Take Home Exercise 2

Published

February 18, 2023

Modified

February 28, 2023

1.0 Overview

1.1 Background

Due to the COVID-19 outbreak in late December 2019, Indonesia commenced its COVID-19 vaccination program on 13 January 2021. Despite having the 4th highest number of vaccines administered as of January 4th, 2022, the distribution was not necessarily even.

This notebook explore how the vaccination rates changed over time for the DKI Jarkarta on a sub-district level.

1.2 Task

  • Choropleth Mapping and Analysis

  • Local Gi* Analysis

  • Emerging Hot Spot Analysis (EHSA)

2.0 Setup

2.1 Import Packages

  • sf - Used for handling geospatial data

  • sfdep - Used for functions not in spdep

  • tmap, maptools, kableExtra, plotly - Used for visualizing dataframes and plots

  • lubridate - Used for handling datetime

  • tidyr - Used for changing the shape and hierarchy of dataframe

  • readxl - to read excel data (.xlsx files)

  • tidyVerse - Used for data transformation and presentation

pacman::p_load(sf, sfdep, tmap, maptools, kableExtra, plotly, lubridate, tidyr, readxl, tidyverse)

3.0 Data Wrangling

3.1 Datasets Used

Type Name
Geopsatial BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA
Aspatial DKI Jakarta Provincial Vaccination Open Data

3.2 Geospatial Data

3.2.1 Load Data

bd_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/shambhavigoenka/Desktop/School/Geo/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 84
glimpse(bd_jakarta)
Rows: 269
Columns: 162
$ OBJECT_ID  <dbl> 25477, 25478, 25397, 25400, 25378, 25379, 25390, 25382, 253…
$ KODE_DESA  <chr> "3173031006", "3173031007", "3171031003", "3171031006", "31…
$ DESA       <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA BARU", "PU…
$ KODE       <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 317102, 310…
$ PROVINSI   <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA",…
$ KAB_KOTA   <chr> "JAKARTA BARAT", "JAKARTA BARAT", "JAKARTA PUSAT", "JAKARTA…
$ KECAMATAN  <chr> "TAMAN SARI", "TAMAN SARI", "KEMAYORAN", "KEMAYORAN", "KEPU…
$ DESA_KELUR <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA BARU", "PU…
$ JUMLAH_PEN <dbl> 21609, 9069, 29085, 41913, 6947, 7059, 15793, 5891, 33383, …
$ JUMLAH_KK  <dbl> 7255, 3273, 9217, 13766, 2026, 2056, 5599, 1658, 11276, 128…
$ LUAS_WILAY <dbl> 0.36, 0.37, 0.53, 0.97, 0.93, 0.95, 1.76, 1.14, 0.47, 1.31,…
$ KEPADATAN  <dbl> 60504, 24527, 54465, 42993, 7497, 7401, 8971, 5156, 71628, …
$ PERPINDAHA <dbl> 102, 25, 131, 170, 17, 26, 58, 13, 113, 178, 13, 87, 56, 12…
$ JUMLAH_MEN <dbl> 68, 52, 104, 151, 14, 32, 36, 10, 60, 92, 5, 83, 21, 70, 93…
$ PERUBAHAN  <dbl> 20464, 8724, 27497, 38323, 6853, 6993, 15006, 5807, 31014, …
$ WAJIB_KTP  <dbl> 16027, 7375, 20926, 30264, 4775, 4812, 12559, 3989, 24784, …
$ SILAM      <dbl> 15735, 1842, 26328, 36813, 6941, 7057, 7401, 5891, 23057, 2…
$ KRISTEN    <dbl> 2042, 2041, 1710, 3392, 6, 0, 3696, 0, 4058, 5130, 1, 3061,…
$ KHATOLIK   <dbl> 927, 1460, 531, 1082, 0, 0, 1602, 0, 2100, 2575, 0, 1838, 7…
$ HINDU      <dbl> 15, 9, 42, 127, 0, 0, 622, 0, 25, 27, 0, 9, 115, 47, 382, 7…
$ BUDHA      <dbl> 2888, 3716, 469, 495, 0, 2, 2462, 0, 4134, 4740, 5, 1559, 3…
$ KONGHUCU   <dbl> 2, 1, 5, 1, 0, 0, 10, 0, 9, 10, 0, 4, 1, 1, 4, 0, 2, 0, 0, …
$ KEPERCAYAA <dbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 22, 0, 3, 3, 0, 0, 0…
$ PRIA       <dbl> 11049, 4404, 14696, 21063, 3547, 3551, 7833, 2954, 16887, 1…
$ WANITA     <dbl> 10560, 4665, 14389, 20850, 3400, 3508, 7960, 2937, 16496, 1…
$ BELUM_KAWI <dbl> 10193, 4240, 14022, 20336, 3366, 3334, 7578, 2836, 15860, 1…
$ KAWIN      <dbl> 10652, 4364, 13450, 19487, 3224, 3404, 7321, 2791, 15945, 1…
$ CERAI_HIDU <dbl> 255, 136, 430, 523, 101, 80, 217, 44, 381, 476, 39, 305, 10…
$ CERAI_MATI <dbl> 509, 329, 1183, 1567, 256, 241, 677, 220, 1197, 993, 79, 90…
$ U0         <dbl> 1572, 438, 2232, 3092, 640, 648, 802, 585, 2220, 2399, 376,…
$ U5         <dbl> 1751, 545, 2515, 3657, 645, 684, 995, 588, 2687, 2953, 331,…
$ U10        <dbl> 1703, 524, 2461, 3501, 620, 630, 1016, 513, 2653, 2754, 309…
$ U15        <dbl> 1493, 521, 2318, 3486, 669, 671, 1106, 548, 2549, 2666, 328…
$ U20        <dbl> 1542, 543, 2113, 3098, 619, 609, 1081, 491, 2313, 2515, 290…
$ U25        <dbl> 1665, 628, 2170, 3024, 639, 582, 1002, 523, 2446, 2725, 325…
$ U30        <dbl> 1819, 691, 2363, 3188, 564, 592, 1236, 478, 2735, 3122, 329…
$ U35        <dbl> 1932, 782, 2595, 3662, 590, 572, 1422, 504, 3034, 3385, 317…
$ U40        <dbl> 1828, 675, 2371, 3507, 480, 486, 1200, 397, 2689, 3037, 250…
$ U45        <dbl> 1600, 607, 2250, 3391, 421, 457, 1163, 365, 2470, 2597, 206…
$ U50        <dbl> 1408, 619, 1779, 2696, 346, 369, 1099, 288, 2129, 2282, 134…
$ U55        <dbl> 1146, 602, 1379, 1909, 252, 318, 979, 235, 1843, 1930, 129,…
$ U60        <dbl> 836, 614, 1054, 1397, 197, 211, 880, 162, 1386, 1394, 75, 9…
$ U65        <dbl> 587, 555, 654, 970, 122, 114, 747, 111, 958, 932, 50, 706, …
$ U70        <dbl> 312, 311, 411, 631, 69, 55, 488, 65, 554, 573, 38, 412, 129…
$ U75        <dbl> 415, 414, 420, 704, 74, 61, 577, 38, 717, 642, 37, 528, 125…
$ TIDAK_BELU <dbl> 3426, 1200, 4935, 7328, 1306, 1318, 2121, 973, 5075, 6089, …
$ BELUM_TAMA <dbl> 1964, 481, 2610, 3763, 730, 676, 1278, 732, 3241, 3184, 383…
$ TAMAT_SD   <dbl> 2265, 655, 2346, 2950, 1518, 2054, 1169, 1266, 4424, 3620, …
$ SLTP       <dbl> 3660, 1414, 3167, 5138, 906, 1357, 2236, 852, 5858, 6159, 5…
$ SLTA       <dbl> 8463, 3734, 12172, 16320, 2040, 1380, 5993, 1570, 12448, 14…
$ DIPLOMA_I  <dbl> 81, 23, 84, 179, 22, 15, 43, 36, 85, 83, 4, 63, 27, 79, 110…
$ DIPLOMA_II <dbl> 428, 273, 1121, 1718, 101, 59, 573, 97, 604, 740, 25, 734, …
$ DIPLOMA_IV <dbl> 1244, 1241, 2477, 4181, 314, 191, 2199, 357, 1582, 1850, 83…
$ STRATA_II  <dbl> 74, 46, 166, 315, 10, 8, 168, 8, 63, 92, 5, 174, 125, 122, …
$ STRATA_III <dbl> 4, 2, 7, 21, 0, 1, 13, 0, 3, 9, 0, 16, 8, 7, 75, 49, 65, 14…
$ BELUM_TIDA <dbl> 3927, 1388, 5335, 8105, 1788, 1627, 2676, 1129, 5985, 6820,…
$ APARATUR_P <dbl> 81, 10, 513, 931, 246, 75, 156, 160, 132, 79, 23, 145, 369,…
$ TENAGA_PEN <dbl> 70, 43, 288, 402, 130, 93, 81, 123, 123, 73, 45, 109, 30, 1…
$ WIRASWASTA <dbl> 8974, 3832, 10662, 14925, 788, 728, 6145, 819, 12968, 14714…
$ PERTANIAN  <dbl> 1, 0, 1, 3, 2, 2, 1, 3, 2, 5, 1, 1, 0, 0, 2, 5, 2, 1, 13, 4…
$ NELAYAN    <dbl> 0, 0, 2, 0, 960, 1126, 1, 761, 1, 2, 673, 0, 0, 0, 0, 0, 2,…
$ AGAMA_DAN  <dbl> 6, 6, 5, 40, 0, 0, 49, 2, 10, 11, 0, 54, 15, 16, 21, 14, 17…
$ PELAJAR_MA <dbl> 4018, 1701, 6214, 9068, 1342, 1576, 3135, 1501, 6823, 6866,…
$ TENAGA_KES <dbl> 28, 29, 80, 142, 34, 26, 60, 11, 48, 55, 16, 68, 89, 93, 28…
$ PENSIUNAN  <dbl> 57, 50, 276, 498, 20, 7, 59, 14, 56, 75, 2, 97, 53, 146, 57…
$ LAINNYA    <dbl> 4447, 2010, 5709, 7799, 1637, 1799, 3430, 1368, 7235, 7206,…
$ GENERATED  <chr> "30 Juni 2019", "30 Juni 2019", "30 Juni 2019", "30 Juni 20…
$ KODE_DES_1 <chr> "3173031006", "3173031007", "3171031003", "3171031006", "31…
$ BELUM_     <dbl> 3099, 1032, 4830, 7355, 1663, 1704, 2390, 1213, 5330, 5605,…
$ MENGUR_    <dbl> 4447, 2026, 5692, 7692, 1576, 1731, 3500, 1323, 7306, 7042,…
$ PELAJAR_   <dbl> 3254, 1506, 6429, 8957, 1476, 1469, 3185, 1223, 6993, 6858,…
$ PENSIUNA_1 <dbl> 80, 65, 322, 603, 24, 8, 70, 20, 75, 97, 2, 132, 67, 165, 6…
$ PEGAWAI_   <dbl> 48, 5, 366, 612, 223, 72, 65, 143, 73, 48, 15, 89, 91, 174,…
$ TENTARA    <dbl> 4, 0, 41, 57, 3, 0, 74, 1, 20, 12, 2, 11, 90, 340, 41, 52, …
$ KEPOLISIAN <dbl> 10, 1, 16, 42, 11, 8, 2, 9, 17, 7, 3, 9, 165, 15, 17, 28, 1…
$ PERDAG_    <dbl> 31, 5, 1, 3, 6, 1, 2, 4, 3, 1, 4, 0, 1, 2, 9, 2, 8, 2, 5, 9…
$ PETANI     <dbl> 0, 0, 1, 2, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 2, 0, 1, 6, 1,…
$ PETERN_    <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ NELAYAN_1  <dbl> 1, 0, 1, 0, 914, 1071, 0, 794, 0, 1, 663, 0, 0, 0, 0, 0, 2,…
$ INDUSTR_   <dbl> 7, 3, 4, 3, 1, 3, 0, 0, 1, 7, 0, 0, 2, 2, 1, 3, 12, 1, 8, 4…
$ KONSTR_    <dbl> 3, 0, 2, 6, 3, 8, 1, 6, 1, 5, 10, 0, 2, 5, 7, 4, 7, 1, 6, 2…
$ TRANSP_    <dbl> 2, 0, 7, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 6, 3, 2, 1, 2, 5,…
$ KARYAW_    <dbl> 6735, 3034, 7347, 10185, 237, 264, 4319, 184, 9405, 10844, …
$ KARYAW1    <dbl> 9, 2, 74, 231, 4, 0, 16, 1, 13, 10, 1, 24, 17, 29, 187, 246…
$ KARYAW1_1  <dbl> 0, 0, 5, 15, 0, 0, 0, 1, 0, 1, 0, 0, 2, 4, 7, 9, 3, 1, 6, 5…
$ KARYAW1_12 <dbl> 23, 4, 25, 35, 141, 50, 16, 157, 6, 9, 40, 11, 11, 15, 22, …
$ BURUH      <dbl> 515, 155, 971, 636, 63, 218, 265, 55, 1085, 652, 17, 357, 2…
$ BURUH_     <dbl> 1, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 2, 1, 0, 1, 1,…
$ BURUH1     <dbl> 0, 0, 1, 0, 1, 25, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ BURUH1_1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
$ PEMBANT_   <dbl> 1, 1, 4, 1, 1, 0, 7, 0, 5, 1, 0, 6, 1, 10, 11, 9, 8, 3, 4, …
$ TUKANG     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ TUKANG_1   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ TUKANG_12  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ TUKANG__13 <dbl> 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,…
$ TUKANG__14 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ TUKANG__15 <dbl> 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,…
$ TUKANG__16 <dbl> 7, 4, 10, 14, 0, 0, 2, 0, 7, 8, 0, 8, 1, 0, 3, 2, 2, 0, 17,…
$ TUKANG__17 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PENATA     <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
$ PENATA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PENATA1_1  <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0,…
$ MEKANIK    <dbl> 11, 1, 10, 8, 0, 0, 4, 0, 7, 8, 0, 9, 0, 15, 10, 10, 3, 0, …
$ SENIMAN_   <dbl> 4, 0, 12, 28, 0, 0, 2, 0, 3, 4, 0, 9, 6, 7, 14, 13, 17, 22,…
$ TABIB      <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ PARAJI_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PERANCA_   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 1, 2, 0, 0,…
$ PENTER_    <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0,…
$ IMAM_M     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PENDETA    <dbl> 2, 4, 5, 33, 0, 0, 20, 0, 10, 8, 0, 30, 14, 14, 18, 12, 1, …
$ PASTOR     <dbl> 0, 1, 0, 1, 0, 0, 8, 0, 0, 0, 0, 23, 0, 0, 0, 0, 2, 0, 0, 0…
$ WARTAWAN   <dbl> 7, 1, 16, 27, 0, 0, 4, 0, 8, 6, 0, 9, 5, 9, 26, 30, 11, 7, …
$ USTADZ     <dbl> 6, 1, 1, 5, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ JURU_M     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0,…
$ PROMOT     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ ANGGOTA_   <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 1, 2, 1, 0, 0,…
$ ANGGOTA1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,…
$ ANGGOTA1_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PRESIDEN   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ WAKIL_PRES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ ANGGOTA1_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ ANGGOTA1_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ DUTA_B     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ GUBERNUR   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ WAKIL_GUBE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ BUPATI     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ WAKIL_BUPA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ WALIKOTA   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ WAKIL_WALI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ ANGGOTA1_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 1,…
$ ANGGOTA1_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
$ DOSEN      <dbl> 3, 2, 23, 36, 1, 2, 11, 0, 3, 5, 0, 14, 6, 28, 69, 73, 58, …
$ GURU       <dbl> 72, 40, 272, 378, 118, 72, 69, 116, 126, 71, 36, 97, 23, 10…
$ PILOT      <dbl> 1, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 1, 0, 0, 0,…
$ PENGACARA_ <dbl> 4, 1, 8, 22, 0, 0, 5, 0, 5, 4, 0, 4, 3, 12, 24, 26, 40, 13,…
$ NOTARIS    <dbl> 0, 0, 3, 5, 0, 0, 4, 0, 0, 0, 0, 5, 0, 5, 10, 3, 7, 1, 0, 4…
$ ARSITEK    <dbl> 1, 0, 2, 3, 0, 0, 2, 0, 0, 0, 0, 4, 1, 2, 7, 3, 9, 2, 0, 4,…
$ AKUNTA_    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 0, 3, 1,…
$ KONSUL_    <dbl> 1, 0, 2, 11, 0, 0, 4, 0, 0, 0, 0, 6, 2, 3, 10, 8, 12, 2, 1,…
$ DOKTER     <dbl> 16, 32, 35, 68, 0, 1, 63, 0, 27, 32, 1, 63, 48, 60, 236, 12…
$ BIDAN      <dbl> 3, 1, 9, 18, 12, 8, 1, 3, 3, 3, 7, 3, 10, 10, 7, 2, 3, 2, 1…
$ PERAWAT    <dbl> 7, 0, 25, 44, 12, 10, 3, 6, 12, 20, 6, 7, 26, 16, 21, 20, 9…
$ APOTEK_    <dbl> 0, 0, 2, 3, 1, 0, 0, 0, 1, 2, 0, 1, 2, 3, 3, 3, 3, 1, 0, 1,…
$ PSIKIATER  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 1, 0, 0, 0,…
$ PENYIA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0,…
$ PENYIA1    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
$ PELAUT     <dbl> 0, 0, 6, 16, 1, 1, 0, 14, 2, 4, 1, 2, 4, 2, 10, 13, 2, 0, 3…
$ PENELITI   <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 2, 0,…
$ SOPIR      <dbl> 65, 3, 94, 123, 0, 1, 61, 0, 76, 79, 0, 63, 44, 101, 71, 59…
$ PIALAN     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ PARANORMAL <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ PEDAGA_    <dbl> 379, 126, 321, 562, 11, 10, 412, 15, 202, 225, 0, 271, 212,…
$ PERANG_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
$ KEPALA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ BIARAW_    <dbl> 0, 1, 0, 0, 0, 0, 22, 0, 3, 0, 0, 2, 1, 0, 4, 0, 17, 1, 0, …
$ WIRASWAST_ <dbl> 1370, 611, 1723, 3099, 131, 119, 1128, 259, 2321, 2677, 79,…
$ LAINNYA_12 <dbl> 94, 57, 82, 122, 12, 10, 41, 6, 89, 158, 24, 37, 15, 94, 12…
$ LUAS_DESA  <dbl> 25476, 25477, 25396, 25399, 25377, 25378, 25389, 25381, 253…
$ KODE_DES_3 <chr> "3173031006", "3173031007", "3171031003", "3171031006", "31…
$ DESA_KEL_1 <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA BARU", "PU…
$ KODE_12    <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 317102, 310…
$ geometry   <MULTIPOLYGON [°]> MULTIPOLYGON (((106.8164 -6..., MULTIPOLYGON (…
plot(st_geometry(bd_jakarta)) #qtm

3.2.1.1 Understanding the Data

DKI Jarkarta is a province divided into municipalities (Kab Kota) which are further divided into districts (kecamatan) and finally divided into sub-districts (Desa Kelur)

3.2.2 Data Preprocessing

3.2.2.1 Invalid Geometries

length(which(st_is_valid(bd_jakarta) == FALSE))
[1] 0

Everything is valid

3.2.2.2 Exclude redundant fields

Exclude all data except Municipality, District name, District Code, Sub-district Code, Sub-district name, Total Population and geometry of the regions.

bd_jakarta <- bd_jakarta %>%
  select(2:4, 6:7, 9,)

3.2.2.3 Rename from Bahasa Indonesia to English

Index Original Name Translated Name
2 KODE_DESA Sub_District_Code
3 DESA Sub_District
4 KODE District_Code
6 KAB_KOTA Municipality
7 KECAMATAN District
9 JUMLAH_PEN Total_Population
Code 
bd_jakarta <- bd_jakarta %>% 
  dplyr::rename(
    Municipality=KAB_KOTA,
    District=KECAMATAN,
    District_Code=KODE,
    Sub_District=DESA,
    Sub_District_Code=KODE_DESA,
    Total_Population=JUMLAH_PEN
    )

3.2.2.4 Handle Missing Values

Code 
cat("There are", sum(is.na(bd_jakarta)), "missing values in", paste(names(which(colSums(is.na(bd_jakarta))>0)), collapse = ", "))
There are 4 missing values in Municipality, District

Remove all rows with missing values

bd_jakarta <- na.omit(bd_jakarta)

3.2.3 Transform Coordinate System

st_crs(bd_jakarta)
Coordinate Reference System:
  User input: WGS 84 
  wkt:
GEOGCRS["WGS 84",
    DATUM["World Geodetic System 1984",
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["latitude",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["longitude",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    ID["EPSG",4326]]

Transform the CRS to DGN95, ESPG code 23845

bd_jakarta <- st_transform(bd_jakarta, 23845)
st_crs(bd_jakarta)
Coordinate Reference System:
  User input: EPSG:23845 
  wkt:
PROJCRS["DGN95 / Indonesia TM-3 zone 54.1",
    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 54.1",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",139.5,
            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 east of 138°E."],
        BBOX[-9.19,138,-1.49,141.01]],
    ID["EPSG",23845]]

3.2.4 Remove outer islands

Based on first glance in View, there are several small islands surrounding the mainland. As this is not relevant to the analysis, they shall be omitted.

# KEPULAUAN SERIBU means thousand islands
bd_jakarta <- filter(bd_jakarta, Municipality != "KEPULAUAN SERIBU")

3.2.5 Data Summary

Code 
c = length(unique(bd_jakarta$"Municipality"))
d = length(unique(bd_jakarta$"District"))
sd = length(unique(bd_jakarta$"Sub_District"))

cat("There are", c, "unique municipalities,", d, "unique districts, and", sd, "unique sub-districs")
There are 5 unique municipalities, 42 unique districts, and 261 unique sub-districs

3.2.5.1 View distribution

Code 
df_grp_municipality = bd_jakarta %>% group_by(Municipality)  %>%
                    summarise(total_districts = n(),
                              .groups = 'drop')
                              
ggplot(df_grp_municipality, aes(x=Municipality, y=total_districts), fill=Municipality) + 
  geom_bar(stat = "identity",
           color="black",
           fill="lightblue4") + 
  ggtitle("Number of Districts by Municipality") +
  xlab("Municipality Name") +
  ylab("No. of Districts")

Code 
df_grp_districs = bd_jakarta %>% group_by(District)  %>%
                    summarise(total_sub_districts = n(),
                              .groups = 'drop')

ggplot(df_grp_districs, aes(x=District, y=total_sub_districts)) + 
  geom_bar(stat = "identity",
           color="black",
           fill="lightblue3") + 
  scale_x_discrete(guide = guide_axis(n.dodge=7))+
  ggtitle("Number of Sub-districts by District") +
  xlab("District Name") +
  ylab("No. of Sub-districts")

3.2.5.2 View by Municipality Divisions

tm_shape(bd_jakarta) + 
  tm_polygons("Municipality")

3.2.5.2 View by Population

ggplot(bd_jakarta, aes(x=Total_Population)) + 
  geom_histogram(bins = 10,
                color="black",
                fill="indianred")

Equal Interval Classification

Code 
tmap_mode("plot")
tm_shape(bd_jakarta) + 
  tm_fill("Total_Population",
          style = "equal",
          palette = "OrRd") + 
  tm_layout(main.title = "Distribution by Population") + 
  tm_borders(alpha = 0.5)

3.3 Aspatial Data

3.3.1 Load Data

All files were downloaded as “Data Vaksinasi Berbasis Kelurahan (<last date of month> <Indonesian month name> <year>).xlsx” were locally renamed as “<last date of month> <English month name> <year>.xlsx”

#get list of aspatial data files
folder_path <- "data/aspatial"
files <- list.files(folder_path, pattern = ".xlsx", full.names = TRUE)

There was no file for Feb 28, 2022 so vaccination rate as of Feb 27, 2022 has been used instead

#combines files into vacc_df
vacc_df <- data.frame()

for (file in files) {
  data <- read_excel(file, sheet = "Data Kelurahan") #sub-district
  data$Date <- dmy(str_extract(file, "\\d{2} [[:alpha:]]+ \\d{4}")) #add date column
  vacc_df <- bind_rows(vacc_df, data)
}

3.3.2 Data Preprocessing

3.3.2.1 Exclude redundant fields

Exclude all values except Municipality name, Sub-district name, Sub-district code, Target number, Number not yet vaccinated, Total vaccines administered and Date.

vacc_df <-  vacc_df %>%
  select(1:2, 4:6, 9, 28)

3.3.2.2 Rename from Bahasa Indonesia to English

Index Original Name Translated Name
1 KODE KELURAHAN Sub_District_Code
2 WILAYAH KOTA Municipality
4 KELURAHAN Sub_District
5 SANSARAN Target
6 BELUM VAKSIN Not_Yet_Vaccinated
9 TOTAL VAKSIN DIBERIKAN Total_Vaccine_Administered
Code 
vacc_df <- vacc_df %>% 
  dplyr::rename(
    Municipality="WILAYAH KOTA",
    Sub_District="KELURAHAN",
    Sub_District_Code="KODE KELURAHAN",
    Target="SASARAN", 
    Not_Yet_Vaccinated="BELUM VAKSIN", 
    Total_Vaccine_Administered="TOTAL VAKSIN\r\nDIBERIKAN"
    )

3.3.2.3 Handle Missing Values

Code 
cat("There are", sum(is.na(vacc_df)), "missing values in", paste(names(which(colSums(is.na(vacc_df))>0)), collapse = ", "))
There are 24 missing values in Sub_District_Code, Municipality

Remove all rows with missing values

vacc_df <- na.omit(vacc_df)

3.3.3 Remove outer islands

vacc_df <- filter(vacc_df, Municipality != "KAB.ADM.KEP.SERIBU")

Remove column Municipality as there is no further need for it in this data frame

vacc_df = vacc_df[,!(names(vacc_df) %in% "Municipality")]

3.4 Combined Data Wrangling

3.4.1 Join vaccination data

df <- left_join(bd_jakarta, vacc_df,
         by=c("Sub_District_Code"="Sub_District_Code",
              "Sub_District"="Sub_District"))

Visualise

Code 
p1 = tm_shape(df)+
  tm_fill("Target") +
  tm_borders(alpha = 0.5) +
  tm_layout(main.title="Target")
  
p2 = tm_shape(df)+
  tm_fill("Total_Vaccine_Administered") +
  tm_borders(alpha = 0.5) +
  tm_layout(main.title="Total Vaccines Administered")


tmap_arrange(p1, p2)

While Target and Total Vaccines Administered seem highly correlated, there several grey Missing patches which will be addressed below

3.4.2 Standardise Data

Although missing values from the geospatial and aspatial data have already been omitted, there is a “Missing” data in the above visualisation. This is because there are discrepancies between the sub-district names in the two datasets.

View Discrepancies

Code 
cases_subdistrict <- c(vacc_df$Sub_District)
bd_subdistrict <- c(bd_jakarta$Sub_District)

aspatial_list <- sort(unique(cases_subdistrict[!(cases_subdistrict %in% bd_subdistrict)]))
aspatial_list <- c(aspatial_list[1:3], aspatial_list[6], aspatial_list[5], aspatial_list[7:9], aspatial_list[4]) #rearrgane - hardcoded

geospatial_list <- sort(unique(bd_subdistrict[!(bd_subdistrict %in% cases_subdistrict)]))


spelling <- data.frame(
  Aspatial_Cases=aspatial_list,
  Geospatial_BD=geospatial_list
  )

# with dataframe a input, outputs a kable
library(knitr)
library(kableExtra)
kable(spelling, caption="Mismatched Records") %>%
  kable_material("hover", latex_options="scale_down")
Mismatched Records
Aspatial_Cases Geospatial_BD
BALE KAMBANG BALEKAMBANG
HALIM PERDANA KUSUMAH HALIM PERDANA KUSUMA
JATI PULO JATIPULO
KRAMAT JATI KRAMATJATI
KERENDANG KRENDANG
PAL MERIAM PALMERIAM
PINANG RANTI PINANGRANTI
RAWA JATI RAWAJATI
KAMPUNG TENGAH TENGAH

Rename geospatial data with aspatial data values

for (i in 1:9) {
  bd_jakarta$Sub_District[bd_jakarta$Sub_District == geospatial_list[i]] <- aspatial_list[i]
}

rm(aspatial_list, geospatial_list, i) #cleanup

3.4.3 Join vaccination data (again)

df <- left_join(bd_jakarta, vacc_df,
        by=c("Sub_District_Code"="Sub_District_Code",
              "Sub_District"="Sub_District"))

Visualise

Code 
p1 = tm_shape(df)+
  tm_fill("Target") +
  tm_borders(alpha = 0.5) +
  tm_layout(main.title="Target")
  
p2 = tm_shape(df)+
  tm_fill("Total_Vaccine_Administered") +
  tm_borders(alpha = 0.5) +
  tm_layout(main.title="Total Vaccines Administered")


tmap_arrange(p1, p2)

Grey Missing data from 3.4.1 has been handled.

4.0 Choropleth Mapping

4.1 Compute Vaccination Rates

\[ \mathrm Vaccination \ Rate = \frac{\mathrm Target \ - \ (\mathrm Not \ Vaccinated) \ }{\mathrm Target} \ * 100 \]

(Target has been chosen over total population to excluse people who are not required to be vaccinated)

Add Vaccination Rate column to main dataframe (df)

df <- df %>%
  mutate(vaccination_rate = ((Target-Not_Yet_Vaccinated)/Target)*100)

Create table vacc_rate_df that groups vaccination rate by sub-district and date

vacc_rate_df <- df %>%
  st_drop_geometry  %>% #remove geometry for pivot
  group_by(Sub_District, Date) %>%
  summarise(vaccination_rate) %>%
  
  ungroup() %>% 
  pivot_wider(names_from = Date, #use pivot table to rearrange
              values_from = vaccination_rate) %>%
  
  left_join(bd_jakarta, by=c("Sub_District"="Sub_District")) #add geometry back

View

head(vacc_rate_df)
# A tibble: 6 × 19
  Sub_District 2021-07…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
# … with 10 more variables: `2022-03-31` <dbl>, `2022-04-30` <dbl>,
#   `2022-05-31` <dbl>, `2022-06-30` <dbl>, Sub_District_Code <chr>,
#   District_Code <dbl>, Municipality <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`

Convert to sf object for plotting

vacc_rate_df <- st_as_sf(vacc_rate_df)

4.2 Visualise monthly vaccination rates

Code 
#helper function for plots
#Can use fisher too


plot <- function(varname) {
  tm_shape(vacc_rate_df) +
    tm_polygons() +
  tm_shape(vacc_rate_df) +
    tm_fill(varname, 
          n= 6,
          style = "jenks", 
          palette="RdPu",
          title = "Vaccination Rate (%)") +
    tm_layout(main.title = paste("Vaccination rate by", paste(month(ymd(varname), label=TRUE), year(ymd(varname)))),
          main.title.position = "center",
          frame = TRUE) +
    tm_borders(alpha = 0.5)
}

Plot

Code 
tmap_mode("plot")
tmap_arrange(plot("2021-07-31"), plot("2021-08-31"), plot("2021-09-30"),plot("2021-10-31"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2021-11-30"), plot("2021-12-31"), plot("2022-01-31"), plot("2022-02-27"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2022-03-31"), plot("2022-04-30"), plot("2022-05-31"), plot("2022-06-30"))

The vaccination rate increases over the year for all regions. The Northern municipality starts with the highest vaccination rates then other regions, particularly the South and East, catch up.

4.3 Observations from Jenks based Mapping

4.3.1 Vaccination Rates by Sub-district

For lowest vaccination rates,

Code 
#temporary dataset
temp_low <- df %>%
  group_by(Date) %>%
  filter(vaccination_rate == min(vaccination_rate)) %>% 
  arrange(Date)

#plot
ggplotly(ggplot(temp_low, aes(x=Date, y=vaccination_rate)) +
  geom_point(aes(color=temp_low$Sub_District)) + 
  ggtitle("Lowest Vaccination Rates over the Year by Sub-district"))

Not only does the minimum vaccination rate increase over year, but the sub-district Bale Kambang has a sharp increase in its rate and no longer has the lowest vaccination rate by July 2022.

For highest vaccination rates,

Code 
#temporary dataset
temp_high <- df %>%
  group_by(Date) %>%
  filter(vaccination_rate == max(vaccination_rate)) %>% 
  arrange(Date)

#plot
ggplotly(ggplot(temp_high, aes(x=Date, y=vaccination_rate)) +
  geom_point(aes(color=temp_high$Sub_District)) + 
  ggtitle("Highest Vaccination Rates over the Year by Sub-district"))

The sub-district Halim Perdana Kusumah has the highest vaccination rate for the most part.

The vaccination rate by July 31, 2021 for this sub-district at 65.21% is much higher than that of Bale Kambang which was at 37.01%. This difference of 28.20% gets smoothed down to 11.47% by June 30, 2022.

temp <- data.frame(temp_low$Date, temp_low$vaccination_rate, temp_high$vaccination_rate) 

colors <- c("Lowest" = "orange", "Highest" = "steelblue")

ggplotly(ggplot(temp, aes(x=temp_low.Date)) + 
  geom_line(aes(y=temp_low.vaccination_rate, color="Lowest"), size = 1.2) + 
  geom_line(aes(y=temp_high.vaccination_rate, color="Highest"), size = 1.2) + 
  ggtitle("Highest and Lowest Vaccination Rates over the Year by Sub-district") + 
  labs(y = "Vaccination Rate (%)",
      color = "Vaccination Rate") +
  scale_color_manual(values = colors))

4.3.2 Spatio-Temporal Mapping with custom breakpoints

View how the vaccination rate increases in all regions over the year based on Jenks defined breakpoints.

With the lowest and highest vaccination rates over the year being 37% and 90%, the Jenks breakpoints are defined as follows

breakpoints = c(37, 55, 72, 80, 84, 90)
Code 
#helper function for plotting
plot <- function(varname) {
  tm_shape(vacc_rate_df) +
    tm_polygons() +
  tm_shape(vacc_rate_df) +
    tm_fill(varname, 
          breaks= breakpoints,
          palette="RdPu",
          title = "Vaccination Rate") +
    tm_layout(main.title = paste("Vaccination rate by", paste(month(ymd(varname), label=TRUE), year(ymd(varname)))),
          main.title.position = "center",
          frame = TRUE) +
    tm_borders(alpha = 0.5)
}

Plot

Code 
tmap_mode("plot")
tmap_arrange(plot("2021-07-31"), plot("2021-08-31"), plot("2021-09-30"),plot("2021-10-31"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2021-11-30"), plot("2021-12-31"), plot("2022-01-31"), plot("2022-02-27"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2022-03-31"), plot("2022-04-30"), plot("2022-05-31"), plot("2022-06-30"))

As aforementioned, the overall vaccination rates grew from July 2021, to June 2022. From the maps, it is observed that the rates grew quickly till December 2021 and then grew at a steady pace.

In July 2021, there is a slightly higher vaccination rate in the North and Western municipalities which quickly becomes uniform by August 2021.

From October 2021, it is seems that the Northern and Southern municipalities have the higher vaccination rates than their surrounding regions. The Eastern municipality catches up the fastest by March 2022 and the rest gradually catch up by June 2022.

5.0 Local Gi* Analysis

5.1 Compute Gi* values

5.1.1. Create attribute table

# make table with Date, Sub_District, Target, Not_Yet_Vaccinated
vacc_attr_table <- df %>% select(10, 8, 7, 2) %>% st_drop_geometry()

# add a new field for Vaccination_Rate
vacc_attr_table$Vaccination_Rate <- (vacc_attr_table$Target - vacc_attr_table$Not_Yet_Vaccinated) / vacc_attr_table$Target*100

# final vaccination attribute table with Date, Sub_District, Vaccination_Rate
vacc_attr_table <- tibble(vacc_attr_table %>% select(1,4,5))

5.1.2 Create a time series cube (spatio-temporal cube)

vacc_rate_st <- spacetime(vacc_attr_table, bd_jakarta,
                          .loc_col = "Sub_District",
                          .time_col = "Date")

#check valid 
is_spacetime_cube(vacc_rate_st)
[1] TRUE

5.1.3 Derive spatial weights

vacc_rate_nb <- vacc_rate_st %>%
  activate("geometry") %>%
         #neighbours
  mutate(nb = include_self(st_contiguity(geometry)), 
         #inverse distance weights
         wt = st_inverse_distance(nb, geometry,
                                  scale=1,
                                  alpha=1),
         .before=1) %>%
  set_nbs("nb") %>%
  set_wts("wt")

View

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]>

5.1.4 Compute Gi* values

Set Seed for replicable results

set.seed(1234)

Compute

#LISA
gi_val <- vacc_rate_nb |>
  group_by(Date) |>
  mutate(gi_val = local_gstar_perm(
    Vaccination_Rate, nb, wt, nsim=99)) |>
      tidyr::unnest(gi_val)

View

colnames(gi_val)
 [1] "Date"             "Sub_District"     "Vaccination_Rate" "nb"              
 [5] "wt"               "gi_star"          "e_gi"             "var_gi"          
 [9] "p_value"          "p_sim"            "p_folded_sim"     "skewness"        
[13] "kurtosis"        
head(gi_val)
# A tibble: 6 × 13
# Groups:   Date [1]
  Date       Sub_Dis…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 KEAGUNGAN    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 BA…    59.3 <int> <dbl>   2.71  0.00383 1.38e-8 6.82e-3  0.02
6 2021-07-31 KARANG A…    52.2 <int> <dbl>   1.67  0.00382 2.17e-8 9.49e-2  0.1 
# … with 3 more variables: p_folded_sim <dbl>, skewness <dbl>, kurtosis <dbl>,
#   and abbreviated variable names ¹​Sub_District, ²​Vaccination_Rate

5.2 Visualise Gi* maps of the monthly vaccination rate

Combine Gi* values to main df

gi_val_df <- df %>%
  left_join(gi_val,
            by = c("Sub_District", "Date"))

Visualising Gi* and p-value of Gi* for significant locations (p-value < 0.05)

Code 
#helper function for plotting
plot <- function(varname){
 gi_plot <- 
    tm_shape(filter(gi_val_df, Date == varname)) +
    tm_polygons() +
    tm_shape(gi_val_df %>% filter(p_sim <0.05)) +
    tm_fill("gi_star",
            style="equal",
            n=5,
            palette = "seq") +
    tm_borders(alpha = 0.5) +
    tm_layout(main.title = paste("Significant Local Gi for", paste(month(ymd(varname), label=TRUE), year(ymd(varname)))),
              main.title.size = 0.8,
              aes.palette = list(seq = "-RdBu"))
  return(gi_plot)
}
Code 
tmap_mode("plot")
tmap_arrange(plot("2021-07-31"), plot("2021-08-31"), plot("2021-09-30"),plot("2021-10-31"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2021-11-30"), plot("2021-12-31"), plot("2022-01-31"), plot("2022-02-27"))

Code 
tmap_mode("plot")
tmap_arrange(plot("2022-03-31"), plot("2022-04-30"), plot("2022-05-31"), plot("2022-06-30"))

5.3 Observations of the Local Gi* Analysis

Filter by significant p-value (<0.05)

p_value_freq = filter(gi_val_df, p_sim<0.05)
temp <- as.data.frame(table(p_value_freq$Sub_District))
Code 
cat("Throughout the 12 months, there were", nrow(temp), "Sub-districts with p-value of Gi* < 0.05 for at least 1 month")
Throughout the 12 months, there were 128 Sub-districts with p-value of Gi* < 0.05 for at least 1 month
Code 
cat("The Gi* value of the vaccination rates for the entire time period were significantly low / high for the", nrow(temp %>% filter(Freq == 12)), "following Sub-districts throughout the year - ")
The Gi* value of the vaccination rates for the entire time period were significantly low / high for the 10 following Sub-districts throughout the year -
Sub-District Municipality Significance
BATU AMPAR East Low
CIPINANG CEMPEDAK East Low
GLODOK West High
KAMPUNG BALI Central Low
KAMPUNG TENGAH East Low
KEAGUNGAN West High
KEBON KACANG Central Low
KEBON MELATI Central Low
MANGGA BESAR West High
PETAMBURAN Central Low

It is observed the Central and Eastern municipalities have very distributed vaccination rates throughout the year whereas the Western municipality has evenly distributed vaccinated rates throughout the year.

6.0 Emerging Hot Spot Analysis (EHSA)

6.1 Mann-Kendall Test

Perform the Mann-Kendall Test using the spatio-temporal local Gi* values on the following Sub-districts - “ANCOL”,“KOJA” and “JOHAR BARU”

sub_list = list("KAMAL","KOJA", "ANCOL")

Filter data

cbg <- gi_val %>% 
  ungroup() %>% 
  filter(Sub_District %in% sub_list) |> 
  select(Date, Sub_District, gi_star)

Plot

Code 
cbg_ <-filter(cbg, Sub_District==sub_list[1])

ggplotly(ggplot(data=cbg_, aes(x=Date, y=gi_star))+
    geom_line()+
    theme_light())

P-value of Mann-Kendall Test

cbg_ |> 
  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 -0.848 0.000162   -56  66.0  213.

The p-value (sl) is 0.0002 which is < 0.05 hence p-value is significant and tau is -0.85 This means there is an overall significant downward trend.

Plot

Code 
cbg_ <-filter(cbg, Sub_District==sub_list[2])

ggplotly(ggplot(data=cbg_, aes(x=Date, y=gi_star))+
    geom_line()+
    theme_light())

P-value of Mann-Kendall Test

cbg_ |> 
  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 0.364 0.115    24  66.0  213.

The p-value (sl) is 0.6 which is > 0.05 hence p-value is significant and tau is 0.21 This means there is an overall insignificant upward trend. This is because the Gi* values initially went down and then rose.

Plot

Code 
cbg_ <-filter(cbg, Sub_District==sub_list[3])

ggplotly(ggplot(data=cbg_, aes(x=Date, y=gi_star))+
    geom_line()+
    theme_light())

P-value of Mann-Kendall Test

cbg_ |> 
  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 -0.273 0.244   -18  66.0  213.

The p-value is 0.24 which is < 0.05 hence p-value is significant and tau is -0.27. This means there is an overall significant downward trend.

6.2 EHSA map of the Gi* values of the vaccination rate

6.2.1. Perform the Mann Kendall Test for each location

ehsa <- gi_val %>%
  group_by(Sub_District) %>%
  summarise(mk = list(
    unclass(
      Kendall::MannKendall(gi_star)))) %>%
  tidyr::unnest_wider(mk)

6.2.2. 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.

6.2.3. Performing Emerging Hotspot Analysis (EHSA)

ehsa <- emerging_hotspot_analysis(
  x = vacc_rate_st, #spacetime object
  .var = "Vaccination_Rate",
  k = 1, #number of time lags
  nsim = 99
)

Plot

Code 
ggplot(data = ehsa,
       aes(x=classification, fill=classification, stat = "identity")) + 
    scale_fill_manual(values=c("#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072")) + 
    geom_bar()

Oscillating hotspot class has the highest number of Sub-districts.

Combine EHSA values to main df nad add classification column

ehsa_df <- df %>%
  left_join(ehsa, by = c("Sub_District" = "location")) %>%
  mutate(NewClassification = case_when(p_value < 0.05 ~ classification,
                                       p_value >= 0.05 ~ "insignificant"))

Visualise

Code 
tmap_mode("plot")
tm_shape(ehsa_df) +
  tm_polygons() +
  tm_borders(alpha = 0.5) +
tm_shape(ehsa_df %>% filter(NewClassification != "insignificant")) +
  tm_fill("NewClassification") + 
  tm_borders(alpha = 0.4)

6.3 Observations of the spatial patterns

As aforementioned, most the places are categorised under oscillating hotspots. These points are evenly spread out throughout the province. This is because more sub-districts had higher vaccination rates over time.

The second most number of places, under sporadic coldspots seem to be more clustered towards the Central municipality. This is because that region had the lowest relative vaccination rates, despite increasing over the year.

Oscillating coldspots are evenly spread out throughout the province, more so towards the provincial borders. This is because those regions started with the highest vaccination rates but were relatively less than its neighbours over time.

Finally, with the fewest classes, the places where no patterns are detected seem to be mainly in the Central municipality.

7.0 Acknowledgements

I’d like to thank Professor Kam for his insights and resource materials provided under IS415 Geospatial Analytics and Applications.