Part 0
Install appropriate packages
packages = c('rgdal', 'spdep', 'tmap', 'tidyverse', 'sf','sp','gifski')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}## Loading required package: rgdal## Loading required package: sp## rgdal: version: 1.5-17, (SVN revision 1070)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: C:/Users/ahhli/OneDrive/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: C:/Users/ahhli/OneDrive/Documents/R/win-library/4.0/rgdal/proj
## Linking to sp version:1.4-4
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.## Loading required package: spdep## Loading required package: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`## Loading required package: sf## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1## Loading required package: tmap## Loading required package: tidyverse## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Loading required package: gifskiLoad data
## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS =
## dumpSRS, : Discarded datum International_Terrestrial_Reference_Frame_2008 in CRS
## definition: +proj=lcc +lat_0=12 +lon_0=-102 +lat_1=17.5 +lat_2=29.5 +x_0=2500000
## +y_0=0 +ellps=GRS80 +units=m +no_defs## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\ahhli\OneDrive\Desktop\Geospatial\Take-home-Ex02\Take-Home-Ex02\data\geospatial", layer: "municipalities_COVID"
## with 2465 features
## It has 198 fieldsWe can look at each attribute and some of its corresponding values below to better understand the data.
## CVEGEO CVE_ENT CVE_MUN NOMGEO Pop2010 Pop2020 new1 new2 new3
## 0 01001 01 001 Aguascalientes 797010 961977 0 0 0
## 1 01002 01 002 Asientos 45492 50864 0 0 0
## 2 01003 01 003 Calvillo 54136 60760 0 0 0
## 3 01004 01 004 CosÃo 15042 16918 0 0 0
## 4 01005 01 005 Jesús MarÃa 99590 130184 0 0 0
## 5 01006 01 006 Pabellón de Arteaga 41862 50032 0 0 0
## new4 new5 new6 new7 new8 new9 new10 new11 new12 new13 new14 new15 new16 new17
## 0 0 0 0 0 0 0 0 1 7 30 8 10 51 60
## 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 8
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 1 0 0 6 3
## 5 0 0 0 0 0 0 0 0 0 4 1 0 0 0
## new18 new19 new20 new21 new22 new23 new24 new25 new26 new27 new28 new29 new30
## 0 87 101 101 99 185 257 312 280 258 234 295 307 267
## 1 1 0 0 5 2 1 8 6 4 9 11 6 7
## 2 1 1 1 0 0 3 3 8 11 8 20 35 13
## 3 0 0 6 7 3 7 6 12 1 0 2 10 9
## 4 3 4 5 5 6 5 11 10 15 14 15 13 10
## 5 6 3 2 14 6 17 8 19 13 7 8 21 14
## new31 new32 cumul1 cumul2 cumul3 cumul4 cumul5 cumul6 cumul7 cumul8 cumul9
## 0 219 23 0 0 0 0 0 0 0 0 0
## 1 11 0 0 0 0 0 0 0 0 0 0
## 2 13 3 0 0 0 0 0 0 0 0 0
## 3 4 0 0 0 0 0 0 0 0 0 0
## 4 15 2 0 0 0 0 0 0 0 0 0
## 5 19 0 0 0 0 0 0 0 0 0 0
## cumul10 cumul11 cumul12 cumul13 cumul14 cumul15 cumul16 cumul17 cumul18
## 0 0 1 8 38 46 56 107 167 254
## 1 0 0 1 1 1 1 1 1 2
## 2 0 0 0 0 0 0 0 8 9
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 1 1 1 7 10 13
## 5 0 0 0 4 5 5 5 5 11
## cumul19 cumul20 cumul21 cumul22 cumul23 cumul24 cumul25 cumul26 cumul27
## 0 355 456 555 740 997 1309 1589 1847 2081
## 1 2 2 7 9 10 18 24 28 37
## 2 10 11 11 11 14 17 25 36 44
## 3 0 6 13 16 23 29 41 42 42
## 4 17 22 27 33 38 49 59 74 88
## 5 14 16 30 36 53 61 80 93 100
## cumul28 cumul29 cumul30 cumul31 cumul32 activ1 activ2 activ3 activ4 activ5
## 0 2376 2683 2950 3169 3192 0 0 0 0 0
## 1 48 54 61 72 72 0 0 0 0 0
## 2 64 99 112 125 128 0 0 0 0 0
## 3 44 54 63 67 67 0 0 0 0 0
## 4 103 116 126 141 143 0 0 0 0 0
## 5 108 129 143 162 162 0 0 0 0 0
## activ6 activ7 activ8 activ9 activ10 activ11 activ12 activ13 activ14 activ15
## 0 0 0 0 0 1 4 16 45 53 75
## 1 0 0 0 0 0 0 1 1 1 1
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 1 1 3
## 5 0 0 0 0 0 0 0 5 5 5
## activ16 activ17 activ18 activ19 activ20 activ21 activ22 activ23 activ24
## 0 139 203 309 393 500 622 848 1133 1424
## 1 1 1 2 2 4 9 10 14 19
## 2 0 9 9 11 11 11 12 15 18
## 3 0 0 0 1 7 14 18 27 34
## 4 7 12 14 19 23 28 34 44 53
## 5 5 5 13 14 22 34 41 60 70
## activ25 activ26 activ27 activ28 activ29 activ30 activ31 activ32 death1 death2
## 0 1713 1972 2248 2544 2845 3079 3189 3192 0 0
## 1 27 31 41 51 56 70 72 72 0 0
## 2 28 39 53 90 108 124 127 128 0 0
## 3 42 42 43 50 56 66 67 67 0 0
## 4 69 83 96 109 122 135 143 143 0 0
## 5 88 95 104 119 135 148 162 162 0 0
## death3 death4 death5 death6 death7 death8 death9 death10 death11 death12
## 0 0 0 0 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## death13 death14 death15 death16 death17 death18 death19 death20 death21
## 0 0 0 1 2 2 3 7 6 4
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 2
## 5 0 0 0 0 0 0 0 0 1
## death22 death23 death24 death25 death26 death27 death28 death29 death30
## 0 17 18 27 25 24 19 19 19 20
## 1 0 0 0 0 2 0 2 2 0
## 2 0 0 0 0 1 0 0 0 1
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 1 3
## 5 1 0 1 1 1 0 1 0 1
## death31 death32 actvrt1 actvrt2 actvrt3 actvrt4 actvrt5 actvrt6 actvrt7
## 0 13 7 0 0 0 0 0 0 0
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## actvrt8 actvrt9 actvr10 actvr11 actvr12 actvr13 actvr14 actvr15
## 0 0 0 0.1039526 0.4158104 1.663241 4.5739139 5.0936769 6.133203
## 1 0 0 0.0000000 0.0000000 1.966027 1.9660271 1.9660271 0.000000
## 2 0 0 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.000000
## 3 0 0 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 0.000000
## 4 0 0 0.0000000 0.0000000 0.000000 0.7681436 0.7681436 2.304431
## 5 0 0 0.0000000 0.0000000 0.000000 9.9936041 9.9936041 9.993604
## actvr16 actvr17 actvr18 actvr19 actvr20 actvr21 actvr22
## 0 9.771543 15.592888 24.324906 26.403958 30.873919 32.537160 47.29843
## 1 0.000000 0.000000 1.966027 1.966027 5.898081 13.762189 15.72822
## 2 0.000000 14.812377 14.812377 18.104016 3.291639 3.291639 1.64582
## 3 0.000000 0.000000 0.000000 5.910864 41.376049 82.752098 100.48469
## 4 4.608861 8.449579 8.449579 9.217723 8.449579 10.754010 11.52215
## 5 0.000000 0.000000 15.989767 17.988487 33.978254 41.973137 53.96546
## actvr23 actvr24 actvr25 actvr26 actvr27 actvr28 actvr29
## 0 65.801989 83.36998 89.91899 87.21622 85.65693 86.38460 90.75061
## 1 19.660271 19.66027 33.42246 33.42246 43.25260 47.18465 49.15068
## 2 6.583278 11.52074 26.33311 39.49967 57.60369 102.04082 113.56155
## 3 118.217283 118.21728 141.86074 88.66296 53.19778 47.28691 82.75210
## 4 16.131015 19.20359 26.88502 29.95760 33.03017 30.72574 29.95760
## 5 75.951391 71.95395 93.93988 69.95523 67.95651 61.96035 79.94883
## actvr30 actvr31 actvr32 dethrt1 dethrt2 dethrt3 dethrt4 dethrt5 dethrt6
## 0 86.38460 67.04942 36.07155 0 0 0 0 0 0
## 1 57.01478 41.28657 31.45643 0 0 0 0 0 0
## 2 116.85319 60.89533 32.91639 0 0 0 0 0 0
## 3 135.94988 100.48469 65.01951 0 0 0 0 0 0
## 4 29.95760 26.11688 16.13101 0 0 0 0 0 0
## 5 87.94372 85.94500 53.96546 0 0 0 0 0 0
## dethrt7 dethrt8 dethrt9 dthrt10 dthrt11 dthrt12 dthrt13 dthrt14 dthrt15
## 0 0 0 0 0 0 0 0 0 0.1039526
## 1 0 0 0 0 0 0 0 0 0.0000000
## 2 0 0 0 0 0 0 0 0 0.0000000
## 3 0 0 0 0 0 0 0 0 0.0000000
## 4 0 0 0 0 0 0 0 0 0.0000000
## 5 0 0 0 0 0 0 0 0 0.0000000
## dthrt16 dthrt17 dthrt18 dthrt19 dthrt20 dthrt21 dthrt22 dthrt23
## 0 0.2079052 0.2079052 0.3118578 0.7276681 0.6237155 0.4158104 1.767194 1.871147
## 1 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000 0.000000
## 2 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000 0.000000
## 3 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000 0.000000
## 4 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.5362871 0.000000 0.000000
## 5 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 1.9987208 1.998721 0.000000
## dthrt24 dthrt25 dthrt26 dthrt27 dthrt28 dthrt29 dthrt30 dthrt31
## 0 2.806720 2.598815 2.494862 1.975099 1.975099 1.9750992 2.079052 1.351384
## 1 0.000000 0.000000 3.932054 0.000000 3.932054 3.9320541 0.000000 0.000000
## 2 0.000000 0.000000 1.645820 0.000000 0.000000 0.0000000 1.645820 0.000000
## 3 0.000000 0.000000 0.000000 0.000000 0.000000 0.0000000 0.000000 0.000000
## 4 0.000000 0.000000 0.000000 0.000000 0.000000 0.7681436 2.304431 0.000000
## 5 1.998721 1.998721 1.998721 0.000000 1.998721 0.0000000 1.998721 0.000000
## dthrt32
## 0 0.7276681
## 1 0.0000000
## 2 0.0000000
## 3 0.0000000
## 4 0.0000000
## 5 0.0000000Part 1
1.1 Extract municipalities located with the study area
As mentioned in the task description, there is a need for data wrangling in order to specifically analyse the spatio-temporal patterns of COVID-19 cases at following areas: 1. Mexico City (9) 2. Mexico State (15) 3. Morelos State (17) Using the attribute “CVE_ENT”, we extract the relevant municipalities.
1.2 Calculate the COVID-19 rate (i.e. cases per 10000 population) from e-week 13 until e-week 32.
In order to calculate the rate of COVID-19, we look at the attributes “cumul” and “Pop2020” in our calculations.
for (i in c(13:32)) {
mexico_city[[paste0("covidrate_wk",i)]] <- mexico_city[[paste0("cumul",i)]] / (mexico_city$Pop2020 / 10000)
mexico_state[[paste0("covidrate_wk",i)]] <- mexico_state[[paste0("cumul",i)]] / (mexico_state$Pop2020 / 10000)
morelos_state[[paste0("covidrate_wk",i)]] <- morelos_state[[paste0("cumul",i)]] / (morelos_state$Pop2020 / 10000)
}## class : SpatialPolygonsDataFrame
## features : 16
## extent : 2776218, 2820850, 786788.9, 846749.5 (xmin, xmax, ymin, ymax)
## crs : +proj=lcc +lat_0=12 +lon_0=-102 +lat_1=17.5 +lat_2=29.5 +x_0=2500000 +y_0=0 +ellps=GRS80 +units=m +no_defs
## variables : 218
## names : CVEGEO, CVE_ENT, CVE_MUN, NOMGEO, Pop2010, Pop2020, new1, new2, new3, new4, new5, new6, new7, new8, new9, ...
## min values : 09002, 09, 002, Ãlvaro Obregón, 130582, 139371, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## max values : 09017, 09, 017, Xochimilco, 1815786, 1815551, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...Part 2
2.1 Data wrangling and preparation for spatio-temporal pattern analysis
Firstly, we will have to convert the 3 spatial polygon dataframes into sf objects for visualisation purpose.
mexico_city_sf <- st_as_sf(mexico_city)
mexico_state_sf <- st_as_sf(mexico_state)
morelos_state_sf <- st_as_sf(morelos_state)Next, as the sf object sizes are big, we should select the variables that we need and use them for the subsequent steps. The variables that are relevant in this part of the study are “NOMGEO” and the previously processed variable, “covidrate_wkX” where 13 <= X <= 32. This step will be repeated for all 3 study areas.
variables = c("NOMGEO")
for (i in c(13:32)) {
variables <- c(variables, paste0("covidrate_wk",i))
}
mexico_city_sf <- subset(mexico_city_sf, select=variables)
mexico_state_sf <- subset(mexico_state_sf, select=variables)
morelos_state_sf <- subset(morelos_state_sf, select=variables)
# Bind sf objects to form central mexico sf
central_mexico_sf <- rbind(mexico_city_sf,mexico_state_sf,morelos_state_sf)
central_mexico_sf## Simple feature collection with 176 features and 21 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 2645804 ymin: 707708.1 xmax: 2855437 ymax: 921773.9
## projected CRS: MEXICO_ITRF_2008_LCC
## First 10 features:
## NOMGEO covidrate_wk13 covidrate_wk14 covidrate_wk15
## 270 Azcapotzalco 0.46518347 1.12623365 2.1300506
## 271 Coyoacán 0.40196028 0.70745009 1.7203900
## 272 Cuajimalpa de Morelos 3.30315451 3.75358467 4.6544450
## 273 Gustavo A. Madero 0.23789962 0.81565583 1.6398081
## 274 Iztacalco 0.27931471 0.68559066 1.6504960
## 275 Iztapalapa 0.22582676 0.55079698 1.2392932
## 276 La Magdalena Contreras 0.61187777 1.10137999 1.8356333
## 277 Milpa Alta 0.00000000 0.07175094 1.6502716
## 278 Ã\201lvaro Obregón 0.74119467 1.19120573 1.9324004
## 279 Tláhuac 0.08183619 0.30006601 0.8183619
## covidrate_wk16 covidrate_wk17 covidrate_wk18 covidrate_wk19 covidrate_wk20
## 270 3.819401 6.218768 9.572986 15.05725 22.79399
## 271 3.424702 6.029404 8.907440 13.36116 18.49017
## 272 6.105831 7.857504 10.109655 13.01243 16.16544
## 273 3.373077 6.236369 9.405531 14.04457 19.79665
## 274 4.773742 8.709541 14.016520 20.46615 28.56628
## 275 2.897192 5.843956 9.660979 14.41436 19.73506
## 276 3.385724 6.893823 8.974207 11.99280 17.41812
## 277 4.233305 9.829879 14.852444 21.59703 30.13539
## 278 3.573617 5.453075 8.060492 11.21057 15.47244
## 279 2.155020 4.773778 8.456406 13.99399 22.25944
## covidrate_wk21 covidrate_wk22 covidrate_wk23 covidrate_wk24 covidrate_wk25
## 270 31.02039 38.92851 47.47320 55.06303 63.50978
## 271 23.98899 30.00232 36.25682 41.06426 47.20622
## 272 20.71979 28.67739 35.73413 42.59067 49.59737
## 273 26.40686 32.65172 39.71224 45.59176 50.78307
## 274 36.31091 43.31917 50.50518 56.09147 62.31257
## 275 25.66163 30.47009 35.33913 39.84465 43.95911
## 276 23.29215 29.08459 34.22436 39.20097 46.58429
## 277 44.12683 55.82223 68.95265 84.95311 97.15077
## 278 20.56815 25.69034 30.75958 36.82149 42.57899
## 279 31.86155 38.21750 46.48295 52.94801 60.55878
## covidrate_wk26 covidrate_wk27 covidrate_wk28 covidrate_wk29 covidrate_wk30
## 270 71.54032 79.27706 86.64654 93.99154 102.12001
## 271 51.57954 57.72150 62.75404 70.63246 77.49794
## 272 55.90339 64.31142 72.26902 80.82719 91.83771
## 273 55.24369 60.11214 64.46230 69.77256 75.40568
## 274 67.54338 71.73310 76.10056 80.82352 86.73991
## 275 47.62191 51.81898 55.50932 59.60725 63.61705
## 276 53.88604 62.37074 71.71207 85.78526 95.86085
## 277 106.98065 119.60881 130.44321 145.08040 159.21533
## 278 47.43646 52.57188 58.11760 65.58249 72.09442
## 279 68.11499 76.16221 84.67317 94.24801 103.14087
## covidrate_wk31 covidrate_wk32 geometry
## 270 108.75500 109.34260 POLYGON ((2794860 837218.4,...
## 271 83.86499 84.34735 POLYGON ((2799635 820691.6,...
## 272 97.54315 97.89349 POLYGON ((2787230 825329.3,...
## 273 79.69637 80.19766 POLYGON ((2802176 843326.7,...
## 274 91.71680 93.29111 POLYGON ((2808291 828067.6,...
## 275 67.03199 67.77557 POLYGON ((2812453 823708.1,...
## 276 107.16019 107.64970 POLYGON ((2792518 818323.1,...
## 277 169.61922 170.40848 POLYGON ((2814877 806710.5,...
## 278 77.71956 78.54016 POLYGON ((2794396 824857.6,...
## 279 110.91531 111.95190 POLYGON ((2816579 817396, 2...Before we proceed further, there are a couple of checks required for central mexico simple feature dataframe that needs to be done before we begin utilising it in the subsequent steps.
- Check for NA values in the central mexico simple feature dataframe.
- Check for invalid gemoetries in the central mexico simple feature dataframe.
## Simple feature collection with 0 features and 21 fields
## bbox: xmin: NA ymin: NA xmax: NA ymax: NA
## projected CRS: MEXICO_ITRF_2008_LCC
## [1] NOMGEO covidrate_wk13 covidrate_wk14 covidrate_wk15 covidrate_wk16
## [6] covidrate_wk17 covidrate_wk18 covidrate_wk19 covidrate_wk20 covidrate_wk21
## [11] covidrate_wk22 covidrate_wk23 covidrate_wk24 covidrate_wk25 covidrate_wk26
## [16] covidrate_wk27 covidrate_wk28 covidrate_wk29 covidrate_wk30 covidrate_wk31
## [21] covidrate_wk32 geometry
## <0 rows> (or 0-length row.names)As seen in the output above, there’s no NA value in central_mexico_sf.
## [1] 0As seen in the output above, there’s no invalid geometries in central_mexico_sf.
2.2 Plotting of animated tmap to show gradual change in COVID-19 rates from e-week 13 to e-week 32
Note: the animation that is functioning properly is stored in the project directory folder. Please refer to the animation in that folder instead of the individual frames below.
central_mexico_sf_munis <- central_mexico_sf$NOMGEO
# Check that municipalities are distinct in central_mexico_sf_munis
if(length(central_mexico_sf_munis) != length(unique(central_mexico_sf_munis))){
central_mexico_sf_munis <- unique(central_mexico_sf_munis)
}
# Create separate df to store relevant variables that will be required in tmap
central_mexico_df <- data.frame(matrix(ncol=0,nrow=0))
for (muni in central_mexico_sf_munis) {
# keep geometry attribute in order to convert the df to sf later on.
geometry <- subset(central_mexico_sf, NOMGEO == muni)$geometry
for (i in c(13:32)) {
covidrate_wk <- subset(central_mexico_sf, NOMGEO == muni)[[paste0("covidrate_wk",i)]]
central_mexico_df <- rbind(central_mexico_df, data.frame(muni, geometry,covidrate_wk, i))
}
}
# typeof(central_mexico_df)
central_mexico_sf_final <- st_as_sf(central_mexico_df)
typeof(central_mexico_sf_final)## [1] "list"tmap_central_mexico <- tm_shape(central_mexico_sf_final) +
tm_fill("covidrate_wk",
style="fisher") +
tm_borders() +
tm_facets("i", nrow=1, ncol=1)
tm_layout(
title = "i",
title.position = c("right","top"))## $tm_layout
## $tm_layout$title
## [1] "i"
##
## $tm_layout$title.position
## [1] "right" "top"
##
## $tm_layout$style
## [1] NA
##
##
## attr(,"class")
## [1] "tm"
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2.3 Analysis of animated tmap
Based on the legend used, darker shades of yellow represent relatively higher level of COVID-19 cases for the particular week, whereas lighter shades represent relatively lower level of COVID-19 cases.
The week number is clearly shown at the top of the map.
As you can observe from the animation, there was no difference in yellow shades across all municipalities from e-week 13 to e-week 16.
However, from e-week 17 onwards, the first municipality located at mexico city was first spotted with darker shade of yellow, meaning that particular city was the first city to experience higher COVID-19 cases as compared to the rest.
Gradually as time passes, we can infer that more COVID-19 cases spread among the neighbouring municipalities as the yellow shades at mexico city become darker and those at mexico state as well as marelos city also experience gradual increase in yellow shades.
By the time it reaches e-week32, there is only a few municipalities left that remain at the lowest range of COVID-19 weekly rate, especially those at the extreme left boundary of Central Mexico.
animated_tmap <- tmap_animation(tmap_central_mexico, filename = "animation.gif", width = NA, height = NA,delay = 40)## Creating frames## ========## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====## ====##
## Creating animation
##
Frame 1 (5%)
Frame 2 (10%)
Frame 3 (15%)
Frame 4 (20%)
Frame 5 (25%)
Frame 6 (30%)
Frame 7 (35%)
Frame 8 (40%)
Frame 9 (45%)
Frame 10 (50%)
Frame 11 (55%)
Frame 12 (60%)
Frame 13 (65%)
Frame 14 (70%)
Frame 15 (75%)
Frame 16 (80%)
Frame 17 (85%)
Frame 18 (90%)
Frame 19 (95%)
Frame 20 (100%)
## Finalizing encoding... done!
## Animation saved to C:\Users\ahhli\OneDrive\Desktop\Geospatial\Take-home-Ex02\Take-Home-Ex02\animation.gifPart 3: Local Moran’s I Analysis
3.1 Derive spatial weight matrix
# Convert sf object of central mexico to spatial object
central_mexico <- as_Spatial(central_mexico_sf)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition## Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum International Terrestrial Reference Frame 2008 in
## CRS definition## Neighbour list object:
## Number of regions: 176
## Number of nonzero links: 962
## Percentage nonzero weights: 3.10563
## Average number of links: 5.465909
## Link number distribution:
##
## 1 2 3 4 5 6 7 8 9 10 11 14
## 3 3 18 41 31 31 22 13 9 3 1 1
## 3 least connected regions:
## 721 739 765 with 1 link
## 1 most connected region:
## 761 with 14 linksThe summary above shows that there are 176 area units in Central Mexico. The most connected area unit has 14 neighbours. There are 3 area units with only one neighbour.
Let’s plot Queen contiguity based neighbours maps.
plot(central_mexico, border="lightgrey")
plot(wm_q, coordinates(central_mexico), pch = 19, cex = 0.6, add = TRUE, col= "red")
## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 176
## Number of nonzero links: 962
## Percentage nonzero weights: 3.10563
## Average number of links: 5.465909
## Link number distribution:
##
## 1 2 3 4 5 6 7 8 9 10 11 14
## 3 3 18 41 31 31 22 13 9 3 1 1
## 3 least connected regions:
## 721 739 765 with 1 link
## 1 most connected region:
## 761 with 14 links
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 176 30976 176 71.10695 731.679fips <- order(central_mexico$NOMGEO)
localMI <- localmoran(central_mexico@data[["covidrate_wk13"]], rswm_q)
localMI_13 <- localmoran(central_mexico@data[["covidrate_wk13"]], rswm_q)
head(localMI_13)## Ii E.Ii Var.Ii Z.Ii Pr(z > 0)
## 270 1.42938461 -0.005714286 0.15624522 3.6306005 0.0001413813
## 271 0.52791359 -0.005714286 0.15624522 1.3500042 0.0885073115
## 272 21.14786143 -0.005714286 0.19615364 47.7623404 0.0000000000
## 273 0.06784766 -0.005714286 0.09638259 0.2369485 0.4063483824
## 274 0.17785066 -0.005714286 0.15624522 0.4643938 0.3211828265
## 275 0.04471045 -0.005714286 0.09638259 0.1624218 0.4354868483for(i in 13:32){
weekly_rate<-paste("covidrate_wk",i, sep="")
localMIObj <- paste("localMI_week", i, sep="")
# weekly_rate
# localMIObj
localMIObj <- localmoran(central_mexico[[weekly_rate]], rswm_q)
localMIObj_name <- paste0(i,"_localMI")
# localMIObj_name
localMIObj_name <- cbind(central_mexico,localMIObj)
# Mapping Local Moran's I
localMI.map_name<- paste("localMIPlot_week", i,sep="")
localMI.map <- tm_shape(localMIObj_name) +
tm_fill(col = "Ii",
style = "pretty",
palette = "RdBu",
title = "local moran statistics") +
tm_borders(alpha = 0.5)
assign(localMI.map_name,localMI.map)
# localMIPlot_week18
# Mapping Local Moran's I p-values
pvalue.map_name<- paste("pvaluePlot_week", i,sep="")
pvalue.map <- tm_shape(localMIObj_name) +
tm_fill(col = "Pr.z...0.",
breaks=c(-Inf, 0.001, 0.01, 0.05, 0.1, Inf),
palette="-Blues",
title = "local Moran's I p-values") +
tm_borders(alpha = 0.5)
assign(pvalue.map_name,pvalue.map)
}Below are the LocalMI plot and Pvalue plot for e-week 13
tmap_arrange(localMIPlot_week13, pvaluePlot_week13,localMIPlot_week14, pvaluePlot_week14,asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week15, pvaluePlot_week15,localMIPlot_week16, pvaluePlot_week16, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week17, pvaluePlot_week17,localMIPlot_week18, pvaluePlot_week18, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week19, pvaluePlot_week19,localMIPlot_week20, pvaluePlot_week20, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week21, pvaluePlot_week21,localMIPlot_week22, pvaluePlot_week22, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week23, pvaluePlot_week23,localMIPlot_week24, pvaluePlot_week24, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week25, pvaluePlot_week25,localMIPlot_week26, pvaluePlot_week26, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week27, pvaluePlot_week27,localMIPlot_week28, pvaluePlot_week28, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week29, pvaluePlot_week29,localMIPlot_week30, pvaluePlot_week30, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
tmap_arrange(localMIPlot_week31, pvaluePlot_week31,localMIPlot_week32, pvaluePlot_week32, asp=2, ncol=2)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
Below are the LocalMI plot and Pvalue plot for e-week 32
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "Ii" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
Part 4: Local Getis-Ord Gi* analysis
4.1 Derive spatial weight matrix
For this part of the study, instead of using a fixed distance to derive the proximity matrix, we will use the adaptive distance method, which is a fixed number of neighbours.
# Convert sf object of central mexico to spatial object
central_mexico <- as_Spatial(central_mexico_sf)## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition## Warning in showSRID(SRS_string, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum International Terrestrial Reference Frame 2008 in
## CRS definition# Prepare knb
coords <- coordinates(central_mexico)
knb <- knn2nb(knearneigh(coords, k=8, longlat = TRUE), row.names=row.names(central_mexico@data$NOMGEO))## Warning in knearneigh(coords, k = 8, longlat = TRUE): knearneigh: coordinates
## are not geographical: longlat argument wrong## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 176
## Number of nonzero links: 1408
## Percentage nonzero weights: 4.545455
## Average number of links: 8
## Non-symmetric neighbours list
## Link number distribution:
##
## 8
## 176
## 176 least connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 with 8 links
## 176 most connected regions:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 with 8 links
##
## Weights style: B
## Weights constants summary:
## n nn S0 S1 S2
## B 176 30976 1408 2502 459884.2 Prepare GI Plot from e-week 13 to e-week 32 using adaptive distance method.
for(i in 13:32){
weekly_rate<-paste0("covidrate_wk",i)
week_num <- paste0("week", i)
GIObj <- paste0("GI",i)
adaptive_df <- data.frame("gstat_adaptive" = as.matrix(localG(central_mexico[[weekly_rate]], knb_lw)))
GIObj <- cbind(central_mexico, adaptive_df)
GI.Plot_name <- paste0("GIPlot_week", i)
# for each e-week, output different GI plot respectively
GI.Plot <- tm_shape(GIObj) +
tm_fill(col = "gstat_adaptive",
style = "pretty",
palette = "-RdBu",
title = "Local Gi Plot") +
tm_borders(alpha=1) +
tm_layout(week_num,
title.size = 0.5,
panel.label.size = 0.5,
legend.title.size = 0.5,
legend.text.size = 0.5)
assign(GI.Plot_name, GI.Plot)
}From e-week13 to e-week 18
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
From e-week19 to e-week 24
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
From e-week25 to e-week 30
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
From e-week31 to e-week 32
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Variable(s) "gstat_adaptive" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
4.3 Analysis of the Plots above
Based on the preset legend, red spots represent the “hot” spots whereas blue spots represent the “cold” spots. As one can observe from the GI plots across the weeks, there are some distinctive pattern that can be noticed.
Based on the visualisations, there are generally less hot and less cold spots during the first few time periods. Althought there is a small number of hot and cold spots, their colours are of lighter shade, meaning that the relative COVID-19 rates during this first time period is low.
However, as time passes and reaches the mid-point of the study time period, both hot and cold spots are of darker shades and are more spread out, illustrating how the COVID-19 cases spread in central mexico and grew rapidly in number.
Towards the end of the study time period, the hot and cold spots become generally lighter in shade, which could be due to the fact that the COVID-19 cases are more evenly spread out across Central Mexico and no particular city has distinctively higher/lower COVID-19 cases, as opposed to during the first few e-weeks.