36689
11 27 February 2022 3174101002 BINTARO 53098
12 27 February 2022 3174011005 BUKIT DURI 34183
13 27 February 2022 3171041006 BUNGUR 18144
14 27 February 2022 3175061007 CAKUNG BARAT 57724
15 27 February 2022 3175061004 CAKUNG TIMUR 58287
16 27 February 2022 3175041007 CAWANG 32762
17 27 February 2022 3175101008 CEGER 18932
18 27 February 2022 3171031006 CEMPAKA BARU 32581
19 27 February 2022 3171051002 CEMPAKA PUTIH BARAT 35885
20 27 February 2022 3171051001 CEMPAKA PUTIH TIMUR 24292
21 27 February 2022 3173011001 CENGKARENG BARAT 66284
22 27 February 2022 3173011006 CENGKARENG TIMUR 81027
23 27 February 2022 3175091002 CIBUBUR 66364
24 27 February 2022 3171011002 CIDENG 15390
25 27 February 2022 3174091003 CIGANJUR 38497
26 27 February 2022 3175051003 CIJANTUNG 41419
27 27 February 2022 3171061003 CIKINI 8274
28 27 February 2022 3174081006 CIKOKO 10547
29 27 February 2022 3174061001 CILANDAK BARAT 51752
30 27 February 2022 3174041003 CILANDAK TIMUR 25145
31 27 February 2022 3175101002 CILANGKAP 27635
32 27 February 2022 3175041006 CILILITAN 40181
33 27 February 2022 3172041001 CILINCING 43609
34 27 February 2022 3175101001 CIPAYUNG 26104
35 27 February 2022 3174091006 CIPEDAK 38303
36 27 February 2022 3174061005 CIPETE SELATAN 26704
37 27 February 2022 3174071010 CIPETE UTARA 34102
38 27 February 2022 3175021003 CIPINANG 39306
39 27 February 2022 3175031007 CIPINANG BESAR SELATAN 35212
40 27 February 2022 3175031008 CIPINANG BESAR UTARA 45319
41 27 February 2022 3175031005 CIPINANG CEMPEDAK 32824
42 27 February 2022 3175081005 CIPINANG MELAYU 42308
43 27 February 2022 3175031006 CIPINANG MUARA 55243
44 27 February 2022 3174051003 CIPULIR 39937
45 27 February 2022 3175091001 CIRACAS 64594
46 27 February 2022 3175041003 DUKUH 24609
47 27 February 2022 3175071001 DUREN SAWIT 61160
48 27 February 2022 3174081004 DUREN TIGA 28319
49 27 February 2022 3173051005 DURI KEPA 60138
50 27 February 2022 3173011002 DURI KOSAMBI 77923
51 27 February 2022 3171011006 DURI PULO 19610
52 27 February 2022 3173041011 DURI SELATAN 14259
53 27 February 2022 3173041003 DURI UTARA 19844
54 27 February 2022 3171081003 GALUR 18243
55 27 February 2022 3171011001 GAMBIR 2191
56 27 February 2022 3174061004 GANDARIA SELATAN 22119
57 27 February 2022 3174071009 GANDARIA UTARA 39922
58 27 February 2022 3175051001 GEDONG 35755
59 27 February 2022 3171071001 GELORA 3127
60 27 February 2022 3173031007 GLODOK 7837
61 27 February 2022 3171061004 GONDANGDIA 4100
62 27 February 2022 3173021001 GROGOL 16876
63 27 February 2022 3174051005 GROGOL SELATAN 44251
64 27 February 2022 3174051004 GROGOL UTARA 42494
65 27 February 2022 3174021007 GUNTUR 3985
66 27 February 2022 3174071002 GUNUNG 9436
67 27 February 2022 3171031005 GUNUNG SAHARI SELATAN 19199
68 27 February 2022 3171021004 GUNUNG SAHARI UTARA 16650
69 27 February 2022 3175081004 HALIM PERDANA KUSUMAH 28363
70 27 February 2022 3171031003 HARAPAN MULIA 22557
71 27 February 2022 3174091001 JAGAKARSA 62389
72 27 February 2022 3175021007 JATI 32757
73 27 February 2022 3174041002 JATI PADANG 37502
74 27 February 2022 3173071004 JATI PULO 28738
75 27 February 2022 3175061001 JATINEGARA 84160
76 27 February 2022 3175021004 JATINEGARA KAUM 24632
77 27 February 2022 3173021004 JELAMBAR 30266
78 27 February 2022 3173021006 JELAMBAR BARU 38246
79 27 February 2022 3173041006 JEMBATAN BESI 29314
80 27 February 2022 3173041008 JEMBATAN LIMA 21021
81 27 February 2022 3173081005 JOGLO 40839
82 27 February 2022 3171081001 JOHAR BARU 37488
83 27 February 2022 3173041002 KALI ANYAR 23856
84 27 February 2022 3172041004 KALIBARU 67684
85 27 February 2022 3174081002 KALIBATA 41994
86 27 February 2022 3173061001 KALIDERES 72447
87 27 February 2022 3175051004 KALISARI 43212
88 27 February 2022 3173061004 KAMAL 53783
89 27 February 2022 3172011002 KAMAL MUARA 12930
90 27 February 2022 3171071007 KAMPUNG BALI 11358
91 27 February 2022 3175031001 KAMPUNG MELAYU 25717
92 27 February 2022 3171081002 KAMPUNG RAWA 21237
93 27 February 2022 3175041002 KAMPUNG TENGAH 43993
94 27 February 2022 3173011005 KAPUK 134466
95 27 February 2022 3172011003 KAPUK MUARA 35781
96 27 February 2022 3171021002 KARANG ANYAR 25761
97 27 February 2022 3174021004 KARET 9623
98 27 February 2022 3174021003 KARET KUNINGAN 16055
99 27 February 2022 3174021002 KARET SEMANGGI 2770
100 27 February 2022 3171071003 KARET TENGSIN 19271
101 27 February 2022 3171021003 KARTINI 22292
102 27 February 2022 3175011003 KAYU MANIS 25284
103 27 February 2022 3175021006 KAYU PUTIH 40696
104 27 February 2022 3173031006 KEAGUNGAN 17387
105 27 February 2022 3174041007 KEBAGUSAN 44552
106 27 February 2022 3174051006 KEBAYORAN LAMA SELATAN 41212
107 27 February 2022 3174051001 KEBAYORAN LAMA UTARA 42422
108 27 February 2022 3174011004 KEBON BARU 35165
109 27 February 2022 3172021005 KEBON BAWANG 50952
110 27 February 2022 3173051001 KEBON JERUK 53887
111 27 February 2022 3171071006 KEBON KACANG 21531
112 27 February 2022 3171011005 KEBON KELAPA 10163
113 27 February 2022 3171031002 KEBON KOSONG 30019
114 27 February 2022 3175011005 KEBON MANGGIS 16490
115 27 February 2022 3171071005 KEBON MELATI 32206
116 27 February 2022 3175081003 KEBON PALA 46614
117 27 February 2022 3171061005 KEBON SIRIH 12299
118 27 February 2022 3173011004 KEDAUNG KALI ANGKE 32612
119 27 February 2022 3173051007 KEDOYA SELATAN 32165
120 27 February 2022 3173051006 KEDOYA UTARA 45740
121 27 February 2022 3173051004 KELAPA DUA 23565
122 27 February 2022 3175091003 KELAPA DUA WETAN 46875
123 27 February 2022 3172061003 KELAPA GADING BARAT 35717
124 27 February 2022 3172061001 KELAPA GADING TIMUR 33592
125 27 February 2022 3173071005 KEMANGGISAN 32139
126 27 February 2022 3171031001 KEMAYORAN 20708
127 27 February 2022 3173081006 KEMBANGAN SELATAN 27130
128 27 February 2022 3173081001 KEMBANGAN UTARA 54451
129 27 February 2022 3171041002 KENARI 9579
130 27 February 2022 3173041005 KERENDANG 19973
131 27 February 2022 3175071003 KLENDER 69978
132 27 February 2022 3172031001 KOJA 28175
133 27 February 2022 3173071006 KOTA BAMBU SELATAN 21158
134 27 February 2022 3173071003 KOTA BAMBU UTARA 24995
135 27 February 2022 3171041004 KRAMAT 28835
136 27 February 2022 3175041001 KRAMAT JATI 34144
137 27 February 2022 3174071003 KRAMAT PELA 14413
138 27 February 2022 3173031002 KRUKUT 19244
139 27 February 2022 3174031005 KUNINGAN BARAT 12785
140 27 February 2022 3174021008 KUNINGAN TIMUR 6008
141 27 February 2022 3171041005 KWITANG 15486
142 27 February 2022 3172031003 LAGOA 59065
143 27 February 2022 3174061002 LEBAK BULUS 37626
144 27 February 2022 3174091004 LENTENG AGUNG 53946
145 27 February 2022 3175101007 LUBANG BUAYA 63547
146 27 February 2022 3175081001 MAKASAR 34390
147 27 February 2022 3175071006 MALAKA JAYA 30927
148 27 February 2022 3175071005 MALAKA SARI 27530
149 27 February 2022 3174031001 MAMPANG PRAPATAN 18159
150 27 February 2022 3173031005 MANGGA BESAR 7707
151 27 February 2022 3171021005 MANGGA DUA SELATAN 27442
152 27 February 2022 3174011007 MANGGARAI 28373
153 27 February 2022 3174011006 MANGGARAI SELATAN 23075
154 27 February 2022 3173031003 MAPHAR 16461
155 27 February 2022 3172041003 MARUNDA 28943
156 27 February 2022 3174071001 MELAWAI 2627
157 27 February 2022 3171061001 MENTENG 25624
158 27 February 2022 3174021005 MENTENG ATAS 27816
159 27 February 2022 3174011003 MENTENG DALAM 36028
160 27 February 2022 3173081003 MERUYA SELATAN 32130
161 27 February 2022 3173081002 MERUYA UTARA 43752
162 27 February 2022 3175101004 MUNJUL 23871
163 27 February 2022 3172051002 PADEMANGAN BARAT 74240
164 27 February 2022 3172051001 PADEMANGAN TIMUR 37736
165 27 February 2022 3175011004 PAL MERIAM 19769
166 27 February 2022 3173071001 PALMERAH 63011
167 27 February 2022 3174081001 PANCORAN 19421
168 27 February 2022 3172021003 PAPANGGO 39097
169 27 February 2022 3171021001 PASAR BARU 12035
170 27 February 2022 3174021006 PASAR MANGGIS 26752
171 27 February 2022 3174041001 PASAR MINGGU 24821
172 27 February 2022 3171041003 PASEBAN 23533
173 27 February 2022 3173061005 PEGADUNGAN 75587
174 27 February 2022 3171061002 PEGANGSAAN 23455
175 27 February 2022 3172061002 PEGANGSAAN DUA 49976
176 27 February 2022 3172011004 PEJAGALAN 74578
177 27 February 2022 3174041006 PEJATEN BARAT 37346
178 27 February 2022 3174041005 PEJATEN TIMUR 57656
179 27 February 2022 3175051005 PEKAYON 43250
180 27 February 2022 3173041009 PEKOJAN 23587
181 27 February 2022 3174031003 PELA MAMPANG 43522
182 27 February 2022 3174081005 PENGADEGAN 21478
183 27 February 2022 3175061003 PENGGILINGAN 99255
184 27 February 2022 3172011001 PENJARINGAN 88308
185 27 February 2022 3174101001 PESANGGRAHAN 27545
186 27 February 2022 3171071004 PETAMBURAN 33271
187 27 February 2022 3174071008 PETOGOGAN 11499
188 27 February 2022 3171011004 PETOJO SELATAN 14692
189 27 February 2022 3171011003 PETOJO UTARA 17458
190 27 February 2022 3174101004 PETUKANGAN SELATAN 37187
191 27 February 2022 3174101003 PETUKANGAN UTARA 53716
192 27 February 2022 3175081002 PINANG RANTI 27198
193 27 February 2022 3173031008 PINANGSIA 10729
194 27 February 2022 3175011001 PISANGAN BARU 31776
195 27 February 2022 3175021002 PISANGAN TIMUR 41011
196 27 February 2022 3172011005 PLUIT 47783
197 27 February 2022 3175071002 PONDOK BAMBU 61550
198 27 February 2022 3175071004 PONDOK KELAPA 72015
199 27 February 2022 3175071007 PONDOK KOPI 35367
200 27 February 2022 3174061003 PONDOK LABU 46464
201 27 February 2022 3174051002 PONDOK PINANG 56169
202 27 February 2022 3175101003 PONDOK RANGGON 25574
203 27 February 2022 3101011003 PULAU HARAPAN 2060
204 27 February 2022 3101011002 PULAU KELAPA 5528
205 27 February 2022 3101011001 PULAU PANGGANG 5450
206 27 February 2022 3101021003 PULAU PARI 2765
207 27 February 2022 3101021002 PULAU TIDUNG 4553
208 27 February 2022 3101021001 PULAU UNTUNG JAWA 1941
209 27 February 2022 3174071007 PULO 5835
210 27 February 2022 3175021001 PULO GADUNG 34422
211 27 February 2022 3175061005 PULO GEBANG 97301
212 27 February 2022 3174041004 RAGUNAN 39616
213 27 February 2022 3175091005 RAMBUTAN 36771
214 27 February 2022 3172031006 RAWA BADAK SELATAN 42179
215 27 February 2022 3172031004 RAWA BADAK UTARA 34408
216 27 February 2022 3174071005 RAWA BARAT 5625
217 27 February 2022 3173011003 RAWA BUAYA 63799
218 27 February 2022 3175031004 RAWA BUNGA 21434
219 27 February 2022 3174081003 RAWA JATI 20509
220 27 February 2022 3175061002 RAWA TERATE 24637
221 27 February 2022 3175021005 RAWAMANGUN 37612
222 27 February 2022 3171051003 RAWASARI 21967
223 27 February 2022 3173041010 ROA MALAKA 3484
224 27 February 2022 3172041006 ROROTAN 41668
225 27 February 2022 3174071004 SELONG 2842
226 27 February 2022 3173061002 SEMANAN 71354
227 27 February 2022 3172041007 SEMPER BARAT 68587
228 27 February 2022 3172041005 SEMPER TIMUR 36267
229 27 February 2022 3174071006 SENAYAN 2771
230 27 February 2022 3171041001 SENEN 6804
231 27 February 2022 3171031004 SERDANG 29668
232 27 February 2022 3174021001 SETIA BUDI 3023
233 27 February 2022 3175101005 SETU 20572
234 27 February 2022 3173071002 SLIPI 16591
235 27 February 2022 3173081004 SRENGSENG 45523
236 27 February 2022 3174091002 SRENGSENG SAWAH 58196
237 27 February 2022 3173051003 SUKABUMI SELATAN 37033
238 27 February 2022 3173051002 SUKABUMI UTARA 38290
239 27 February 2022 3172041002 SUKAPURA 53949
240 27 February 2022 3171031007 SUMUR BATU 22717
241 27 February 2022 3172021004 SUNGAI BAMBU 29876
242 27 February 2022 3172021006 SUNTER AGUNG 71805
243 27 February 2022 3172021002 SUNTER JAYA 63031
244 27 February 2022 3175091004 SUSUKAN 38257
245 27 February 2022 3173031001 TAMAN SARI 14348
246 27 February 2022 3173041001 TAMBORA 10204
247 27 February 2022 3173041004 TANAH SEREAL 26387
248 27 February 2022 3171081004 TANAH TINGGI 37693
249 27 February 2022 3173031004 TANGKI 13324
250 27 February 2022 3174091005 TANJUNG BARAT 39770
251 27 February 2022 3173021005 TANJUNG DUREN SELATAN 25337
252 27 February 2022 3173021002 TANJUNG DUREN UTARA 17010
253 27 February 2022 3172021001 TANJUNG PRIOK 34588
254 27 February 2022 3174011002 TEBET BARAT 21194
255 27 February 2022 3174011001 TEBET TIMUR 18199
256 27 February 2022 3173061003 TEGAL ALUR 83647
257 27 February 2022 3174031004 TEGAL PARANG 32401
258 27 February 2022 3173021003 TOMANG 29605
259 27 February 2022 3172031005 TUGU SELATAN 39122
260 27 February 2022 3172031002 TUGU UTARA 67670
261 27 February 2022 3175061006 UJUNG MENTENG 29934
262 27 February 2022 3174101005 ULUJAMI 40544
263 27 February 2022 3175011006 UTAN KAYU SELATAN 33511
264 27 February 2022 3175011002 UTAN KAYU UTARA 28169
265 27 February 2022 3171031008 UTAN PANJANG 29208
266 27 February 2022 3172021007 WARAKAS 44401
267 27 February 2022 3173021007 WIJAYA KUSUMA 38934
268 30 April 2022 3172051003 ANCOL 23947
269 30 April 2022 3173041007 ANGKE 29381
270 30 April 2022 3175041005 BALE KAMBANG 29074
271 30 April 2022 3175031003 BALI MESTER 9752
272 30 April 2022 3175101006 BAMBU APUS 26285
273 30 April 2022 3174031002 BANGKA 21566
274 30 April 2022 3175051002 BARU 23886
275 30 April 2022 3175041004 BATU AMPAR 47898
276 30 April 2022 3171071002 BENDUNGAN HILIR 21494
277 30 April 2022 3175031002 BIDARA CINA 36689
278 30 April 2022 3174101002 BINTARO 53098
279 30 April 2022 3174011005 BUKIT DURI 34183
280 30 April 2022 3171041006 BUNGUR 18144
281 30 April 2022 3175061007 CAKUNG BARAT 57724
282 30 April 2022 3175061004 CAKUNG TIMUR 58287
283 30 April 2022 3175041007 CAWANG 32762
284 30 April 2022 3175101008 CEGER 18932
285 30 April 2022 3171031006 CEMPAKA BARU 32581
286 30 April 2022 3171051002 CEMPAKA PUTIH BARAT 35885
287 30 April 2022 3171051001 CEMPAKA PUTIH TIMUR 24292
288 30 April 2022 3173011001 CENGKARENG BARAT 66284
289 30 April 2022 3173011006 CENGKARENG TIMUR 81027
290 30 April 2022 3175091002 CIBUBUR 66364
291 30 April 2022 3171011002 CIDENG 15390
292 30 April 2022 3174091003 CIGANJUR 38497
293 30 April 2022 3175051003 CIJANTUNG 41419
294 30 April 2022 3171061003 CIKINI 8274
295 30 April 2022 3174081006 CIKOKO 10547
296 30 April 2022 3174061001 CILANDAK BARAT 51752
297 30 April 2022 3174041003 CILANDAK TIMUR 25145
298 30 April 2022 3175101002 CILANGKAP 27635
299 30 April 2022 3175041006 CILILITAN 40181
300 30 April 2022 3172041001 CILINCING 43609
301 30 April 2022 3175101001 CIPAYUNG 26104
302 30 April 2022 3174091006 CIPEDAK 38303
303 30 April 2022 3174061005 CIPETE SELATAN 26704
304 30 April 2022 3174071010 CIPETE UTARA 34102
305 30 April 2022 3175021003 CIPINANG 39306
306 30 April 2022 3175031007 CIPINANG BESAR SELATAN 35212
307 30 April 2022 3175031008 CIPINANG BESAR UTARA 45319
308 30 April 2022 3175031005 CIPINANG CEMPEDAK 32824
309 30 April 2022 3175081005 CIPINANG MELAYU 42308
310 30 April 2022 3175031006 CIPINANG MUARA 55243
311 30 April 2022 3174051003 CIPULIR 39937
312 30 April 2022 3175091001 CIRACAS 64594
313 30 April 2022 3175041003 DUKUH 24609
314 30 April 2022 3175071001 DUREN SAWIT 61160
315 30 April 2022 3174081004 DUREN TIGA 28319
316 30 April 2022 3173051005 DURI KEPA 60138
317 30 April 2022 3173011002 DURI KOSAMBI 77923
318 30 April 2022 3171011006 DURI PULO 19610
319 30 April 2022 3173041011 DURI SELATAN 14259
320 30 April 2022 3173041003 DURI UTARA 19844
321 30 April 2022 3171081003 GALUR 18243
322 30 April 2022 3171011001 GAMBIR 2191
323 30 April 2022 3174061004 GANDARIA SELATAN 22119
324 30 April 2022 3174071009 GANDARIA UTARA 39922
325 30 April 2022 3175051001 GEDONG 35755
326 30 April 2022 3171071001 GELORA 3127
327 30 April 2022 3173031007 GLODOK 7837
328 30 April 2022 3171061004 GONDANGDIA 4100
329 30 April 2022 3173021001 GROGOL 16876
330 30 April 2022 3174051005 GROGOL SELATAN 44251
331 30 April 2022 3174051004 GROGOL UTARA 42494
332 30 April 2022 3174021007 GUNTUR 3985
333 30 April 2022 3174071002 GUNUNG 9436
334 30 April 2022 3171031005 GUNUNG SAHARI SELATAN 19199
335 30 April 2022 3171021004 GUNUNG SAHARI UTARA 16650
336 30 April 2022 3175081004 HALIM PERDANA KUSUMAH 28363
337 30 April 2022 3171031003 HARAPAN MULIA 22557
338 30 April 2022 3174091001 JAGAKARSA 62389
339 30 April 2022 3175021007 JATI 32757
340 30 April 2022 3174041002 JATI PADANG 37502
341 30 April 2022 3173071004 JATI PULO 28738
342 30 April 2022 3175061001 JATINEGARA 84160
343 30 April 2022 3175021004 JATINEGARA KAUM 24632
344 30 April 2022 3173021004 JELAMBAR 30266
345 30 April 2022 3173021006 JELAMBAR BARU 38246
346 30 April 2022 3173041006 JEMBATAN BESI 29314
347 30 April 2022 3173041008 JEMBATAN LIMA 21021
348 30 April 2022 3173081005 JOGLO 40839
349 30 April 2022 3171081001 JOHAR BARU 37488
350 30 April 2022 3173041002 KALI ANYAR 23856
351 30 April 2022 3172041004 KALIBARU 67684
352 30 April 2022 3174081002 KALIBATA 41994
353 30 April 2022 3173061001 KALIDERES 72447
354 30 April 2022 3175051004 KALISARI 43212
355 30 April 2022 3173061004 KAMAL 53783
356 30 April 2022 3172011002 KAMAL MUARA 12930
357 30 April 2022 3171071007 KAMPUNG BALI 11358
358 30 April 2022 3175031001 KAMPUNG MELAYU 25717
359 30 April 2022 3171081002 KAMPUNG RAWA 21237
360 30 April 2022 3175041002 KAMPUNG TENGAH 43993
361 30 April 2022 3173011005 KAPUK 134466
362 30 April 2022 3172011003 KAPUK MUARA 35781
363 30 April 2022 3171021002 KARANG ANYAR 25761
364 30 April 2022 3174021004 KARET 9623
365 30 April 2022 3174021003 KARET KUNINGAN 16055
366 30 April 2022 3174021002 KARET SEMANGGI 2770
367 30 April 2022 3171071003 KARET TENGSIN 19271
368 30 April 2022 3171021003 KARTINI 22292
369 30 April 2022 3175011003 KAYU MANIS 25284
370 30 April 2022 3175021006 KAYU PUTIH 40696
371 30 April 2022 3173031006 KEAGUNGAN 17387
372 30 April 2022 3174041007 KEBAGUSAN 44552
373 30 April 2022 3174051006 KEBAYORAN LAMA SELATAN 41212
374 30 April 2022 3174051001 KEBAYORAN LAMA UTARA 42422
375 30 April 2022 3174011004 KEBON BARU 35165
376 30 April 2022 3172021005 KEBON BAWANG 50952
377 30 April 2022 3173051001 KEBON JERUK 53887
378 30 April 2022 3171071006 KEBON KACANG 21531
379 30 April 2022 3171011005 KEBON KELAPA 10163
380 30 April 2022 3171031002 KEBON KOSONG 30019
381 30 April 2022 3175011005 KEBON MANGGIS 16490
382 30 April 2022 3171071005 KEBON MELATI 32206
383 30 April 2022 3175081003 KEBON PALA 46614
384 30 April 2022 3171061005 KEBON SIRIH 12299
385 30 April 2022 3173011004 KEDAUNG KALI ANGKE 32612
386 30 April 2022 3173051007 KEDOYA SELATAN 32165
387 30 April 2022 3173051006 KEDOYA UTARA 45740
388 30 April 2022 3173051004 KELAPA DUA 23565
389 30 April 2022 3175091003 KELAPA DUA WETAN 46875
390 30 April 2022 3172061003 KELAPA GADING BARAT 35717
391 30 April 2022 3172061001 KELAPA GADING TIMUR 33592
392 30 April 2022 3173071005 KEMANGGISAN 32139
393 30 April 2022 3171031001 KEMAYORAN 20708
394 30 April 2022 3173081006 KEMBANGAN SELATAN 27130
395 30 April 2022 3173081001 KEMBANGAN UTARA 54451
396 30 April 2022 3171041002 KENARI 9579
397 30 April 2022 3173041005 KERENDANG 19973
398 30 April 2022 3175071003 KLENDER 69978
399 30 April 2022 3172031001 KOJA 28175
400 30 April 2022 3173071006 KOTA BAMBU SELATAN 21158
401 30 April 2022 3173071003 KOTA BAMBU UTARA 24995
402 30 April 2022 3171041004 KRAMAT 28835
403 30 April 2022 3175041001 KRAMAT JATI 34144
404 30 April 2022 3174071003 KRAMAT PELA 14413
405 30 April 2022 3173031002 KRUKUT 19244
406 30 April 2022 3174031005 KUNINGAN BARAT 12785
407 30 April 2022 3174021008 KUNINGAN TIMUR 6008
408 30 April 2022 3171041005 KWITANG 15486
409 30 April 2022 3172031003 LAGOA 59065
410 30 April 2022 3174061002 LEBAK BULUS 37626
411 30 April 2022 3174091004 LENTENG AGUNG 53946
412 30 April 2022 3175101007 LUBANG BUAYA 63547
413 30 April 2022 3175081001 MAKASAR 34390
414 30 April 2022 3175071006 MALAKA JAYA 30927
415 30 April 2022 3175071005 MALAKA SARI 27530
416 30 April 2022 3174031001 MAMPANG PRAPATAN 18159
417 30 April 2022 3173031005 MANGGA BESAR 7707
418 30 April 2022 3171021005 MANGGA DUA SELATAN 27442
419 30 April 2022 3174011007 MANGGARAI 28373
420 30 April 2022 3174011006 MANGGARAI SELATAN 23075
421 30 April 2022 3173031003 MAPHAR 16461
422 30 April 2022 3172041003 MARUNDA 28943
423 30 April 2022 3174071001 MELAWAI 2627
424 30 April 2022 3171061001 MENTENG 25624
425 30 April 2022 3174021005 MENTENG ATAS 27816
426 30 April 2022 3174011003 MENTENG DALAM 36028
427 30 April 2022 3173081003 MERUYA SELATAN 32130
428 30 April 2022 3173081002 MERUYA UTARA 43752
429 30 April 2022 3175101004 MUNJUL 23871
430 30 April 2022 3172051002 PADEMANGAN BARAT 74240
431 30 April 2022 3172051001 PADEMANGAN TIMUR 37736
432 30 April 2022 3175011004 PAL MERIAM 19769
433 30 April 2022 3173071001 PALMERAH 63011
434 30 April 2022 3174081001 PANCORAN 19421
435 30 April 2022 3172021003 PAPANGGO 39097
436 30 April 2022 3171021001 PASAR BARU 12035
437 30 April 2022 3174021006 PASAR MANGGIS 26752
438 30 April 2022 3174041001 PASAR MINGGU 24821
439 30 April 2022 3171041003 PASEBAN 23533
440 30 April 2022 3173061005 PEGADUNGAN 75587
441 30 April 2022 3171061002 PEGANGSAAN 23455
442 30 April 2022 3172061002 PEGANGSAAN DUA 49976
443 30 April 2022 3172011004 PEJAGALAN 74578
444 30 April 2022 3174041006 PEJATEN BARAT 37346
445 30 April 2022 3174041005 PEJATEN TIMUR 57656
446 30 April 2022 3175051005 PEKAYON 43250
447 30 April 2022 3173041009 PEKOJAN 23587
448 30 April 2022 3174031003 PELA MAMPANG 43522
449 30 April 2022 3174081005 PENGADEGAN 21478
450 30 April 2022 3175061003 PENGGILINGAN 99255
451 30 April 2022 3172011001 PENJARINGAN 88308
452 30 April 2022 3174101001 PESANGGRAHAN 27545
453 30 April 2022 3171071004 PETAMBURAN 33271
454 30 April 2022 3174071008 PETOGOGAN 11499
455 30 April 2022 3171011004 PETOJO SELATAN 14692
456 30 April 2022 3171011003 PETOJO UTARA 17458
457 30 April 2022 3174101004 PETUKANGAN SELATAN 37187
458 30 April 2022 3174101003 PETUKANGAN UTARA 53716
459 30 April 2022 3175081002 PINANG RANTI 27198
460 30 April 2022 3173031008 PINANGSIA 10729
461 30 April 2022 3175011001 PISANGAN BARU 31776
462 30 April 2022 3175021002 PISANGAN TIMUR 41011
463 30 April 2022 3172011005 PLUIT 47783
464 30 April 2022 3175071002 PONDOK BAMBU 61550
465 30 April 2022 3175071004 PONDOK KELAPA 72015
466 30 April 2022 3175071007 PONDOK KOPI 35367
467 30 April 2022 3174061003 PONDOK LABU 46464
468 30 April 2022 3174051002 PONDOK PINANG 56169
469 30 April 2022 3175101003 PONDOK RANGGON 25574
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471 30 April 2022 3101011002 PULAU KELAPA 5528
472 30 April 2022 3101011001 PULAU PANGGANG 5450
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474 30 April 2022 3101021002 PULAU TIDUNG 4553
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478 30 April 2022 3175061005 PULO GEBANG 97301
479 30 April 2022 3174041004 RAGUNAN 39616
480 30 April 2022 3175091005 RAMBUTAN 36771
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482 30 April 2022 3172031004 RAWA BADAK UTARA 34408
483 30 April 2022 3174071005 RAWA BARAT 5625
484 30 April 2022 3173011003 RAWA BUAYA 63799
485 30 April 2022 3175031004 RAWA BUNGA 21434
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487 30 April 2022 3175061002 RAWA TERATE 24637
488 30 April 2022 3175021005 RAWAMANGUN 37612
489 30 April 2022 3171051003 RAWASARI 21967
490 30 April 2022 3173041010 ROA MALAKA 3484
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498 30 April 2022 3171031004 SERDANG 29668
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502 30 April 2022 3173081004 SRENGSENG 45523
503 30 April 2022 3174091002 SRENGSENG SAWAH 58196
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508 30 April 2022 3172021004 SUNGAI BAMBU 29876
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511 30 April 2022 3175091004 SUSUKAN 38257
512 30 April 2022 3173031001 TAMAN SARI 14348
513 30 April 2022 3173041001 TAMBORA 10204
514 30 April 2022 3173041004 TANAH SEREAL 26387
515 30 April 2022 3171081004 TANAH TINGGI 37693
516 30 April 2022 3173031004 TANGKI 13324
517 30 April 2022 3174091005 TANJUNG BARAT 39770
518 30 April 2022 3173021005 TANJUNG DUREN SELATAN 25337
519 30 April 2022 3173021002 TANJUNG DUREN UTARA 17010
520 30 April 2022 3172021001 TANJUNG PRIOK 34588
521 30 April 2022 3174011002 TEBET BARAT 21194
522 30 April 2022 3174011001 TEBET TIMUR 18199
523 30 April 2022 3173061003 TEGAL ALUR 83647
524 30 April 2022 3174031004 TEGAL PARANG 32401
525 30 April 2022 3173021003 TOMANG 29605
526 30 April 2022 3172031005 TUGU SELATAN 39122
527 30 April 2022 3172031002 TUGU UTARA 67670
528 30 April 2022 3175061006 UJUNG MENTENG 29934
529 30 April 2022 3174101005 ULUJAMI 40544
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531 30 April 2022 3175011002 UTAN KAYU UTARA 28169
532 30 April 2022 3171031008 UTAN PANJANG 29208
533 30 April 2022 3172021007 WARAKAS 44401
534 30 April 2022 3173021007 WIJAYA KUSUMA 38934
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537 30 June 2022 3175041005 BALE KAMBANG 29074
538 30 June 2022 3175031003 BALI MESTER 9752
539 30 June 2022 3175101006 BAMBU APUS 26285
540 30 June 2022 3174031002 BANGKA 21566
541 30 June 2022 3175051002 BARU 23886
542 30 June 2022 3175041004 BATU AMPAR 47898
543 30 June 2022 3171071002 BENDUNGAN HILIR 21494
544 30 June 2022 3175031002 BIDARA CINA 36689
545 30 June 2022 3174101002 BINTARO 53098
546 30 June 2022 3174011005 BUKIT DURI 34183
547 30 June 2022 3171041006 BUNGUR 18144
548 30 June 2022 3175061007 CAKUNG BARAT 57724
549 30 June 2022 3175061004 CAKUNG TIMUR 58287
550 30 June 2022 3175041007 CAWANG 32762
551 30 June 2022 3175101008 CEGER 18932
552 30 June 2022 3171031006 CEMPAKA BARU 32581
553 30 June 2022 3171051002 CEMPAKA PUTIH BARAT 35885
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555 30 June 2022 3173011001 CENGKARENG BARAT 66284
556 30 June 2022 3173011006 CENGKARENG TIMUR 81027
557 30 June 2022 3175091002 CIBUBUR 66364
558 30 June 2022 3171011002 CIDENG 15390
559 30 June 2022 3174091003 CIGANJUR 38497
560 30 June 2022 3175051003 CIJANTUNG 41419
561 30 June 2022 3171061003 CIKINI 8274
562 30 June 2022 3174081006 CIKOKO 10547
563 30 June 2022 3174061001 CILANDAK BARAT 51752
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565 30 June 2022 3175101002 CILANGKAP 27635
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567 30 June 2022 3172041001 CILINCING 43609
568 30 June 2022 3175101001 CIPAYUNG 26104
569 30 June 2022 3174091006 CIPEDAK 38303
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571 30 June 2022 3174071010 CIPETE UTARA 34102
572 30 June 2022 3175021003 CIPINANG 39306
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574 30 June 2022 3175031008 CIPINANG BESAR UTARA 45319
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576 30 June 2022 3175081005 CIPINANG MELAYU 42308
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581 30 June 2022 3175071001 DUREN SAWIT 61160
582 30 June 2022 3174081004 DUREN TIGA 28319
583 30 June 2022 3173051005 DURI KEPA 60138
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585 30 June 2022 3171011006 DURI PULO 19610
586 30 June 2022 3173041011 DURI SELATAN 14259
587 30 June 2022 3173041003 DURI UTARA 19844
588 30 June 2022 3171081003 GALUR 18243
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590 30 June 2022 3174061004 GANDARIA SELATAN 22119
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593 30 June 2022 3171071001 GELORA 3127
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595 30 June 2022 3171061004 GONDANGDIA 4100
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597 30 June 2022 3174051005 GROGOL SELATAN 44251
598 30 June 2022 3174051004 GROGOL UTARA 42494
599 30 June 2022 3174021007 GUNTUR 3985
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602 30 June 2022 3171021004 GUNUNG SAHARI UTARA 16650
603 30 June 2022 3175081004 HALIM PERDANA KUSUMAH 28363
604 30 June 2022 3171031003 HARAPAN MULIA 22557
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607 30 June 2022 3174041002 JATI PADANG 37502
608 30 June 2022 3173071004 JATI PULO 28738
609 30 June 2022 3175061001 JATINEGARA 84160
610 30 June 2022 3175021004 JATINEGARA KAUM 24632
611 30 June 2022 3173021004 JELAMBAR 30266
612 30 June 2022 3173021006 JELAMBAR BARU 38246
613 30 June 2022 3173041006 JEMBATAN BESI 29314
614 30 June 2022 3173041008 JEMBATAN LIMA 21021
615 30 June 2022 3173081005 JOGLO 40839
616 30 June 2022 3171081001 JOHAR BARU 37488
617 30 June 2022 3173041002 KALI ANYAR 23856
618 30 June 2022 3172041004 KALIBARU 67684
619 30 June 2022 3174081002 KALIBATA 41994
620 30 June 2022 3173061001 KALIDERES 72447
621 30 June 2022 3175051004 KALISARI 43212
622 30 June 2022 3173061004 KAMAL 53783
623 30 June 2022 3172011002 KAMAL MUARA 12930
624 30 June 2022 3171071007 KAMPUNG BALI 11358
625 30 June 2022 3175031001 KAMPUNG MELAYU 25717
626 30 June 2022 3171081002 KAMPUNG RAWA 21237
627 30 June 2022 3175041002 KAMPUNG TENGAH 43993
628 30 June 2022 3173011005 KAPUK 134466
629 30 June 2022 3172011003 KAPUK MUARA 35781
630 30 June 2022 3171021002 KARANG ANYAR 25761
631 30 June 2022 3174021004 KARET 9623
632 30 June 2022 3174021003 KARET KUNINGAN 16055
633 30 June 2022 3174021002 KARET SEMANGGI 2770
634 30 June 2022 3171071003 KARET TENGSIN 19271
635 30 June 2022 3171021003 KARTINI 22292
636 30 June 2022 3175011003 KAYU MANIS 25284
637 30 June 2022 3175021006 KAYU PUTIH 40696
638 30 June 2022 3173031006 KEAGUNGAN 17387
639 30 June 2022 3174041007 KEBAGUSAN 44552
640 30 June 2022 3174051006 KEBAYORAN LAMA SELATAN 41212
641 30 June 2022 3174051001 KEBAYORAN LAMA UTARA 42422
642 30 June 2022 3174011004 KEBON BARU 35165
643 30 June 2022 3172021005 KEBON BAWANG 50952
644 30 June 2022 3173051001 KEBON JERUK 53887
645 30 June 2022 3171071006 KEBON KACANG 21531
646 30 June 2022 3171011005 KEBON KELAPA 10163
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649 30 June 2022 3171071005 KEBON MELATI 32206
650 30 June 2022 3175081003 KEBON PALA 46614
651 30 June 2022 3171061005 KEBON SIRIH 12299
652 30 June 2022 3173011004 KEDAUNG KALI ANGKE 32612
653 30 June 2022 3173051007 KEDOYA SELATAN 32165
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655 30 June 2022 3173051004 KELAPA DUA 23565
656 30 June 2022 3175091003 KELAPA DUA WETAN 46875
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658 30 June 2022 3172061001 KELAPA GADING TIMUR 33592
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660 30 June 2022 3171031001 KEMAYORAN 20708
661 30 June 2022 3173081006 KEMBANGAN SELATAN 27130
662 30 June 2022 3173081001 KEMBANGAN UTARA 54451
663 30 June 2022 3171041002 KENARI 9579
664 30 June 2022 3173041005 KERENDANG 19973
665 30 June 2022 3175071003 KLENDER 69978
666 30 June 2022 3172031001 KOJA 28175
667 30 June 2022 3173071006 KOTA BAMBU SELATAN 21158
668 30 June 2022 3173071003 KOTA BAMBU UTARA 24995
669 30 June 2022 3171041004 KRAMAT 28835
670 30 June 2022 3175041001 KRAMAT JATI 34144
671 30 June 2022 3174071003 KRAMAT PELA 14413
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673 30 June 2022 3174031005 KUNINGAN BARAT 12785
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675 30 June 2022 3171041005 KWITANG 15486
676 30 June 2022 3172031003 LAGOA 59065
677 30 June 2022 3174061002 LEBAK BULUS 37626
678 30 June 2022 3174091004 LENTENG AGUNG 53946
679 30 June 2022 3175101007 LUBANG BUAYA 63547
680 30 June 2022 3175081001 MAKASAR 34390
681 30 June 2022 3175071006 MALAKA JAYA 30927
682 30 June 2022 3175071005 MALAKA SARI 27530
683 30 June 2022 3174031001 MAMPANG PRAPATAN 18159
684 30 June 2022 3173031005 MANGGA BESAR 7707
685 30 June 2022 3171021005 MANGGA DUA SELATAN 27442
686 30 June 2022 3174011007 MANGGARAI 28373
687 30 June 2022 3174011006 MANGGARAI SELATAN 23075
688 30 June 2022 3173031003 MAPHAR 16461
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692 30 June 2022 3174021005 MENTENG ATAS 27816
693 30 June 2022 3174011003 MENTENG DALAM 36028
694 30 June 2022 3173081003 MERUYA SELATAN 32130
695 30 June 2022 3173081002 MERUYA UTARA 43752
696 30 June 2022 3175101004 MUNJUL 23871
697 30 June 2022 3172051002 PADEMANGAN BARAT 74240
698 30 June 2022 3172051001 PADEMANGAN TIMUR 37736
699 30 June 2022 3175011004 PAL MERIAM 19769
700 30 June 2022 3173071001 PALMERAH 63011
701 30 June 2022 3174081001 PANCORAN 19421
702 30 June 2022 3172021003 PAPANGGO 39097
703 30 June 2022 3171021001 PASAR BARU 12035
704 30 June 2022 3174021006 PASAR MANGGIS 26752
705 30 June 2022 3174041001 PASAR MINGGU 24821
706 30 June 2022 3171041003 PASEBAN 23533
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708 30 June 2022 3171061002 PEGANGSAAN 23455
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710 30 June 2022 3172011004 PEJAGALAN 74578
711 30 June 2022 3174041006 PEJATEN BARAT 37346
712 30 June 2022 3174041005 PEJATEN TIMUR 57656
713 30 June 2022 3175051005 PEKAYON 43250
714 30 June 2022 3173041009 PEKOJAN 23587
715 30 June 2022 3174031003 PELA MAMPANG 43522
716 30 June 2022 3174081005 PENGADEGAN 21478
717 30 June 2022 3175061003 PENGGILINGAN 99255
718 30 June 2022 3172011001 PENJARINGAN 88308
719 30 June 2022 3174101001 PESANGGRAHAN 27545
720 30 June 2022 3171071004 PETAMBURAN 33271
721 30 June 2022 3174071008 PETOGOGAN 11499
722 30 June 2022 3171011004 PETOJO SELATAN 14692
723 30 June 2022 3171011003 PETOJO UTARA 17458
724 30 June 2022 3174101004 PETUKANGAN SELATAN 37187
725 30 June 2022 3174101003 PETUKANGAN UTARA 53716
726 30 June 2022 3175081002 PINANG RANTI 27198
727 30 June 2022 3173031008 PINANGSIA 10729
728 30 June 2022 3175011001 PISANGAN BARU 31776
729 30 June 2022 3175021002 PISANGAN TIMUR 41011
730 30 June 2022 3172011005 PLUIT 47783
731 30 June 2022 3175071002 PONDOK BAMBU 61550
732 30 June 2022 3175071004 PONDOK KELAPA 72015
733 30 June 2022 3175071007 PONDOK KOPI 35367
734 30 June 2022 3174061003 PONDOK LABU 46464
735 30 June 2022 3174051002 PONDOK PINANG 56169
736 30 June 2022 3175101003 PONDOK RANGGON 25574
737 30 June 2022 3101011003 PULAU HARAPAN 2060
738 30 June 2022 3101011002 PULAU KELAPA 5528
739 30 June 2022 3101011001 PULAU PANGGANG 5450
740 30 June 2022 3101021003 PULAU PARI 2765
741 30 June 2022 3101021002 PULAU TIDUNG 4553
742 30 June 2022 3101021001 PULAU UNTUNG JAWA 1941
743 30 June 2022 3174071007 PULO 5835
744 30 June 2022 3175021001 PULO GADUNG 34422
745 30 June 2022 3175061005 PULO GEBANG 97301
746 30 June 2022 3174041004 RAGUNAN 39616
747 30 June 2022 3175091005 RAMBUTAN 36771
748 30 June 2022 3172031006 RAWA BADAK SELATAN 42179
749 30 June 2022 3172031004 RAWA BADAK UTARA 34408
750 30 June 2022 3174071005 RAWA BARAT 5625
751 30 June 2022 3173011003 RAWA BUAYA 63799
752 30 June 2022 3175031004 RAWA BUNGA 21434
753 30 June 2022 3174081003 RAWA JATI 20509
754 30 June 2022 3175061002 RAWA TERATE 24637
755 30 June 2022 3175021005 RAWAMANGUN 37612
756 30 June 2022 3171051003 RAWASARI 21967
757 30 June 2022 3173041010 ROA MALAKA 3484
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760 30 June 2022 3173061002 SEMANAN 71354
761 30 June 2022 3172041007 SEMPER BARAT 68587
762 30 June 2022 3172041005 SEMPER TIMUR 36267
763 30 June 2022 3174071006 SENAYAN 2771
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765 30 June 2022 3171031004 SERDANG 29668
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768 30 June 2022 3173071002 SLIPI 16591
769 30 June 2022 3173081004 SRENGSENG 45523
770 30 June 2022 3174091002 SRENGSENG SAWAH 58196
771 30 June 2022 3173051003 SUKABUMI SELATAN 37033
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775 30 June 2022 3172021004 SUNGAI BAMBU 29876
776 30 June 2022 3172021006 SUNTER AGUNG 71805
777 30 June 2022 3172021002 SUNTER JAYA 63031
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779 30 June 2022 3173031001 TAMAN SARI 14348
780 30 June 2022 3173041001 TAMBORA 10204
781 30 June 2022 3173041004 TANAH SEREAL 26387
782 30 June 2022 3171081004 TANAH TINGGI 37693
783 30 June 2022 3173031004 TANGKI 13324
784 30 June 2022 3174091005 TANJUNG BARAT 39770
785 30 June 2022 3173021005 TANJUNG DUREN SELATAN 25337
786 30 June 2022 3173021002 TANJUNG DUREN UTARA 17010
787 30 June 2022 3172021001 TANJUNG PRIOK 34588
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789 30 June 2022 3174011001 TEBET TIMUR 18199
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791 30 June 2022 3174031004 TEGAL PARANG 32401
792 30 June 2022 3173021003 TOMANG 29605
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794 30 June 2022 3172031002 TUGU UTARA 67670
795 30 June 2022 3175061006 UJUNG MENTENG 29934
796 30 June 2022 3174101005 ULUJAMI 40544
797 30 June 2022 3175011006 UTAN KAYU SELATAN 33511
798 30 June 2022 3175011002 UTAN KAYU UTARA 28169
799 30 June 2022 3171031008 UTAN PANJANG 29208
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801 30 June 2022 3173021007 WIJAYA KUSUMA 38934
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803 30 November 2021 3173041007 ANGKE 29381
804 30 November 2021 3175041005 BALE KAMBANG 29074
805 30 November 2021 3175031003 BALI MESTER 9752
806 30 November 2021 3175101006 BAMBU APUS 26285
807 30 November 2021 3174031002 BANGKA 21566
808 30 November 2021 3175051002 BARU 23886
809 30 November 2021 3175041004 BATU AMPAR 47898
810 30 November 2021 3171071002 BENDUNGAN HILIR 21494
811 30 November 2021 3175031002 BIDARA CINA 36689
812 30 November 2021 3174101002 BINTARO 53098
813 30 November 2021 3174011005 BUKIT DURI 34183
814 30 November 2021 3171041006 BUNGUR 18144
815 30 November 2021 3175061007 CAKUNG BARAT 57724
816 30 November 2021 3175061004 CAKUNG TIMUR 58287
817 30 November 2021 3175041007 CAWANG 32762
818 30 November 2021 3175101008 CEGER 18932
819 30 November 2021 3171031006 CEMPAKA BARU 32581
820 30 November 2021 3171051002 CEMPAKA PUTIH BARAT 35885
821 30 November 2021 3171051001 CEMPAKA PUTIH TIMUR 24292
822 30 November 2021 3173011001 CENGKARENG BARAT 66284
823 30 November 2021 3173011006 CENGKARENG TIMUR 81027
824 30 November 2021 3175091002 CIBUBUR 66364
825 30 November 2021 3171011002 CIDENG 15390
826 30 November 2021 3174091003 CIGANJUR 38497
827 30 November 2021 3175051003 CIJANTUNG 41419
828 30 November 2021 3171061003 CIKINI 8274
829 30 November 2021 3174081006 CIKOKO 10547
830 30 November 2021 3174061001 CILANDAK BARAT 51752
831 30 November 2021 3174041003 CILANDAK TIMUR 25145
832 30 November 2021 3175101002 CILANGKAP 27635
833 30 November 2021 3175041006 CILILITAN 40181
834 30 November 2021 3172041001 CILINCING 43609
835 30 November 2021 3175101001 CIPAYUNG 26104
836 30 November 2021 3174091006 CIPEDAK 38303
837 30 November 2021 3174061005 CIPETE SELATAN 26704
838 30 November 2021 3174071010 CIPETE UTARA 34102
839 30 November 2021 3175021003 CIPINANG 39306
840 30 November 2021 3175031007 CIPINANG BESAR SELATAN 35212
841 30 November 2021 3175031008 CIPINANG BESAR UTARA 45319
842 30 November 2021 3175031005 CIPINANG CEMPEDAK 32824
843 30 November 2021 3175081005 CIPINANG MELAYU 42308
844 30 November 2021 3175031006 CIPINANG MUARA 55243
845 30 November 2021 3174051003 CIPULIR 39937
846 30 November 2021 3175091001 CIRACAS 64594
847 30 November 2021 3175041003 DUKUH 24609
848 30 November 2021 3175071001 DUREN SAWIT 61160
849 30 November 2021 3174081004 DUREN TIGA 28319
850 30 November 2021 3173051005 DURI KEPA 60138
851 30 November 2021 3173011002 DURI KOSAMBI 77923
852 30 November 2021 3171011006 DURI PULO 19610
853 30 November 2021 3173041011 DURI SELATAN 14259
854 30 November 2021 3173041003 DURI UTARA 19844
855 30 November 2021 3171081003 GALUR 18243
856 30 November 2021 3171011001 GAMBIR 2191
857 30 November 2021 3174061004 GANDARIA SELATAN 22119
858 30 November 2021 3174071009 GANDARIA UTARA 39922
859 30 November 2021 3175051001 GEDONG 35755
860 30 November 2021 3171071001 GELORA 3127
861 30 November 2021 3173031007 GLODOK 7837
862 30 November 2021 3171061004 GONDANGDIA 4100
863 30 November 2021 3173021001 GROGOL 16876
864 30 November 2021 3174051005 GROGOL SELATAN 44251
865 30 November 2021 3174051004 GROGOL UTARA 42494
866 30 November 2021 3174021007 GUNTUR 3985
867 30 November 2021 3174071002 GUNUNG 9436
868 30 November 2021 3171031005 GUNUNG SAHARI SELATAN 19199
869 30 November 2021 3171021004 GUNUNG SAHARI UTARA 16650
870 30 November 2021 3175081004 HALIM PERDANA KUSUMAH 28363
871 30 November 2021 3171031003 HARAPAN MULIA 22557
872 30 November 2021 3174091001 JAGAKARSA 62389
873 30 November 2021 3175021007 JATI 32757
874 30 November 2021 3174041002 JATI PADANG 37502
875 30 November 2021 3173071004 JATI PULO 28738
876 30 November 2021 3175061001 JATINEGARA 84160
877 30 November 2021 3175021004 JATINEGARA KAUM 24632
878 30 November 2021 3173021004 JELAMBAR 30266
879 30 November 2021 3173021006 JELAMBAR BARU 38246
880 30 November 2021 3173041006 JEMBATAN BESI 29314
881 30 November 2021 3173041008 JEMBATAN LIMA 21021
882 30 November 2021 3173081005 JOGLO 40839
883 30 November 2021 3171081001 JOHAR BARU 37488
884 30 November 2021 3173041002 KALI ANYAR 23856
885 30 November 2021 3172041004 KALIBARU 67684
886 30 November 2021 3174081002 KALIBATA 41994
887 30 November 2021 3173061001 KALIDERES 72447
888 30 November 2021 3175051004 KALISARI 43212
889 30 November 2021 3173061004 KAMAL 53783
890 30 November 2021 3172011002 KAMAL MUARA 12930
891 30 November 2021 3171071007 KAMPUNG BALI 11358
892 30 November 2021 3175031001 KAMPUNG MELAYU 25717
893 30 November 2021 3171081002 KAMPUNG RAWA 21237
894 30 November 2021 3175041002 KAMPUNG TENGAH 43993
895 30 November 2021 3173011005 KAPUK 134466
896 30 November 2021 3172011003 KAPUK MUARA 35781
897 30 November 2021 3171021002 KARANG ANYAR 25761
898 30 November 2021 3174021004 KARET 9623
899 30 November 2021 3174021003 KARET KUNINGAN 16055
900 30 November 2021 3174021002 KARET SEMANGGI 2770
901 30 November 2021 3171071003 KARET TENGSIN 19271
902 30 November 2021 3171021003 KARTINI 22292
903 30 November 2021 3175011003 KAYU MANIS 25284
904 30 November 2021 3175021006 KAYU PUTIH 40696
905 30 November 2021 3173031006 KEAGUNGAN 17387
906 30 November 2021 3174041007 KEBAGUSAN 44552
907 30 November 2021 3174051006 KEBAYORAN LAMA SELATAN 41212
908 30 November 2021 3174051001 KEBAYORAN LAMA UTARA 42422
909 30 November 2021 3174011004 KEBON BARU 35165
910 30 November 2021 3172021005 KEBON BAWANG 50952
911 30 November 2021 3173051001 KEBON JERUK 53887
912 30 November 2021 3171071006 KEBON KACANG 21531
913 30 November 2021 3171011005 KEBON KELAPA 10163
914 30 November 2021 3171031002 KEBON KOSONG 30019
915 30 November 2021 3175011005 KEBON MANGGIS 16490
916 30 November 2021 3171071005 KEBON MELATI 32206
917 30 November 2021 3175081003 KEBON PALA 46614
918 30 November 2021 3171061005 KEBON SIRIH 12299
919 30 November 2021 3173011004 KEDAUNG KALI ANGKE 32612
920 30 November 2021 3173051007 KEDOYA SELATAN 32165
921 30 November 2021 3173051006 KEDOYA UTARA 45740
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924 30 November 2021 3172061003 KELAPA GADING BARAT 35717
925 30 November 2021 3172061001 KELAPA GADING TIMUR 33592
926 30 November 2021 3173071005 KEMANGGISAN 32139
927 30 November 2021 3171031001 KEMAYORAN 20708
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929 30 November 2021 3173081001 KEMBANGAN UTARA 54451
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934 30 November 2021 3173071006 KOTA BAMBU SELATAN 21158
935 30 November 2021 3173071003 KOTA BAMBU UTARA 24995
936 30 November 2021 3171041004 KRAMAT 28835
937 30 November 2021 3175041001 KRAMAT JATI 34144
938 30 November 2021 3174071003 KRAMAT PELA 14413
939 30 November 2021 3173031002 KRUKUT 19244
940 30 November 2021 3174031005 KUNINGAN BARAT 12785
941 30 November 2021 3174021008 KUNINGAN TIMUR 6008
942 30 November 2021 3171041005 KWITANG 15486
943 30 November 2021 3172031003 LAGOA 59065
944 30 November 2021 3174061002 LEBAK BULUS 37626
945 30 November 2021 3174091004 LENTENG AGUNG 53946
946 30 November 2021 3175101007 LUBANG BUAYA 63547
947 30 November 2021 3175081001 MAKASAR 34390
948 30 November 2021 3175071006 MALAKA JAYA 30927
949 30 November 2021 3175071005 MALAKA SARI 27530
950 30 November 2021 3174031001 MAMPANG PRAPATAN 18159
951 30 November 2021 3173031005 MANGGA BESAR 7707
952 30 November 2021 3171021005 MANGGA DUA SELATAN 27442
953 30 November 2021 3174011007 MANGGARAI 28373
954 30 November 2021 3174011006 MANGGARAI SELATAN 23075
955 30 November 2021 3173031003 MAPHAR 16461
956 30 November 2021 3172041003 MARUNDA 28943
957 30 November 2021 3174071001 MELAWAI 2627
958 30 November 2021 3171061001 MENTENG 25624
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960 30 November 2021 3174011003 MENTENG DALAM 36028
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962 30 November 2021 3173081002 MERUYA UTARA 43752
963 30 November 2021 3175101004 MUNJUL 23871
964 30 November 2021 3172051002 PADEMANGAN BARAT 74240
965 30 November 2021 3172051001 PADEMANGAN TIMUR 37736
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967 30 November 2021 3173071001 PALMERAH 63011
968 30 November 2021 3174081001 PANCORAN 19421
969 30 November 2021 3172021003 PAPANGGO 39097
970 30 November 2021 3171021001 PASAR BARU 12035
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972 30 November 2021 3174041001 PASAR MINGGU 24821
973 30 November 2021 3171041003 PASEBAN 23533
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975 30 November 2021 3171061002 PEGANGSAAN 23455
976 30 November 2021 3172061002 PEGANGSAAN DUA 49976
977 30 November 2021 3172011004 PEJAGALAN 74578
978 30 November 2021 3174041006 PEJATEN BARAT 37346
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980 30 November 2021 3175051005 PEKAYON 43250
981 30 November 2021 3173041009 PEKOJAN 23587
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983 30 November 2021 3174081005 PENGADEGAN 21478
984 30 November 2021 3175061003 PENGGILINGAN 99255
985 30 November 2021 3172011001 PENJARINGAN 88308
986 30 November 2021 3174101001 PESANGGRAHAN 27545
987 30 November 2021 3171071004 PETAMBURAN 33271
988 30 November 2021 3174071008 PETOGOGAN 11499
989 30 November 2021 3171011004 PETOJO SELATAN 14692
990 30 November 2021 3171011003 PETOJO UTARA 17458
991 30 November 2021 3174101004 PETUKANGAN SELATAN 37187
992 30 November 2021 3174101003 PETUKANGAN UTARA 53716
993 30 November 2021 3175081002 PINANG RANTI 27198
994 30 November 2021 3173031008 PINANGSIA 10729
995 30 November 2021 3175011001 PISANGAN BARU 31776
996 30 November 2021 3175021002 PISANGAN TIMUR 41011
997 30 November 2021 3172011005 PLUIT 47783
998 30 November 2021 3175071002 PONDOK BAMBU 61550
999 30 November 2021 3175071004 PONDOK KELAPA 72015
1000 30 November 2021 3175071007 PONDOK KOPI 35367
1001 30 November 2021 3174061003 PONDOK LABU 46464
1002 30 November 2021 3174051002 PONDOK PINANG 56169
1003 30 November 2021 3175101003 PONDOK RANGGON 25574
1004 30 November 2021 3101011003 PULAU HARAPAN 2060
1005 30 November 2021 3101011002 PULAU KELAPA 5528
1006 30 November 2021 3101011001 PULAU PANGGANG 5450
1007 30 November 2021 3101021003 PULAU PARI 2765
1008 30 November 2021 3101021002 PULAU TIDUNG 4553
1009 30 November 2021 3101021001 PULAU UNTUNG JAWA 1941
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1011 30 November 2021 3175021001 PULO GADUNG 34422
1012 30 November 2021 3175061005 PULO GEBANG 97301
1013 30 November 2021 3174041004 RAGUNAN 39616
1014 30 November 2021 3175091005 RAMBUTAN 36771
1015 30 November 2021 3172031006 RAWA BADAK SELATAN 42179
1016 30 November 2021 3172031004 RAWA BADAK UTARA 34408
1017 30 November 2021 3174071005 RAWA BARAT 5625
1018 30 November 2021 3173011003 RAWA BUAYA 63799
1019 30 November 2021 3175031004 RAWA BUNGA 21434
1020 30 November 2021 3174081003 RAWA JATI 20509
1021 30 November 2021 3175061002 RAWA TERATE 24637
1022 30 November 2021 3175021005 RAWAMANGUN 37612
1023 30 November 2021 3171051003 RAWASARI 21967
1024 30 November 2021 3173041010 ROA MALAKA 3484
1025 30 November 2021 3172041006 ROROTAN 41668
1026 30 November 2021 3174071004 SELONG 2842
1027 30 November 2021 3173061002 SEMANAN 71354
1028 30 November 2021 3172041007 SEMPER BARAT 68587
1029 30 November 2021 3172041005 SEMPER TIMUR 36267
1030 30 November 2021 3174071006 SENAYAN 2771
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1032 30 November 2021 3171031004 SERDANG 29668
1033 30 November 2021 3174021001 SETIA BUDI 3023
1034 30 November 2021 3175101005 SETU 20572
1035 30 November 2021 3173071002 SLIPI 16591
1036 30 November 2021 3173081004 SRENGSENG 45523
1037 30 November 2021 3174091002 SRENGSENG SAWAH 58196
1038 30 November 2021 3173051003 SUKABUMI SELATAN 37033
1039 30 November 2021 3173051002 SUKABUMI UTARA 38290
1040 30 November 2021 3172041002 SUKAPURA 53949
1041 30 November 2021 3171031007 SUMUR BATU 22717
1042 30 November 2021 3172021004 SUNGAI BAMBU 29876
1043 30 November 2021 3172021006 SUNTER AGUNG 71805
1044 30 November 2021 3172021002 SUNTER JAYA 63031
1045 30 November 2021 3175091004 SUSUKAN 38257
1046 30 November 2021 3173031001 TAMAN SARI 14348
1047 30 November 2021 3173041001 TAMBORA 10204
1048 30 November 2021 3173041004 TANAH SEREAL 26387
1049 30 November 2021 3171081004 TANAH TINGGI 37693
1050 30 November 2021 3173031004 TANGKI 13324
1051 30 November 2021 3174091005 TANJUNG BARAT 39770
1052 30 November 2021 3173021005 TANJUNG DUREN SELATAN 25337
1053 30 November 2021 3173021002 TANJUNG DUREN UTARA 17010
1054 30 November 2021 3172021001 TANJUNG PRIOK 34588
1055 30 November 2021 3174011002 TEBET BARAT 21194
1056 30 November 2021 3174011001 TEBET TIMUR 18199
1057 30 November 2021 3173061003 TEGAL ALUR 83647
1058 30 November 2021 3174031004 TEGAL PARANG 32401
1059 30 November 2021 3173021003 TOMANG 29605
1060 30 November 2021 3172031005 TUGU SELATAN 39122
1061 30 November 2021 3172031002 TUGU UTARA 67670
1062 30 November 2021 3175061006 UJUNG MENTENG 29934
1063 30 November 2021 3174101005 ULUJAMI 40544
1064 30 November 2021 3175011006 UTAN KAYU SELATAN 33511
1065 30 November 2021 3175011002 UTAN KAYU UTARA 28169
1066 30 November 2021 3171031008 UTAN PANJANG 29208
1067 30 November 2021 3172021007 WARAKAS 44401
1068 30 November 2021 3173021007 WIJAYA KUSUMA 38934
1069 30 September 2021 3172051003 ANCOL 23947
1070 30 September 2021 3173041007 ANGKE 29381
1071 30 September 2021 3175041005 BALE KAMBANG 29074
1072 30 September 2021 3175031003 BALI MESTER 9752
1073 30 September 2021 3175101006 BAMBU APUS 26285
1074 30 September 2021 3174031002 BANGKA 21566
1075 30 September 2021 3175051002 BARU 23886
1076 30 September 2021 3175041004 BATU AMPAR 47898
1077 30 September 2021 3171071002 BENDUNGAN HILIR 21494
1078 30 September 2021 3175031002 BIDARA CINA 36689
1079 30 September 2021 3174101002 BINTARO 53098
1080 30 September 2021 3174011005 BUKIT DURI 34183
1081 30 September 2021 3171041006 BUNGUR 18144
1082 30 September 2021 3175061007 CAKUNG BARAT 57724
1083 30 September 2021 3175061004 CAKUNG TIMUR 58287
1084 30 September 2021 3175041007 CAWANG 32762
1085 30 September 2021 3175101008 CEGER 18932
1086 30 September 2021 3171031006 CEMPAKA BARU 32581
1087 30 September 2021 3171051002 CEMPAKA PUTIH BARAT 35885
1088 30 September 2021 3171051001 CEMPAKA PUTIH TIMUR 24292
1089 30 September 2021 3173011001 CENGKARENG BARAT 66284
1090 30 September 2021 3173011006 CENGKARENG TIMUR 81027
1091 30 September 2021 3175091002 CIBUBUR 66364
1092 30 September 2021 3171011002 CIDENG 15390
1093 30 September 2021 3174091003 CIGANJUR 38497
1094 30 September 2021 3175051003 CIJANTUNG 41419
1095 30 September 2021 3171061003 CIKINI 8274
1096 30 September 2021 3174081006 CIKOKO 10547
1097 30 September 2021 3174061001 CILANDAK BARAT 51752
1098 30 September 2021 3174041003 CILANDAK TIMUR 25145
1099 30 September 2021 3175101002 CILANGKAP 27635
1100 30 September 2021 3175041006 CILILITAN 40181
1101 30 September 2021 3172041001 CILINCING 43609
1102 30 September 2021 3175101001 CIPAYUNG 26104
1103 30 September 2021 3174091006 CIPEDAK 38303
1104 30 September 2021 3174061005 CIPETE SELATAN 26704
1105 30 September 2021 3174071010 CIPETE UTARA 34102
1106 30 September 2021 3175021003 CIPINANG 39306
1107 30 September 2021 3175031007 CIPINANG BESAR SELATAN 35212
1108 30 September 2021 3175031008 CIPINANG BESAR UTARA 45319
1109 30 September 2021 3175031005 CIPINANG CEMPEDAK 32824
1110 30 September 2021 3175081005 CIPINANG MELAYU 42308
1111 30 September 2021 3175031006 CIPINANG MUARA 55243
1112 30 September 2021 3174051003 CIPULIR 39937
1113 30 September 2021 3175091001 CIRACAS 64594
1114 30 September 2021 3175041003 DUKUH 24609
1115 30 September 2021 3175071001 DUREN SAWIT 61160
1116 30 September 2021 3174081004 DUREN TIGA 28319
1117 30 September 2021 3173051005 DURI KEPA 60138
1118 30 September 2021 3173011002 DURI KOSAMBI 77923
1119 30 September 2021 3171011006 DURI PULO 19610
1120 30 September 2021 3173041011 DURI SELATAN 14259
1121 30 September 2021 3173041003 DURI UTARA 19844
1122 30 September 2021 3171081003 GALUR 18243
1123 30 September 2021 3171011001 GAMBIR 2191
1124 30 September 2021 3174061004 GANDARIA SELATAN 22119
1125 30 September 2021 3174071009 GANDARIA UTARA 39922
1126 30 September 2021 3175051001 GEDONG 35755
1127 30 September 2021 3171071001 GELORA 3127
1128 30 September 2021 3173031007 GLODOK 7837
1129 30 September 2021 3171061004 GONDANGDIA 4100
1130 30 September 2021 3173021001 GROGOL 16876
1131 30 September 2021 3174051005 GROGOL SELATAN 44251
1132 30 September 2021 3174051004 GROGOL UTARA 42494
1133 30 September 2021 3174021007 GUNTUR 3985
1134 30 September 2021 3174071002 GUNUNG 9436
1135 30 September 2021 3171031005 GUNUNG SAHARI SELATAN 19199
1136 30 September 2021 3171021004 GUNUNG SAHARI UTARA 16650
1137 30 September 2021 3175081004 HALIM PERDANA KUSUMAH 28363
1138 30 September 2021 3171031003 HARAPAN MULIA 22557
1139 30 September 2021 3174091001 JAGAKARSA 62389
1140 30 September 2021 3175021007 JATI 32757
1141 30 September 2021 3174041002 JATI PADANG 37502
1142 30 September 2021 3173071004 JATI PULO 28738
1143 30 September 2021 3175061001 JATINEGARA 84160
1144 30 September 2021 3175021004 JATINEGARA KAUM 24632
1145 30 September 2021 3173021004 JELAMBAR 30266
1146 30 September 2021 3173021006 JELAMBAR BARU 38246
1147 30 September 2021 3173041006 JEMBATAN BESI 29314
1148 30 September 2021 3173041008 JEMBATAN LIMA 21021
1149 30 September 2021 3173081005 JOGLO 40839
1150 30 September 2021 3171081001 JOHAR BARU 37488
1151 30 September 2021 3173041002 KALI ANYAR 23856
1152 30 September 2021 3172041004 KALIBARU 67684
1153 30 September 2021 3174081002 KALIBATA 41994
1154 30 September 2021 3173061001 KALIDERES 72447
1155 30 September 2021 3175051004 KALISARI 43212
1156 30 September 2021 3173061004 KAMAL 53783
1157 30 September 2021 3172011002 KAMAL MUARA 12930
1158 30 September 2021 3171071007 KAMPUNG BALI 11358
1159 30 September 2021 3175031001 KAMPUNG MELAYU 25717
1160 30 September 2021 3171081002 KAMPUNG RAWA 21237
1161 30 September 2021 3175041002 KAMPUNG TENGAH 43993
1162 30 September 2021 3173011005 KAPUK 134466
1163 30 September 2021 3172011003 KAPUK MUARA 35781
1164 30 September 2021 3171021002 KARANG ANYAR 25761
1165 30 September 2021 3174021004 KARET 9623
1166 30 September 2021 3174021003 KARET KUNINGAN 16055
1167 30 September 2021 3174021002 KARET SEMANGGI 2770
1168 30 September 2021 3171071003 KARET TENGSIN 19271
1169 30 September 2021 3171021003 KARTINI 22292
1170 30 September 2021 3175011003 KAYU MANIS 25284
1171 30 September 2021 3175021006 KAYU PUTIH 40696
1172 30 September 2021 3173031006 KEAGUNGAN 17387
1173 30 September 2021 3174041007 KEBAGUSAN 44552
1174 30 September 2021 3174051006 KEBAYORAN LAMA SELATAN 41212
1175 30 September 2021 3174051001 KEBAYORAN LAMA UTARA 42422
1176 30 September 2021 3174011004 KEBON BARU 35165
1177 30 September 2021 3172021005 KEBON BAWANG 50952
1178 30 September 2021 3173051001 KEBON JERUK 53887
1179 30 September 2021 3171071006 KEBON KACANG 21531
1180 30 September 2021 3171011005 KEBON KELAPA 10163
1181 30 September 2021 3171031002 KEBON KOSONG 30019
1182 30 September 2021 3175011005 KEBON MANGGIS 16490
1183 30 September 2021 3171071005 KEBON MELATI 32206
1184 30 September 2021 3175081003 KEBON PALA 46614
1185 30 September 2021 3171061005 KEBON SIRIH 12299
1186 30 September 2021 3173011004 KEDAUNG KALI ANGKE 32612
1187 30 September 2021 3173051007 KEDOYA SELATAN 32165
1188 30 September 2021 3173051006 KEDOYA UTARA 45740
1189 30 September 2021 3173051004 KELAPA DUA 23565
1190 30 September 2021 3175091003 KELAPA DUA WETAN 46875
1191 30 September 2021 3172061003 KELAPA GADING BARAT 35717
1192 30 September 2021 3172061001 KELAPA GADING TIMUR 33592
1193 30 September 2021 3173071005 KEMANGGISAN 32139
1194 30 September 2021 3171031001 KEMAYORAN 20708
1195 30 September 2021 3173081006 KEMBANGAN SELATAN 27130
1196 30 September 2021 3173081001 KEMBANGAN UTARA 54451
1197 30 September 2021 3171041002 KENARI 9579
1198 30 September 2021 3173041005 KERENDANG 19973
1199 30 September 2021 3175071003 KLENDER 69978
1200 30 September 2021 3172031001 KOJA 28175
1201 30 September 2021 3173071006 KOTA BAMBU SELATAN 21158
1202 30 September 2021 3173071003 KOTA BAMBU UTARA 24995
1203 30 September 2021 3171041004 KRAMAT 28835
1204 30 September 2021 3175041001 KRAMAT JATI 34144
1205 30 September 2021 3174071003 KRAMAT PELA 14413
1206 30 September 2021 3173031002 KRUKUT 19244
1207 30 September 2021 3174031005 KUNINGAN BARAT 12785
1208 30 September 2021 3174021008 KUNINGAN TIMUR 6008
1209 30 September 2021 3171041005 KWITANG 15486
1210 30 September 2021 3172031003 LAGOA 59065
1211 30 September 2021 3174061002 LEBAK BULUS 37626
1212 30 September 2021 3174091004 LENTENG AGUNG 53946
1213 30 September 2021 3175101007 LUBANG BUAYA 63547
1214 30 September 2021 3175081001 MAKASAR 34390
1215 30 September 2021 3175071006 MALAKA JAYA 30927
1216 30 September 2021 3175071005 MALAKA SARI 27530
1217 30 September 2021 3174031001 MAMPANG PRAPATAN 18159
1218 30 September 2021 3173031005 MANGGA BESAR 7707
1219 30 September 2021 3171021005 MANGGA DUA SELATAN 27442
1220 30 September 2021 3174011007 MANGGARAI 28373
1221 30 September 2021 3174011006 MANGGARAI SELATAN 23075
1222 30 September 2021 3173031003 MAPHAR 16461
1223 30 September 2021 3172041003 MARUNDA 28943
1224 30 September 2021 3174071001 MELAWAI 2627
1225 30 September 2021 3171061001 MENTENG 25624
1226 30 September 2021 3174021005 MENTENG ATAS 27816
1227 30 September 2021 3174011003 MENTENG DALAM 36028
1228 30 September 2021 3173081003 MERUYA SELATAN 32130
1229 30 September 2021 3173081002 MERUYA UTARA 43752
1230 30 September 2021 3175101004 MUNJUL 23871
1231 30 September 2021 3172051002 PADEMANGAN BARAT 74240
1232 30 September 2021 3172051001 PADEMANGAN TIMUR 37736
1233 30 September 2021 3175011004 PAL MERIAM 19769
1234 30 September 2021 3173071001 PALMERAH 63011
1235 30 September 2021 3174081001 PANCORAN 19421
1236 30 September 2021 3172021003 PAPANGGO 39097
1237 30 September 2021 3171021001 PASAR BARU 12035
1238 30 September 2021 3174021006 PASAR MANGGIS 26752
1239 30 September 2021 3174041001 PASAR MINGGU 24821
1240 30 September 2021 3171041003 PASEBAN 23533
1241 30 September 2021 3173061005 PEGADUNGAN 75587
1242 30 September 2021 3171061002 PEGANGSAAN 23455
1243 30 September 2021 3172061002 PEGANGSAAN DUA 49976
1244 30 September 2021 3172011004 PEJAGALAN 74578
1245 30 September 2021 3174041006 PEJATEN BARAT 37346
1246 30 September 2021 3174041005 PEJATEN TIMUR 57656
1247 30 September 2021 3175051005 PEKAYON 43250
1248 30 September 2021 3173041009 PEKOJAN 23587
1249 30 September 2021 3174031003 PELA MAMPANG 43522
1250 30 September 2021 3174081005 PENGADEGAN 21478
1251 30 September 2021 3175061003 PENGGILINGAN 99255
1252 30 September 2021 3172011001 PENJARINGAN 88308
1253 30 September 2021 3174101001 PESANGGRAHAN 27545
1254 30 September 2021 3171071004 PETAMBURAN 33271
1255 30 September 2021 3174071008 PETOGOGAN 11499
1256 30 September 2021 3171011004 PETOJO SELATAN 14692
1257 30 September 2021 3171011003 PETOJO UTARA 17458
1258 30 September 2021 3174101004 PETUKANGAN SELATAN 37187
1259 30 September 2021 3174101003 PETUKANGAN UTARA 53716
1260 30 September 2021 3175081002 PINANG RANTI 27198
1261 30 September 2021 3173031008 PINANGSIA 10729
1262 30 September 2021 3175011001 PISANGAN BARU 31776
1263 30 September 2021 3175021002 PISANGAN TIMUR 41011
1264 30 September 2021 3172011005 PLUIT 47783
1265 30 September 2021 3175071002 PONDOK BAMBU 61550
1266 30 September 2021 3175071004 PONDOK KELAPA 72015
1267 30 September 2021 3175071007 PONDOK KOPI 35367
1268 30 September 2021 3174061003 PONDOK LABU 46464
1269 30 September 2021 3174051002 PONDOK PINANG 56169
1270 30 September 2021 3175101003 PONDOK RANGGON 25574
1271 30 September 2021 3101011003 PULAU HARAPAN 2060
1272 30 September 2021 3101011002 PULAU KELAPA 5528
1273 30 September 2021 3101011001 PULAU PANGGANG 5450
1274 30 September 2021 3101021003 PULAU PARI 2765
1275 30 September 2021 3101021002 PULAU TIDUNG 4553
1276 30 September 2021 3101021001 PULAU UNTUNG JAWA 1941
1277 30 September 2021 3174071007 PULO 5835
1278 30 September 2021 3175021001 PULO GADUNG 34422
1279 30 September 2021 3175061005 PULO GEBANG 97301
1280 30 September 2021 3174041004 RAGUNAN 39616
1281 30 September 2021 3175091005 RAMBUTAN 36771
1282 30 September 2021 3172031006 RAWA BADAK SELATAN 42179
1283 30 September 2021 3172031004 RAWA BADAK UTARA 34408
1284 30 September 2021 3174071005 RAWA BARAT 5625
1285 30 September 2021 3173011003 RAWA BUAYA 63799
1286 30 September 2021 3175031004 RAWA BUNGA 21434
1287 30 September 2021 3174081003 RAWA JATI 20509
1288 30 September 2021 3175061002 RAWA TERATE 24637
1289 30 September 2021 3175021005 RAWAMANGUN 37612
1290 30 September 2021 3171051003 RAWASARI 21967
1291 30 September 2021 3173041010 ROA MALAKA 3484
1292 30 September 2021 3172041006 ROROTAN 41668
1293 30 September 2021 3174071004 SELONG 2842
1294 30 September 2021 3173061002 SEMANAN 71354
1295 30 September 2021 3172041007 SEMPER BARAT 68587
1296 30 September 2021 3172041005 SEMPER TIMUR 36267
1297 30 September 2021 3174071006 SENAYAN 2771
1298 30 September 2021 3171041001 SENEN 6804
1299 30 September 2021 3171031004 SERDANG 29668
1300 30 September 2021 3174021001 SETIA BUDI 3023
1301 30 September 2021 3175101005 SETU 20572
1302 30 September 2021 3173071002 SLIPI 16591
1303 30 September 2021 3173081004 SRENGSENG 45523
1304 30 September 2021 3174091002 SRENGSENG SAWAH 58196
1305 30 September 2021 3173051003 SUKABUMI SELATAN 37033
1306 30 September 2021 3173051002 SUKABUMI UTARA 38290
1307 30 September 2021 3172041002 SUKAPURA 53949
1308 30 September 2021 3171031007 SUMUR BATU 22717
1309 30 September 2021 3172021004 SUNGAI BAMBU 29876
1310 30 September 2021 3172021006 SUNTER AGUNG 71805
1311 30 September 2021 3172021002 SUNTER JAYA 63031
1312 30 September 2021 3175091004 SUSUKAN 38257
1313 30 September 2021 3173031001 TAMAN SARI 14348
1314 30 September 2021 3173041001 TAMBORA 10204
1315 30 September 2021 3173041004 TANAH SEREAL 26387
1316 30 September 2021 3171081004 TANAH TINGGI 37693
1317 30 September 2021 3173031004 TANGKI 13324
1318 30 September 2021 3174091005 TANJUNG BARAT 39770
1319 30 September 2021 3173021005 TANJUNG DUREN SELATAN 25337
1320 30 September 2021 3173021002 TANJUNG DUREN UTARA 17010
1321 30 September 2021 3172021001 TANJUNG PRIOK 34588
1322 30 September 2021 3174011002 TEBET BARAT 21194
1323 30 September 2021 3174011001 TEBET TIMUR 18199
1324 30 September 2021 3173061003 TEGAL ALUR 83647
1325 30 September 2021 3174031004 TEGAL PARANG 32401
1326 30 September 2021 3173021003 TOMANG 29605
1327 30 September 2021 3172031005 TUGU SELATAN 39122
1328 30 September 2021 3172031002 TUGU UTARA 67670
1329 30 September 2021 3175061006 UJUNG MENTENG 29934
1330 30 September 2021 3174101005 ULUJAMI 40544
1331 30 September 2021 3175011006 UTAN KAYU SELATAN 33511
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1333 30 September 2021 3171031008 UTAN PANJANG 29208
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1794 31 December 2021 3175081002 PINANG RANTI 27198
1795 31 December 2021 3173031008 PINANGSIA 10729
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1799 31 December 2021 3175071002 PONDOK BAMBU 61550
1800 31 December 2021 3175071004 PONDOK KELAPA 72015
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1810 31 December 2021 3101021001 PULAU UNTUNG JAWA 1941
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1813 31 December 2021 3175061005 PULO GEBANG 97301
1814 31 December 2021 3174041004 RAGUNAN 39616
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1818 31 December 2021 3174071005 RAWA BARAT 5625
1819 31 December 2021 3173011003 RAWA BUAYA 63799
1820 31 December 2021 3175031004 RAWA BUNGA 21434
1821 31 December 2021 3174081003 RAWA JATI 20509
1822 31 December 2021 3175061002 RAWA TERATE 24637
1823 31 December 2021 3175021005 RAWAMANGUN 37612
1824 31 December 2021 3171051003 RAWASARI 21967
1825 31 December 2021 3173041010 ROA MALAKA 3484
1826 31 December 2021 3172041006 ROROTAN 41668
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1829 31 December 2021 3172041007 SEMPER BARAT 68587
1830 31 December 2021 3172041005 SEMPER TIMUR 36267
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1833 31 December 2021 3171031004 SERDANG 29668
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1835 31 December 2021 3175101005 SETU 20572
1836 31 December 2021 3173071002 SLIPI 16591
1837 31 December 2021 3173081004 SRENGSENG 45523
1838 31 December 2021 3174091002 SRENGSENG SAWAH 58196
1839 31 December 2021 3173051003 SUKABUMI SELATAN 37033
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1841 31 December 2021 3172041002 SUKAPURA 53949
1842 31 December 2021 3171031007 SUMUR BATU 22717
1843 31 December 2021 3172021004 SUNGAI BAMBU 29876
1844 31 December 2021 3172021006 SUNTER AGUNG 71805
1845 31 December 2021 3172021002 SUNTER JAYA 63031
1846 31 December 2021 3175091004 SUSUKAN 38257
1847 31 December 2021 3173031001 TAMAN SARI 14348
1848 31 December 2021 3173041001 TAMBORA 10204
1849 31 December 2021 3173041004 TANAH SEREAL 26387
1850 31 December 2021 3171081004 TANAH TINGGI 37693
1851 31 December 2021 3173031004 TANGKI 13324
1852 31 December 2021 3174091005 TANJUNG BARAT 39770
1853 31 December 2021 3173021005 TANJUNG DUREN SELATAN 25337
1854 31 December 2021 3173021002 TANJUNG DUREN UTARA 17010
1855 31 December 2021 3172021001 TANJUNG PRIOK 34588
1856 31 December 2021 3174011002 TEBET BARAT 21194
1857 31 December 2021 3174011001 TEBET TIMUR 18199
1858 31 December 2021 3173061003 TEGAL ALUR 83647
1859 31 December 2021 3174031004 TEGAL PARANG 32401
1860 31 December 2021 3173021003 TOMANG 29605
1861 31 December 2021 3172031005 TUGU SELATAN 39122
1862 31 December 2021 3172031002 TUGU UTARA 67670
1863 31 December 2021 3175061006 UJUNG MENTENG 29934
1864 31 December 2021 3174101005 ULUJAMI 40544
1865 31 December 2021 3175011006 UTAN KAYU SELATAN 33511
1866 31 December 2021 3175011002 UTAN KAYU UTARA 28169
1867 31 December 2021 3171031008 UTAN PANJANG 29208
1868 31 December 2021 3172021007 WARAKAS 44401
1869 31 December 2021 3173021007 WIJAYA KUSUMA 38934
1870 31 January 2022 3172051003 ANCOL 23947
1871 31 January 2022 3173041007 ANGKE 29381
1872 31 January 2022 3175041005 BALE KAMBANG 29074
1873 31 January 2022 3175031003 BALI MESTER 9752
1874 31 January 2022 3175101006 BAMBU APUS 26285
1875 31 January 2022 3174031002 BANGKA 21566
1876 31 January 2022 3175051002 BARU 23886
1877 31 January 2022 3175041004 BATU AMPAR 47898
1878 31 January 2022 3171071002 BENDUNGAN HILIR 21494
1879 31 January 2022 3175031002 BIDARA CINA 36689
1880 31 January 2022 3174101002 BINTARO 53098
1881 31 January 2022 3174011005 BUKIT DURI 34183
1882 31 January 2022 3171041006 BUNGUR 18144
1883 31 January 2022 3175061007 CAKUNG BARAT 57724
1884 31 January 2022 3175061004 CAKUNG TIMUR 58287
1885 31 January 2022 3175041007 CAWANG 32762
1886 31 January 2022 3175101008 CEGER 18932
1887 31 January 2022 3171031006 CEMPAKA BARU 32581
1888 31 January 2022 3171051002 CEMPAKA PUTIH BARAT 35885
1889 31 January 2022 3171051001 CEMPAKA PUTIH TIMUR 24292
1890 31 January 2022 3173011001 CENGKARENG BARAT 66284
1891 31 January 2022 3173011006 CENGKARENG TIMUR 81027
1892 31 January 2022 3175091002 CIBUBUR 66364
1893 31 January 2022 3171011002 CIDENG 15390
1894 31 January 2022 3174091003 CIGANJUR 38497
1895 31 January 2022 3175051003 CIJANTUNG 41419
1896 31 January 2022 3171061003 CIKINI 8274
1897 31 January 2022 3174081006 CIKOKO 10547
1898 31 January 2022 3174061001 CILANDAK BARAT 51752
1899 31 January 2022 3174041003 CILANDAK TIMUR 25145
1900 31 January 2022 3175101002 CILANGKAP 27635
1901 31 January 2022 3175041006 CILILITAN 40181
1902 31 January 2022 3172041001 CILINCING 43609
1903 31 January 2022 3175101001 CIPAYUNG 26104
1904 31 January 2022 3174091006 CIPEDAK 38303
1905 31 January 2022 3174061005 CIPETE SELATAN 26704
1906 31 January 2022 3174071010 CIPETE UTARA 34102
1907 31 January 2022 3175021003 CIPINANG 39306
1908 31 January 2022 3175031007 CIPINANG BESAR SELATAN 35212
1909 31 January 2022 3175031008 CIPINANG BESAR UTARA 45319
1910 31 January 2022 3175031005 CIPINANG CEMPEDAK 32824
1911 31 January 2022 3175081005 CIPINANG MELAYU 42308
1912 31 January 2022 3175031006 CIPINANG MUARA 55243
1913 31 January 2022 3174051003 CIPULIR 39937
1914 31 January 2022 3175091001 CIRACAS 64594
1915 31 January 2022 3175041003 DUKUH 24609
1916 31 January 2022 3175071001 DUREN SAWIT 61160
1917 31 January 2022 3174081004 DUREN TIGA 28319
1918 31 January 2022 3173051005 DURI KEPA 60138
1919 31 January 2022 3173011002 DURI KOSAMBI 77923
1920 31 January 2022 3171011006 DURI PULO 19610
1921 31 January 2022 3173041011 DURI SELATAN 14259
1922 31 January 2022 3173041003 DURI UTARA 19844
1923 31 January 2022 3171081003 GALUR 18243
1924 31 January 2022 3171011001 GAMBIR 2191
1925 31 January 2022 3174061004 GANDARIA SELATAN 22119
1926 31 January 2022 3174071009 GANDARIA UTARA 39922
1927 31 January 2022 3175051001 GEDONG 35755
1928 31 January 2022 3171071001 GELORA 3127
1929 31 January 2022 3173031007 GLODOK 7837
1930 31 January 2022 3171061004 GONDANGDIA 4100
1931 31 January 2022 3173021001 GROGOL 16876
1932 31 January 2022 3174051005 GROGOL SELATAN 44251
1933 31 January 2022 3174051004 GROGOL UTARA 42494
1934 31 January 2022 3174021007 GUNTUR 3985
1935 31 January 2022 3174071002 GUNUNG 9436
1936 31 January 2022 3171031005 GUNUNG SAHARI SELATAN 19199
1937 31 January 2022 3171021004 GUNUNG SAHARI UTARA 16650
1938 31 January 2022 3175081004 HALIM PERDANA KUSUMAH 28363
1939 31 January 2022 3171031003 HARAPAN MULIA 22557
1940 31 January 2022 3174091001 JAGAKARSA 62389
1941 31 January 2022 3175021007 JATI 32757
1942 31 January 2022 3174041002 JATI PADANG 37502
1943 31 January 2022 3173071004 JATI PULO 28738
1944 31 January 2022 3175061001 JATINEGARA 84160
1945 31 January 2022 3175021004 JATINEGARA KAUM 24632
1946 31 January 2022 3173021004 JELAMBAR 30266
1947 31 January 2022 3173021006 JELAMBAR BARU 38246
1948 31 January 2022 3173041006 JEMBATAN BESI 29314
1949 31 January 2022 3173041008 JEMBATAN LIMA 21021
1950 31 January 2022 3173081005 JOGLO 40839
1951 31 January 2022 3171081001 JOHAR BARU 37488
1952 31 January 2022 3173041002 KALI ANYAR 23856
1953 31 January 2022 3172041004 KALIBARU 67684
1954 31 January 2022 3174081002 KALIBATA 41994
1955 31 January 2022 3173061001 KALIDERES 72447
1956 31 January 2022 3175051004 KALISARI 43212
1957 31 January 2022 3173061004 KAMAL 53783
1958 31 January 2022 3172011002 KAMAL MUARA 12930
1959 31 January 2022 3171071007 KAMPUNG BALI 11358
1960 31 January 2022 3175031001 KAMPUNG MELAYU 25717
1961 31 January 2022 3171081002 KAMPUNG RAWA 21237
1962 31 January 2022 3175041002 KAMPUNG TENGAH 43993
1963 31 January 2022 3173011005 KAPUK 134466
1964 31 January 2022 3172011003 KAPUK MUARA 35781
1965 31 January 2022 3171021002 KARANG ANYAR 25761
1966 31 January 2022 3174021004 KARET 9623
1967 31 January 2022 3174021003 KARET KUNINGAN 16055
1968 31 January 2022 3174021002 KARET SEMANGGI 2770
1969 31 January 2022 3171071003 KARET TENGSIN 19271
1970 31 January 2022 3171021003 KARTINI 22292
1971 31 January 2022 3175011003 KAYU MANIS 25284
1972 31 January 2022 3175021006 KAYU PUTIH 40696
1973 31 January 2022 3173031006 KEAGUNGAN 17387
1974 31 January 2022 3174041007 KEBAGUSAN 44552
1975 31 January 2022 3174051006 KEBAYORAN LAMA SELATAN 41212
1976 31 January 2022 3174051001 KEBAYORAN LAMA UTARA 42422
1977 31 January 2022 3174011004 KEBON BARU 35165
1978 31 January 2022 3172021005 KEBON BAWANG 50952
1979 31 January 2022 3173051001 KEBON JERUK 53887
1980 31 January 2022 3171071006 KEBON KACANG 21531
1981 31 January 2022 3171011005 KEBON KELAPA 10163
1982 31 January 2022 3171031002 KEBON KOSONG 30019
1983 31 January 2022 3175011005 KEBON MANGGIS 16490
1984 31 January 2022 3171071005 KEBON MELATI 32206
1985 31 January 2022 3175081003 KEBON PALA 46614
1986 31 January 2022 3171061005 KEBON SIRIH 12299
1987 31 January 2022 3173011004 KEDAUNG KALI ANGKE 32612
1988 31 January 2022 3173051007 KEDOYA SELATAN 32165
1989 31 January 2022 3173051006 KEDOYA UTARA 45740
1990 31 January 2022 3173051004 KELAPA DUA 23565
1991 31 January 2022 3175091003 KELAPA DUA WETAN 46875
1992 31 January 2022 3172061003 KELAPA GADING BARAT 35717
1993 31 January 2022 3172061001 KELAPA GADING TIMUR 33592
1994 31 January 2022 3173071005 KEMANGGISAN 32139
1995 31 January 2022 3171031001 KEMAYORAN 20708
1996 31 January 2022 3173081006 KEMBANGAN SELATAN 27130
1997 31 January 2022 3173081001 KEMBANGAN UTARA 54451
1998 31 January 2022 3171041002 KENARI 9579
1999 31 January 2022 3173041005 KERENDANG 19973
2000 31 January 2022 3175071003 KLENDER 69978
2001 31 January 2022 3172031001 KOJA 28175
2002 31 January 2022 3173071006 KOTA BAMBU SELATAN 21158
2003 31 January 2022 3173071003 KOTA BAMBU UTARA 24995
2004 31 January 2022 3171041004 KRAMAT 28835
2005 31 January 2022 3175041001 KRAMAT JATI 34144
2006 31 January 2022 3174071003 KRAMAT PELA 14413
2007 31 January 2022 3173031002 KRUKUT 19244
2008 31 January 2022 3174031005 KUNINGAN BARAT 12785
2009 31 January 2022 3174021008 KUNINGAN TIMUR 6008
2010 31 January 2022 3171041005 KWITANG 15486
2011 31 January 2022 3172031003 LAGOA 59065
2012 31 January 2022 3174061002 LEBAK BULUS 37626
2013 31 January 2022 3174091004 LENTENG AGUNG 53946
2014 31 January 2022 3175101007 LUBANG BUAYA 63547
2015 31 January 2022 3175081001 MAKASAR 34390
2016 31 January 2022 3175071006 MALAKA JAYA 30927
2017 31 January 2022 3175071005 MALAKA SARI 27530
2018 31 January 2022 3174031001 MAMPANG PRAPATAN 18159
2019 31 January 2022 3173031005 MANGGA BESAR 7707
2020 31 January 2022 3171021005 MANGGA DUA SELATAN 27442
2021 31 January 2022 3174011007 MANGGARAI 28373
2022 31 January 2022 3174011006 MANGGARAI SELATAN 23075
2023 31 January 2022 3173031003 MAPHAR 16461
2024 31 January 2022 3172041003 MARUNDA 28943
2025 31 January 2022 3174071001 MELAWAI 2627
2026 31 January 2022 3171061001 MENTENG 25624
2027 31 January 2022 3174021005 MENTENG ATAS 27816
2028 31 January 2022 3174011003 MENTENG DALAM 36028
2029 31 January 2022 3173081003 MERUYA SELATAN 32130
2030 31 January 2022 3173081002 MERUYA UTARA 43752
2031 31 January 2022 3175101004 MUNJUL 23871
2032 31 January 2022 3172051002 PADEMANGAN BARAT 74240
2033 31 January 2022 3172051001 PADEMANGAN TIMUR 37736
2034 31 January 2022 3175011004 PAL MERIAM 19769
2035 31 January 2022 3173071001 PALMERAH 63011
2036 31 January 2022 3174081001 PANCORAN 19421
2037 31 January 2022 3172021003 PAPANGGO 39097
2038 31 January 2022 3171021001 PASAR BARU 12035
2039 31 January 2022 3174021006 PASAR MANGGIS 26752
2040 31 January 2022 3174041001 PASAR MINGGU 24821
2041 31 January 2022 3171041003 PASEBAN 23533
2042 31 January 2022 3173061005 PEGADUNGAN 75587
2043 31 January 2022 3171061002 PEGANGSAAN 23455
2044 31 January 2022 3172061002 PEGANGSAAN DUA 49976
2045 31 January 2022 3172011004 PEJAGALAN 74578
2046 31 January 2022 3174041006 PEJATEN BARAT 37346
2047 31 January 2022 3174041005 PEJATEN TIMUR 57656
2048 31 January 2022 3175051005 PEKAYON 43250
2049 31 January 2022 3173041009 PEKOJAN 23587
2050 31 January 2022 3174031003 PELA MAMPANG 43522
2051 31 January 2022 3174081005 PENGADEGAN 21478
2052 31 January 2022 3175061003 PENGGILINGAN 99255
2053 31 January 2022 3172011001 PENJARINGAN 88308
2054 31 January 2022 3174101001 PESANGGRAHAN 27545
2055 31 January 2022 3171071004 PETAMBURAN 33271
2056 31 January 2022 3174071008 PETOGOGAN 11499
2057 31 January 2022 3171011004 PETOJO SELATAN 14692
2058 31 January 2022 3171011003 PETOJO UTARA 17458
2059 31 January 2022 3174101004 PETUKANGAN SELATAN 37187
2060 31 January 2022 3174101003 PETUKANGAN UTARA 53716
2061 31 January 2022 3175081002 PINANG RANTI 27198
2062 31 January 2022 3173031008 PINANGSIA 10729
2063 31 January 2022 3175011001 PISANGAN BARU 31776
2064 31 January 2022 3175021002 PISANGAN TIMUR 41011
2065 31 January 2022 3172011005 PLUIT 47783
2066 31 January 2022 3175071002 PONDOK BAMBU 61550
2067 31 January 2022 3175071004 PONDOK KELAPA 72015
2068 31 January 2022 3175071007 PONDOK KOPI 35367
2069 31 January 2022 3174061003 PONDOK LABU 46464
2070 31 January 2022 3174051002 PONDOK PINANG 56169
2071 31 January 2022 3175101003 PONDOK RANGGON 25574
2072 31 January 2022 3101011003 PULAU HARAPAN 2060
2073 31 January 2022 3101011002 PULAU KELAPA 5528
2074 31 January 2022 3101011001 PULAU PANGGANG 5450
2075 31 January 2022 3101021003 PULAU PARI 2765
2076 31 January 2022 3101021002 PULAU TIDUNG 4553
2077 31 January 2022 3101021001 PULAU UNTUNG JAWA 1941
2078 31 January 2022 3174071007 PULO 5835
2079 31 January 2022 3175021001 PULO GADUNG 34422
2080 31 January 2022 3175061005 PULO GEBANG 97301
2081 31 January 2022 3174041004 RAGUNAN 39616
2082 31 January 2022 3175091005 RAMBUTAN 36771
2083 31 January 2022 3172031006 RAWA BADAK SELATAN 42179
2084 31 January 2022 3172031004 RAWA BADAK UTARA 34408
2085 31 January 2022 3174071005 RAWA BARAT 5625
2086 31 January 2022 3173011003 RAWA BUAYA 63799
2087 31 January 2022 3175031004 RAWA BUNGA 21434
2088 31 January 2022 3174081003 RAWA JATI 20509
2089 31 January 2022 3175061002 RAWA TERATE 24637
2090 31 January 2022 3175021005 RAWAMANGUN 37612
2091 31 January 2022 3171051003 RAWASARI 21967
2092 31 January 2022 3173041010 ROA MALAKA 3484
2093 31 January 2022 3172041006 ROROTAN 41668
2094 31 January 2022 3174071004 SELONG 2842
2095 31 January 2022 3173061002 SEMANAN 71354
2096 31 January 2022 3172041007 SEMPER BARAT 68587
2097 31 January 2022 3172041005 SEMPER TIMUR 36267
2098 31 January 2022 3174071006 SENAYAN 2771
2099 31 January 2022 3171041001 SENEN 6804
2100 31 January 2022 3171031004 SERDANG 29668
2101 31 January 2022 3174021001 SETIA BUDI 3023
2102 31 January 2022 3175101005 SETU 20572
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2104 31 January 2022 3173081004 SRENGSENG 45523
2105 31 January 2022 3174091002 SRENGSENG SAWAH 58196
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2107 31 January 2022 3173051002 SUKABUMI UTARA 38290
2108 31 January 2022 3172041002 SUKAPURA 53949
2109 31 January 2022 3171031007 SUMUR BATU 22717
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2111 31 January 2022 3172021006 SUNTER AGUNG 71805
2112 31 January 2022 3172021002 SUNTER JAYA 63031
2113 31 January 2022 3175091004 SUSUKAN 38257
2114 31 January 2022 3173031001 TAMAN SARI 14348
2115 31 January 2022 3173041001 TAMBORA 10204
2116 31 January 2022 3173041004 TANAH SEREAL 26387
2117 31 January 2022 3171081004 TANAH TINGGI 37693
2118 31 January 2022 3173031004 TANGKI 13324
2119 31 January 2022 3174091005 TANJUNG BARAT 39770
2120 31 January 2022 3173021005 TANJUNG DUREN SELATAN 25337
2121 31 January 2022 3173021002 TANJUNG DUREN UTARA 17010
2122 31 January 2022 3172021001 TANJUNG PRIOK 34588
2123 31 January 2022 3174011002 TEBET BARAT 21194
2124 31 January 2022 3174011001 TEBET TIMUR 18199
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2126 31 January 2022 3174031004 TEGAL PARANG 32401
2127 31 January 2022 3173021003 TOMANG 29605
2128 31 January 2022 3172031005 TUGU SELATAN 39122
2129 31 January 2022 3172031002 TUGU UTARA 67670
2130 31 January 2022 3175061006 UJUNG MENTENG 29934
2131 31 January 2022 3174101005 ULUJAMI 40544
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2133 31 January 2022 3175011002 UTAN KAYU UTARA 28169
2134 31 January 2022 3171031008 UTAN PANJANG 29208
2135 31 January 2022 3172021007 WARAKAS 44401
2136 31 January 2022 3173021007 WIJAYA KUSUMA 38934
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2139 31 July 2021 3175041005 BALE KAMBANG 29074
2140 31 July 2021 3175031003 BALI MESTER 9752
2141 31 July 2021 3175101006 BAMBU APUS 26285
2142 31 July 2021 3174031002 BANGKA 21566
2143 31 July 2021 3175051002 BARU 23886
2144 31 July 2021 3175041004 BATU AMPAR 47898
2145 31 July 2021 3171071002 BENDUNGAN HILIR 21494
2146 31 July 2021 3175031002 BIDARA CINA 36689
2147 31 July 2021 3174101002 BINTARO 53098
2148 31 July 2021 3174011005 BUKIT DURI 34183
2149 31 July 2021 3171041006 BUNGUR 18144
2150 31 July 2021 3175061007 CAKUNG BARAT 57724
2151 31 July 2021 3175061004 CAKUNG TIMUR 58287
2152 31 July 2021 3175041007 CAWANG 32762
2153 31 July 2021 3175101008 CEGER 18932
2154 31 July 2021 3171031006 CEMPAKA BARU 32581
2155 31 July 2021 3171051002 CEMPAKA PUTIH BARAT 35885
2156 31 July 2021 3171051001 CEMPAKA PUTIH TIMUR 24292
2157 31 July 2021 3173011001 CENGKARENG BARAT 66284
2158 31 July 2021 3173011006 CENGKARENG TIMUR 81027
2159 31 July 2021 3175091002 CIBUBUR 66364
2160 31 July 2021 3171011002 CIDENG 15390
2161 31 July 2021 3174091003 CIGANJUR 38497
2162 31 July 2021 3175051003 CIJANTUNG 41419
2163 31 July 2021 3171061003 CIKINI 8274
2164 31 July 2021 3174081006 CIKOKO 10547
2165 31 July 2021 3174061001 CILANDAK BARAT 51752
2166 31 July 2021 3174041003 CILANDAK TIMUR 25145
2167 31 July 2021 3175101002 CILANGKAP 27635
2168 31 July 2021 3175041006 CILILITAN 40181
2169 31 July 2021 3172041001 CILINCING 43609
2170 31 July 2021 3175101001 CIPAYUNG 26104
2171 31 July 2021 3174091006 CIPEDAK 38303
2172 31 July 2021 3174061005 CIPETE SELATAN 26704
2173 31 July 2021 3174071010 CIPETE UTARA 34102
2174 31 July 2021 3175021003 CIPINANG 39306
2175 31 July 2021 3175031007 CIPINANG BESAR SELATAN 35212
2176 31 July 2021 3175031008 CIPINANG BESAR UTARA 45319
2177 31 July 2021 3175031005 CIPINANG CEMPEDAK 32824
2178 31 July 2021 3175081005 CIPINANG MELAYU 42308
2179 31 July 2021 3175031006 CIPINANG MUARA 55243
2180 31 July 2021 3174051003 CIPULIR 39937
2181 31 July 2021 3175091001 CIRACAS 64594
2182 31 July 2021 3175041003 DUKUH 24609
2183 31 July 2021 3175071001 DUREN SAWIT 61160
2184 31 July 2021 3174081004 DUREN TIGA 28319
2185 31 July 2021 3173051005 DURI KEPA 60138
2186 31 July 2021 3173011002 DURI KOSAMBI 77923
2187 31 July 2021 3171011006 DURI PULO 19610
2188 31 July 2021 3173041011 DURI SELATAN 14259
2189 31 July 2021 3173041003 DURI UTARA 19844
2190 31 July 2021 3171081003 GALUR 18243
2191 31 July 2021 3171011001 GAMBIR 2191
2192 31 July 2021 3174061004 GANDARIA SELATAN 22119
2193 31 July 2021 3174071009 GANDARIA UTARA 39922
2194 31 July 2021 3175051001 GEDONG 35755
2195 31 July 2021 3171071001 GELORA 3127
2196 31 July 2021 3173031007 GLODOK 7837
2197 31 July 2021 3171061004 GONDANGDIA 4100
2198 31 July 2021 3173021001 GROGOL 16876
2199 31 July 2021 3174051005 GROGOL SELATAN 44251
2200 31 July 2021 3174051004 GROGOL UTARA 42494
2201 31 July 2021 3174021007 GUNTUR 3985
2202 31 July 2021 3174071002 GUNUNG 9436
2203 31 July 2021 3171031005 GUNUNG SAHARI SELATAN 19199
2204 31 July 2021 3171021004 GUNUNG SAHARI UTARA 16650
2205 31 July 2021 3175081004 HALIM PERDANA KUSUMAH 28363
2206 31 July 2021 3171031003 HARAPAN MULIA 22557
2207 31 July 2021 3174091001 JAGAKARSA 62389
2208 31 July 2021 3175021007 JATI 32757
2209 31 July 2021 3174041002 JATI PADANG 37502
2210 31 July 2021 3173071004 JATI PULO 28738
2211 31 July 2021 3175061001 JATINEGARA 84160
2212 31 July 2021 3175021004 JATINEGARA KAUM 24632
2213 31 July 2021 3173021004 JELAMBAR 30266
2214 31 July 2021 3173021006 JELAMBAR BARU 38246
2215 31 July 2021 3173041006 JEMBATAN BESI 29314
2216 31 July 2021 3173041008 JEMBATAN LIMA 21021
2217 31 July 2021 3173081005 JOGLO 40839
2218 31 July 2021 3171081001 JOHAR BARU 37488
2219 31 July 2021 3173041002 KALI ANYAR 23856
2220 31 July 2021 3172041004 KALIBARU 67684
2221 31 July 2021 3174081002 KALIBATA 41994
2222 31 July 2021 3173061001 KALIDERES 72447
2223 31 July 2021 3175051004 KALISARI 43212
2224 31 July 2021 3173061004 KAMAL 53783
2225 31 July 2021 3172011002 KAMAL MUARA 12930
2226 31 July 2021 3171071007 KAMPUNG BALI 11358
2227 31 July 2021 3175031001 KAMPUNG MELAYU 25717
2228 31 July 2021 3171081002 KAMPUNG RAWA 21237
2229 31 July 2021 3175041002 KAMPUNG TENGAH 43993
2230 31 July 2021 3173011005 KAPUK 134466
2231 31 July 2021 3172011003 KAPUK MUARA 35781
2232 31 July 2021 3171021002 KARANG ANYAR 25761
2233 31 July 2021 3174021004 KARET 9623
2234 31 July 2021 3174021003 KARET KUNINGAN 16055
2235 31 July 2021 3174021002 KARET SEMANGGI 2770
2236 31 July 2021 3171071003 KARET TENGSIN 19271
2237 31 July 2021 3171021003 KARTINI 22292
2238 31 July 2021 3175011003 KAYU MANIS 25284
2239 31 July 2021 3175021006 KAYU PUTIH 40696
2240 31 July 2021 3173031006 KEAGUNGAN 17387
2241 31 July 2021 3174041007 KEBAGUSAN 44552
2242 31 July 2021 3174051006 KEBAYORAN LAMA SELATAN 41212
2243 31 July 2021 3174051001 KEBAYORAN LAMA UTARA 42422
2244 31 July 2021 3174011004 KEBON BARU 35165
2245 31 July 2021 3172021005 KEBON BAWANG 50952
2246 31 July 2021 3173051001 KEBON JERUK 53887
2247 31 July 2021 3171071006 KEBON KACANG 21531
2248 31 July 2021 3171011005 KEBON KELAPA 10163
2249 31 July 2021 3171031002 KEBON KOSONG 30019
2250 31 July 2021 3175011005 KEBON MANGGIS 16490
2251 31 July 2021 3171071005 KEBON MELATI 32206
2252 31 July 2021 3175081003 KEBON PALA 46614
2253 31 July 2021 3171061005 KEBON SIRIH 12299
2254 31 July 2021 3173011004 KEDAUNG KALI ANGKE 32612
2255 31 July 2021 3173051007 KEDOYA SELATAN 32165
2256 31 July 2021 3173051006 KEDOYA UTARA 45740
2257 31 July 2021 3173051004 KELAPA DUA 23565
2258 31 July 2021 3175091003 KELAPA DUA WETAN 46875
2259 31 July 2021 3172061003 KELAPA GADING BARAT 35717
2260 31 July 2021 3172061001 KELAPA GADING TIMUR 33592
2261 31 July 2021 3173071005 KEMANGGISAN 32139
2262 31 July 2021 3171031001 KEMAYORAN 20708
2263 31 July 2021 3173081006 KEMBANGAN SELATAN 27130
2264 31 July 2021 3173081001 KEMBANGAN UTARA 54451
2265 31 July 2021 3171041002 KENARI 9579
2266 31 July 2021 3173041005 KERENDANG 19973
2267 31 July 2021 3175071003 KLENDER 69978
2268 31 July 2021 3172031001 KOJA 28175
2269 31 July 2021 3173071006 KOTA BAMBU SELATAN 21158
2270 31 July 2021 3173071003 KOTA BAMBU UTARA 24995
2271 31 July 2021 3171041004 KRAMAT 28835
2272 31 July 2021 3175041001 KRAMAT JATI 34144
2273 31 July 2021 3174071003 KRAMAT PELA 14413
2274 31 July 2021 3173031002 KRUKUT 19244
2275 31 July 2021 3174031005 KUNINGAN BARAT 12785
2276 31 July 2021 3174021008 KUNINGAN TIMUR 6008
2277 31 July 2021 3171041005 KWITANG 15486
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2279 31 July 2021 3174061002 LEBAK BULUS 37626
2280 31 July 2021 3174091004 LENTENG AGUNG 53946
2281 31 July 2021 3175101007 LUBANG BUAYA 63547
2282 31 July 2021 3175081001 MAKASAR 34390
2283 31 July 2021 3175071006 MALAKA JAYA 30927
2284 31 July 2021 3175071005 MALAKA SARI 27530
2285 31 July 2021 3174031001 MAMPANG PRAPATAN 18159
2286 31 July 2021 3173031005 MANGGA BESAR 7707
2287 31 July 2021 3171021005 MANGGA DUA SELATAN 27442
2288 31 July 2021 3174011007 MANGGARAI 28373
2289 31 July 2021 3174011006 MANGGARAI SELATAN 23075
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2292 31 July 2021 3174071001 MELAWAI 2627
2293 31 July 2021 3171061001 MENTENG 25624
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2295 31 July 2021 3174011003 MENTENG DALAM 36028
2296 31 July 2021 3173081003 MERUYA SELATAN 32130
2297 31 July 2021 3173081002 MERUYA UTARA 43752
2298 31 July 2021 3175101004 MUNJUL 23871
2299 31 July 2021 3172051002 PADEMANGAN BARAT 74240
2300 31 July 2021 3172051001 PADEMANGAN TIMUR 37736
2301 31 July 2021 3175011004 PAL MERIAM 19769
2302 31 July 2021 3173071001 PALMERAH 63011
2303 31 July 2021 3174081001 PANCORAN 19421
2304 31 July 2021 3172021003 PAPANGGO 39097
2305 31 July 2021 3171021001 PASAR BARU 12035
2306 31 July 2021 3174021006 PASAR MANGGIS 26752
2307 31 July 2021 3174041001 PASAR MINGGU 24821
2308 31 July 2021 3171041003 PASEBAN 23533
2309 31 July 2021 3173061005 PEGADUNGAN 75587
2310 31 July 2021 3171061002 PEGANGSAAN 23455
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2312 31 July 2021 3172011004 PEJAGALAN 74578
2313 31 July 2021 3174041006 PEJATEN BARAT 37346
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2315 31 July 2021 3175051005 PEKAYON 43250
2316 31 July 2021 3173041009 PEKOJAN 23587
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2318 31 July 2021 3174081005 PENGADEGAN 21478
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2321 31 July 2021 3174101001 PESANGGRAHAN 27545
2322 31 July 2021 3171071004 PETAMBURAN 33271
2323 31 July 2021 3174071008 PETOGOGAN 11499
2324 31 July 2021 3171011004 PETOJO SELATAN 14692
2325 31 July 2021 3171011003 PETOJO UTARA 17458
2326 31 July 2021 3174101004 PETUKANGAN SELATAN 37187
2327 31 July 2021 3174101003 PETUKANGAN UTARA 53716
2328 31 July 2021 3175081002 PINANG RANTI 27198
2329 31 July 2021 3173031008 PINANGSIA 10729
2330 31 July 2021 3175011001 PISANGAN BARU 31776
2331 31 July 2021 3175021002 PISANGAN TIMUR 41011
2332 31 July 2021 3172011005 PLUIT 47783
2333 31 July 2021 3175071002 PONDOK BAMBU 61550
2334 31 July 2021 3175071004 PONDOK KELAPA 72015
2335 31 July 2021 3175071007 PONDOK KOPI 35367
2336 31 July 2021 3174061003 PONDOK LABU 46464
2337 31 July 2021 3174051002 PONDOK PINANG 56169
2338 31 July 2021 3175101003 PONDOK RANGGON 25574
2339 31 July 2021 3101011003 PULAU HARAPAN 2060
2340 31 July 2021 3101011002 PULAU KELAPA 5528
2341 31 July 2021 3101011001 PULAU PANGGANG 5450
2342 31 July 2021 3101021003 PULAU PARI 2765
2343 31 July 2021 3101021002 PULAU TIDUNG 4553
2344 31 July 2021 3101021001 PULAU UNTUNG JAWA 1941
2345 31 July 2021 3174071007 PULO 5835
2346 31 July 2021 3175021001 PULO GADUNG 34422
2347 31 July 2021 3175061005 PULO GEBANG 97301
2348 31 July 2021 3174041004 RAGUNAN 39616
2349 31 July 2021 3175091005 RAMBUTAN 36771
2350 31 July 2021 3172031006 RAWA BADAK SELATAN 42179
2351 31 July 2021 3172031004 RAWA BADAK UTARA 34408
2352 31 July 2021 3174071005 RAWA BARAT 5625
2353 31 July 2021 3173011003 RAWA BUAYA 63799
2354 31 July 2021 3175031004 RAWA BUNGA 21434
2355 31 July 2021 3174081003 RAWA JATI 20509
2356 31 July 2021 3175061002 RAWA TERATE 24637
2357 31 July 2021 3175021005 RAWAMANGUN 37612
2358 31 July 2021 3171051003 RAWASARI 21967
2359 31 July 2021 3173041010 ROA MALAKA 3484
2360 31 July 2021 3172041006 ROROTAN 41668
2361 31 July 2021 3174071004 SELONG 2842
2362 31 July 2021 3173061002 SEMANAN 71354
2363 31 July 2021 3172041007 SEMPER BARAT 68587
2364 31 July 2021 3172041005 SEMPER TIMUR 36267
2365 31 July 2021 3174071006 SENAYAN 2771
2366 31 July 2021 3171041001 SENEN 6804
2367 31 July 2021 3171031004 SERDANG 29668
2368 31 July 2021 3174021001 SETIA BUDI 3023
2369 31 July 2021 3175101005 SETU 20572
2370 31 July 2021 3173071002 SLIPI 16591
2371 31 July 2021 3173081004 SRENGSENG 45523
2372 31 July 2021 3174091002 SRENGSENG SAWAH 58196
2373 31 July 2021 3173051003 SUKABUMI SELATAN 37033
2374 31 July 2021 3173051002 SUKABUMI UTARA 38290
2375 31 July 2021 3172041002 SUKAPURA 53949
2376 31 July 2021 3171031007 SUMUR BATU 22717
2377 31 July 2021 3172021004 SUNGAI BAMBU 29876
2378 31 July 2021 3172021006 SUNTER AGUNG 71805
2379 31 July 2021 3172021002 SUNTER JAYA 63031
2380 31 July 2021 3175091004 SUSUKAN 38257
2381 31 July 2021 3173031001 TAMAN SARI 14348
2382 31 July 2021 3173041001 TAMBORA 10204
2383 31 July 2021 3173041004 TANAH SEREAL 26387
2384 31 July 2021 3171081004 TANAH TINGGI 37693
2385 31 July 2021 3173031004 TANGKI 13324
2386 31 July 2021 3174091005 TANJUNG BARAT 39770
2387 31 July 2021 3173021005 TANJUNG DUREN SELATAN 25337
2388 31 July 2021 3173021002 TANJUNG DUREN UTARA 17010
2389 31 July 2021 3172021001 TANJUNG PRIOK 34588
2390 31 July 2021 3174011002 TEBET BARAT 21194
2391 31 July 2021 3174011001 TEBET TIMUR 18199
2392 31 July 2021 3173061003 TEGAL ALUR 83647
2393 31 July 2021 3174031004 TEGAL PARANG 32401
2394 31 July 2021 3173021003 TOMANG 29605
2395 31 July 2021 3172031005 TUGU SELATAN 39122
2396 31 July 2021 3172031002 TUGU UTARA 67670
2397 31 July 2021 3175061006 UJUNG MENTENG 29934
2398 31 July 2021 3174101005 ULUJAMI 40544
2399 31 July 2021 3175011006 UTAN KAYU SELATAN 33511
2400 31 July 2021 3175011002 UTAN KAYU UTARA 28169
2401 31 July 2021 3171031008 UTAN PANJANG 29208
2402 31 July 2021 3172021007 WARAKAS 44401
2403 31 July 2021 3173021007 WIJAYA KUSUMA 38934
2404 31 March 2022 3172051003 ANCOL 23947
2405 31 March 2022 3173041007 ANGKE 29381
2406 31 March 2022 3175041005 BALE KAMBANG 29074
2407 31 March 2022 3175031003 BALI MESTER 9752
2408 31 March 2022 3175101006 BAMBU APUS 26285
2409 31 March 2022 3174031002 BANGKA 21566
2410 31 March 2022 3175051002 BARU 23886
2411 31 March 2022 3175041004 BATU AMPAR 47898
2412 31 March 2022 3171071002 BENDUNGAN HILIR 21494
2413 31 March 2022 3175031002 BIDARA CINA 36689
2414 31 March 2022 3174101002 BINTARO 53098
2415 31 March 2022 3174011005 BUKIT DURI 34183
2416 31 March 2022 3171041006 BUNGUR 18144
2417 31 March 2022 3175061007 CAKUNG BARAT 57724
2418 31 March 2022 3175061004 CAKUNG TIMUR 58287
2419 31 March 2022 3175041007 CAWANG 32762
2420 31 March 2022 3175101008 CEGER 18932
2421 31 March 2022 3171031006 CEMPAKA BARU 32581
2422 31 March 2022 3171051002 CEMPAKA PUTIH BARAT 35885
2423 31 March 2022 3171051001 CEMPAKA PUTIH TIMUR 24292
2424 31 March 2022 3173011001 CENGKARENG BARAT 66284
2425 31 March 2022 3173011006 CENGKARENG TIMUR 81027
2426 31 March 2022 3175091002 CIBUBUR 66364
2427 31 March 2022 3171011002 CIDENG 15390
2428 31 March 2022 3174091003 CIGANJUR 38497
2429 31 March 2022 3175051003 CIJANTUNG 41419
2430 31 March 2022 3171061003 CIKINI 8274
2431 31 March 2022 3174081006 CIKOKO 10547
2432 31 March 2022 3174061001 CILANDAK BARAT 51752
2433 31 March 2022 3174041003 CILANDAK TIMUR 25145
2434 31 March 2022 3175101002 CILANGKAP 27635
2435 31 March 2022 3175041006 CILILITAN 40181
2436 31 March 2022 3172041001 CILINCING 43609
2437 31 March 2022 3175101001 CIPAYUNG 26104
2438 31 March 2022 3174091006 CIPEDAK 38303
2439 31 March 2022 3174061005 CIPETE SELATAN 26704
2440 31 March 2022 3174071010 CIPETE UTARA 34102
2441 31 March 2022 3175021003 CIPINANG 39306
2442 31 March 2022 3175031007 CIPINANG BESAR SELATAN 35212
2443 31 March 2022 3175031008 CIPINANG BESAR UTARA 45319
2444 31 March 2022 3175031005 CIPINANG CEMPEDAK 32824
2445 31 March 2022 3175081005 CIPINANG MELAYU 42308
2446 31 March 2022 3175031006 CIPINANG MUARA 55243
2447 31 March 2022 3174051003 CIPULIR 39937
2448 31 March 2022 3175091001 CIRACAS 64594
2449 31 March 2022 3175041003 DUKUH 24609
2450 31 March 2022 3175071001 DUREN SAWIT 61160
2451 31 March 2022 3174081004 DUREN TIGA 28319
2452 31 March 2022 3173051005 DURI KEPA 60138
2453 31 March 2022 3173011002 DURI KOSAMBI 77923
2454 31 March 2022 3171011006 DURI PULO 19610
2455 31 March 2022 3173041011 DURI SELATAN 14259
2456 31 March 2022 3173041003 DURI UTARA 19844
2457 31 March 2022 3171081003 GALUR 18243
2458 31 March 2022 3171011001 GAMBIR 2191
2459 31 March 2022 3174061004 GANDARIA SELATAN 22119
2460 31 March 2022 3174071009 GANDARIA UTARA 39922
2461 31 March 2022 3175051001 GEDONG 35755
2462 31 March 2022 3171071001 GELORA 3127
2463 31 March 2022 3173031007 GLODOK 7837
2464 31 March 2022 3171061004 GONDANGDIA 4100
2465 31 March 2022 3173021001 GROGOL 16876
2466 31 March 2022 3174051005 GROGOL SELATAN 44251
2467 31 March 2022 3174051004 GROGOL UTARA 42494
2468 31 March 2022 3174021007 GUNTUR 3985
2469 31 March 2022 3174071002 GUNUNG 9436
2470 31 March 2022 3171031005 GUNUNG SAHARI SELATAN 19199
2471 31 March 2022 3171021004 GUNUNG SAHARI UTARA 16650
2472 31 March 2022 3175081004 HALIM PERDANA KUSUMAH 28363
2473 31 March 2022 3171031003 HARAPAN MULIA 22557
2474 31 March 2022 3174091001 JAGAKARSA 62389
2475 31 March 2022 3175021007 JATI 32757
2476 31 March 2022 3174041002 JATI PADANG 37502
2477 31 March 2022 3173071004 JATI PULO 28738
2478 31 March 2022 3175061001 JATINEGARA 84160
2479 31 March 2022 3175021004 JATINEGARA KAUM 24632
2480 31 March 2022 3173021004 JELAMBAR 30266
2481 31 March 2022 3173021006 JELAMBAR BARU 38246
2482 31 March 2022 3173041006 JEMBATAN BESI 29314
2483 31 March 2022 3173041008 JEMBATAN LIMA 21021
2484 31 March 2022 3173081005 JOGLO 40839
2485 31 March 2022 3171081001 JOHAR BARU 37488
2486 31 March 2022 3173041002 KALI ANYAR 23856
2487 31 March 2022 3172041004 KALIBARU 67684
2488 31 March 2022 3174081002 KALIBATA 41994
2489 31 March 2022 3173061001 KALIDERES 72447
2490 31 March 2022 3175051004 KALISARI 43212
2491 31 March 2022 3173061004 KAMAL 53783
2492 31 March 2022 3172011002 KAMAL MUARA 12930
2493 31 March 2022 3171071007 KAMPUNG BALI 11358
2494 31 March 2022 3175031001 KAMPUNG MELAYU 25717
2495 31 March 2022 3171081002 KAMPUNG RAWA 21237
2496 31 March 2022 3175041002 KAMPUNG TENGAH 43993
2497 31 March 2022 3173011005 KAPUK 134466
2498 31 March 2022 3172011003 KAPUK MUARA 35781
2499 31 March 2022 3171021002 KARANG ANYAR 25761
2500 31 March 2022 3174021004 KARET 9623
2501 31 March 2022 3174021003 KARET KUNINGAN 16055
2502 31 March 2022 3174021002 KARET SEMANGGI 2770
2503 31 March 2022 3171071003 KARET TENGSIN 19271
2504 31 March 2022 3171021003 KARTINI 22292
2505 31 March 2022 3175011003 KAYU MANIS 25284
2506 31 March 2022 3175021006 KAYU PUTIH 40696
2507 31 March 2022 3173031006 KEAGUNGAN 17387
2508 31 March 2022 3174041007 KEBAGUSAN 44552
2509 31 March 2022 3174051006 KEBAYORAN LAMA SELATAN 41212
2510 31 March 2022 3174051001 KEBAYORAN LAMA UTARA 42422
2511 31 March 2022 3174011004 KEBON BARU 35165
2512 31 March 2022 3172021005 KEBON BAWANG 50952
2513 31 March 2022 3173051001 KEBON JERUK 53887
2514 31 March 2022 3171071006 KEBON KACANG 21531
2515 31 March 2022 3171011005 KEBON KELAPA 10163
2516 31 March 2022 3171031002 KEBON KOSONG 30019
2517 31 March 2022 3175011005 KEBON MANGGIS 16490
2518 31 March 2022 3171071005 KEBON MELATI 32206
2519 31 March 2022 3175081003 KEBON PALA 46614
2520 31 March 2022 3171061005 KEBON SIRIH 12299
2521 31 March 2022 3173011004 KEDAUNG KALI ANGKE 32612
2522 31 March 2022 3173051007 KEDOYA SELATAN 32165
2523 31 March 2022 3173051006 KEDOYA UTARA 45740
2524 31 March 2022 3173051004 KELAPA DUA 23565
2525 31 March 2022 3175091003 KELAPA DUA WETAN 46875
2526 31 March 2022 3172061003 KELAPA GADING BARAT 35717
2527 31 March 2022 3172061001 KELAPA GADING TIMUR 33592
2528 31 March 2022 3173071005 KEMANGGISAN 32139
2529 31 March 2022 3171031001 KEMAYORAN 20708
2530 31 March 2022 3173081006 KEMBANGAN SELATAN 27130
2531 31 March 2022 3173081001 KEMBANGAN UTARA 54451
2532 31 March 2022 3171041002 KENARI 9579
2533 31 March 2022 3173041005 KERENDANG 19973
2534 31 March 2022 3175071003 KLENDER 69978
2535 31 March 2022 3172031001 KOJA 28175
2536 31 March 2022 3173071006 KOTA BAMBU SELATAN 21158
2537 31 March 2022 3173071003 KOTA BAMBU UTARA 24995
2538 31 March 2022 3171041004 KRAMAT 28835
2539 31 March 2022 3175041001 KRAMAT JATI 34144
2540 31 March 2022 3174071003 KRAMAT PELA 14413
2541 31 March 2022 3173031002 KRUKUT 19244
2542 31 March 2022 3174031005 KUNINGAN BARAT 12785
2543 31 March 2022 3174021008 KUNINGAN TIMUR 6008
2544 31 March 2022 3171041005 KWITANG 15486
2545 31 March 2022 3172031003 LAGOA 59065
2546 31 March 2022 3174061002 LEBAK BULUS 37626
2547 31 March 2022 3174091004 LENTENG AGUNG 53946
2548 31 March 2022 3175101007 LUBANG BUAYA 63547
2549 31 March 2022 3175081001 MAKASAR 34390
2550 31 March 2022 3175071006 MALAKA JAYA 30927
2551 31 March 2022 3175071005 MALAKA SARI 27530
2552 31 March 2022 3174031001 MAMPANG PRAPATAN 18159
2553 31 March 2022 3173031005 MANGGA BESAR 7707
2554 31 March 2022 3171021005 MANGGA DUA SELATAN 27442
2555 31 March 2022 3174011007 MANGGARAI 28373
2556 31 March 2022 3174011006 MANGGARAI SELATAN 23075
2557 31 March 2022 3173031003 MAPHAR 16461
2558 31 March 2022 3172041003 MARUNDA 28943
2559 31 March 2022 3174071001 MELAWAI 2627
2560 31 March 2022 3171061001 MENTENG 25624
2561 31 March 2022 3174021005 MENTENG ATAS 27816
2562 31 March 2022 3174011003 MENTENG DALAM 36028
2563 31 March 2022 3173081003 MERUYA SELATAN 32130
2564 31 March 2022 3173081002 MERUYA UTARA 43752
2565 31 March 2022 3175101004 MUNJUL 23871
2566 31 March 2022 3172051002 PADEMANGAN BARAT 74240
2567 31 March 2022 3172051001 PADEMANGAN TIMUR 37736
2568 31 March 2022 3175011004 PAL MERIAM 19769
2569 31 March 2022 3173071001 PALMERAH 63011
2570 31 March 2022 3174081001 PANCORAN 19421
2571 31 March 2022 3172021003 PAPANGGO 39097
2572 31 March 2022 3171021001 PASAR BARU 12035
2573 31 March 2022 3174021006 PASAR MANGGIS 26752
2574 31 March 2022 3174041001 PASAR MINGGU 24821
2575 31 March 2022 3171041003 PASEBAN 23533
2576 31 March 2022 3173061005 PEGADUNGAN 75587
2577 31 March 2022 3171061002 PEGANGSAAN 23455
2578 31 March 2022 3172061002 PEGANGSAAN DUA 49976
2579 31 March 2022 3172011004 PEJAGALAN 74578
2580 31 March 2022 3174041006 PEJATEN BARAT 37346
2581 31 March 2022 3174041005 PEJATEN TIMUR 57656
2582 31 March 2022 3175051005 PEKAYON 43250
2583 31 March 2022 3173041009 PEKOJAN 23587
2584 31 March 2022 3174031003 PELA MAMPANG 43522
2585 31 March 2022 3174081005 PENGADEGAN 21478
2586 31 March 2022 3175061003 PENGGILINGAN 99255
2587 31 March 2022 3172011001 PENJARINGAN 88308
2588 31 March 2022 3174101001 PESANGGRAHAN 27545
2589 31 March 2022 3171071004 PETAMBURAN 33271
2590 31 March 2022 3174071008 PETOGOGAN 11499
2591 31 March 2022 3171011004 PETOJO SELATAN 14692
2592 31 March 2022 3171011003 PETOJO UTARA 17458
2593 31 March 2022 3174101004 PETUKANGAN SELATAN 37187
2594 31 March 2022 3174101003 PETUKANGAN UTARA 53716
2595 31 March 2022 3175081002 PINANG RANTI 27198
2596 31 March 2022 3173031008 PINANGSIA 10729
2597 31 March 2022 3175011001 PISANGAN BARU 31776
2598 31 March 2022 3175021002 PISANGAN TIMUR 41011
2599 31 March 2022 3172011005 PLUIT 47783
2600 31 March 2022 3175071002 PONDOK BAMBU 61550
2601 31 March 2022 3175071004 PONDOK KELAPA 72015
2602 31 March 2022 3175071007 PONDOK KOPI 35367
2603 31 March 2022 3174061003 PONDOK LABU 46464
2604 31 March 2022 3174051002 PONDOK PINANG 56169
2605 31 March 2022 3175101003 PONDOK RANGGON 25574
2606 31 March 2022 3101011003 PULAU HARAPAN 2060
2607 31 March 2022 3101011002 PULAU KELAPA 5528
2608 31 March 2022 3101011001 PULAU PANGGANG 5450
2609 31 March 2022 3101021003 PULAU PARI 2765
2610 31 March 2022 3101021002 PULAU TIDUNG 4553
2611 31 March 2022 3101021001 PULAU UNTUNG JAWA 1941
2612 31 March 2022 3174071007 PULO 5835
2613 31 March 2022 3175021001 PULO GADUNG 34422
2614 31 March 2022 3175061005 PULO GEBANG 97301
2615 31 March 2022 3174041004 RAGUNAN 39616
2616 31 March 2022 3175091005 RAMBUTAN 36771
2617 31 March 2022 3172031006 RAWA BADAK SELATAN 42179
2618 31 March 2022 3172031004 RAWA BADAK UTARA 34408
2619 31 March 2022 3174071005 RAWA BARAT 5625
2620 31 March 2022 3173011003 RAWA BUAYA 63799
2621 31 March 2022 3175031004 RAWA BUNGA 21434
2622 31 March 2022 3174081003 RAWA JATI 20509
2623 31 March 2022 3175061002 RAWA TERATE 24637
2624 31 March 2022 3175021005 RAWAMANGUN 37612
2625 31 March 2022 3171051003 RAWASARI 21967
2626 31 March 2022 3173041010 ROA MALAKA 3484
2627 31 March 2022 3172041006 ROROTAN 41668
2628 31 March 2022 3174071004 SELONG 2842
2629 31 March 2022 3173061002 SEMANAN 71354
2630 31 March 2022 3172041007 SEMPER BARAT 68587
2631 31 March 2022 3172041005 SEMPER TIMUR 36267
2632 31 March 2022 3174071006 SENAYAN 2771
2633 31 March 2022 3171041001 SENEN 6804
2634 31 March 2022 3171031004 SERDANG 29668
2635 31 March 2022 3174021001 SETIA BUDI 3023
2636 31 March 2022 3175101005 SETU 20572
2637 31 March 2022 3173071002 SLIPI 16591
2638 31 March 2022 3173081004 SRENGSENG 45523
2639 31 March 2022 3174091002 SRENGSENG SAWAH 58196
2640 31 March 2022 3173051003 SUKABUMI SELATAN 37033
2641 31 March 2022 3173051002 SUKABUMI UTARA 38290
2642 31 March 2022 3172041002 SUKAPURA 53949
2643 31 March 2022 3171031007 SUMUR BATU 22717
2644 31 March 2022 3172021004 SUNGAI BAMBU 29876
2645 31 March 2022 3172021006 SUNTER AGUNG 71805
2646 31 March 2022 3172021002 SUNTER JAYA 63031
2647 31 March 2022 3175091004 SUSUKAN 38257
2648 31 March 2022 3173031001 TAMAN SARI 14348
2649 31 March 2022 3173041001 TAMBORA 10204
2650 31 March 2022 3173041004 TANAH SEREAL 26387
2651 31 March 2022 3171081004 TANAH TINGGI 37693
2652 31 March 2022 3173031004 TANGKI 13324
2653 31 March 2022 3174091005 TANJUNG BARAT 39770
2654 31 March 2022 3173021005 TANJUNG DUREN SELATAN 25337
2655 31 March 2022 3173021002 TANJUNG DUREN UTARA 17010
2656 31 March 2022 3172021001 TANJUNG PRIOK 34588
2657 31 March 2022 3174011002 TEBET BARAT 21194
2658 31 March 2022 3174011001 TEBET TIMUR 18199
2659 31 March 2022 3173061003 TEGAL ALUR 83647
2660 31 March 2022 3174031004 TEGAL PARANG 32401
2661 31 March 2022 3173021003 TOMANG 29605
2662 31 March 2022 3172031005 TUGU SELATAN 39122
2663 31 March 2022 3172031002 TUGU UTARA 67670
2664 31 March 2022 3175061006 UJUNG MENTENG 29934
2665 31 March 2022 3174101005 ULUJAMI 40544
2666 31 March 2022 3175011006 UTAN KAYU SELATAN 33511
2667 31 March 2022 3175011002 UTAN KAYU UTARA 28169
2668 31 March 2022 3171031008 UTAN PANJANG 29208
2669 31 March 2022 3172021007 WARAKAS 44401
2670 31 March 2022 3173021007 WIJAYA KUSUMA 38934
2671 31 May 2022 3172051003 ANCOL 23947
2672 31 May 2022 3173041007 ANGKE 29381
2673 31 May 2022 3175041005 BALE KAMBANG 29074
2674 31 May 2022 3175031003 BALI MESTER 9752
2675 31 May 2022 3175101006 BAMBU APUS 26285
2676 31 May 2022 3174031002 BANGKA 21566
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2679 31 May 2022 3171071002 BENDUNGAN HILIR 21494
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2694 31 May 2022 3171011002 CIDENG 15390
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2712 31 May 2022 3175081005 CIPINANG MELAYU 42308
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2738 31 May 2022 3171021004 GUNUNG SAHARI UTARA 16650
2739 31 May 2022 3175081004 HALIM PERDANA KUSUMAH 28363
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2741 31 May 2022 3174091001 JAGAKARSA 62389
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2744 31 May 2022 3173071004 JATI PULO 28738
2745 31 May 2022 3175061001 JATINEGARA 84160
2746 31 May 2022 3175021004 JATINEGARA KAUM 24632
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2751 31 May 2022 3173081005 JOGLO 40839
2752 31 May 2022 3171081001 JOHAR BARU 37488
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2806 31 May 2022 3175041001 KRAMAT JATI 34144
2807 31 May 2022 3174071003 KRAMAT PELA 14413
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2809 31 May 2022 3174031005 KUNINGAN BARAT 12785
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2836 31 May 2022 3173071001 PALMERAH 63011
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2871 31 May 2022 3174051002 PONDOK PINANG 56169
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2883 31 May 2022 3175091005 RAMBUTAN 36771
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2888 31 May 2022 3175031004 RAWA BUNGA 21434
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2913 31 May 2022 3172021002 SUNTER JAYA 63031
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2959 31 October 2021 3173011006 CENGKARENG TIMUR 81027
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3047 31 October 2021 3173051001 KEBON JERUK 53887
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3049 31 October 2021 3171011005 KEBON KELAPA 10163
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3051 31 October 2021 3175011005 KEBON MANGGIS 16490
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3056 31 October 2021 3173051007 KEDOYA SELATAN 32165
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3058 31 October 2021 3173051004 KELAPA DUA 23565
3059 31 October 2021 3175091003 KELAPA DUA WETAN 46875
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3061 31 October 2021 3172061001 KELAPA GADING TIMUR 33592
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3069 31 October 2021 3172031001 KOJA 28175
3070 31 October 2021 3173071006 KOTA BAMBU SELATAN 21158
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3072 31 October 2021 3171041004 KRAMAT 28835
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3074 31 October 2021 3174071003 KRAMAT PELA 14413
3075 31 October 2021 3173031002 KRUKUT 19244
3076 31 October 2021 3174031005 KUNINGAN BARAT 12785
3077 31 October 2021 3174021008 KUNINGAN TIMUR 6008
3078 31 October 2021 3171041005 KWITANG 15486
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3080 31 October 2021 3174061002 LEBAK BULUS 37626
3081 31 October 2021 3174091004 LENTENG AGUNG 53946
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3083 31 October 2021 3175081001 MAKASAR 34390
3084 31 October 2021 3175071006 MALAKA JAYA 30927
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3086 31 October 2021 3174031001 MAMPANG PRAPATAN 18159
3087 31 October 2021 3173031005 MANGGA BESAR 7707
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3089 31 October 2021 3174011007 MANGGARAI 28373
3090 31 October 2021 3174011006 MANGGARAI SELATAN 23075
3091 31 October 2021 3173031003 MAPHAR 16461
3092 31 October 2021 3172041003 MARUNDA 28943
3093 31 October 2021 3174071001 MELAWAI 2627
3094 31 October 2021 3171061001 MENTENG 25624
3095 31 October 2021 3174021005 MENTENG ATAS 27816
3096 31 October 2021 3174011003 MENTENG DALAM 36028
3097 31 October 2021 3173081003 MERUYA SELATAN 32130
3098 31 October 2021 3173081002 MERUYA UTARA 43752
3099 31 October 2021 3175101004 MUNJUL 23871
3100 31 October 2021 3172051002 PADEMANGAN BARAT 74240
3101 31 October 2021 3172051001 PADEMANGAN TIMUR 37736
3102 31 October 2021 3175011004 PAL MERIAM 19769
3103 31 October 2021 3173071001 PALMERAH 63011
3104 31 October 2021 3174081001 PANCORAN 19421
3105 31 October 2021 3172021003 PAPANGGO 39097
3106 31 October 2021 3171021001 PASAR BARU 12035
3107 31 October 2021 3174021006 PASAR MANGGIS 26752
3108 31 October 2021 3174041001 PASAR MINGGU 24821
3109 31 October 2021 3171041003 PASEBAN 23533
3110 31 October 2021 3173061005 PEGADUNGAN 75587
3111 31 October 2021 3171061002 PEGANGSAAN 23455
3112 31 October 2021 3172061002 PEGANGSAAN DUA 49976
3113 31 October 2021 3172011004 PEJAGALAN 74578
3114 31 October 2021 3174041006 PEJATEN BARAT 37346
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2759 2018
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2999 3498
3000 9220
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3119 4090
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3196 9035
3197 16560
3198 5398
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3200 6294
3201 6107
3202 6151
3203 8425
3204 8427jakarta_vac_points$Date <- c(jakarta_vac_points$Date) %>%
as.Date(jakarta_vac_points$Date, format ="%d %B %Y")
glimpse(jakarta_vac_points)Rows: 3,204
Columns: 5
$ Date <date> 2022-02-27, 2022-02-27, 2022-02-27, 2022-02-27, 2022-0…
$ KODE.KELURAHAN <chr> "3172051003", "3173041007", "3175041005", "3175031003",…
$ KELURAHAN <chr> "ANCOL", "ANGKE", "BALE KAMBANG", "BALI MESTER", "BAMBU…
$ SASARAN <dbl> 23947, 29381, 29074, 9752, 26285, 21566, 23886, 47898, …
$ BELUM.VAKSIN <dbl> 4592, 5319, 5903, 1649, 4030, 3950, 3344, 9382, 3772, 7…3.2.1.3 Renaming Columns to English
# renames the columns in the style New_Name = OLD_NAME
jakarta_vac_points <- jakarta_vac_points %>%
dplyr::rename(
Date=Date,
Sub_District_Code=KODE.KELURAHAN,
Sub_District=KELURAHAN,
Target=SASARAN,
Unvaccinated=BELUM.VAKSIN
)glimpse(jakarta_vac_points)Rows: 3,204
Columns: 5
$ Date <date> 2022-02-27, 2022-02-27, 2022-02-27, 2022-02-27, 202…
$ Sub_District_Code <chr> "3172051003", "3173041007", "3175041005", "317503100…
$ Sub_District <chr> "ANCOL", "ANGKE", "BALE KAMBANG", "BALI MESTER", "BA…
$ Target <dbl> 23947, 29381, 29074, 9752, 26285, 21566, 23886, 4789…
$ Unvaccinated <dbl> 4592, 5319, 5903, 1649, 4030, 3950, 3344, 9382, 3772…3.2.1.4 Further Data Processing
Now that we have our confirmed dataframe, let’s execute any pre-processing we might have missed. Firstly, let’s check for missing values:
# returns rows that contain NA values
jakarta_vac_points[rowSums(is.na(jakarta_vac_points))!=0,][1] Date Sub_District_Code Sub_District Target
[5] Unvaccinated
<0 rows> (or 0-length row.names)There are no missing values!
4.0 Geospatial Data Integration
4.1 Preliminary joining + EDA
Now that we have both the geospatial and aspatial data frames, we’ll need to join them. A quick look at their headers tell us what their common fields are:
# checks for column names of the dataframes
colnames(jakarta_renamed) [1] "Object_ID" "Village_Code" "Village" "Code"
[5] "Province" "City" "District" "Sub_District"
[9] "Total_Population" "geometry" colnames(jakarta_vac_points)[1] "Date" "Sub_District_Code" "Sub_District"
[4] "Target" "Unvaccinated" It seems that the Sub_District should match up. Let’s try doing that first:
# joins cases_jakarta to bd_jakarta based on Sub_District
combined_jakarta <- left_join(jakarta_renamed, jakarta_vac_points,
by=c(
"Sub_District"="Sub_District")
)glimpse(combined_jakarta)Rows: 3,033
Columns: 14
$ Object_ID <dbl> 25477, 25477, 25477, 25477, 25477, 25477, 25477, 254…
$ Village_Code <chr> "3173031006", "3173031006", "3173031006", "317303100…
$ Village <chr> "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", …
$ Code <dbl> 317303, 317303, 317303, 317303, 317303, 317303, 3173…
$ Province <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI JA…
$ City <chr> "JAKARTA BARAT", "JAKARTA BARAT", "JAKARTA BARAT", "…
$ District <chr> "TAMAN SARI", "TAMAN SARI", "TAMAN SARI", "TAMAN SAR…
$ Sub_District <chr> "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", …
$ Total_Population <dbl> 21609, 21609, 21609, 21609, 21609, 21609, 21609, 216…
$ Date <date> 2022-02-27, 2022-04-30, 2022-06-30, 2021-11-30, 202…
$ Sub_District_Code <chr> "3173031006", "3173031006", "3173031006", "317303100…
$ Target <dbl> 17387, 17387, 17387, 17387, 17387, 17387, 17387, 173…
$ Unvaccinated <dbl> 2755, 2593, 2553, 3099, 4203, 6054, 2924, 2783, 8126…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-764674.9 8..., MULTIPO…Now, let’s visualise our current combined_jakarta in terms of vaccinated and unvaccinated:
# maps the Vaccinated and Unvaccinated side-by-side
total_pop = tm_shape(combined_jakarta)+
tm_fill("Total_Population") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Total Population")
unvacc_cases = tm_shape(combined_jakarta)+
tm_fill("Unvaccinated") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Unvaccinated People")
target_count = tm_shape(combined_jakarta)+
tm_fill("Target") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Target Count")
tmap_arrange(total_pop, unvacc_cases, target_count)
That’s not quite right! There are still some ‘missing’ values, but we’ve taken care to remove NA records from both sets of data. The most likely reason for this is due to mistmatched records (one says Berenstein while the other says Berenstain, for example) - which requires addition tweaking and investigation.
4.1.1 Identifying Mismatched Sub-District Records
A brief comparison of the geospatial vs aspatial values reveals to us the mismatch: for the Sub_District field, there isn’t standardisation of the sub-district names, which leads to the same sub-district being referred to by slightly different names, such as KRAMAJATI vs KRAMAT JATI. This is why these records appear as ‘missing values’.
# checks for unique values of Sub_District
jakarta_renamed_subdistrict <- c(jakarta_renamed$Sub_District)
jakarta_vac_points_subdistrict <- c(jakarta_vac_points$Sub_District)
unique(jakarta_vac_points_subdistrict[!(jakarta_vac_points_subdistrict %in% jakarta_renamed_subdistrict)]) [1] "BALE KAMBANG" "HALIM PERDANA KUSUMAH" "JATI PULO"
[4] "KAMPUNG TENGAH" "KERENDANG" "KRAMAT JATI"
[7] "PAL MERIAM" "PINANG RANTI" "PULAU HARAPAN"
[10] "PULAU KELAPA" "PULAU PANGGANG" "PULAU PARI"
[13] "PULAU TIDUNG" "PULAU UNTUNG JAWA" "RAWA JATI" unique(jakarta_renamed_subdistrict[!(jakarta_renamed_subdistrict %in% jakarta_vac_points_subdistrict)])[1] "KRENDANG" "RAWAJATI" "TENGAH"
[4] "BALEKAMBANG" "PINANGRANTI" "JATIPULO"
[7] "PALMERIAM" "KRAMATJATI" "HALIM PERDANA KUSUMA"# initialise a dataframe of our cases vs bd subdistrict spelling
spelling <- data.frame(
Aspatial_Cases=c("BALE KAMBANG", "HALIM PERDANA KUSUMAH", "JATI PULO", "KAMPUNG TENGAH", "KERENDANG", "KRAMAT JATI", "PAL MERIAM", "PINANG RANTI", "RAWA JATI" ),
Geospatial_BD=c("BALEKAMBANG", "HALIM PERDANA KUSUMA", "JATIPULO", "TENGAH", "KRENDANG", "KRAMATJATI","PALMERIAM", "PINANGRANTI", "RAWAJATI")
)
# with dataframe a input, outputs a kable
library(knitr)
library(kableExtra)
kable(spelling, caption="Mismatched Records") %>%
kable_material("hover", latex_options="scale_down")| Aspatial_Cases | Geospatial_BD |
|---|---|
| BALE KAMBANG | BALEKAMBANG |
| HALIM PERDANA KUSUMAH | HALIM PERDANA KUSUMA |
| JATI PULO | JATIPULO |
| KAMPUNG TENGAH | TENGAH |
| KERENDANG | KRENDANG |
| KRAMAT JATI | KRAMATJATI |
| PAL MERIAM | PALMERIAM |
| PINANG RANTI | PINANGRANTI |
| RAWA JATI | RAWAJATI |
4.1.2 Correcting Mismatched Sub-District Records
Now that we know which sub-district records are mismatched, we need to rectify the mismatches by renaming them:
# where bd_jakarta is a mismatched value, replace with the correct value
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'BALEKAMBANG'] <- 'BALE KAMBANG'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'HALIM PERDANA KUSUMA'] <- 'HALIM PERDANA KUSUMAH'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'JATIPULO'] <- 'JATI PULO'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'TENGAH'] <- 'KAMPUNG TENGAH'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'KRAMATJATI'] <- 'KRAMAT JATI'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'KRENDANG'] <- 'KERENDANG'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'PALMERIAM'] <- 'PAL MERIAM'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'PINANGRANTI'] <- 'PINANG RANTI'
jakarta_renamed$Sub_District[jakarta_renamed$Sub_District == 'RAWAJATI'] <- 'RAWA JATI'4.1.3 Joining + EDA
Now, we have a standardised common identifier among our geospatial and aspatial dataframes. Let’s join them once more:
# joins cases_jakarta to jakarta_renamed based on Sub_District
combined_jakarta <- left_join(jakarta_renamed, jakarta_vac_points,
by=c("Sub_District"="Sub_District")
)combined_jakartaSimple feature collection with 3132 features and 13 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -779259.9 ymin: 787584.9 xmax: -747172.2 ymax: 819039
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
First 10 features:
Object_ID Village_Code Village Code Province City District
1 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
2 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
3 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
4 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
5 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
6 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
7 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
8 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
9 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
10 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
Sub_District Total_Population Date Sub_District_Code Target
1 KEAGUNGAN 21609 2022-02-27 3173031006 17387
2 KEAGUNGAN 21609 2022-04-30 3173031006 17387
3 KEAGUNGAN 21609 2022-06-30 3173031006 17387
4 KEAGUNGAN 21609 2021-11-30 3173031006 17387
5 KEAGUNGAN 21609 2021-09-30 3173031006 17387
6 KEAGUNGAN 21609 2021-08-31 3173031006 17387
7 KEAGUNGAN 21609 2021-12-31 3173031006 17387
8 KEAGUNGAN 21609 2022-01-31 3173031006 17387
9 KEAGUNGAN 21609 2021-07-31 3173031006 17387
10 KEAGUNGAN 21609 2022-03-31 3173031006 17387
Unvaccinated geometry
1 2755 MULTIPOLYGON (((-764674.9 8...
2 2593 MULTIPOLYGON (((-764674.9 8...
3 2553 MULTIPOLYGON (((-764674.9 8...
4 3099 MULTIPOLYGON (((-764674.9 8...
5 4203 MULTIPOLYGON (((-764674.9 8...
6 6054 MULTIPOLYGON (((-764674.9 8...
7 2924 MULTIPOLYGON (((-764674.9 8...
8 2783 MULTIPOLYGON (((-764674.9 8...
9 8126 MULTIPOLYGON (((-764674.9 8...
10 2675 MULTIPOLYGON (((-764674.9 8...Now, let’s once again visualise our updated combined_jakarta in terms of vaccinated and unvaccinated:
# maps the Vaccinated and Unvaccinated side-by-side
updated_total_pop = tm_shape(combined_jakarta)+
tm_fill("Total_Population") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Total Population")
updated_unvacc_cases = tm_shape(combined_jakarta)+
tm_fill("Unvaccinated") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Unvaccinated People")
updated_target_count = tm_shape(combined_jakarta)+
tm_fill("Target") +
tm_borders(alpha = 0.5) +
tm_layout(main.title="Target Count")
tmap_arrange(updated_total_pop, updated_unvacc_cases, updated_target_count)
Hooray! We got rid of the missing values!
5.0 Calculating Vaccination Rate
To get the vaccination rate:
Vaccination rate % = ( (Target - Unvaccinated) / Target ) * 100
Let’s find the cummulative vaccination rate by summing up the number of cases (based on the name of the sub-district and the date), and then dividing by the target. Lastly, we get the percentage rate by * 100.
# grouping based on the sub-district and date
# the vaccination_rate is based on the sum of cases over the total population
vaccination_rate <- jakarta_vac_points %>%
inner_join(jakarta_renamed, by=c("Sub_District" = "Sub_District")) %>%
group_by(Sub_District, Date) %>%
dplyr::summarise(`vaccination_rate` =(( (Target - Unvaccinated) /(Target))*100) )%>%
#afterwards, pivots the table based on the Dates, using the cumulative case rate as the values
ungroup() %>% pivot_wider(names_from = Date,
values_from = vaccination_rate)glimpse(vaccination_rate)Rows: 261
Columns: 13
$ Sub_District <chr> "ANCOL", "ANGKE", "BALE KAMBANG", "BALI MESTER", "BAMBU A…
$ `2021-07-31` <dbl> 48.49877, 52.77560, 37.00901, 46.95447, 47.59368, 51.5765…
$ `2021-08-31` <dbl> 61.61941, 64.60297, 56.97186, 62.01805, 64.17729, 61.3048…
$ `2021-09-30` <dbl> 72.07166, 74.19761, 70.04884, 74.18991, 76.21457, 73.1753…
$ `2021-10-31` <dbl> 74.98225, 77.68286, 73.90796, 78.20960, 80.92448, 78.0116…
$ `2021-11-30` <dbl> 76.91986, 79.62629, 76.60453, 80.31173, 82.45387, 79.8479…
$ `2021-12-31` <dbl> 78.86165, 80.85157, 78.21077, 81.72683, 83.36313, 80.6686…
$ `2022-01-31` <dbl> 80.59465, 81.66162, 79.47651, 82.79327, 84.48925, 81.4847…
$ `2022-02-27` <dbl> 80.82432, 81.89646, 79.69664, 83.09065, 84.66806, 81.6841…
$ `2022-03-31` <dbl> 81.11663, 82.34914, 80.11969, 83.37777, 84.98383, 81.9669…
$ `2022-04-30` <dbl> 81.42147, 82.63844, 80.39829, 83.61362, 85.32623, 82.2962…
$ `2022-05-31` <dbl> 81.45906, 82.69630, 80.47740, 83.70591, 85.41373, 82.3611…
$ `2022-06-30` <dbl> 81.61774, 82.84265, 80.62874, 83.83921, 85.57733, 82.4816…5.1 Converting Dataframes to sf Objects
Before we move on into the mapping, we should convert these dataframes into sf objects.
combined_jakarta <- st_as_sf(combined_jakarta)
# need to join our previous dataframes with the geospatial data to ensure that geometry column is present
vaccination_rate <- vaccination_rate%>% left_join(jakarta_renamed, by=c("Sub_District"="Sub_District"))
vaccination_rate <- st_as_sf(vaccination_rate)glimpse(vaccination_rate)Rows: 261
Columns: 22
$ Sub_District <chr> "ANCOL", "ANGKE", "BALE KAMBANG", "BALI MESTER", "BAM…
$ `2021-07-31` <dbl> 48.49877, 52.77560, 37.00901, 46.95447, 47.59368, 51.…
$ `2021-08-31` <dbl> 61.61941, 64.60297, 56.97186, 62.01805, 64.17729, 61.…
$ `2021-09-30` <dbl> 72.07166, 74.19761, 70.04884, 74.18991, 76.21457, 73.…
$ `2021-10-31` <dbl> 74.98225, 77.68286, 73.90796, 78.20960, 80.92448, 78.…
$ `2021-11-30` <dbl> 76.91986, 79.62629, 76.60453, 80.31173, 82.45387, 79.…
$ `2021-12-31` <dbl> 78.86165, 80.85157, 78.21077, 81.72683, 83.36313, 80.…
$ `2022-01-31` <dbl> 80.59465, 81.66162, 79.47651, 82.79327, 84.48925, 81.…
$ `2022-02-27` <dbl> 80.82432, 81.89646, 79.69664, 83.09065, 84.66806, 81.…
$ `2022-03-31` <dbl> 81.11663, 82.34914, 80.11969, 83.37777, 84.98383, 81.…
$ `2022-04-30` <dbl> 81.42147, 82.63844, 80.39829, 83.61362, 85.32623, 82.…
$ `2022-05-31` <dbl> 81.45906, 82.69630, 80.47740, 83.70591, 85.41373, 82.…
$ `2022-06-30` <dbl> 81.61774, 82.84265, 80.62874, 83.83921, 85.57733, 82.…
$ Object_ID <dbl> 25455, 25486, 25605, 25595, 25642, 25531, 25609, 2560…
$ Village_Code <chr> "3172051003", "3173041007", "3175041005", "3175031003…
$ Village <chr> "ANCOL", "ANGKE", "BALEKAMBANG", "BALI MESTER", "BAMB…
$ Code <dbl> 317205, 317304, 317504, 317503, 317510, 317403, 31750…
$ Province <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI JAK…
$ City <chr> "JAKARTA UTARA", "JAKARTA BARAT", "JAKARTA TIMUR", "J…
$ District <chr> "PADEMANGAN", "TAMBORA", "KRAMATJATI", "JATINEGARA", …
$ Total_Population <dbl> 29886, 36428, 35376, 11695, 31774, 26261, 29389, 5889…
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-759512 815..., MULTIPOL…6.0 Mapping: Monthly Vaccination Rate
I’m planning to use the jenks classification method. It ‘seeks to minimize the average deviation from the class mean while maximizing the deviation from the means of the other groups’ (source) and tends to identify real, natural classes within the data. However, jenks will not work as well if the data has a low variance, so let’s check the variance:
# commented out most of the variances due to the increasing variance over time
#last date in record
var(vaccination_rate$`2022-06-30`)[1] 2.592474Since the variance is increasing over time and seems significant, we can continue with jenks classification. After some testing, having 6 classes seems to be the optimum: too many classes and it becomes hard for the human eye to differentiate between the gradients, while too few classes makes it hard for any differentiation to be seen.
Let’s first plot a map for the first month - July 2021.
# using the jenks method, with 6 classes
tmap_mode("view")
tm_shape(vaccination_rate)+
tm_fill("2021-07-31",
n= 6,
style = "jenks",
title = "Vaccination Rate") +
tm_layout(main.title = "Distribution of Vaccination Rate of Jakarta in July 2021",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.5,
legend.width = 0.4,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(11,16))We should plot it for all the months - so let’s have a helper function for plotting this!
# input: the dataframe and the variable name - in this case, the month
# with style="jenks" for the jenks classification method
jenks_plot <- function(df, varname) {
tm_shape(vaccination_rate) +
tm_polygons() +
tm_shape(df) +
tm_fill(varname,
n= 6,
style = "jenks",
title = "Vaccination Rate") +
tm_layout(main.title = varname,
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5)
}vaccination_rateSimple feature collection with 261 features and 21 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -779259.9 ymin: 787584.9 xmax: -747172.2 ymax: 819039
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
# A tibble: 261 × 22
Sub_District 2021-…¹ 2021-…² 2021-…³ 2021-…⁴ 2021-…⁵ 2021-…⁶ 2022-…⁷ 2022-…⁸
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ANCOL 48.5 61.6 72.1 75.0 76.9 78.9 80.6 80.8
2 ANGKE 52.8 64.6 74.2 77.7 79.6 80.9 81.7 81.9
3 BALE KAMBANG 37.0 57.0 70.0 73.9 76.6 78.2 79.5 79.7
4 BALI MESTER 47.0 62.0 74.2 78.2 80.3 81.7 82.8 83.1
5 BAMBU APUS 47.6 64.2 76.2 80.9 82.5 83.4 84.5 84.7
6 BANGKA 51.6 61.3 73.2 78.0 79.8 80.7 81.5 81.7
7 BARU 57.9 67.6 79.5 82.9 84.2 85.0 85.8 86.0
8 BATU AMPAR 39.8 58.4 70.6 74.5 77.1 78.8 80.1 80.4
9 BENDUNGAN HI… 53.6 62.6 75.6 79.1 80.5 81.4 82.3 82.5
10 BIDARA CINA 40.6 57.6 71.0 75.2 77.0 78.2 79.2 79.5
# … with 251 more rows, 13 more variables: `2022-03-31` <dbl>,
# `2022-04-30` <dbl>, `2022-05-31` <dbl>, `2022-06-30` <dbl>,
# Object_ID <dbl>, Village_Code <chr>, Village <chr>, Code <dbl>,
# Province <chr>, City <chr>, District <chr>, Total_Population <dbl>,
# geometry <MULTIPOLYGON [m]>, and abbreviated variable names ¹`2021-07-31`,
# ²`2021-08-31`, ³`2021-09-30`, ⁴`2021-10-31`, ⁵`2021-11-30`, ⁶`2021-12-31`,
# ⁷`2022-01-31`, ⁸`2022-02-27`tmap_mode("plot")
tmap_arrange(jenks_plot(vaccination_rate, "2021-07-31"),
jenks_plot(vaccination_rate, "2021-08-31"),
jenks_plot(vaccination_rate, "2021-09-30"),
jenks_plot(vaccination_rate, "2021-10-31"))
tmap_mode("plot")
tmap_arrange(jenks_plot(vaccination_rate, "2021-07-31"),
jenks_plot(vaccination_rate, "2021-08-31"),
jenks_plot(vaccination_rate, "2021-09-30"),
jenks_plot(vaccination_rate, "2021-10-31"))
tmap_mode("plot")
tmap_arrange(jenks_plot(vaccination_rate, "2021-11-30"),
jenks_plot(vaccination_rate, "2021-12-31"),
jenks_plot(vaccination_rate, "2022-01-31"),
jenks_plot(vaccination_rate, "2022-02-27"))
tmap_mode("plot")
tmap_arrange(jenks_plot(vaccination_rate, "2022-03-31"),
jenks_plot(vaccination_rate, "2022-04-30"),
jenks_plot(vaccination_rate, "2022-05-31"),
jenks_plot(vaccination_rate, "2022-06-30"))
6.1 Observations from Jenks Choropleth Map
Places with the highest vaccination rate tends to be
# shades the map according to the city divisions
tm_shape(jakarta_renamed) +
tm_polygons("City")
- West Jakarta –> JAKARTA BARAT
- Central Jakarta –> JAKARTA PUSAT
- South Jakarta –> JAKARTA SELATAN
- East Jakarta –> JAKARTA TIMUR
- North Jakarta –> JAKARTA Utara
Generally across the months, the regions with the darkest colours and hence with the highest vaccination rates would be the South and East of Jakarta.
Let’s first look for the City regions for the places with the greatest vaccination rate for a macroscopic view.
Vaccination rate of North Jakarta (blue) used to be high from July 2021 to December 2021. After which, the vaccination rate decreased.
October 2021 has the greatest number of regions in Jakarta with high vaccination rates. After doing a search online on the timeline of COVID 19 in Jakarta, the high vaccination rates in October 2021 proved to help Jakarta a lot in fighting the pandemic. On 31 October 2021, the number of recorded deceased patients was 17 - the lowest since 16 May 2020.
# to check for darkest sub-district in early stages
vaccination_rate$Sub_District[which.max(vaccination_rate$`2021-07-31`)] [1] "KAMAL MUARA"# using the jenks method, with 6 classes
tmap_mode("view")
tm_shape(vaccination_rate)+
tm_fill("2021-07-31",
n= 6,
style = "jenks",
title = "Vaccination Rate") +
tm_layout(main.title = "Distribution of Vaccination Rate of Jakarta in July 2021",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.5,
legend.width = 0.4,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(11,16))# using the jenks method, with 6 classes
tmap_mode("view")
tm_shape(vaccination_rate)+
tm_fill("2022-06-30",
n= 6,
style = "jenks",
title = "Vaccination Rate") +
tm_layout(main.title = "Distribution of Vaccination Rate of Jakarta in June 2022",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.5,
legend.width = 0.4,
frame = TRUE) +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(11,16))From 31 October 2021 - 30 June 2022, a few sub-districts in the South of Jakarta have been consistently having high vaccination rates. These districts are: PONDOK LABU, JAGAKARSA, SRENGSENG SAWAH. 3 sub-districts from South Jakarta (located towards the centre of the map) are BUKIT DURI, TEBET BARAT, TEBET TIMUR. Another sub-district from East Jakarta that has been having consistently high vaccination rates over the months is: HALIM PERDANA KUSUMAH. Generally, sub-districts from the South and East of Jakarta tend to have high vaccination rates.
Low-vaccination rate trend here…
Why? We could filter dataset and get places w high vaccination rates vs those with low vaccination rates to see major contributors and search online for reasons why.
Top half of Jakarta vaccination rate drops over time.
Bottom half of Jakarta vaccination rate rises over time.
–> expect high number of health workers vaccinated -> hence high number of elderly (South Jakarta) https://m.beritajakarta.id/en/read/38680/160-elderly-in-cilandak-injected-with-covid-19-vaccines
6.1.1 Spatio-Temporal Mapping with custom breakpoints
In the above section, we saw each map by their relative vaccination rate - but to see the spatio-temporal progression of case rates, we should set a fixed range. In this case, customising our breakpoints would be ideal - just like above, we’ll be breaking into 6 classes.
Firstly, let’s find the maximum of the vaccination_rate - since the highest vaccination rate is logically in the latest month (June 2022), we’ll find the maximum vaccination_rate from there:
# gets maximum value (case rate) of July 2021 from vaccination_rate df
max(vaccination_rate$`2022-06-30`)[1] 89.77894Now that we know our maximum number, let’s set our breakpoints:
# these breakpoints are chosen based on the jenks breaks ranges from the previous section
breakpoints = c(35, 50, 70, 80, 85, 92)tmap_mode("plot")
tm_shape(vaccination_rate)+
tm_fill("2022-06-30",
breaks= breakpoints,
title = "Vaccination Rate") +
tm_layout(main.title = "Distribution of COVID-19 Case Rate in June 2022 (Spatio-Temporal)",
main.title.position = "center",
main.title.size = 1,
legend.height = 0.5,
legend.width = 0.4,
frame = TRUE) +
tm_borders(alpha = 0.5)
It might seem pretty skewed towards the ‘heavier’ end, but this is to be expected: after all, this is the end of our dataset where COVID-19 has had over a year of presence in Jakarta. Once again, let’s create a helper function to help us:
break_plot <- function(df, varname) {
tm_shape(vaccination_rate) +
tm_polygons() +
tm_shape(df) +
tm_fill(varname,
breaks= breakpoints,
title = "Vaccination Rate") +
tm_layout(main.title = varname,
main.title.position = "center",
main.title.size = 1.2,
legend.height = 0.45,
legend.width = 0.35,
frame = TRUE) +
tm_borders(alpha = 0.5)
}Now, let’s visualise the custom break plots for all months:
tmap_mode("plot")
tmap_arrange(break_plot(vaccination_rate, "2021-07-31"),
break_plot(vaccination_rate, "2021-08-31"),
break_plot(vaccination_rate, "2021-09-30"),
break_plot(vaccination_rate, "2021-10-31"))
tmap_mode("plot")
tmap_arrange(break_plot(vaccination_rate, "2021-11-30"),
break_plot(vaccination_rate, "2021-12-31"),
break_plot(vaccination_rate, "2022-01-31"),
break_plot(vaccination_rate, "2022-02-27"))
tmap_mode("plot")
tmap_arrange(break_plot(vaccination_rate, "2022-03-31"),
break_plot(vaccination_rate, "2022-04-30"),
break_plot(vaccination_rate, "2022-05-31"),
break_plot(vaccination_rate, "2022-06-30"))
7.0 Local Gi* Analysis
Here are some tasks to complete for this section:
- Compute local Gi* values of the monthly vaccination rate
- Display the Gi* maps of the monthly vaccination rate. The maps should only display the significant (i.e. p-value < 0.05)
- With reference to the analysis results, draw statistical conclusions (not more than 250 words)
To detect hot spot areas with high vaccination rates, and cold spot areas with low vaccination rates in Jakarta, Local Gi* statistics will be used. Time-series analysis will be conducted to understand the evolution of spatial hot spots and cold spots across time.
Interpretation of Gi* values:
Gi∗>0 : indicates grouping of areas with values higher than average
Gi∗<0 : indicates grouping of areas with values lower than average
A larger magnitude represents a greater intensity of grouping.
For significant Gi* statistic values, two spatial associations can be inferred:
Hot spot areas: where Gi*>0, indicating that a location is associated with relatively high values in the surrounding locations.
Cold spot areas: where Gi*<0, indicating that a location is associated with relatively low values in the surrounding locations.
7.1 New Data For Time Series Cube Creation
We will need to prepare our data in the following manner - (TIME, SPACE, ATTRIBUTE). In our case, the TIME would be Date feature, SPACE would be Sub_District feature and ATTRIBUTE would be Vaccination Rate feature.
data_for_time_series_cube = combined_jakarta %>% select("Date", "Sub_District", "Unvaccinated", "Target") %>% st_drop_geometry()
glimpse(data_for_time_series_cube)Rows: 3,132
Columns: 4
$ Date <date> 2022-02-27, 2022-04-30, 2022-06-30, 2021-11-30, 2021-09-…
$ Sub_District <chr> "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "KEAG…
$ Unvaccinated <dbl> 2755, 2593, 2553, 3099, 4203, 6054, 2924, 2783, 8126, 267…
$ Target <dbl> 17387, 17387, 17387, 17387, 17387, 17387, 17387, 17387, 1…We took “Vaccinated” and “Target” so that we can create the “Vaccination_Rate” feature.
data_for_time_series_cube$Vaccination_Rate <- (data_for_time_series_cube$Target - data_for_time_series_cube$Unvaccinated ) / data_for_time_series_cube$Target * 100
glimpse(data_for_time_series_cube)Rows: 3,132
Columns: 5
$ Date <date> 2022-02-27, 2022-04-30, 2022-06-30, 2021-11-30, 2021…
$ Sub_District <chr> "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "KEAGUNGAN", "…
$ Unvaccinated <dbl> 2755, 2593, 2553, 3099, 4203, 6054, 2924, 2783, 8126,…
$ Target <dbl> 17387, 17387, 17387, 17387, 17387, 17387, 17387, 1738…
$ Vaccination_Rate <dbl> 84.15483, 85.08656, 85.31662, 82.17634, 75.82677, 65.…data_for_time_series_cube = data_for_time_series_cube %>% select("Date", "Sub_District", "Vaccination_Rate")
data_for_time_series_cube_tib = tibble(data_for_time_series_cube)
data_for_time_series_cube_tib# A tibble: 3,132 × 3
Date Sub_District Vaccination_Rate
<date> <chr> <dbl>
1 2022-02-27 KEAGUNGAN 84.2
2 2022-04-30 KEAGUNGAN 85.1
3 2022-06-30 KEAGUNGAN 85.3
4 2021-11-30 KEAGUNGAN 82.2
5 2021-09-30 KEAGUNGAN 75.8
6 2021-08-31 KEAGUNGAN 65.2
7 2021-12-31 KEAGUNGAN 83.2
8 2022-01-31 KEAGUNGAN 84.0
9 2021-07-31 KEAGUNGAN 53.3
10 2022-03-31 KEAGUNGAN 84.6
# … with 3,122 more rows7.2 Create Spatio-Temporal Cube
As of our now, the “Date” column in data_for_time_series_cube_tib tibble dataframe is not numeric. In order to create a spacetime cube, this field has to be converted. For example, from 27 Februari 2022 to 2022-02-27 (YYYY-MM-DD) format.
In the code chunk below, spacetime() of sfdep is used to create an spatio-temporal cube.
time_series_cube_vacc_rate <- spacetime(data_for_time_series_cube_tib, jakarta_renamed,
.loc_col = "Sub_District",
.time_col = "Date")is_spacetime_cube(time_series_cube_vacc_rate)[1] TRUEtime_series_cube_vacc_rate is a spacetime cube!
7.3 Computing Gi*
7.3.1 Deriving Spatial Weights
The code chunk below will be used to identify neighbors and to derive an inverse distance weights. We’ll get all the neighbours including the target as well due to the function include_self() function.
time_series_cube_vacc_rate# A tibble: 3,132 × 3
Date Sub_District Vaccination_Rate
* <date> <chr> <dbl>
1 2022-02-27 KEAGUNGAN 84.2
2 2022-04-30 KEAGUNGAN 85.1
3 2022-06-30 KEAGUNGAN 85.3
4 2021-11-30 KEAGUNGAN 82.2
5 2021-09-30 KEAGUNGAN 75.8
6 2021-08-31 KEAGUNGAN 65.2
7 2021-12-31 KEAGUNGAN 83.2
8 2022-01-31 KEAGUNGAN 84.0
9 2021-07-31 KEAGUNGAN 53.3
10 2022-03-31 KEAGUNGAN 84.6
# … with 3,122 more rowsvacc_rate_nb <- time_series_cube_vacc_rate %>%
activate("geometry") %>%
mutate(nb = include_self(st_contiguity(geometry)),
wt = st_inverse_distance(nb, geometry,
scale = 1,
alpha = 1),
.before = 1) %>%
set_nbs("nb") %>%
set_wts("wt")- activate() of dplyr package is used to activate the geometry context
- mutate() of dplyr package is used to create two new columns nb and wt.
Then we will activate the data context again and copy over the nb and wt columns to each time-slice using set_nbs() and set_wts() row order is very important so do not rearrange the observations after using set_nbs() or set_wts().
head(vacc_rate_nb)# A tibble: 6 × 5
Date Sub_District Vaccination_Rate nb wt
<date> <chr> <dbl> <list> <list>
1 2021-07-31 KEAGUNGAN 53.3 <int [6]> <dbl [6]>
2 2021-07-31 GLODOK 61.6 <int [7]> <dbl [7]>
3 2021-07-31 HARAPAN MULIA 49.7 <int [6]> <dbl [6]>
4 2021-07-31 CEMPAKA BARU 46.7 <int [7]> <dbl [7]>
5 2021-07-31 PASAR BARU 59.3 <int [9]> <dbl [9]>
6 2021-07-31 KARANG ANYAR 52.2 <int [7]> <dbl [7]>7.3.2 Computing Gi*
set.seed(1234)We can use these new columns to manually calculate the local Gi* for each location. We can do this by grouping by Date and using local_gstar_perm() of sfdep package. After which, we use unnest() to unnest gi_star column of the newly created gi_starts data.frame.
gi_stars <- vacc_rate_nb %>%
group_by(Date) %>%
mutate(gi_star = local_gstar_perm(
Vaccination_Rate, nb, wt, nsim = 99)) %>%
tidyr::unnest(gi_star)gi_stars# A tibble: 3,132 × 13
# Groups: Date [12]
Date Sub_Di…¹ Vacci…² nb wt gi_star e_gi var_gi p_value p_sim
<date> <chr> <dbl> <lis> <lis> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2021-07-31 KEAGUNG… 53.3 <int> <dbl> 2.44 0.00383 2.13e-8 1.46e-2 0.02
2 2021-07-31 GLODOK 61.6 <int> <dbl> 3.85 0.00384 1.56e-8 1.18e-4 0.02
3 2021-07-31 HARAPAN… 49.7 <int> <dbl> 0.309 0.00382 2.20e-8 7.57e-1 0.84
4 2021-07-31 CEMPAKA… 46.7 <int> <dbl> -1.05 0.00383 1.53e-8 2.96e-1 0.34
5 2021-07-31 PASAR B… 59.3 <int> <dbl> 2.71 0.00383 1.38e-8 6.82e-3 0.02
6 2021-07-31 KARANG … 52.2 <int> <dbl> 1.67 0.00382 2.17e-8 9.49e-2 0.1
7 2021-07-31 MANGGA … 51.6 <int> <dbl> 1.35 0.00384 1.80e-8 1.77e-1 0.22
8 2021-07-31 PETOJO … 47.2 <int> <dbl> -0.179 0.00382 1.92e-8 8.58e-1 0.96
9 2021-07-31 SENEN 54.4 <int> <dbl> 1.51 0.00382 1.20e-8 1.32e-1 0.1
10 2021-07-31 BUNGUR 52.8 <int> <dbl> 0.797 0.00385 1.54e-8 4.25e-1 0.48
# … with 3,122 more rows, 3 more variables: p_folded_sim <dbl>, skewness <dbl>,
# kurtosis <dbl>, and abbreviated variable names ¹Sub_District,
# ²Vaccination_Rate7.4 Visualise Gi* With Maps
We want to see the gi_stars values on the map. The gi_stars dataframe does not have a “geometry” column. As such, we will perform a join.
gi_values_map <- combined_jakarta %>%
left_join(gi_stars)gi_values_mapSimple feature collection with 3132 features and 24 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -779259.9 ymin: 787584.9 xmax: -747172.2 ymax: 819039
Projected CRS: DGN95 / Indonesia TM-3 zone 50.1
First 10 features:
Object_ID Village_Code Village Code Province City District
1 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
2 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
3 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
4 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
5 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
6 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
7 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
8 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
9 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
10 25477 3173031006 KEAGUNGAN 317303 DKI JAKARTA JAKARTA BARAT TAMAN SARI
Sub_District Total_Population Date Sub_District_Code Target
1 KEAGUNGAN 21609 2022-02-27 3173031006 17387
2 KEAGUNGAN 21609 2022-04-30 3173031006 17387
3 KEAGUNGAN 21609 2022-06-30 3173031006 17387
4 KEAGUNGAN 21609 2021-11-30 3173031006 17387
5 KEAGUNGAN 21609 2021-09-30 3173031006 17387
6 KEAGUNGAN 21609 2021-08-31 3173031006 17387
7 KEAGUNGAN 21609 2021-12-31 3173031006 17387
8 KEAGUNGAN 21609 2022-01-31 3173031006 17387
9 KEAGUNGAN 21609 2021-07-31 3173031006 17387
10 KEAGUNGAN 21609 2022-03-31 3173031006 17387
Unvaccinated Vaccination_Rate nb
1 2755 84.15483 1, 2, 39, 152, 158, 166
2 2593 85.08656 1, 2, 39, 152, 158, 166
3 2553 85.31662 1, 2, 39, 152, 158, 166
4 3099 82.17634 1, 2, 39, 152, 158, 166
5 4203 75.82677 1, 2, 39, 152, 158, 166
6 6054 65.18088 1, 2, 39, 152, 158, 166
7 2924 83.18284 1, 2, 39, 152, 158, 166
8 2783 83.99379 1, 2, 39, 152, 158, 166
9 8126 53.26393 1, 2, 39, 152, 158, 166
10 2675 84.61494 1, 2, 39, 152, 158, 166
wt
1 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
2 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
3 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
4 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
5 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
6 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
7 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
8 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
9 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
10 0.000000000, 0.001301072, 0.001220558, 0.001569882, 0.001225179, 0.001502357
gi_star e_gi var_gi p_value p_sim p_folded_sim skewness
1 2.478859 0.003831172 8.419778e-10 0.013180341 0.02 0.01 -0.03194341
2 2.825027 0.003827108 7.494269e-10 0.004727670 0.04 0.02 -0.06356613
3 2.856078 0.003828377 6.980492e-10 0.004289104 0.02 0.01 0.09797571
4 2.400280 0.003831389 1.665572e-09 0.016382514 0.02 0.01 -0.75383323
5 2.340497 0.003839767 1.969413e-09 0.019258078 0.02 0.01 -0.45706127
6 2.697283 0.003843766 4.229906e-09 0.006990777 0.02 0.01 0.19649652
7 2.737341 0.003831004 9.440138e-10 0.006193804 0.02 0.01 -0.13372915
8 2.601179 0.003833862 7.779184e-10 0.009290410 0.02 0.01 -0.51408533
9 2.442508 0.003830045 2.133669e-08 0.014585591 0.02 0.01 -0.40723702
10 2.631489 0.003829259 7.973859e-10 0.008501151 0.04 0.02 0.23980167
kurtosis geometry
1 -0.81316154 MULTIPOLYGON (((-764674.9 8...
2 0.55021722 MULTIPOLYGON (((-764674.9 8...
3 -0.50749002 MULTIPOLYGON (((-764674.9 8...
4 0.54499553 MULTIPOLYGON (((-764674.9 8...
5 0.28836755 MULTIPOLYGON (((-764674.9 8...
6 0.03187466 MULTIPOLYGON (((-764674.9 8...
7 -0.02562365 MULTIPOLYGON (((-764674.9 8...
8 0.07882560 MULTIPOLYGON (((-764674.9 8...
9 0.28865884 MULTIPOLYGON (((-764674.9 8...
10 -0.20275849 MULTIPOLYGON (((-764674.9 8...tmap_mode("plot")
gi_star_map = tm_shape(filter(gi_values_map, Date == '2021-07-31')) +
tm_fill("gi_star") +
tm_borders(alpha=0.5) +
tm_layout(main.title = "Gi* values for vaccination rates in July 2021", main.title.size=0.8)
# tm_view(set.zoom.limits = c(10,16))
p_sim_map = tm_shape(filter(gi_values_map, Date == '2021-07-31')) +
tm_fill("p_sim", breaks = c(0, 0.05, 1)) +
tm_borders(alpha=0.5) +
tm_layout(main.title = "p-values of Gi* for vaccination rates in July 2021", main.title.size=0.8)
# tm_view(set.zoom.limits = c(10,16))
tmap_arrange(gi_star_map, p_sim_map)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-07-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-07-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-07-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-08-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-07-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-07-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-09-30")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-09-30",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-09-30",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-10-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-10-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-10-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-11-30")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-11-30",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-11-30",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-12-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2021-12-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2021-12-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-01-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-01-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-01-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-02-27")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-02-27",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-02-27",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-03-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-03-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-03-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-04-30")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-04-30",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-04-30",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-05-31")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-05-31",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-05-31",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-06-30")
map1 <- tm_shape(wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_view(set.zoom.limits = c(6,8)) +
tm_layout(main.title = "Gi* value vaccination rate 2022-06-30",
main.title.size = 0.8)
map2 <- tm_shape(wanted_records) +
tm_fill("p_sim",
breaks = c(0, 0.001, 0.01, 0.05, 1),
labels = c("0.001", "0.01", "0.05", "Not sig")) +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "p-value of Gi vaccination rate 2022-06-30",
main.title.size = 0.8)
tmap_arrange(map1, map2, ncol = 2)
7.5 Visualising Hot Spot and Cold Spot Areas
How would the visualisations in this section differ from those in 7.4? The visualisations in 7.4 included both statistically significant (hotspot and coldspot) and statistically insignificant areas. This time round, we just want to focus on the statistically significant areas.
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-07-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-07-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-08-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-08-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-09-30")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-09-30",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-10-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-10-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-11-30")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-11-30",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2021-12-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2021-12-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-01-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-01-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-02-27")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-02-27",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-03-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-03-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-04-30")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-04-30",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-05-31")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-05-31",
main.title.size = 0.8)
tmap_mode("plot")
wanted_records = filter(gi_values_map, Date == "2022-06-30")
only_sig_wanted_records = wanted_records %>% filter(p_sim < 0.05)
tm_shape(wanted_records) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(only_sig_wanted_records) +
tm_fill("gi_star") +
tm_borders(alpha = 0.5) +
tm_layout(main.title = "Hot Spots & Cold Spots In 2022-06-30",
main.title.size = 0.8)
7.6 Draw Statistical Conclusion
For the Gi* value vaccination rate maps on the left, the darker green regions with high significance level (as seen from the right graph when p-value < 0.05) are known as hot spots. For the Gi* value vaccination rate maps on the left, the darker red regions with high significance level (as seen from the right graph when p-value < 0.05) are known as cold spots.
From 31 October 2021 onwards, a significant proportion of East Jakarta is a hot spot with high vaccination rate, with Gi* > 0 and p-value < 0.05. Over the next few months, we see sub-districts in this area continually having increasingly high vaccination rates as the green region becomes darker (more intense), indicating a higher Gi* value. This tells us that the sub-districts in East Jakarta are doing a good job in promoting vaccines, given the high and sustained vaccination rates.
A consistent but small cold spot is seen on the edge of West Jakarta boundary, towards the centre of the map. This is a cold spot as the Gi* value is consistently < 0 and is statistically significant as p-value < 0.05. This means that the sub-districts in this cold spot have generally low vaccination rates. More should be done to help increase the vaccination rates in the sub-districts from this cold spot.
Initially in the earlier months (July 2021 - September 2021), North Jakarta had a hot spot as Gi* value > 0 and was statistically significant, indicating that some sub-districts in the region had high vaccination rates. However, after September 2021, these sub-districts had much lower vaccination rates.
8.0 Emerging Hot Spot Analysis (EHSA)
8.1 Mann-Kendall Test
The Mann-Kendall statistical test for trend is used to assess whether a set of data values is increasing over time or decreasing over time, and whether the trend in either direction is statistically significant. The Mann-Kendall test does NOT assess the magnitude of change.
H0: There is no monotonic trend in the series.
H1: There is a monotonic trend in the series. (This trend can be positive, negative, or non-null.)
With these Gi* measures we can then evaluate each location for a trend using the Mann-Kendall test. We’ll be analysing the following sub-districts: JAGAKARSA, SRENGSENG SAWAH, CIGANJUR. These are the sub-districts from East Jakarta that we discussed in 7.5. From 31 October 2021 onwards, these sub-districts have been having high and sustained vaccination rates.
jagakarsa <- gi_stars %>%
ungroup() %>%
filter(Sub_District == "JAGAKARSA") |>
select(Sub_District, Date, gi_star)Next, we plot the result by using ggplot2 functions.
y <- ggplot(data = jagakarsa,
aes(x = Date,
y = gi_star)) +
geom_line() +
theme_light()
ggplotly(y)jagakarsa %>%
summarise(mk = list(
unclass(
Kendall::MannKendall(gi_star)))) %>%
tidyr::unnest_wider(mk)# A tibble: 1 × 5
tau sl S D varS
<dbl> <dbl> <dbl> <dbl> <dbl>
1 0.667 0.00319 44 66.0 213.sl is the p-value. Since sl < 0.005, the result tells us that upward spike after June 2022 is a significant trend.
srengseng_sawah <- gi_stars %>%
ungroup() %>%
filter(Sub_District == "SRENGSENG SAWAH") |>
select(Sub_District, Date, gi_star)Next, we plot the result by using ggplot2 functions.
s <- ggplot(data = srengseng_sawah,
aes(x = Date,
y = gi_star)) +
geom_line() +
theme_light()
ggplotly(s)srengseng_sawah %>%
summarise(mk = list(
unclass(
Kendall::MannKendall(gi_star)))) %>%
tidyr::unnest_wider(mk)# A tibble: 1 × 5
tau sl S D varS
<dbl> <dbl> <dbl> <dbl> <dbl>
1 0.667 0.00319 44 66.0 213.sl is the p-value. Since sl < 0.005, the result tells us that downward trend after June 2022 is a significant trend.
ciganjur <- gi_stars %>%
ungroup() %>%
filter(Sub_District == "CIGANJUR") |>
select(Sub_District, Date, gi_star)Next, we plot the result by using ggplot2 functions.
p <- ggplot(data = ciganjur,
aes(x = Date,
y = gi_star)) +
geom_line() +
theme_light()
ggplotly(p)ciganjur %>%
summarise(mk = list(
unclass(
Kendall::MannKendall(gi_star)))) %>%
tidyr::unnest_wider(mk)# A tibble: 1 × 5
tau sl S D varS
<dbl> <dbl> <dbl> <dbl> <dbl>
1 0.485 0.0335 32 66.0 213.sl is the p-value. Since sl > 0.005, the result tells us that downward spike after April 2022 is not a significant trend.
These 3 sub_district plots generally have positive Gi* values. JAGAKARSA looks like it would increase in Gi* value in the future and produce a darker green region on the map.
While SRENGSENG SAWAH has always had a positive Gi* value, its value is starting to decrease sometime in June 2022 onwards. Sub-district SRENGSENG SAWAH’s administration should keep an eye on this and try to ensure that the vaccination rate does not plummet too rapidly especially since the Mann-Kendall test proves that this is a significant downward trend.
Similar to SRENGSENG SAWAH, CIGANJUR had always had a positive Gi* value. However from April 2022, the Gi* has been decreasing. The Mann-Kendall test proves that this is a NOT significant downward trend. Hence, we should continue to observe the pattern for upcoming months before resorting to more drastic measures in attempt to increase vaccination rates.
This rise in vaccination rate for SRENGSENG SAWAH could be due to effective vaccination campaigns, support or even a sharp increase in COVID cases, scaring the public into getting their vaccines.
8.2 Visualising EHSA
Similarly, we can replicate this for each location by using group_by() of dplyr package.
ehsa <- gi_stars %>%
group_by(Sub_District) %>%
summarise(mk = list(
unclass(
Kendall::MannKendall(gi_star)))) %>%
tidyr::unnest_wider(mk)8.2.1 Arrange to show significant emerging hot/cold spots
emerging <- ehsa %>%
arrange(sl, abs(tau)) %>%
slice(1:5)
emerging# A tibble: 5 × 6
Sub_District tau sl S D varS
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 PETOJO UTARA -0.970 0.0000156 -64 66.0 213.
2 KAYU MANIS 0.970 0.0000156 64 66.0 213.
3 JATINEGARA KAUM 0.939 0.0000287 62 66.0 213.
4 PISANGAN BARU 0.939 0.0000287 62 66.0 213.
5 PASAR BARU -0.939 0.0000288 -62 66.0 213.8.2.2 Performing Emerging Hotspot Analysis
Lastly, we will perform EHSA analysis by using emerging_hotspot_analysis() of sfdep package. It takes a spacetime object x, and the quoted name of the variable of interest for .var argument. The k argument is used to specify the number of time lags which is set to 1 by default. Lastly, nsim map numbers of simulation to be performed.
ehsa <- emerging_hotspot_analysis(
x = time_series_cube_vacc_rate,
.var = "Vaccination_Rate",
k = 1,
nsim = 99
)ehsa# A tibble: 261 × 4
location tau p_value classification
<chr> <dbl> <dbl> <chr>
1 KEAGUNGAN 0.576 0.0112 oscilating coldspot
2 GLODOK 0.545 0.0164 sporadic coldspot
3 HARAPAN MULIA 0.727 0.00127 sporadic coldspot
4 CEMPAKA BARU 0.727 0.00127 sporadic coldspot
5 PASAR BARU 0.848 0.000162 oscilating hotspot
6 KARANG ANYAR 0.424 0.0641 oscilating coldspot
7 MANGGA DUA SELATAN 0.848 0.000162 oscilating hotspot
8 PETOJO UTARA 0.576 0.0112 sporadic coldspot
9 SENEN 0.0606 0.837 sporadic coldspot
10 BUNGUR 0.697 0.00203 sporadic coldspot
# … with 251 more rows8.2.3 Visualising the distribution of EHSA classes
In the code chunk below, ggplot2 functions ised used to reveal the distribution of EHSA classes as a bar chart.
ggplot(data = ehsa,
aes(x = classification)) +
geom_bar()
Figure above shows that oscilating hotspots class has the highest numbers of sub-districts. What does this mean for us?
- This means that many hotspots in Jakarta are statistically significant hot spots for the final time-step interval that has a history of also being a statistically significant cold spot during a prior time step. Less than 90 percent of the time-step intervals have been statistically significant hot spots.
8.2.4 Visualising EHSA
In this section, we will learn how to visualise the geographic distribution EHSA classes. However, before we can do so, we need to join both jakarta_renamed and ehsa together by using the code chunk below.
jakarta_ehsa <- jakarta_renamed %>%
left_join(ehsa,
by = c("Sub_District" = "location"))Next, tmap functions will be used to plot a categorical choropleth map by using the code chunk below.
ehsa_sig <- jakarta_ehsa %>%
filter(p_value < 0.05)
tmap_mode("plot")
tm_shape(jakarta_ehsa) +
tm_polygons() +
tm_borders(alpha = 0.5) +
tm_shape(ehsa_sig) +
tm_fill("classification") +
tm_borders(alpha = 0.4)
8.3 Observations From Emerging Hot Spot Analysis (EHSA)
Oscillating hotspot is the most prevalent type of hotspot trend being detected throughout Jakarta. This means that generally throughout Jakarta, the regions that are currently considered as a statistically significant hotspot were once significant cold spots previously.Less than 90 percent of the time-step intervals have been statistically significant hot spots. This means that these places that currently have high vaccination rates had their moments of having low vaccination rates.
Another prevalent type of statistically significant region would be the sporadic coldspot which is more commonly found on the left part of Jakarta (more so in the East and West Jakarta). This means that the sub-districts with currently low vaccination rates have never had high vaccination rates before. These are the places that should more vaccines or campaigns to improve vaccination rates as they’ve never been high before.
The areas that are grey are statistically insignificant.