Reading in Files from DHS • DHSHarmonization

library(DHSHarmonization)

Here we demonstrate how to load and view the DHS datasets that will be harmonized in this package. Each section builds the function needed to read in the data, and then tests it to ensure it works as expected. The functions are then implemented in the targets pipeline.

Some basic EDA is provided for you to help you understand what data can be made available to you through a data request.

library(targets)
library(here)
#> here() starts at /n/holylabs/cgolden_lab/Lab/projects/DHSHarmonization
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.1     ✔ stringr   1.5.2
#> ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
#> ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
#> ✔ purrr     1.1.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(purrr)
library(rdhs)
#> Thank you for using rdhs. If you are using rdhs regularly
#> or for automated tasks, please register for your own API key by
#> emailing api@dhsprogram.com. 
#> 
#> More info at <https://api.dhsprogram.com/#/introdevelop.html>
library(glue)
library(sf)
#> Linking to GEOS 3.7.2, GDAL 3.0.4, PROJ 6.3.2; sf_use_s2() is TRUE

load_flat_dhs_data

So long as the data has a flat structure, it should be straightforward to load it into R and view it using the rdhs package. The structure is taken from this DHS page.

Example:
To give an example of how distribution files for a survey are organized, the following table shows the available files, along with the names that they are given for the Kenya 2003 DHS survey.
Kenya 2003 DHS Survey
    ASCII File Types    Software-Specific Data File Types
Unit of Analysis    Hierarchical    Flat    SAS SPSS    Stata
Households      KEHR42FL.ZIP    KEHR42SD.ZIP    KEHR42SV.ZIP    KEHR42DT.ZIP
Household Members       KEPR42FL.ZIP    KEPR42SD.ZIP    KEPR42SV.ZIP    KEPR42DT.ZIP
Women   KEIR42.ZIP  KEIR42FL.ZIP    KEIR42SD.ZIP    KEIR42SV.ZIP    KEIR42DT.ZIP
Men KEMR42.ZIP  KEMR42FL.ZIP    KEMR42SD.ZIP    KEMR42SV.ZIP    KEMR42DT.ZIP
Births      KEBR42FL.ZIP    KEBR42SD.ZIP    KEBR42SV.ZIP    KEBR42DT.ZIP
Children        KEKR42FL.ZIP    KEKR42SD.ZIP    KEKR42SV.ZIP    KEKR42DT.ZIP
Couples     KECR42FL.ZIP    KECR42SD.ZIP    KECR42SV.ZIP    KECR42DT.ZIP
HIV Test Results    KEAR42.ZIP  KEAR42FL.ZIP    KEAR42SD.ZIP    KEAR42SV.ZIP    KEAR42DT.ZIP

The following reference tables contain the descriptions for the four different types of filename codes (country, data type, data version, and file format).

In our case, we have the following:

tree -L 3 data/DHS\ Data/

data/DHS Data/
├── DHS 1992
│   ├── MDBR21FL
│   │   ├── MDBR21FL.DAT
│   │   ├── MDBR21FL.DCF
│   │   ├── MDBR21FL.DCT
│   │   ├── MDBR21FL.DO
│   │   ├── MDBR21FL.frq
│   │   ├── MDBR21FL.frw
│   │   ├── MDBR21FL.MAP
│   │   ├── MDBR21FL.SAS
│   │   └── MDBR21FL.SPS
│   ├── MDHR21FL
│   │   ├── MDHR21FL.DAT
│   │   ├── MDHR21FL.DCT
│   │   ├── MDHR21FL.DO
│   │   ├── MDHR21FL.FRQ
│   │   ├── MDHR21FL.FRW
│   │   ├── MDHR21FL.MAP
│   │   ├── MDHR21FL.SAS
│   │   └── MDHR21FL.SPS
│   ├── MDHW21FL
│   │   ├── MDHW21FL.DAT
│   │   ├── MDHW21FL.DCF
│   │   ├── MDHW21FL.DCT
│   │   ├── MDHW21FL.DO
│   │   ├── MDHW21FL.MAP
│   │   ├── MDHW21FL.SAS
│   │   ├── MDHW21FL.SPS
│   │   └── MERGE.DOC
│   ├── MDIR21FL
│   │   ├── MDIR21FL.DAT
│   │   ├── MDIR21FL.DCT
│   │   ├── MDIR21FL.DO
│   │   ├── MDIR21FL.DOC
│   │   ├── MDIR21FL.FRQ
│   │   ├── MDIR21FL.FRW
│   │   ├── MDIR21FL.MAP
│   │   ├── MDIR21FL.SAS
│   │   └── MDIR21FL.SPS
│   ├── MDKR21FL
│   │   ├── MDKR21FL.DAT
│   │   ├── MDKR21FL.DCT
│   │   ├── MDKR21FL.DO
│   │   ├── MDKR21FL.DOC
│   │   ├── MDKR21FL.FRQ
│   │   ├── MDKR21FL.MAP
│   │   ├── MDKR21FL.SAS
│   │   └── MDKR21FL.SPS
│   ├── MDPR21FL
│   │   ├── MDPR21FL.DAT
│   │   ├── MDPR21FL.DCT
│   │   ├── MDPR21FL.DO
│   │   ├── MDPR21FL.FRQ
│   │   ├── MDPR21FL.FRW
│   │   ├── MDPR21FL.MAP
│   │   ├── MDPR21FL.SAS
│   │   └── MDPR21FL.SPS
│   └── MDSQ21FL
│       ├── MDSQ21FL.DAT
│       ├── MDSQ21FL.DCT
│       ├── MDSQ21FL.DO
│       ├── MDSQ21FL.MAP
│       ├── MDSQ21FL.SAS
│       └── MDSQ21FL.SPS

So, for the generic ones, we know that we have BR (Birth rates), HR (Household), HW (height and weight), IR (individual), KR (kids), PR (household member), and SQ (Service Availability). We also have WI (wealth), CR (couples), MR (men’s), GE (Geographic Data), GC (geospatial covariates), FW (Fieldworker), and MIS data (malaria response survey).

To read in the generic flat files, we can use the rdhs package. This has to be done by first zipping the files to a temporary location, and then using the rdhs::read_flat() function.

library(rdhs)
library(dplyr)
library(here)
library(zip)
#> 
#> Attaching package: 'zip'
#> The following objects are masked from 'package:utils':
#> 
#>     unzip, zip

ex_data <- here("data", "DHS Data", "DHS 1992", "MDIR21FL")
temp_zip <- tempfile(fileext = ".zip")

zip::zipr(zipfile = temp_zip, files = list.files(ex_data, full.names = TRUE))
file.exists(temp_zip)
#> [1] TRUE

dhs_data <- rdhs:::read_dhs_flat(temp_zip)
tibble(dhs_data)
#> # A tibble: 6,260 × 2,347
#>    caseid     v000   v001  v002  v003  v004   v005  v006  v007  v008  v009  v010
#>    <chr>      <chr> <int> <int> <int> <int>  <int> <int> <int> <int> <int> <int>
#>  1 "        … MD2       1     1     6     1 1.26e6     6    92  1110     2    70
#>  2 "        … MD2       1     1     8     1 1.26e6     6    92  1110     6    73
#>  3 "        … MD2       1     1     9     1 1.26e6     6    92  1110     1    75
#>  4 "        … MD2       1     2     2     1 1.26e6     6    92  1110    11    63
#>  5 "        … MD2       1     3     2     1 1.26e6     6    92  1110     7    55
#>  6 "        … MD2       1     3     3     1 1.26e6     6    92  1110     3    77
#>  7 "        … MD2       1     4     2     1 1.26e6     6    92  1110     8    67
#>  8 "        … MD2       1     5     1     1 1.26e6     6    92  1110     7    53
#>  9 "        … MD2       1     6     2     1 1.26e6     6    92  1110    11    53
#> 10 "        … MD2       1     6     3     1 1.26e6     6    92  1110     9    69
#> # ℹ 6,250 more rows
#> # ℹ 2,335 more variables: v011 <int>, v012 <int>, v013 <int+lbl>,
#> #   v014 <int+lbl>, v015 <int+lbl>, v016 <int>, v017 <int>, v018 <int+lbl>,
#> #   v019 <int+lbl>, v020 <int+lbl>, v021 <int>, v022 <int>, v023 <int+lbl>,
#> #   v024 <int+lbl>, v025 <int+lbl>, v026 <int+lbl>, v027 <int>, v028 <int>,
#> #   v029 <int>, v101 <int+lbl>, v102 <int+lbl>, v103 <int+lbl>, v104 <int+lbl>,
#> #   v105 <int+lbl>, v106 <int+lbl>, v107 <int>, v108 <int+lbl>, …

We can see that the variables do come with labels and attributes. This is useful information for understanding the data.

get_variable_labels(dhs_data) %>%
  head()
#>   variable              description
#> 1   caseid      Case Identification
#> 2     v000   Country code and phase
#> 3     v001           Cluster number
#> 4     v002         Household number
#> 5     v003 Respondent's line number
#> 6     v004       Ultimate area unit

So, to read in a generic flat file from DHS, we can define the function load_flat_dhs_data():

ex_data <- here("data", "DHS Data", "DHS 1992", "MDIR21FL")
load_flat_dhs_data(ex_data) %>%
  tibble() %>%
  head()
#> # A tibble: 6 × 2,347
#>   caseid      v000   v001  v002  v003  v004   v005  v006  v007  v008  v009  v010
#>   <chr>       <chr> <int> <int> <int> <int>  <int> <int> <int> <int> <int> <int>
#> 1 "         … MD2       1     1     6     1 1.26e6     6    92  1110     2    70
#> 2 "         … MD2       1     1     8     1 1.26e6     6    92  1110     6    73
#> 3 "         … MD2       1     1     9     1 1.26e6     6    92  1110     1    75
#> 4 "         … MD2       1     2     2     1 1.26e6     6    92  1110    11    63
#> 5 "         … MD2       1     3     2     1 1.26e6     6    92  1110     7    55
#> 6 "         … MD2       1     3     3     1 1.26e6     6    92  1110     3    77
#> # ℹ 2,335 more variables: v011 <int>, v012 <int>, v013 <int+lbl>,
#> #   v014 <int+lbl>, v015 <int+lbl>, v016 <int>, v017 <int>, v018 <int+lbl>,
#> #   v019 <int+lbl>, v020 <int+lbl>, v021 <int>, v022 <int>, v023 <int+lbl>,
#> #   v024 <int+lbl>, v025 <int+lbl>, v026 <int+lbl>, v027 <int>, v028 <int>,
#> #   v029 <int>, v101 <int+lbl>, v102 <int+lbl>, v103 <int+lbl>, v104 <int+lbl>,
#> #   v105 <int+lbl>, v106 <int+lbl>, v107 <int>, v108 <int+lbl>, v109 <int+lbl>,
#> #   v110 <int+lbl>, v111 <int>, v112 <int+lbl>, v113 <int+lbl>, …

We can also test that this works for the malaria response survey data:

ex_data <- here("data", "DHS Data", "MIS 2016", "MDIR71FL")

load_flat_dhs_data(ex_data) %>%
  tibble() %>%
  head()
#> # A tibble: 6 × 3,532
#>   caseid      v000   v001  v002  v003  v004   v005  v006  v007  v008 v008a  v009
#>   <chr>       <chr> <int> <int> <int> <int>  <int> <int> <int> <int> <int> <int>
#> 1 "    00010… MD7       1     9     4     1 3.47e6     4  2016  1396 42489     3
#> 2 "    00010… MD7       1    15     4     1 3.47e6     4  2016  1396 42489     7
#> 3 "    00010… MD7       1    21     2     1 3.47e6     4  2016  1396 42490     1
#> 4 "    00010… MD7       1    27     2     1 3.47e6     4  2016  1396 42490     9
#> 5 "    00010… MD7       1    39     2     1 3.47e6     4  2016  1396 42489     7
#> 6 "    00010… MD7       1    39     3     1 3.47e6     4  2016  1396 42489     9
#> # ℹ 3,520 more variables: v010 <int>, v011 <int>, v012 <int>, v013 <int+lbl>,
#> #   v014 <int+lbl>, v015 <int+lbl>, v016 <int>, v017 <int>, v018 <int+lbl>,
#> #   v019 <int+lbl>, v019a <int+lbl>, v020 <int+lbl>, v021 <int>,
#> #   v022 <int+lbl>, v023 <int+lbl>, v024 <int+lbl>, v025 <int+lbl>,
#> #   v026 <int+lbl>, v027 <int+lbl>, v028 <int+lbl>, v029 <int>, v030 <int+lbl>,
#> #   v031 <int>, v032 <int>, v034 <int+lbl>, v040 <int+lbl>, v042 <int+lbl>,
#> #   v044 <int+lbl>, v045a <int+lbl>, v045b <int+lbl>, v045c <int+lbl>, …

Because the different kinds of surveys have different variable structures, we will read each type separately.

To do this, we’ll read in a list of file paths, and then map over them to read them in.

In the targets pipeline, we’ve assigned the BR files to a target called dhs_data_BR, which has multiple files per target.

tar_read(dhs_data_BR, store = here("_targets"))[[1]] -> ex_data_1
tar_read(dhs_data_BR, store = here("_targets"))[[5]] -> ex_data_2

tibble(ex_data_1)
#> # A tibble: 18,931 × 580
#>    caseid      bidx v000   v001  v002  v003  v004   v005  v006  v007  v008  v009
#>    <chr>      <int> <chr> <int> <int> <int> <int>  <int> <int> <int> <int> <int>
#>  1 "        …     1 MD2       1     2     2     1 1.26e6     6    92  1110    11
#>  2 "        …     2 MD2       1     2     2     1 1.26e6     6    92  1110    11
#>  3 "        …     3 MD2       1     2     2     1 1.26e6     6    92  1110    11
#>  4 "        …     4 MD2       1     2     2     1 1.26e6     6    92  1110    11
#>  5 "        …     1 MD2       1     3     2     1 1.26e6     6    92  1110     7
#>  6 "        …     2 MD2       1     3     2     1 1.26e6     6    92  1110     7
#>  7 "        …     3 MD2       1     3     2     1 1.26e6     6    92  1110     7
#>  8 "        …     4 MD2       1     3     2     1 1.26e6     6    92  1110     7
#>  9 "        …     5 MD2       1     3     2     1 1.26e6     6    92  1110     7
#> 10 "        …     6 MD2       1     3     2     1 1.26e6     6    92  1110     7
#> # ℹ 18,921 more rows
#> # ℹ 568 more variables: v010 <int>, v011 <int>, v012 <int>, v013 <int+lbl>,
#> #   v014 <int+lbl>, v015 <int+lbl>, v016 <int>, v017 <int>, v018 <int+lbl>,
#> #   v019 <int+lbl>, v020 <int+lbl>, v021 <int>, v022 <int>, v023 <int+lbl>,
#> #   v024 <int+lbl>, v025 <int+lbl>, v026 <int+lbl>, v027 <int>, v028 <int+lbl>,
#> #   v029 <int+lbl>, v101 <int+lbl>, v102 <int+lbl>, v103 <int+lbl>,
#> #   v104 <int+lbl>, v105 <int+lbl>, v106 <int+lbl>, v107 <int+lbl>, …

tibble(ex_data_2)
#> # A tibble: 47,720 × 1,162
#>    caseid      bidx v000   v001  v002  v003  v004   v005  v006  v007  v008 v008a
#>    <chr>      <int> <chr> <int> <int> <int> <int>  <int> <int> <int> <int> <int>
#>  1 "       1…     1 MD7       1    10     2     1 733467     3  2021  1455 44259
#>  2 "       1…     2 MD7       1    10     2     1 733467     3  2021  1455 44259
#>  3 "       1…     1 MD7       1    16     1     1 733467     3  2021  1455 44262
#>  4 "       1…     1 MD7       1    23     2     1 733467     3  2021  1455 44261
#>  5 "       1…     2 MD7       1    23     2     1 733467     3  2021  1455 44261
#>  6 "       1…     3 MD7       1    23     2     1 733467     3  2021  1455 44261
#>  7 "       1…     1 MD7       1    30     2     1 733467     3  2021  1455 44263
#>  8 "       1…     1 MD7       1    45     2     1 733467     3  2021  1455 44259
#>  9 "       1…     2 MD7       1    45     2     1 733467     3  2021  1455 44259
#> 10 "       1…     1 MD7       1    52     2     1 733467     3  2021  1455 44259
#> # ℹ 47,710 more rows
#> # ℹ 1,150 more variables: v009 <int>, v010 <int>, v011 <int>, v012 <int>,
#> #   v013 <int+lbl>, v014 <int+lbl>, v015 <int+lbl>, v016 <int>, v017 <int>,
#> #   v018 <int+lbl>, v019 <int+lbl>, v019a <int+lbl>, v020 <int+lbl>,
#> #   v021 <int>, v022 <int>, v023 <int>, v024 <int+lbl>, v025 <int+lbl>,
#> #   v026 <int+lbl>, v027 <int+lbl>, v028 <int>, v029 <int>, v030 <int>,
#> #   v031 <int>, v032 <int>, v034 <int+lbl>, v040 <int+lbl>, v042 <int+lbl>, …

So even though they come from the same type of survey, the variables are different and cannot be easily merged with the rhds::rbind_labelled() function, because they change year over year:

# try to merge all of the BR datasets
all_br_data <- tar_read(dhs_data_BR, store = here("_targets"))
tryCatch({
  rdhs::rbind_labelled(all_br_data) %>% tibble()
}, error = function(e) {
  message("Error merging BR datasets: ", e$message)
  NULL
})
#> Error merging BR datasets: undefined columns selected
#> NULL

So with that in mind, we should take a look at the actual variable dictionaries for each survey type to plan how we will eventually harmonize and release them (descriptions are provided by ChatGPT and verified with DHS documentation available here).

summarize_dhs_flat_dictionary

The function summarize_dhs_flat_dictionary() will read in all of the flat files of a given survey type (e.g., BR, HR, IR) and summarize the variable names.

Birth Recode Variables

A dataset where each record represents a live birth to a surveyed woman. It is used primarily for fertility and mortality analyses (e.g., age‐specific fertility rates, infant/child mortality) by linking births to the mother’s survey responses.

survey_data <- tar_read(dhs_data_BR, store = here("_targets"))
var_descriptions_br <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_BR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_br, caption = glue("Variable Descriptions for Birth Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Couples Recode Variables

A dataset where each record is a couple (husband + wife) interviewed in the survey. Used for analyses of spousal/partner characteristics, family planning dynamics, and household reproduction.

survey_data <- tar_read(dhs_data_CR, store = here("_targets"))
var_descriptions_cr <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_CR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_cr, caption = glue("Variable Descriptions for Couples Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Height/Weight Variables

A dataset focusing on anthropometric measurements for children under age 5 (height/length, weight, nutritional status z-scores) designed to support child nutrition and growth‐monitoring analyses.

survey_data <- tar_read(dhs_data_HW, store = here("_targets"))
var_descriptions_hw <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_HW, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_hw, caption = glue("Variable Descriptions for Height/Weight Surveys ({n_surveys} Distinct Surveys Total)"))

Household Recode Variables

A dataset in which each record is a household. It contains information on household structure, living conditions, assets, water/sanitation, and biomarkers for household members. Useful for household‐level analysis and linking to individual recodes

survey_data <- tar_read(dhs_data_HR, store = here("_targets"))
var_descriptions_hr <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_HR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_hr, caption = glue("Variable Descriptions for Household Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Individual Recode (Women) Variables

A dataset with one record per interviewed woman (usually ages 15-49). Contains their birth histories, reproductive health, contraceptive use, maternity care, and child health modules. It is the primary unit for women’s health and fertility research.

survey_data <- tar_read(dhs_data_IR, store = here("_targets"))
var_descriptions_ir <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_IR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_ir, caption = glue("Variable Descriptions for Individual Recode (Women) Surveys ({n_surveys} Distinct Surveys Total)"))
#> Warning in instance$preRenderHook(instance): It seems your data is too big for
#> client-side DataTables. You may consider server-side processing:
#> https://rstudio.github.io/DT/server.html

Kids Recode Variables

A dataset with one record per child under age 5 born to a surveyed woman. It includes immunization, illness episodes, nutrition, growth, and survival status for each child.

survey_data <- tar_read(dhs_data_KR, store = here("_targets"))
var_descriptions_kr <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_KR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_kr, caption = glue("Variable Descriptions for Kids Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Household Member Recode Variables

A dataset with one record per household member (both male and female) in the selected households. Contains demographic, socio‐economic, and biomarker information. Useful for analyses of all household members—not just women or children.

survey_data <- tar_read(dhs_data_PR, store = here("_targets"))
var_descriptions_pr <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_PR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_pr, caption = glue("Variable Descriptions for Household Member Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Wealth Index Variables

An optional dataset (in older surveys) where each record is a household and it specifically provides wealth score and quintile for that survey, used when wealth index was not yet integrated in other recode files.

survey_data <- tar_read(dhs_data_WI, store = here("_targets"))
var_descriptions_wi <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_WI, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_wi, caption = glue("Variable Descriptions for Wealth Index Surveys ({n_surveys} Distinct Surveys Total)"))

Men’s Recode Variables

A dataset where each record is an interviewed man (often ages 15-59). It includes his fertility, contraception, HIV/AIDS, and health‐behavior modules. Useful for research on men’s health and reproductive behavior.

survey_data <- tar_read(dhs_data_MR, store = here("_targets"))
var_descriptions_mr <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_MR, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_mr, caption = glue("Variable Descriptions for Men's Recode Surveys ({n_surveys} Distinct Surveys Total)"))

Fieldworker Variables

A dataset with one record for each fieldworker/interviewer in the survey. It contains characteristics of data collection staff (age, education, languages, prior experience), used for survey‐quality and interviewer bias analyses

survey_data <- tar_read(dhs_data_FW, store = here("_targets"))
var_descriptions_fw <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_FW, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_fw, caption = glue("Variable Descriptions for Fieldworker Surveys ({n_surveys} Distinct Surveys Total)"))

Supplemental Questionnaire Variables

A file type associated with additional modules or supplemental questionnaires (e.g., field worker interviews, special modules) beyond the core recode files. Use is more specialized depending on the country/survey.

survey_data <- tar_read(dhs_data_SQ, store = here("_targets"))
var_descriptions_sq <- summarize_dhs_flat_dictionary(survey_data)
n_surveys <- length(tar_read(dhs_data_SQ, store = here("_targets"))) # shows the number of branches
DT::datatable(var_descriptions_sq, caption = glue("Variable Descriptions for Supplemental Questionnaire Surveys ({n_surveys} Distinct Surveys Total)"))

load_gps_dhs_data

Reading in the geospatial data is a little bit different:

ex_data <- here("data", "DHS Data", "DHS 1997", "GPS Data", "MDGE32FL")
list.files(ex_data)
#> [1] "DHS_README.txt"   "MDGE32FL.dbf"     "MDGE32FL.prj"     "MDGE32FL.sbn"    
#> [5] "MDGE32FL.sbx"     "MDGE32FL.shp"     "MDGE32FL.shp.xml" "MDGE32FL.shx"

To read in shapefiles, we can use the sf package:

library(sf)
gps_data <- sf::st_read(dsn = ex_data)
#> Reading layer `MDGE32FL' from data source 
#>   `/n/holylabs/cgolden_lab/Lab/data_freeze/golden_googledrive_rclone/Climate-Smart Public Health - Madagascar/4. Datasets/DHS Data/DHS 1997/GPS Data/MDGE32FL' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 269 features and 20 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 6.661338e-16 ymin: -25.28438 xmax: 50.45773 ymax: 0
#> Geodetic CRS:  WGS 84
gps_data
#> Simple feature collection with 269 features and 20 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 6.661338e-16 ymin: -25.28438 xmax: 50.45773 ymax: 0
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSCC DHSYEAR DHSCLUST CCFIPS ADM1FIPS ADM1FIPSNA ADM1SALBNA
#> 1  MD199700000001    MD    1997        1     MA     NULL       NULL       NULL
#> 2  MD199700000002    MD    1997        2     MA     NULL       NULL       NULL
#> 3  MD199700000003    MD    1997        3     MA     NULL       NULL       NULL
#> 4  MD199700000004    MD    1997        4     MA     NULL       NULL       NULL
#> 5  MD199700000005    MD    1997        5     MA     NULL       NULL       NULL
#> 6  MD199700000006    MD    1997        6     MA     NULL       NULL       NULL
#> 7  MD199700000007    MD    1997        7     MA     NULL       NULL       NULL
#> 8  MD199700000008    MD    1997        8     MA     NULL       NULL       NULL
#> 9  MD199700000009    MD    1997        9     MA     NULL       NULL       NULL
#> 10 MD199700000010    MD    1997       10     MA     NULL       NULL       NULL
#>    ADM1SALBCO ADM1DHS     ADM1NAME DHSREGCO     DHSREGNA SOURCE URBAN_RURA
#> 1        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 2        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 3        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 4        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 5        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 6        NULL       1 antananarivo        1 antananarivo    MIS          U
#> 7        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 8        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 9        NULL       1 antananarivo        1 antananarivo    GPS          U
#> 10       NULL       1 antananarivo        1 antananarivo    GPS          U
#>       LATNUM  LONGNUM ALT_GPS ALT_DEM DATUM                   geometry
#> 1  -18.92010 47.51332    9999    1290 WGS84  POINT (47.51332 -18.9201)
#> 2  -18.90276 47.50940    9999    1289 WGS84  POINT (47.5094 -18.90276)
#> 3  -18.90329 47.50141    9999    1290 WGS84 POINT (47.50141 -18.90329)
#> 4  -18.89096 47.51720    9999    1297 WGS84  POINT (47.5172 -18.89096)
#> 5  -18.88167 47.54719    9999    1334 WGS84 POINT (47.54719 -18.88167)
#> 6    0.00000  0.00000    9999    9999 WGS84     POINT (6.661338e-16 0)
#> 7  -18.88372 47.51306    9999    1301 WGS84 POINT (47.51306 -18.88372)
#> 8  -18.93616 47.51758    9999    1285 WGS84 POINT (47.51758 -18.93616)
#> 9  -18.92530 47.52272    9999    1285 WGS84  POINT (47.52272 -18.9253)
#> 10 -18.93636 47.51839    9999    1277 WGS84 POINT (47.51839 -18.93636)

We can plot the data as well:

plot(st_geometry(gps_data))

What Geospatial Data Do we have?

Let’s do some simple EDA of the geospatial data to understand what we have.

From the manual:

In most recent DHS surveys, the groupings of households that participated in the survey, known as clusters, are geo referenced. These survey cluster coordinates are collected in the field using GPS receivers, usually during the survey sample listing process. In general, the GPS readings for most clusters are accurate to less than 15 meters.

So a DHS cluster is a group of households surveyed at a given location.

We have the following number of years of GPS surveys:

gps_data <- tar_read(gps_data, store = here("_targets"))
length(gps_data)
#> [1] 6

This tells us how many unique clusters (features, in the sf df) we have across each year:

map(
  gps_data,
  ~ .x %>%
    select(DHSID, DHSYEAR) %>%
    distinct()
)
#> $gps_data_8298b5191d2ae010
#> Simple feature collection with 269 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 6.661338e-16 ymin: -25.28438 xmax: 50.45773 ymax: 0
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD199700000001    1997  POINT (47.51332 -18.9201)
#> 2  MD199700000002    1997  POINT (47.5094 -18.90276)
#> 3  MD199700000003    1997 POINT (47.50141 -18.90329)
#> 4  MD199700000004    1997  POINT (47.5172 -18.89096)
#> 5  MD199700000005    1997 POINT (47.54719 -18.88167)
#> 6  MD199700000006    1997     POINT (6.661338e-16 0)
#> 7  MD199700000007    1997 POINT (47.51306 -18.88372)
#> 8  MD199700000008    1997 POINT (47.51758 -18.93616)
#> 9  MD199700000009    1997  POINT (47.52272 -18.9253)
#> 10 MD199700000010    1997 POINT (47.51839 -18.93636)
#> 
#> $gps_data_f50ff43195c3431a
#> Simple feature collection with 594 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0 ymin: -25.52226 xmax: 50.29224 ymax: 0
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD200800000001    2008  POINT (47.50036 -18.9088)
#> 2  MD200800000002    2008 POINT (47.49953 -18.90939)
#> 3  MD200800000003    2008 POINT (47.51908 -18.90449)
#> 4  MD200800000004    2008 POINT (47.50856 -18.91917)
#> 5  MD200800000005    2008 POINT (47.49968 -18.92357)
#> 6  MD200800000006    2008 POINT (47.52111 -18.91133)
#> 7  MD200800000007    2008 POINT (47.50696 -18.88818)
#> 8  MD200800000008    2008 POINT (47.50463 -18.92391)
#> 9  MD200800000009    2008  POINT (47.52437 -18.9085)
#> 10 MD200800000010    2008   POINT (47.528 -18.92913)
#> 
#> $gps_data_ae75628c32935f2f
#> Simple feature collection with 594 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0 ymin: -25.52226 xmax: 50.29224 ymax: 0
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD200800000001    2008  POINT (47.50036 -18.9088)
#> 2  MD200800000002    2008 POINT (47.49953 -18.90939)
#> 3  MD200800000003    2008 POINT (47.51908 -18.90449)
#> 4  MD200800000004    2008 POINT (47.50856 -18.91917)
#> 5  MD200800000005    2008 POINT (47.49968 -18.92357)
#> 6  MD200800000006    2008 POINT (47.52111 -18.91133)
#> 7  MD200800000007    2008 POINT (47.50696 -18.88818)
#> 8  MD200800000008    2008 POINT (47.50463 -18.92391)
#> 9  MD200800000009    2008  POINT (47.52437 -18.9085)
#> 10 MD200800000010    2008   POINT (47.528 -18.92913)
#> 
#> $gps_data_b505060d1bee8a8f
#> Simple feature collection with 650 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 43.3746 ymin: -25.5548 xmax: 50.36067 ymax: -11.99102
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD202100000001    2021  POINT (47.51655 -18.9005)
#> 2  MD202100000002    2021  POINT (47.50252 -18.9037)
#> 3  MD202100000003    2021 POINT (47.51845 -18.90855)
#> 4  MD202100000004    2021 POINT (47.51001 -18.89412)
#> 5  MD202100000005    2021 POINT (47.50749 -18.91743)
#> 6  MD202100000006    2021 POINT (47.50042 -18.89919)
#> 7  MD202100000007    2021 POINT (47.51526 -18.91396)
#> 8  MD202100000008    2021 POINT (47.49963 -18.93202)
#> 9  MD202100000009    2021 POINT (47.54904 -18.90876)
#> 10 MD202100000010    2021 POINT (47.55657 -18.92492)
#> 
#> $gps_data_b28c64131ce2bbdc
#> Simple feature collection with 267 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0 ymin: -25.55782 xmax: 50.27262 ymax: 0
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD201100000001    2011 POINT (47.54769 -18.81059)
#> 2  MD201100000002    2011 POINT (47.56216 -18.82146)
#> 3  MD201100000003    2011 POINT (47.57843 -18.85815)
#> 4  MD201100000004    2011 POINT (47.62707 -18.75119)
#> 5  MD201100000005    2011 POINT (47.45572 -18.79091)
#> 6  MD201100000006    2011 POINT (47.51928 -18.82442)
#> 7  MD201100000007    2011 POINT (47.48078 -18.80002)
#> 8  MD201100000008    2011 POINT (47.36249 -18.74972)
#> 9  MD201100000009    2011 POINT (47.40767 -18.88466)
#> 10 MD201100000010    2011 POINT (47.09381 -18.28693)
#> 
#> $gps_data_64ab67e0f91b63ce
#> Simple feature collection with 358 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 43.64903 ymin: -25.47617 xmax: 50.31325 ymax: -12.27554
#> Geodetic CRS:  WGS 84
#> First 10 features:
#>             DHSID DHSYEAR                   geometry
#> 1  MD201600000001    2016 POINT (47.56798 -18.96995)
#> 2  MD201600000002    2016 POINT (47.57159 -18.83351)
#> 3  MD201600000003    2016 POINT (47.63072 -18.84299)
#> 4  MD201600000004    2016 POINT (47.41192 -18.83302)
#> 5  MD201600000005    2016 POINT (47.44783 -18.82412)
#> 6  MD201600000006    2016 POINT (47.55835 -18.75685)
#> 7  MD201600000007    2016 POINT (47.07283 -18.31702)
#> 8  MD201600000008    2016 POINT (46.98167 -18.01371)
#> 9  MD201600000009    2016 POINT (47.65978 -18.92267)
#> 10 MD201600000010    2016  POINT (47.8041 -18.48754)

There’s a weird data point in the data that is far outside of Madagascar.

Let’s remove this:

map(
  gps_data,
  ~ .x %>%
    select(LATNUM, LONGNUM) %>%
    summary()
)
#> $gps_data_8298b5191d2ae010
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.28   Min.   : 0.00   POINT        :269  
#>  1st Qu.:-20.35   1st Qu.:46.97   epsg:4326    :  0  
#>  Median :-18.90   Median :47.52   +proj=long...:  0  
#>  Mean   :-18.65   Mean   :47.38                      
#>  3rd Qu.:-16.27   3rd Qu.:48.52                      
#>  Max.   :  0.00   Max.   :50.46                      
#> 
#> $gps_data_f50ff43195c3431a
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.52   Min.   : 0.00   POINT        :594  
#>  1st Qu.:-21.45   1st Qu.:46.29   epsg:4326    :  0  
#>  Median :-18.95   Median :47.27   +proj=long...:  0  
#>  Mean   :-19.04   Mean   :46.45                      
#>  3rd Qu.:-17.34   3rd Qu.:47.92                      
#>  Max.   :  0.00   Max.   :50.29                      
#> 
#> $gps_data_ae75628c32935f2f
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.52   Min.   : 0.00   POINT        :594  
#>  1st Qu.:-21.45   1st Qu.:46.29   epsg:4326    :  0  
#>  Median :-18.95   Median :47.27   +proj=long...:  0  
#>  Mean   :-19.04   Mean   :46.45                      
#>  3rd Qu.:-17.34   3rd Qu.:47.92                      
#>  Max.   :  0.00   Max.   :50.29                      
#> 
#> $gps_data_b505060d1bee8a8f
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.55   Min.   :43.37   POINT        :650  
#>  1st Qu.:-21.46   1st Qu.:46.31   epsg:4326    :  0  
#>  Median :-18.95   Median :47.29   +proj=long...:  0  
#>  Mean   :-19.25   Mean   :47.17                      
#>  3rd Qu.:-17.36   3rd Qu.:48.07                      
#>  Max.   :-11.99   Max.   :50.36                      
#> 
#> $gps_data_b28c64131ce2bbdc
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.56   Min.   : 0.00   POINT        :267  
#>  1st Qu.:-23.93   1st Qu.:45.91   epsg:4326    :  0  
#>  Median :-20.30   Median :47.01   +proj=long...:  0  
#>  Mean   :-20.28   Mean   :46.77                      
#>  3rd Qu.:-18.01   3rd Qu.:47.95                      
#>  Max.   :  0.00   Max.   :50.27                      
#> 
#> $gps_data_64ab67e0f91b63ce
#>      LATNUM          LONGNUM               geometry  
#>  Min.   :-25.48   Min.   :43.65   POINT        :358  
#>  1st Qu.:-22.05   1st Qu.:46.13   epsg:4326    :  0  
#>  Median :-19.04   Median :47.18   +proj=long...:  0  
#>  Mean   :-19.42   Mean   :47.17                      
#>  3rd Qu.:-17.23   3rd Qu.:48.31                      
#>  Max.   :-12.28   Max.   :50.31

The data point at 0-0 is clearly an error. Let’s remove any points with latitudes or longitudes equal to 0.

gps_data2 <- map(
  gps_data,
  ~ .x %>%
    filter(LATNUM != 0 & LONGNUM != 0)
)

Now, what is the overlap of our clusters with our Madagascar healthsheds? To do this, we will perform a spatial join between the GPS points and the healthshed polygons.

Fetching our healthsheds:

# note this is not part of the pipeline yet
healthsheds <- st_read(here("sandbox", "mdg_healthsheds2022", "healthsheds2022.shp"))
#> Reading layer `healthsheds2022' from data source 
#>   `/n/holylabs/cgolden_lab/Lab/projects/DHSHarmonization/sandbox/mdg_healthsheds2022/healthsheds2022.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 2773 features and 13 fields (with 7 geometries empty)
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 43.17692 ymin: -25.60575 xmax: 50.48485 ymax: -11.95139
#> Geodetic CRS:  WGS 84

Ensure both datasets use the same coordinate reference system (CRS):

map(
  gps_data2,
  ~ st_crs(.x)
)
#> $gps_data_8298b5191d2ae010
#> 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]]
#> 
#> $gps_data_f50ff43195c3431a
#> 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]]
#> 
#> $gps_data_ae75628c32935f2f
#> 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]]
#> 
#> $gps_data_b505060d1bee8a8f
#> 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]]
#> 
#> $gps_data_b28c64131ce2bbdc
#> 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]]
#> 
#> $gps_data_64ab67e0f91b63ce
#> 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]]

st_crs(healthsheds) == st_crs(gps_data2[[1]])
#> [1] TRUE

Here’s what the healthsheds look like:

ggplot(healthsheds) +
  geom_sf(aes(fill = fs_pop))

What we want to know: Our healthsheds represent areas of healthcare access. Any analysis that we want to think about should be done with respect to these areas, so that we can know how different variables relate to “healthcare access”.

So, we want to know how many survey clusters fall within each healthshed. To do this, we should do a spatial join between the GPS points and the healthshed polygons, where the goal is to know how many GPS points fall within each healthshed.

At first, this may not look to be very successful because the surveys are relatively sparse:

bind_rows(gps_data2) %>%
  ggplot() +
  geom_sf() +
  facet_grid(~ DHSYEAR)

So we need to figure out the degree of overlap between the GPS points and the healthsheds.

# Spatial join: assign each GPS point to a healthshed polygon
# st_intersects means we are looking for points in the DHS that overlap on the healthshed polygons
pts <- gps_data2[[1]]
joined <- st_join(healthsheds, pts, left = TRUE, join = st_intersects)

joined %>%
  st_drop_geometry() %>% 
  group_by(fs_uid, fs_name) %>%
  # how many DHSIDs are in each fs_uid
  summarise(n_points = n_distinct(DHSID, na.rm = TRUE), .groups = "drop") -> summary_counts

DT::datatable(summary_counts)

Most of the healthsheds have zero GPS points overlapping them, but at most there are 11. Let’s visualize these for one year:

summary_counts %>%
  left_join(healthsheds, ., by = c("fs_uid", "fs_name")) %>%
  ggplot() +
  geom_sf(aes(fill = n_points)) +
  scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
  theme_minimal() +
  labs(fill = "# GPS points")

With the same strategy, we can plot all of the years:

map(
  gps_data2,
  function(x) {
    year <- unique(x$DHSYEAR)
    pts <- x
    joined <- st_join(pts, healthsheds, left = FALSE)
    summary_counts <- joined %>% 
      st_drop_geometry() %>%
      group_by(fs_uid, fs_name) %>% 
      summarise(n_points = n(), .groups = "drop") %>%
      mutate(DHSYEAR = year)

    return(summary_counts)

  }
) %>%
  list_rbind() %>%
  left_join(healthsheds, ., by = c("fs_uid", "fs_name")) %>%
  ggplot() +
  geom_sf(aes(fill = n_points)) +
  scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
  theme_minimal() +
  labs(fill = "# GPS points") +
  facet_wrap(DHSYEAR ~ ., nrow=2)

You may notice that the join created more rows than the original healthsheds. This is because healthsheds are not mutually exclusive (topologically disjoint) and have some overlap:

# st_intersects shows indeces of healthsheds with overlaps; the fact that
# some have lengths > 1 indicates overlap
lengths(st_intersects(healthsheds))
#>    [1]  7  6  6  6  7  6  8  9  8  6  7  9  5  8  9  8  8  5  6  6  5  6  9  8
#>   [25]  7  7  6  7  7  7  4  4  9  6  4  9  7  6  3  3  6  4  7  9  5  7  6  7
#>   [49] 12  5  5  9  7  8  7  8  9  9  6  9 10  6  6  3  9  6 10  2 10  9  9  6
#>   [73]  7  4  5  4  6  5  9 10  9  9  7  7  5  9  7  4  6  7  9 11  6  8  6  4
#>   [97]  7  5  7  7  7 11  7  6  6  7  8  5  5  4  8  7  3  4 10 10  5  7  7  4
#>  [121]  4  9  6  5 10  9  5 13  8  8  6  6  6  6  4  7  9  8  7  7  3  9  4  5
#>  [145]  4  6  6  3  5  4  9 11  5  7  8  6  6  8  7  6  8  9  7  8  9  5  9  7
#>  [169]  7 10  7  6  7  5  7  7 10  7  8 11 11  5  7  7 11  5  9  4  9  5  9  7
#>  [193]  8  4  7  8  7  4 10  8  5  7  7  8  5  8  5  6  9  5  7  5  8  5  8  6
#>  [217]  6  5 10  7  6  4  7  5  6  9  7  6  9  6  8 10  5  1  7  8  5  4  9  6
#>  [241]  6  6  5  6  4  7  9  6 12  4  6  8  6  8  6  8  8  7  7  7  5  7  8  5
#>  [265]  7  6  6  4  7  7  8  6  8  8 11 10  6  6  7  7  7 11  5  7  7  5  6  6
#>  [289]  3  6  8  7  6  4  4 10  8  6  8  9  6  6  8  5  9  6  8  8  4  6 10  5
#>  [313]  6  4  6  8  6  4  8  9  6  5  8  6  7  6  8  7  5  8  5  9 12  5  4  5
#>  [337]  8  5  9  8  6  6  6  6  5  8  9  8  6  7  8  6  6  4  5  9  8  9  6  4
#>  [361]  8  7  2  9  4  8  6  7  6  3  8  5  8  5  7  8  6  6  5  8  8  8  4  5
#>  [385]  5  9  5  9  5  7  6 11 11  9  7  7 10  4  7  6  6  7 11  3  4  6  7  7
#>  [409]  7  8  7  9  5  4  6  6  6 11  3  8  5  6  9  5  6  6  6  7  8  6  8  9
#>  [433]  7  5  4  4  8  7  4  5  7  7  5 12  4  6  9 11  7  6  4  7  6  9  4  6
#>  [457]  7  9  9  6  7  9  6  7  5  4  6  5 10  8  9  7  5  5  4  3  8  9  8  7
#>  [481]  7  5  7  9  7  7  9  6  7  7  7  5  8  7  7  8  8  8  7  0  9  6  3  6
#>  [505]  7  8  6  6  9  6  8  7  3  6  6  5  6 11  6  5  8  9  6  6  7  6  8  4
#>  [529]  7  9  7  6  9  6  5  8  6  6  7  7 10  8  6  1  5  8  5  6  8  7  4  6
#>  [553]  6  6  5 11  9  5  7  5  7  6  5  4  7  8  6 12  5  7  5  4  6  7  8 11
#>  [577]  7  7  9  6  9  6  5  9 10  6  6  9  5  3 13 10  4  5  7  6  6  6  5 10
#>  [601]  5  8  6  4  9  6  7  6  6  4  8  6  6  5  6  8  7  7  6  8 10  6 10  8
#>  [625]  9  6  8  8  8  6  8  6  4  7  4  7  6  7  6  9  8  9  7  8  6  7  9  5
#>  [649]  8  5  5  7  4  8  9  7  8  4  5  5  7 10  5  7 10  6  7  7 11  7  9 11
#>  [673]  6 10  7  6  6 10  8  8  8  3  8  7  8  4  7  7  5  5  8  5  6  8  4  6
#>  [697]  7  6  5  6  9 10 10  7  5  7  5  8  3  8 10  9  5  5  8  6  6  7  7 10
#>  [721]  7  6  9  7  5  8 11  7  8  8  7  5  5  6  6  9  7  6  7  7 12  4 10  6
#>  [745]  6  7 13  8  7 11  8  7  6  6  8  8  5  5  5  5  5  6  7  7  5  6  6  9
#>  [769]  9 10  7  5  8  7  5  6  9  6 10  4  4  4  7  6  8  8  5  5  7  7  8  8
#>  [793]  8  6  5  5  6 12  7  3  6  8 12  6  4  6  5  8  5  5  5  5  3  6  5  9
#>  [817]  5  6  6  4  9  5  9 11 13  9  7 11  8 13  4  5  6  9  8  2  3  9  9  9
#>  [841]  7  3 12  5  6  4  6  8  4  6  7  5  5  8 10  6  5  8  5  5  6  8  5  4
#>  [865]  4 10  7  9  7  7  7  9  8  5  6  6  7  8  6  7  6  8  5  7  6  7  8 12
#>  [889]  5  7  6  8  5  8  7  8  5  7  5  5 10  6  8 10 10 10  8 15 10  9  6  5
#>  [913]  7  5  5  5  7  8  7  5  8  6  5  8  7  7  5  6  5  5  6  6  7 10  4  7
#>  [937]  6  8  5  5  5  7  9 11  7  8  7  9  5  6  7  8  4  7  8  6  4  7  9  8
#>  [961]  6 10  5  4  4  7  9  5  7  6 12  6  5 10  4  4  9  6  9  5  5  5  5  7
#>  [985]  7  9  9  8  7  7  6  7  6  7  6  6  7  8  6  6  8  5  5  9  6  7  7  7
#> [1009] 10  7  6  7 12  7  5 10  6  6  5  7  7  8  6  7  5  5  6 12  6  6 10  5
#> [1033] 11  4  4  9  6  4  7  5  6  4  7  6  5  6  5  7  5  7  5  7  7  7  4  5
#> [1057]  7  7  6  7  9  6  5  7  6  8  5  4  8  5  0  6  5  5  5  9  6 10 10 10
#> [1081]  6  8  9  4  7  8  6  6  6  6  5  7 11  8  8  8  8  4  8  5  8  7  8 10
#> [1105]  7  6  7  8  7  9  6  9  5  7  7  4  5  7  8  6  7  7  8  8  5  6  7  7
#> [1129]  6  8  4  4  4  8  5  6  3  4  4  6 12  4  4  8  8  6  6  4  8  4  5  4
#> [1153]  6  8 10 10  7  2 10  6  6 10  8  8  6 10  5  9  6  5  3  7  5  7  7  8
#> [1177]  9  6  6  4  8  6  6  6  5  8 10  8  8  0  6 11  7  7  6  8  8  7  5 13
#> [1201]  6  6  8  9  7 10  7  8  9  9  8  6  9  4  4  9  5  7  8  7  8  5  4  8
#> [1225]  5  4  9  7  7  6  9  5  5  3  4  7  8  0  4 11  8  7  5  5 11  8  5 14
#> [1249]  7  5  5  8  5  5  6  7  4  8  7  5  7  9  6  6  9  7  7  6  4  7  6  8
#> [1273]  3  5  8  8  7  8  7  6  3  9  7  4  6  5  5  8  7  8  9  8  5  7  4  5
#> [1297]  7  7  4 10  7  7  7  8 14  5  4  5  9  4  6  7  6  7  3  8  6  9  6  9
#> [1321]  4  5 10 10  8  5  8  6  8  7  8  6  5  9  7  7  5  7  9  7  8  9 10  6
#> [1345]  6  9  8  9  7  7  8  8  7  8  9  6  6  6  4  9  6  2  6  5  6  6  9  4
#> [1369]  7  5  8  5  9  7  4  8  5  5  8  7 10 11  6  7  7  7  6  8  6  7  6  8
#> [1393]  0  9 10  9  6 12  7  6  6  6  9  4  7  9  7  8  6  6  5  7  7  5  5  7
#> [1417]  7  6  8  6  7  5  9  5  7 10  6 10  7  7  9  8  8  4  8 12  8  5  0  4
#> [1441]  7  9  4  7  6  8  8  6  9  9  8  9  6  6  6  8  6  6  4  7  8  8  6  9
#> [1465]  8  6  8  9  6 10  8  6  8  6  4  8  7  7  7  6  7  8  6  2  7  6  7  4
#> [1489]  7  8  9  7  6  6  4  4  9  7  8  8  7  4  9  5  6  6  9  7  7  4  5  7
#> [1513]  6  7  8  7  7  6  3  8  5  5  6  6 13  4  6  7  5  8  7  6  6  7  6  7
#> [1537]  9  7  6  8 10  5  6  6  7  7  6  3  6  9  6  6  7  5 11  5  7  4  8  7
#> [1561]  6  8  8  6  5  7  6  7  7  6  7 10  7  7  9  6  5  4  6  8  6  5  7  8
#> [1585]  9  5 10  4  7  8  6  7  7  8  4  6  5  3  7  6  5  5  9  5  7  7  6  4
#> [1609]  6 11  7  7  8  6  5  6  6  6  5  7  7  9  7  6 11  6  8  6  9  6  7  7
#> [1633]  7 10  8  4  6  5 11  4  8  9  5  9  8  6  5  8  7 11  8 12  4 12  9  6
#> [1657]  6  8  6  5  8  4  4  8  4  7  9  4  7  6  7  5  8  6  7  4  7  8  8  8
#> [1681]  6  8  5  5  7 10  6  8  8  6  8  6  5  9  8  5  7  4  8  6  7  6  6  7
#> [1705]  6  4  6  9  9  5  9  4  6 10  7  5  9  9  8  7  5  6  6  7  5  8  4  7
#> [1729]  8  7  5  7  4  4  5  6  7  6  7  6  7  3  8  7  6  7  5  6  6 11 11  5
#> [1753]  7  8  9  6  6  4  5  6  5  8  6  7 10  4  6  7  3  7  7  6  5  8  7  7
#> [1777]  6  9  4  7  7  7 12  7  6  8  5  8 10  3  5  8 14  5  5  7  5  6  6  3
#> [1801]  6  8  6  6  5  5  7  6 11  5  5  8  6  6  9  5  6  5  5  6  6  6  4  6
#> [1825]  6  7  8  6  6  6  7  7  6 14  4  5  7  6  8  8  6  7  6  5  6  9  4  8
#> [1849]  8  8  7  8  7  8 10  5  4  9  5  6  8  6  7  8  5  8  7  5  5  5  7  6
#> [1873]  5  7  5  8  7 11  9  4  6  8  8  4  6  7  8  7  5  8  8  8  3  6  2 10
#> [1897]  8  7  7  7  6  5 10  9  5  9  9  7  3  3  9  8  7  5  5  7  6  4  6 10
#> [1921]  6  3  5  9 10  8  5  8 10  6  6  6 11  5  7  7  6  5  9 10  7  8  6  5
#> [1945]  6  5  6  5  6  7  6  3  4  7  6  5  6  5  6  7 10  9  6  7  5  4  8 13
#> [1969]  6  5  6  7  5  8  4  9  3  5  5  4  7  6  9  4  8  7  8  6  4  9  7  8
#> [1993]  6  9  5  5  8  8  3  9  8  5  5  9  7  6  5 10 13  5  9  9  8  8  5  7
#> [2017]  8  7  6  6  7  5  6  6  5  8  8  7  9  7  5  9  6  6 10  7  9  9  4 11
#> [2041]  6  5  8  5  7  6  4  6  4  6  5  5  8  7  4 10  4  7 12  7  6  7  7  7
#> [2065]  7  7  6  6  6  8 12  6  7  8  6  5  8  6  7  8  7  9  9  9  6  6  7  7
#> [2089]  5  7  5  3  8  8  9  9  7  6  9  9  6 10  7  5  6  6  6  6  6  5  7  3
#> [2113]  6  6  5  8  7  8  8  5  7 10  5 10  6  8  9  6  6  8  7  7 10  9  0 10
#> [2137]  5  7  5 10  8  6  8  5 10  4 11  7  8  5  9  5  6  6  4  9  8  7  5  5
#> [2161]  7  8  8  8  7  4 10  9  6  5  7  8  7  9  5  5  7  7  9  5  5 11  9  5
#> [2185]  7  7  5  6  6  5  7  4  8  8  5  7  8 10  9  9  8  5  6  5  4 10  3  6
#> [2209]  8  4  5  5  5  8  6 12  4 11  6  5  6  7  9  9  7  6  6  7  6  2  6  8
#> [2233]  6  5  8  8  8  6  7  5  6  6  4  8  7  9  7  9  5  7  6  7  4  5  6  8
#> [2257]  6  4  8  7  7  3  8 10  5  7 10  7  6  4  7  6  9  8  7  7  7  7  6  5
#> [2281]  7 10  5 14  7  7  6  6  6 10  6  6  7  8  5  6  4  6  9  9 10 10 11  6
#> [2305]  4  9  8  7  7  8  9  9  8 10  8 11  6  8  4  4  5  7 10  9  9  5 11  6
#> [2329]  9  5  5  7  6  6 11  7  6  7  7  8  8  7  9 10  6  6  7  9  7  4  5  8
#> [2353]  9 10  5  4  5  6  7  5  7  9  9  7  7  7  5  3  5  5  8  8  7  6  5  4
#> [2377]  7  6  9  6  8  6  6  7  8 10  7  5  4  5  3  9  6  4  9  5  6  6  7  4
#> [2401]  6  4  6  6  4  8  4  5  8  6  7  8  7  9  5  6  7  5  4  7  7 10  9  7
#> [2425]  4  7  4  6  8  6  6  6  6 11  6  8  6  5  7 11  4  3  5  4  3  7  7  9
#> [2449]  5  6  4  7  6  8  7  6  6  8  7  8  5  3  7  6  4  6  6 10  6 13  6  3
#> [2473]  6  8  6  7  8  5 10  6  8  5  7 11  5  8  8  4  5  8  5  8  7  9  5  5
#> [2497]  5  5  5  8  7  7  6  7 10 11  7  6  6  5  7  8 10  6  9  6  7  9  8  9
#> [2521]  6  4  8  8 10  5  6  7  6  7  6  8  4  7  5 10  5  9  7  8  4  6  8  8
#> [2545]  8  8  7  3  5 11  6 13  9  6  8  6  7  8  6  6 10  6  6  5  6  7  6  2
#> [2569]  7  7  5  6 14  6 10  3  7  5  7  5  4  6  6  6  9  6  8  9  5  4  6  6
#> [2593]  5  7  6  9  6  5  6  8  6  5 10  9  4  9  4 11  4 11  6  5  3  6  9  7
#> [2617]  3  6  6  7  9  9  7  6  6  7  6  6 10 10  7  7  3  5  7  9  7  5  9  6
#> [2641]  5  6  6  5  9  6  7  4  5  6  7 11 11 10  6  8  6  5  4  6  5  9  7  8
#> [2665] 10  6  4  7  7  5  6  7  5  6  5  8  8  3  6  8  9  9  6  6  7  8  5  6
#> [2689]  6  9  5  7  6 10  8 10  8  8  6  6  8  9  7  7  7  4  6 10  8  9  4  6
#> [2713]  6  7  8  8  6  5  7  7  5  7  8  4  4  7  8 12  7  6  5  4  6  7  5  4
#> [2737]  6  9  7  5  9  5  7  4  9  3  8  7 13  7  6  5  5  9  5  6 10  5  6  7
#> [2761]  4  8  8  8  6  8 10  9  5  7  6  7  6

This is important to note for future analyses.

load_gps_covars

There’s a covariate file that comes with the GPS data as well.

ex_data <- here("data", "DHS Data", "DHS 1997", "GPS Data", "MDGC32FL") %>%
  list.files(full.names = TRUE) %>%
  grep(pattern = "csv$", value = TRUE)
ex_data
#> [1] "/n/holylabs/cgolden_lab/Lab/projects/DHSHarmonization/data/DHS Data/DHS 1997/GPS Data/MDGC32FL/MDGC32FL.csv"

library(readr)
library(skimr)

gps_covars <- read_csv(ex_data)
#> Rows: 269 Columns: 131
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr   (4): DHSID, GPS_Dataset, DHSCC, SurveyID
#> dbl (127): DHSYEAR, DHSCLUST, All_Population_Count_2005, All_Population_Coun...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
skim(gps_covars)

Data summary
Name	gps_covars
Number of rows	269
Number of columns	131
_______________________
Column type frequency:
character	4
numeric	127
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
DHSID	1	14	14	269
GPS_Dataset	1	8	8	1
DHSCC	1	2	2	1
SurveyID	1	9	9	1

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
DHSYEAR	1	1997.00	0.00	1997	1997.00	1997.00	1997.00	1997.00	▁▁▇▁▁
DHSCLUST	1	135.38	78.21	1	68.00	135.00	203.00	270.00	▇▇▇▇▇
All_Population_Count_2005	1	62355.44	132320.25	-9999	5312.18	16197.22	59501.18	1387207.75	▇▁▁▁▁
All_Population_Count_2010	1	71869.81	152494.94	-9999	6122.24	18667.16	68574.62	1598745.75	▇▁▁▁▁
All_Population_Count_2015	1	82635.15	175322.28	-9999	7038.82	21461.86	78841.06	1838097.12	▇▁▁▁▁
Annual_Precipitation_2000	1	-928.14	3097.83	-9999	96.01	127.00	137.94	220.57	▁▁▁▁▇
Annual_Precipitation_2005	1	-919.87	3100.68	-9999	101.47	132.21	153.38	226.19	▁▁▁▁▇
Annual_Precipitation_2010	1	-930.06	3097.30	-9999	87.10	106.68	142.65	230.76	▁▁▁▁▇
Annual_Precipitation_2015	1	-931.87	3096.53	-9999	95.94	117.21	131.85	230.28	▁▁▁▁▇
Aridity_2000	1	-1004.64	3071.52	-9999	27.14	40.16	46.11	70.39	▁▁▁▁▇
Aridity_2005	1	-1002.82	3072.14	-9999	28.10	42.27	49.34	69.74	▁▁▁▁▇
Aridity_2010	1	-1007.59	3070.52	-9999	26.64	34.62	40.79	67.70	▁▁▁▁▇
Aridity_2015	1	-1007.99	3070.37	-9999	26.88	35.86	39.07	67.07	▁▁▁▁▇
BUILT_Population_1990	1	-37.06	609.66	-9999	0.00	0.00	0.05	0.82	▁▁▁▁▇
BUILT_Population_2000	1	-37.04	609.66	-9999	0.00	0.00	0.06	0.86	▁▁▁▁▇
BUILT_Population_2014	1	-37.03	609.66	-9999	0.00	0.00	0.07	0.89	▁▁▁▁▇
Day_Land_Surface_Temp_2000	1	-83.13	1055.02	-9999	26.09	27.39	31.43	36.91	▁▁▁▁▇
Day_Land_Surface_Temp_2005	1	-83.86	1054.94	-9999	25.72	26.30	30.76	35.23	▁▁▁▁▇
Day_Land_Surface_Temp_2010	1	-82.83	1055.05	-9999	26.54	27.93	31.55	37.75	▁▁▁▁▇
Day_Land_Surface_Temp_2015	1	-82.81	1055.06	-9999	26.73	28.03	31.54	37.64	▁▁▁▁▇
Diurnal_Temperature_Range_2000	1	-1032.63	3061.93	-9999	8.40	9.08	9.32	11.95	▁▁▁▁▇
Diurnal_Temperature_Range_2005	1	-1032.63	3061.93	-9999	8.40	9.08	9.32	11.95	▁▁▁▁▇
Diurnal_Temperature_Range_2010	1	-1032.63	3061.93	-9999	8.40	9.08	9.32	11.95	▁▁▁▁▇
Diurnal_Temperature_Range_2015	1	-1032.63	3061.93	-9999	8.40	9.08	9.32	11.95	▁▁▁▁▇
Drought_Episodes	1	-5164.38	5008.49	-9999	-9999.00	-9999.00	5.00	10.00	▇▁▁▁▇
Enhanced_Vegetation_Index_1985	1	2641.85	1628.89	-9999	2127.82	2486.00	3271.57	5015.00	▁▁▁▁▇
Enhanced_Vegetation_Index_1990	1	2668.10	1674.59	-9999	2046.83	2475.45	3382.38	5532.00	▁▁▁▇▇
Enhanced_Vegetation_Index_1995	1	2745.19	1672.87	-9999	2179.00	2584.80	3458.50	5208.00	▁▁▁▂▇
Enhanced_Vegetation_Index_2000	1	2786.01	1667.32	-9999	2235.00	2633.70	3519.38	5156.00	▁▁▁▂▇
Enhanced_Vegetation_Index_2005	1	3045.12	1828.62	-9999	2322.00	2790.90	4004.44	5861.50	▁▁▁▆▇
Enhanced_Vegetation_Index_2010	1	2965.26	1801.94	-9999	2213.00	2756.54	3947.89	5738.00	▁▁▁▅▇
Enhanced_Vegetation_Index_2015	1	2900.93	1815.97	-9999	2096.00	2662.73	3898.33	5616.56	▁▁▁▅▇
Frost_Days_2000	1	-1040.79	3059.15	-9999	0.00	0.00	0.00	0.02	▁▁▁▁▇
Frost_Days_2005	1	-1040.79	3059.15	-9999	0.00	0.00	0.00	0.02	▁▁▁▁▇
Frost_Days_2010	1	-1040.79	3059.15	-9999	0.00	0.00	0.00	0.00	▁▁▁▁▇
Frost_Days_2015	1	-1040.79	3059.15	-9999	0.00	0.00	0.00	0.00	▁▁▁▁▇
Global_Human_Footprint	1	-35.28	864.18	-9999	25.94	30.80	46.37	87.00	▁▁▁▁▇
Gross_Cell_Production	1	759.79	940.29	-9999	741.36	809.05	992.86	1009.43	▁▁▁▁▇
Growing_Season_Length	1	-360.92	1897.36	-9999	9.00	11.00	14.00	15.00	▁▁▁▁▇
Irrigation	1	-289.91	1703.01	-9999	0.00	3.07	15.86	58.66	▁▁▁▁▇
ITN_Coverage_2005	1	-185.75	1353.02	-9999	0.00	0.09	0.21	0.31	▁▁▁▁▇
ITN_Coverage_2010	1	-185.32	1353.08	-9999	0.22	0.65	0.78	0.87	▁▁▁▁▇
ITN_Coverage_2015	1	-185.11	1353.11	-9999	0.42	0.97	1.00	1.00	▁▁▁▁▇
Land_Surface_Temperature_2000	1	-89.60	1054.33	-9999	19.75	20.75	25.31	28.43	▁▁▁▁▇
Land_Surface_Temperature_2005	1	-89.54	1054.34	-9999	20.14	21.02	24.92	28.58	▁▁▁▁▇
Land_Surface_Temperature_2010	1	-88.72	1054.43	-9999	20.97	21.78	25.98	29.09	▁▁▁▁▇
Land_Surface_Temperature_2015	1	-88.72	1054.43	-9999	21.03	21.88	25.94	29.32	▁▁▁▁▇
Livestock_Cattle	1	-19.22	611.00	-9999	7.35	14.13	23.47	189.52	▁▁▁▁▇
Livestock_Chickens	1	242.55	791.62	-9999	30.02	72.96	224.79	2216.75	▁▁▁▁▇
Livestock_Goats	1	-31.36	614.43	-9999	0.00	0.01	0.48	1206.19	▁▁▁▁▇
Livestock_Pigs	1	-30.85	610.10	-9999	0.71	2.28	9.82	53.50	▁▁▁▁▇
Livestock_Sheep	1	-34.20	610.56	-9999	0.00	0.19	1.07	487.91	▁▁▁▁▇
Malaria_Incidence_2000	1	-185.63	1353.04	-9999	0.05	0.28	0.34	0.48	▁▁▁▁▇
Malaria_Incidence_2005	1	-185.62	1353.04	-9999	0.12	0.25	0.31	0.44	▁▁▁▁▇
Malaria_Incidence_2010	1	-185.76	1353.02	-9999	0.05	0.07	0.12	0.29	▁▁▁▁▇
Malaria_Incidence_2015	1	-185.79	1353.02	-9999	0.03	0.05	0.09	0.24	▁▁▁▁▇
Malaria_Prevalence_2000	1	-185.67	1353.03	-9999	0.03	0.22	0.29	0.47	▁▁▁▁▇
Malaria_Prevalence_2005	1	-185.67	1353.03	-9999	0.08	0.20	0.26	0.45	▁▁▁▁▇
Malaria_Prevalence_2010	1	-185.80	1353.01	-9999	0.03	0.04	0.07	0.25	▁▁▁▁▇
Malaria_Prevalence_2015	1	-185.82	1353.01	-9999	0.02	0.03	0.05	0.18	▁▁▁▁▇
Maximum_Temperature_2000	1	-1017.68	3067.04	-9999	23.08	24.28	27.53	31.38	▁▁▁▁▇
Maximum_Temperature_2005	1	-1017.46	3067.12	-9999	23.44	24.56	27.80	31.74	▁▁▁▁▇
Maximum_Temperature_2010	1	-1017.07	3067.25	-9999	23.86	25.02	28.23	32.03	▁▁▁▁▇
Maximum_Temperature_2015	1	-1017.16	3067.22	-9999	23.78	24.93	28.17	31.94	▁▁▁▁▇
Mean_Temperature_2000	1	-1021.78	3065.64	-9999	18.45	19.98	23.38	26.54	▁▁▁▁▇
Mean_Temperature_2005	1	-1021.56	3065.72	-9999	18.81	20.26	23.48	26.91	▁▁▁▁▇
Mean_Temperature_2010	1	-1021.17	3065.85	-9999	19.23	20.76	23.99	27.19	▁▁▁▁▇
Mean_Temperature_2015	1	-1021.26	3065.82	-9999	19.15	20.64	23.90	27.11	▁▁▁▁▇
Minimum_Temperature_2000	1	-1025.84	3064.26	-9999	13.88	15.82	18.98	21.75	▁▁▁▁▇
Minimum_Temperature_2005	1	-1025.61	3064.33	-9999	14.23	16.00	19.31	22.12	▁▁▁▁▇
Minimum_Temperature_2010	1	-1025.22	3064.47	-9999	14.65	16.58	19.72	22.40	▁▁▁▁▇
Minimum_Temperature_2015	1	-1025.32	3064.43	-9999	14.58	16.40	19.53	22.32	▁▁▁▁▇
Nightlights_Composite	1	-35.45	609.76	-9999	0.00	0.00	1.41	11.27	▁▁▁▁▇
Night_Land_Surface_Temp2010	1	-94.62	1053.80	-9999	14.41	16.63	19.42	23.41	▁▁▁▁▇
Night_Land_Surface_Temp2015	1	-94.63	1053.80	-9999	14.56	16.50	19.25	23.54	▁▁▁▁▇
Night_Land_Surface_Temp_2000	1	-96.07	1053.65	-9999	12.73	15.47	18.56	22.17	▁▁▁▁▇
Night_Land_Surface_Temp_2005	1	-95.23	1053.73	-9999	14.01	16.11	18.47	22.98	▁▁▁▁▇
Night_Land_Surface_Temp_2010	1	-94.62	1053.80	-9999	14.41	16.63	19.42	23.41	▁▁▁▁▇
Night_Land_Surface_Temp_2015	1	-94.63	1053.80	-9999	14.56	16.50	19.25	23.54	▁▁▁▁▇
PET_2000	1	-1037.92	3060.13	-9999	2.87	2.97	3.32	4.59	▁▁▁▁▇
PET_2005	1	-1037.85	3060.15	-9999	2.92	3.00	3.38	4.62	▁▁▁▁▇
PET_2010	1	-1037.76	3060.18	-9999	3.00	3.11	3.51	4.78	▁▁▁▁▇
PET_2015	1	-1037.75	3060.18	-9999	3.00	3.13	3.53	4.73	▁▁▁▁▇
Proximity_to_National_Borders	1	88565.00	67415.32	-9999	21794.74	87227.09	159217.98	230387.86	▇▃▃▆▂
Proximity_to_Protected_Areas	1	56452.86	37423.60	-9999	25979.61	56410.99	79306.85	169207.51	▆▆▇▂▁
Proximity_to_Water	1	54201.34	39922.04	-9999	16918.51	61091.25	74927.64	180764.07	▇▅▇▂▁
Rainfall_1985	1	1491.35	2000.77	-9999	1527.90	1702.00	2103.56	3977.00	▁▁▁▁▇
Rainfall_1990	1	1060.67	1897.19	-9999	1039.00	1150.00	1610.20	3521.00	▁▁▁▁▇
Rainfall_1995	1	1330.30	1949.23	-9999	1355.30	1494.00	1938.73	3848.00	▁▁▁▁▇
Rainfall_2000	1	1190.16	1903.70	-9999	1240.38	1378.00	1737.38	3161.00	▁▁▁▁▇
Rainfall_2005	1	1279.92	1927.88	-9999	1269.00	1460.00	1837.00	3380.00	▁▁▁▁▇
Rainfall_2010	1	1207.21	1936.52	-9999	1175.00	1280.80	1761.92	3483.00	▁▁▁▁▇
Rainfall_2015	1	1405.53	1964.57	-9999	1455.08	1552.50	1970.00	4471.27	▁▁▁▇▇
Slope	1	-35.86	609.73	-9999	0.45	0.83	1.94	5.30	▁▁▁▁▇
SMOD_Population_1990	1	-36.28	609.71	-9999	0.00	0.00	1.00	3.00	▁▁▁▁▇
SMOD_Population_2000	1	-36.28	609.71	-9999	0.00	0.00	2.00	3.00	▁▁▁▁▇
SMOD_Population_2015	1	-36.26	609.71	-9999	0.00	0.00	2.00	3.00	▁▁▁▁▇
Temperature_April	1	-275.16	1705.58	-9999	20.00	23.25	25.27	28.36	▁▁▁▁▇
Temperature_August	1	-279.16	1704.88	-9999	15.44	19.30	21.06	25.99	▁▁▁▁▇
Temperature_December	1	-274.10	1705.76	-9999	21.16	24.60	26.47	28.88	▁▁▁▁▇
Temperature_February	1	-273.92	1705.80	-9999	21.48	24.77	26.72	28.16	▁▁▁▁▇
Temperature_January	1	-273.92	1705.80	-9999	21.46	24.70	26.66	28.09	▁▁▁▁▇
Temperature_July	1	-279.65	1704.79	-9999	15.04	18.53	20.86	25.48	▁▁▁▁▇
Temperature_June	1	-278.85	1704.93	-9999	15.94	19.27	21.55	25.82	▁▁▁▁▇
Temperature_March	1	-274.24	1705.74	-9999	21.04	24.26	26.20	28.47	▁▁▁▁▇
Temperature_May	1	-277.06	1705.25	-9999	17.96	21.06	23.31	27.29	▁▁▁▁▇
Temperature_November	1	-274.66	1705.67	-9999	20.64	23.77	25.77	29.64	▁▁▁▁▇
Temperature_October	1	-276.08	1705.42	-9999	18.97	22.07	24.36	28.90	▁▁▁▁▇
Temperature_September	1	-277.90	1705.10	-9999	16.75	20.17	22.41	27.11	▁▁▁▁▇
Travel_Times_2000	1	142.31	646.12	-9999	42.17	145.11	251.90	1288.22	▁▁▁▁▇
Travel_Times_2015	1	241.97	698.14	-9999	12.19	155.14	460.69	1167.04	▁▁▁▁▇
U5_Population_2000	1	301.75	900.46	-9999	3.50	8.67	315.58	2734.98	▁▁▁▇▃
U5_Population_2005	1	356.55	979.57	-9999	4.06	10.07	366.60	3177.16	▁▁▁▇▂
U5_Population_2010	1	416.59	1071.14	-9999	4.68	11.61	422.51	3661.65	▁▁▁▇▂
U5_Population_2015	1	484.52	1179.48	-9999	5.38	13.35	485.76	4209.84	▁▁▁▇▂
UN_Population_Count_2000	1	37487.35	100974.59	-9999	2567.17	11510.33	43717.18	889610.56	▇▁▁▁▁
UN_Population_Count_2005	1	45552.20	126914.37	-9999	3193.92	13363.54	48181.36	1114080.00	▇▁▁▁▁
UN_Population_Count_2010	1	54739.64	157552.86	-9999	3928.02	15522.96	49277.87	1374629.50	▇▁▁▁▁
UN_Population_Count_2015	1	65410.76	194319.29	-9999	4831.01	18494.60	51252.00	1680597.75	▇▁▁▁▁
UN_Population_Density_2000	1	1058.04	2308.08	-9999	23.84	68.95	256.78	10035.58	▁▁▇▂▁
UN_Population_Density_2005	1	1308.82	2789.51	-9999	27.86	79.23	335.49	12247.07	▁▁▇▂▁
UN_Population_Density_2010	1	1592.16	3336.65	-9999	32.68	83.84	408.00	14843.68	▁▁▇▂▁
UN_Population_Density_2015	1	1916.81	3971.12	-9999	36.47	91.00	500.29	17814.64	▁▇▁▂▁
Wet_Days_2000	1	-1028.84	3063.23	-9999	10.94	13.05	14.14	20.15	▁▁▁▁▇
Wet_Days_2005	1	-1028.22	3063.44	-9999	10.86	13.28	15.77	22.42	▁▁▁▁▇
Wet_Days_2010	1	-1029.66	3062.95	-9999	9.17	10.22	13.66	21.56	▁▁▁▁▇
Wet_Days_2015	1	-1029.94	3062.86	-9999	9.04	10.19	13.45	23.67	▁▁▁▁▇

Interestingly, there is data from many years included, even though the folder is for 1997. From ChatGPT:

The geospatial covariates provided with DHS GPS data (e.g., Annual_Precipitation_2000, Day_Land_Surface_Temp_2010) come from external environmental and remote-sensing datasets, not from the DHS survey itself. The year indicated in each variable name (e.g., _2000, _2005, _2010, _2015) refers to the reference year of the underlying satellite or modeled dataset, not the year when the DHS survey was conducted. These layers are standardized across all DHS surveys so users can compare environmental conditions over time or across countries. For a given survey, analysts typically use covariates from the year closest to the survey year (e.g., use 2000 data for a 1997 survey).

In short:

DHSYEAR = when the survey happened Variable suffix (e.g., _2000) = when the environmental data were measured

Even when two DHS surveys include covariates with the same reference year (e.g., Annual_Precipitation_2000), the values will differ because each survey’s clusters are unique. The covariate year indicates the year of the environmental dataset, not that the same locations or values are shared across surveys.

To clean this, we’ll also need to acknowledge that NA is represented by -9999. It looks like all of the covariates are numeric except for DHSCLUST, which is the cluster ID.

ex_data <- here("data", "DHS Data", "DHS 1997", "GPS Data", "MDGC32FL") %>%
  list.files(full.names = TRUE) %>%
  grep(pattern = "csv$", value = TRUE)
load_gps_covars(ex_data) %>%
  skimr::skim()
#> Rows: 269 Columns: 131
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr   (4): DHSID, GPS_Dataset, DHSCC, SurveyID
#> dbl (127): DHSYEAR, DHSCLUST, All_Population_Count_2005, All_Population_Coun...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data summary
Name	Piped data
Number of rows	269
Number of columns	131
_______________________
Column type frequency:
factor	5
numeric	126
________________________
Group variables	None

Variable type: factor

skim_variable	complete_rate	ordered	n_unique	top_counts
DHSID	1	FALSE	269	MD1: 1, MD1: 1, MD1: 1, MD1: 1
GPS_Dataset	1	FALSE	1	MDG: 269
DHSCC	1	FALSE	1	MD: 269
DHSCLUST	1	FALSE	269	1: 1, 2: 1, 3: 1, 4: 1
SurveyID	1	FALSE	1	MD1: 269

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
DHSYEAR	0	1.00	1997.00	0.00	1997.00	1997.00	1997.00	1997.00	1997.00	▁▁▇▁▁
All_Population_Count_2005	1	1.00	62625.42	132493.56	33.21	5425.68	16297.45	59803.31	1387207.75	▇▁▁▁▁
All_Population_Count_2010	1	1.00	72175.29	152697.76	38.28	6253.05	18782.68	68922.83	1598745.75	▇▁▁▁▁
All_Population_Count_2015	1	1.00	82980.80	175558.44	44.01	7189.21	21594.67	79241.40	1838097.12	▇▁▁▁▁
Annual_Precipitation_2000	28	0.90	125.74	37.86	29.52	107.09	132.18	145.60	220.57	▂▂▇▃▁
Annual_Precipitation_2005	28	0.90	134.97	40.59	35.26	110.41	132.21	162.32	226.19	▁▅▇▃▂
Annual_Precipitation_2010	28	0.90	123.59	48.22	18.94	91.03	106.68	151.27	230.76	▁▅▇▂▃
Annual_Precipitation_2015	28	0.90	121.57	35.93	36.83	102.33	117.21	135.94	230.28	▁▆▇▂▁
Aridity_2000	28	0.90	40.35	13.52	7.78	32.46	43.62	46.11	70.39	▂▃▆▇▂
Aridity_2005	28	0.90	42.38	13.95	9.10	34.69	45.33	51.26	69.74	▂▃▇▃▂
Aridity_2010	28	0.90	37.06	14.15	4.84	29.46	35.56	45.82	67.70	▁▃▇▁▃
Aridity_2015	28	0.90	36.60	11.34	9.34	30.87	36.78	40.75	67.07	▂▃▇▂▁
BUILT_Population_1990	1	1.00	0.12	0.22	0.00	0.00	0.00	0.05	0.82	▇▁▁▁▁
BUILT_Population_2000	1	1.00	0.13	0.24	0.00	0.00	0.00	0.07	0.86	▇▁▁▁▁
BUILT_Population_2014	1	1.00	0.14	0.26	0.00	0.00	0.00	0.08	0.89	▇▁▁▁▁
Day_Land_Surface_Temp_2000	3	0.99	28.70	3.57	20.68	26.21	27.39	31.45	36.91	▁▇▇▃▃
Day_Land_Surface_Temp_2005	3	0.99	27.96	3.28	21.21	25.73	26.37	30.81	35.23	▁▇▂▃▂
Day_Land_Surface_Temp_2010	3	0.99	29.01	3.39	22.07	26.60	27.93	31.63	37.75	▁▇▂▂▂
Day_Land_Surface_Temp_2015	3	0.99	29.03	3.10	22.70	26.79	28.03	31.61	37.64	▁▇▂▃▁
Diurnal_Temperature_Range_2000	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2005	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2010	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2015	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Drought_Episodes	139	0.48	4.95	2.91	1.00	2.00	5.00	8.00	10.00	▇▁▇▂▅
Enhanced_Vegetation_Index_1985	3	0.99	2784.42	924.10	144.00	2148.74	2501.60	3292.59	5015.00	▁▃▇▂▃
Enhanced_Vegetation_Index_1990	3	0.99	2810.96	999.52	144.00	2096.75	2479.80	3387.27	5532.00	▁▇▇▃▂
Enhanced_Vegetation_Index_1995	3	0.99	2888.93	985.29	141.00	2195.50	2597.86	3470.80	5208.00	▁▃▇▂▃
Enhanced_Vegetation_Index_2000	3	0.99	2930.20	969.60	123.00	2235.00	2647.80	3529.59	5156.00	▁▃▇▂▃
Enhanced_Vegetation_Index_2005	3	0.99	3192.23	1197.40	95.00	2322.00	2794.29	4044.61	5861.50	▁▅▇▂▃
Enhanced_Vegetation_Index_2010	3	0.99	3111.48	1166.01	113.00	2213.00	2773.92	3959.77	5738.00	▁▅▇▂▃
Enhanced_Vegetation_Index_2015	3	0.99	3046.41	1195.82	113.00	2112.94	2667.18	3904.76	5616.56	▁▆▇▂▃
Frost_Days_2000	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2005	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2010	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2015	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Global_Human_Footprint	2	0.99	39.36	19.79	11.08	26.28	30.81	46.44	87.00	▅▇▁▁▂
Gross_Cell_Production	2	0.99	840.38	118.15	682.18	742.02	809.05	993.11	1009.43	▇▅▃▁▇
Growing_Season_Length	10	0.96	11.21	2.85	4.00	9.00	11.00	14.00	15.00	▂▃▅▆▇
Irrigation	8	0.97	7.68	9.49	0.00	0.05	3.10	16.02	58.66	▇▂▁▁▁
ITN_Coverage_2005	5	0.98	0.11	0.11	0.00	0.00	0.10	0.21	0.31	▇▂▃▂▃
ITN_Coverage_2010	5	0.98	0.55	0.26	0.15	0.23	0.66	0.78	0.87	▆▁▁▃▇
ITN_Coverage_2015	5	0.98	0.76	0.29	0.30	0.43	0.97	1.00	1.00	▃▁▁▁▇
Land_Surface_Temperature_2000	3	0.99	22.16	3.28	16.45	19.88	20.87	25.48	28.43	▂▇▃▂▅
Land_Surface_Temperature_2005	3	0.99	22.22	3.05	17.16	20.14	21.04	25.12	28.58	▃▇▂▂▃
Land_Surface_Temperature_2010	3	0.99	23.05	3.06	17.79	20.99	21.84	26.16	29.09	▂▇▂▁▅
Land_Surface_Temperature_2015	3	0.99	23.05	2.94	17.86	21.13	21.90	26.02	29.32	▂▇▃▂▃
Livestock_Cattle	1	1.00	18.02	17.55	1.13	7.35	14.14	23.52	189.52	▇▁▁▁▁
Livestock_Chickens	1	1.00	280.77	484.46	0.79	30.81	73.53	225.77	2216.75	▇▁▁▁▁
Livestock_Goats	1	1.00	5.83	73.74	0.00	0.00	0.01	0.48	1206.19	▇▁▁▁▁
Livestock_Pigs	1	1.00	6.34	8.71	0.00	0.71	2.29	9.85	53.50	▇▂▁▁▁
Livestock_Sheep	1	1.00	2.98	29.88	0.00	0.00	0.19	1.09	487.91	▇▁▁▁▁
Malaria_Incidence_2000	5	0.98	0.23	0.15	0.02	0.06	0.29	0.34	0.48	▇▁▂▇▂
Malaria_Incidence_2005	5	0.98	0.24	0.10	0.09	0.13	0.25	0.31	0.44	▇▂▆▅▂
Malaria_Incidence_2010	5	0.98	0.09	0.05	0.02	0.05	0.07	0.12	0.29	▇▅▃▁▁
Malaria_Incidence_2015	5	0.98	0.07	0.04	0.02	0.03	0.06	0.09	0.24	▇▃▂▁▁
Malaria_Prevalence_2000	5	0.98	0.19	0.14	0.01	0.03	0.23	0.29	0.47	▇▁▆▃▂
Malaria_Prevalence_2005	5	0.98	0.19	0.10	0.05	0.08	0.20	0.26	0.45	▇▃▆▂▂
Malaria_Prevalence_2010	5	0.98	0.06	0.04	0.01	0.03	0.04	0.07	0.25	▇▃▁▁▁
Malaria_Prevalence_2015	5	0.98	0.04	0.03	0.01	0.02	0.03	0.05	0.18	▇▂▁▁▁
Maximum_Temperature_2000	28	0.90	25.79	2.86	21.08	23.08	25.63	27.66	31.38	▇▅▅▆▃
Maximum_Temperature_2005	28	0.90	26.04	2.84	21.33	23.44	25.95	27.92	31.74	▂▇▅▃▃
Maximum_Temperature_2010	28	0.90	26.48	2.81	21.77	23.86	26.37	28.38	32.03	▂▇▃▅▃
Maximum_Temperature_2015	28	0.90	26.37	2.81	21.66	23.78	26.30	28.27	31.94	▂▇▅▃▃
Mean_Temperature_2000	28	0.90	21.21	2.80	16.79	18.45	21.14	23.48	26.54	▇▅▃▇▃
Mean_Temperature_2005	28	0.90	21.47	2.78	17.03	18.81	21.44	23.63	26.91	▇▅▃▇▃
Mean_Temperature_2010	28	0.90	21.90	2.76	17.48	19.23	21.88	24.19	27.19	▇▅▃▇▃
Mean_Temperature_2015	28	0.90	21.79	2.75	17.37	19.15	21.81	24.05	27.11	▇▅▃▇▃
Minimum_Temperature_2000	28	0.90	16.69	2.78	12.57	13.88	16.55	19.02	21.75	▇▃▂▇▃
Minimum_Temperature_2005	28	0.90	16.94	2.77	12.82	14.23	16.77	19.32	22.12	▇▃▃▆▃
Minimum_Temperature_2010	28	0.90	17.38	2.75	13.27	14.65	17.19	19.78	22.40	▇▃▂▇▃
Minimum_Temperature_2015	28	0.90	17.27	2.75	13.15	14.58	17.12	19.68	22.32	▇▃▂▆▃
Nightlights_Composite	1	1.00	1.73	3.27	0.00	0.00	0.00	1.43	11.27	▇▁▁▁▁
Night_Land_Surface_Temp2010	3	0.99	17.08	3.23	10.10	14.41	16.78	19.43	23.41	▂▇▅▅▅
Night_Land_Surface_Temp2015	3	0.99	17.08	3.23	9.83	14.56	16.68	19.29	23.54	▂▇▆▇▅
Night_Land_Surface_Temp_2000	3	0.99	15.62	3.53	8.80	12.83	15.51	18.56	22.17	▃▇▅▆▅
Night_Land_Surface_Temp_2005	3	0.99	16.47	3.20	9.50	14.01	16.16	18.50	22.98	▂▇▅▅▃
Night_Land_Surface_Temp_2010	3	0.99	17.08	3.23	10.10	14.41	16.78	19.43	23.41	▂▇▅▅▅
Night_Land_Surface_Temp_2015	3	0.99	17.08	3.23	9.83	14.56	16.68	19.29	23.54	▂▇▆▇▅
PET_2000	28	0.90	3.20	0.45	2.73	2.87	3.08	3.47	4.59	▇▃▂▁▁
PET_2005	28	0.90	3.27	0.46	2.79	2.92	3.15	3.54	4.62	▇▃▂▂▁
PET_2010	28	0.90	3.38	0.47	2.85	3.00	3.27	3.66	4.78	▇▅▂▂▁
PET_2015	28	0.90	3.39	0.47	2.85	3.00	3.28	3.60	4.73	▇▅▂▁▁
Proximity_to_National_Borders	1	1.00	88932.78	67270.55	316.62	22396.25	87766.24	159230.69	230387.86	▇▃▃▆▁
Proximity_to_Protected_Areas	1	1.00	56700.81	37271.58	0.00	26160.14	56481.23	79314.84	169207.51	▇▃▇▂▁
Proximity_to_Water	1	1.00	54440.89	39802.56	0.00	16925.04	61911.76	74937.83	180764.07	▇▆▅▂▁
Rainfall_1985	7	0.97	1798.34	689.09	227.00	1553.00	1702.00	2155.33	3977.00	▂▇▅▂▁
Rainfall_1990	7	0.97	1356.16	572.44	314.30	1039.00	1164.50	1665.90	3521.00	▂▇▂▁▁
Rainfall_1995	7	0.97	1633.00	605.79	278.00	1368.00	1510.35	1946.44	3848.00	▂▇▃▂▁
Rainfall_2000	7	0.97	1489.11	523.24	393.00	1259.00	1378.00	1745.64	3161.00	▂▇▃▂▁
Rainfall_2005	7	0.97	1581.27	559.80	351.00	1290.05	1464.00	1848.91	3380.00	▂▇▃▂▁
Rainfall_2010	7	0.97	1506.61	626.63	235.00	1175.00	1294.06	1781.44	3483.00	▂▇▃▂▁
Rainfall_2015	7	0.97	1710.23	617.71	414.00	1479.75	1561.05	1983.10	4471.27	▂▇▂▁▁
Slope	1	1.00	1.32	1.14	0.03	0.45	0.83	1.95	5.30	▇▃▂▁▁
SMOD_Population_1990	1	1.00	0.90	1.14	0.00	0.00	0.00	1.00	3.00	▇▃▁▁▃
SMOD_Population_2000	1	1.00	0.89	1.19	0.00	0.00	0.00	2.00	3.00	▇▂▁▁▃
SMOD_Population_2015	1	1.00	0.91	1.24	0.00	0.00	0.00	2.00	3.00	▇▂▁▁▃
Temperature_April	8	0.97	22.89	3.09	16.19	20.06	23.86	25.31	28.36	▂▇▂▇▅
Temperature_August	8	0.97	18.76	3.58	12.10	15.44	19.61	21.22	25.99	▂▇▅▆▃
Temperature_December	8	0.97	23.98	2.96	17.23	21.16	24.83	26.51	28.88	▂▇▂▇▆
Temperature_February	8	0.97	24.16	2.84	17.27	21.51	25.04	26.75	28.16	▁▆▂▃▇
Temperature_January	8	0.97	24.16	2.83	17.46	21.48	25.09	26.69	28.09	▁▆▂▃▇
Temperature_July	8	0.97	18.26	3.56	11.49	15.04	18.56	20.87	25.48	▂▇▅▅▃
Temperature_June	8	0.97	19.09	3.47	12.05	15.97	19.36	21.67	25.82	▂▇▅▇▃
Temperature_March	8	0.97	23.84	2.93	17.23	21.12	24.66	26.29	28.47	▂▇▃▇▇
Temperature_May	8	0.97	20.93	3.31	13.92	18.04	21.23	23.36	27.29	▂▇▃▇▃
Temperature_November	8	0.97	23.40	3.07	16.94	20.70	23.87	25.87	29.64	▂▇▆▆▃
Temperature_October	8	0.97	21.94	3.23	15.57	18.97	22.19	24.41	28.90	▂▇▅▅▃
Temperature_September	8	0.97	20.06	3.47	13.65	16.75	20.45	22.43	27.11	▂▇▅▅▃
Travel_Times_2000	1	1.00	180.15	180.01	1.44	42.29	147.08	252.44	1288.22	▇▂▁▁▁
Travel_Times_2015	1	1.00	280.19	308.16	0.00	12.52	156.13	461.26	1167.04	▇▂▁▂▁
U5_Population_2000	1	1.00	340.19	644.19	0.06	3.52	8.75	323.07	2734.98	▇▁▁▁▁
U5_Population_2005	1	1.00	395.19	748.34	0.07	4.09	10.17	375.31	3177.16	▇▁▁▁▁
U5_Population_2010	1	1.00	455.45	862.45	0.09	4.71	11.72	432.54	3661.65	▇▁▁▁▁
U5_Population_2015	1	1.00	523.64	991.57	0.10	5.42	13.47	497.30	4209.84	▇▁▁▁▁
UN_Population_Count_2000	1	1.00	37664.54	101121.60	53.69	2589.59	11530.82	43719.60	889610.56	▇▁▁▁▁
UN_Population_Count_2005	1	1.00	45759.48	127106.19	74.21	3210.11	13711.80	48352.12	1114080.00	▇▁▁▁▁
UN_Population_Count_2010	1	1.00	54981.20	157797.71	86.27	3928.02	15695.22	49745.27	1374629.50	▇▁▁▁▁
UN_Population_Count_2015	1	1.00	65692.14	194627.93	91.03	4894.93	18500.81	51867.09	1680597.75	▇▁▁▁▁
UN_Population_Density_2000	1	1.00	1099.30	2210.79	2.54	23.86	68.99	266.12	10035.58	▇▁▁▁▁
UN_Population_Density_2005	1	1.00	1351.01	2707.36	2.64	28.19	79.42	335.49	12247.07	▇▁▁▁▁
UN_Population_Density_2010	1	1.00	1635.41	3266.47	2.69	33.04	83.86	413.77	14843.68	▇▁▁▁▁
UN_Population_Density_2015	1	1.00	1961.27	3910.89	2.69	37.38	92.24	505.47	17814.64	▇▁▁▁▁
Wet_Days_2000	28	0.90	13.34	2.85	6.65	11.84	13.05	14.49	20.15	▁▃▇▂▂
Wet_Days_2005	28	0.90	14.03	3.76	4.91	11.98	13.28	16.43	22.42	▁▃▇▂▂
Wet_Days_2010	28	0.90	12.42	4.23	4.38	10.22	10.84	14.34	21.56	▁▇▃▁▂
Wet_Days_2015	28	0.90	12.11	4.02	4.38	9.93	10.64	13.71	23.67	▁▇▃▁▁

So what covariate data do we have?

gps_covar_data <- tar_read(gps_covar_data, store = here("_targets"))

GPS covars cover the following number of years:

length(gps_covar_data)
#> [1] 6

This is fairly easy to summarize:

list_rbind(gps_covar_data) %>%
  group_by(DHSYEAR) %>%
  skimr::skim()

Data summary
Name	Piped data
Number of rows	2732
Number of columns	160
_______________________
Column type frequency:
factor	5
numeric	154
________________________
Group variables	DHSYEAR

Variable type: factor

skim_variable	DHSYEAR	complete_rate	ordered	n_unique	top_counts
DHSID	1997	1	FALSE	269	MD1: 1, MD1: 1, MD1: 1, MD1: 1
DHSID	2008	1	FALSE	594	MD2: 2, MD2: 2, MD2: 2, MD2: 2
DHSID	2011	1	FALSE	267	MD2: 1, MD2: 1, MD2: 1, MD2: 1
DHSID	2016	1	FALSE	358	MD2: 1, MD2: 1, MD2: 1, MD2: 1
DHSID	2021	1	FALSE	650	MD2: 1, MD2: 1, MD2: 1, MD2: 1
GPS_Dataset	1997	1	FALSE	1	MDG: 269, MDG: 0, MDG: 0, MDG: 0
GPS_Dataset	2008	1	FALSE	1	MDG: 1188, MDG: 0, MDG: 0, MDG: 0
GPS_Dataset	2011	1	FALSE	1	MDG: 267, MDG: 0, MDG: 0, MDG: 0
GPS_Dataset	2016	1	FALSE	1	MDG: 358, MDG: 0, MDG: 0, MDG: 0
GPS_Dataset	2021	1	FALSE	1	MDG: 650, MDG: 0, MDG: 0, MDG: 0
DHSCC	1997	1	FALSE	1	MD: 269
DHSCC	2008	1	FALSE	1	MD: 1188
DHSCC	2011	1	FALSE	1	MD: 267
DHSCC	2016	1	FALSE	1	MD: 358
DHSCC	2021	1	FALSE	1	MD: 650
DHSCLUST	1997	1	FALSE	269	1: 1, 2: 1, 3: 1, 4: 1
DHSCLUST	2008	1	FALSE	594	1: 2, 2: 2, 3: 2, 4: 2
DHSCLUST	2011	1	FALSE	267	1: 1, 2: 1, 3: 1, 4: 1
DHSCLUST	2016	1	FALSE	358	1: 1, 2: 1, 3: 1, 4: 1
DHSCLUST	2021	1	FALSE	650	1: 1, 2: 1, 3: 1, 4: 1
SurveyID	1997	1	FALSE	1	MD1: 269, MD2: 0, MD2: 0, MD2: 0
SurveyID	2008	1	FALSE	1	MD2: 1188, MD1: 0, MD2: 0, MD2: 0
SurveyID	2011	1	FALSE	1	MD2: 267, MD1: 0, MD2: 0, MD2: 0
SurveyID	2016	1	FALSE	1	MD2: 358, MD1: 0, MD2: 0, MD2: 0
SurveyID	2021	1	FALSE	1	MD2: 650, MD1: 0, MD2: 0, MD2: 0

Variable type: numeric

skim_variable	DHSYEAR	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
All_Population_Count_2005	1997	1	1.00	62625.42	132493.56	33.21	5425.68	16297.45	59803.31	1387207.75	▇▁▁▁▁
All_Population_Count_2005	2008	18	0.98	49966.86	142234.98	86.36	4836.21	12184.74	32956.28	1398645.75	▇▁▁▁▁
All_Population_Count_2005	2011	1	1.00	64700.44	202184.90	9.89	5163.50	11315.28	31629.46	1387369.75	▇▁▁▁▁
All_Population_Count_2005	2016	0	1.00	41113.96	162515.26	199.23	4161.75	9961.93	24260.69	1342054.25	▇▁▁▁▁
All_Population_Count_2005	2021	0	1.00	10944.89	34074.80	7.00	354.41	1116.98	3041.23	272811.59	▇▁▁▁▁
All_Population_Count_2010	1997	1	1.00	72175.29	152697.76	38.28	6253.05	18782.68	68922.83	1598745.75	▇▁▁▁▁
All_Population_Count_2010	2008	18	0.98	57586.40	163924.67	99.53	5573.70	14042.81	37981.85	1611927.88	▇▁▁▁▁
All_Population_Count_2010	2011	1	1.00	74566.74	233016.48	11.40	5950.89	13040.77	36452.70	1598932.50	▇▁▁▁▁
All_Population_Count_2010	2016	0	1.00	47383.51	187297.53	229.61	4796.38	11481.05	27960.25	1546706.75	▇▁▁▁▁
All_Population_Count_2010	2021	0	1.00	14401.78	44280.29	6.16	428.57	1240.63	3184.25	328731.16	▇▁▁▁▁
All_Population_Count_2015	1997	1	1.00	82980.80	175558.44	44.01	7189.21	21594.67	79241.40	1838097.12	▇▁▁▁▁
All_Population_Count_2015	2008	18	0.98	66207.77	188466.15	114.43	6408.15	16145.19	43668.19	1853252.88	▇▁▁▁▁
All_Population_Count_2015	2011	1	1.00	85730.28	267901.83	13.11	6841.81	14993.13	41910.10	1838311.88	▇▁▁▁▁
All_Population_Count_2015	2016	0	1.00	54477.39	215338.20	263.99	5514.45	13199.90	32146.23	1778267.12	▇▁▁▁▁
All_Population_Count_2015	2021	0	1.00	16507.11	50087.42	19.05	526.83	1460.90	3550.97	396934.06	▇▁▁▁▁
Annual_Precipitation_2000	1997	28	0.90	125.74	37.86	29.52	107.09	132.18	145.60	220.57	▂▂▇▃▁
Annual_Precipitation_2000	2008	154	0.87	117.94	37.32	29.52	96.86	114.19	133.74	220.57	▂▅▇▃▁
Annual_Precipitation_2000	2011	43	0.84	109.76	45.59	29.52	69.92	106.17	133.74	220.57	▆▇▇▅▂
Annual_Precipitation_2000	2016	51	0.86	117.90	40.88	29.52	92.08	112.05	139.22	220.57	▂▅▇▃▂
Annual_Precipitation_2000	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Annual_Precipitation_2005	1997	28	0.90	134.97	40.59	35.26	110.41	132.21	162.32	226.19	▁▅▇▃▂
Annual_Precipitation_2005	2008	154	0.87	127.70	41.87	35.26	101.47	119.42	153.09	226.19	▂▆▇▃▂
Annual_Precipitation_2005	2011	43	0.84	129.12	43.20	35.26	102.04	119.42	153.16	226.19	▂▇▇▃▂
Annual_Precipitation_2005	2016	51	0.86	129.14	45.12	35.26	99.83	116.88	167.90	226.19	▂▇▆▃▃
Annual_Precipitation_2005	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Annual_Precipitation_2010	1997	28	0.90	123.59	48.22	18.94	91.03	106.68	151.27	230.76	▁▅▇▂▃
Annual_Precipitation_2010	2008	154	0.87	113.48	44.88	18.25	87.03	106.29	136.45	230.76	▁▇▆▂▂
Annual_Precipitation_2010	2011	43	0.84	111.48	48.75	18.25	83.47	98.03	136.45	230.76	▁▇▃▂▂
Annual_Precipitation_2010	2016	51	0.86	115.92	48.64	18.25	83.45	94.63	145.09	230.76	▁▇▃▂▂
Annual_Precipitation_2010	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Annual_Precipitation_2015	1997	28	0.90	121.57	35.93	36.83	102.33	117.21	135.94	230.28	▁▆▇▂▁
Annual_Precipitation_2015	2008	154	0.87	115.03	35.20	36.83	94.91	107.78	133.19	230.28	▂▇▆▂▁
Annual_Precipitation_2015	2011	43	0.84	113.75	41.23	36.83	92.40	103.68	131.93	230.28	▂▇▅▁▁
Annual_Precipitation_2015	2016	51	0.86	116.85	39.58	36.83	92.40	103.68	142.60	230.28	▂▇▅▁▁
Annual_Precipitation_2015	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Aridity_2000	1997	28	0.90	40.35	13.52	7.78	32.46	43.62	46.11	70.39	▂▃▆▇▂
Aridity_2000	2008	154	0.87	37.42	13.21	7.78	28.12	36.90	46.11	70.39	▃▆▇▆▂
Aridity_2000	2011	43	0.84	34.67	15.96	7.78	18.84	34.37	46.11	70.39	▇▅▇▅▂
Aridity_2000	2016	51	0.86	36.89	14.17	7.78	27.50	35.50	45.80	70.39	▃▆▇▅▂
Aridity_2000	2021	0	1.00	36.22	12.41	8.11	27.36	35.79	43.76	67.16	▂▅▇▅▂
Aridity_2005	1997	28	0.90	42.38	13.95	9.10	34.69	45.33	51.26	69.74	▂▃▇▃▂
Aridity_2005	2008	154	0.87	39.78	14.29	9.10	29.35	40.58	47.61	69.74	▂▅▇▃▂
Aridity_2005	2011	43	0.84	39.60	14.71	9.10	29.39	37.75	47.62	69.74	▂▅▇▃▃
Aridity_2005	2016	51	0.86	39.64	15.13	9.10	29.35	36.78	52.19	69.74	▃▆▇▃▅
Aridity_2005	2021	0	1.00	39.03	13.51	9.65	29.81	40.31	49.59	68.05	▃▅▇▅▂
Aridity_2010	1997	28	0.90	37.06	14.15	4.84	29.46	35.56	45.82	67.70	▁▃▇▁▃
Aridity_2010	2008	154	0.87	33.87	13.41	4.77	26.11	31.19	39.46	67.70	▂▇▇▂▂
Aridity_2010	2011	43	0.84	32.94	14.78	4.77	24.37	29.77	38.70	67.70	▂▇▆▂▂
Aridity_2010	2016	51	0.86	34.12	14.54	4.77	24.78	30.12	41.68	67.70	▁▇▅▂▃
Aridity_2010	2021	0	1.00	33.85	12.83	5.23	26.01	32.25	40.36	64.42	▂▇▇▂▃
Aridity_2015	1997	28	0.90	36.60	11.34	9.34	30.87	36.78	40.75	67.07	▂▃▇▂▁
Aridity_2015	2008	154	0.87	34.39	11.25	9.34	27.94	33.94	39.07	67.07	▂▆▇▂▁
Aridity_2015	2011	43	0.84	33.65	13.02	9.34	26.00	32.92	40.08	67.07	▃▇▇▂▂
Aridity_2015	2016	51	0.86	34.40	12.37	9.34	26.07	32.21	41.33	67.07	▃▇▆▂▂
Aridity_2015	2021	0	1.00	33.79	10.32	9.75	27.31	33.83	38.71	63.24	▂▅▇▂▁
BUILT_Population_1990	1997	1	1.00	0.12	0.22	0.00	0.00	0.00	0.05	0.82	▇▁▁▁▁
BUILT_Population_1990	2008	18	0.98	0.06	0.16	0.00	0.00	0.00	0.00	0.84	▇▁▁▁▁
BUILT_Population_1990	2011	1	1.00	0.04	0.11	0.00	0.00	0.00	0.00	0.63	▇▁▁▁▁
BUILT_Population_1990	2016	0	1.00	0.01	0.06	0.00	0.00	0.00	0.00	0.61	▇▁▁▁▁
BUILT_Population_1990	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
BUILT_Population_2000	1997	1	1.00	0.13	0.24	0.00	0.00	0.00	0.07	0.86	▇▁▁▁▁
BUILT_Population_2000	2008	18	0.98	0.06	0.17	0.00	0.00	0.00	0.00	0.86	▇▁▁▁▁
BUILT_Population_2000	2011	1	1.00	0.04	0.12	0.00	0.00	0.00	0.00	0.64	▇▁▁▁▁
BUILT_Population_2000	2016	0	1.00	0.02	0.06	0.00	0.00	0.00	0.00	0.62	▇▁▁▁▁
BUILT_Population_2000	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
BUILT_Population_2014	1997	1	1.00	0.14	0.26	0.00	0.00	0.00	0.08	0.89	▇▁▁▁▁
BUILT_Population_2014	2008	18	0.98	0.07	0.19	0.00	0.00	0.00	0.00	0.90	▇▁▁▁▁
BUILT_Population_2014	2011	1	1.00	0.05	0.12	0.00	0.00	0.00	0.00	0.66	▇▁▁▁▁
BUILT_Population_2014	2016	0	1.00	0.02	0.07	0.00	0.00	0.00	0.00	0.62	▇▁▁▁▁
BUILT_Population_2014	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Day_Land_Surface_Temp_2000	1997	3	0.99	28.70	3.57	20.68	26.21	27.39	31.45	36.91	▁▇▇▃▃
Day_Land_Surface_Temp_2000	2008	36	0.97	29.30	3.64	21.08	26.66	28.34	32.51	36.51	▁▇▇▅▅
Day_Land_Surface_Temp_2000	2011	8	0.97	30.13	4.16	19.98	26.45	29.81	34.08	36.60	▁▆▆▃▇
Day_Land_Surface_Temp_2000	2016	12	0.97	29.60	3.78	20.19	26.50	29.14	32.78	36.96	▁▇▇▇▅
Day_Land_Surface_Temp_2000	2021	0	1.00	29.25	3.64	20.35	26.41	28.38	32.45	36.84	▁▇▇▆▅
Day_Land_Surface_Temp_2005	1997	3	0.99	27.96	3.28	21.21	25.73	26.37	30.81	35.23	▁▇▂▃▂
Day_Land_Surface_Temp_2005	2008	36	0.97	28.62	3.36	20.82	25.95	27.58	31.75	35.55	▁▇▅▅▃
Day_Land_Surface_Temp_2005	2011	8	0.97	29.10	3.55	20.37	26.04	29.18	32.34	34.83	▁▆▆▅▇
Day_Land_Surface_Temp_2005	2016	12	0.97	28.95	3.36	21.56	26.06	28.37	31.97	35.31	▁▇▅▆▅
Day_Land_Surface_Temp_2005	2021	0	1.00	28.60	3.35	19.62	25.88	27.82	31.66	35.59	▁▆▇▆▅
Day_Land_Surface_Temp_2010	1997	3	0.99	29.01	3.39	22.07	26.60	27.93	31.63	37.75	▁▇▂▂▂
Day_Land_Surface_Temp_2010	2008	36	0.97	29.60	3.45	21.86	26.94	28.58	32.60	37.43	▁▇▃▅▂
Day_Land_Surface_Temp_2010	2011	8	0.97	30.41	4.02	20.99	26.95	29.83	34.04	36.83	▁▆▆▅▇
Day_Land_Surface_Temp_2010	2016	12	0.97	29.92	3.56	21.60	26.95	29.19	33.07	38.07	▁▇▅▆▂
Day_Land_Surface_Temp_2010	2021	0	1.00	29.55	3.48	20.91	26.94	28.68	32.41	38.53	▁▇▅▅▂
Day_Land_Surface_Temp_2015	1997	3	0.99	29.03	3.10	22.70	26.79	28.03	31.61	37.64	▁▇▂▃▁
Day_Land_Surface_Temp_2015	2008	36	0.97	29.54	3.23	22.27	27.05	28.38	32.21	36.28	▁▇▅▃▃
Day_Land_Surface_Temp_2015	2011	8	0.97	30.24	3.67	21.93	26.96	29.94	33.83	37.02	▁▇▃▆▆
Day_Land_Surface_Temp_2015	2016	12	0.97	29.83	3.30	22.47	27.14	29.07	32.79	36.83	▁▇▃▅▃
Day_Land_Surface_Temp_2015	2021	0	1.00	29.50	3.20	21.67	27.07	28.52	32.24	37.33	▁▇▅▅▂
Diurnal_Temperature_Range_2000	1997	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2000	2008	154	0.87	9.29	0.90	6.82	8.64	9.21	9.91	12.19	▁▆▇▃▁
Diurnal_Temperature_Range_2000	2011	43	0.84	9.11	0.92	6.82	8.53	8.96	9.90	12.19	▁▇▆▂▁
Diurnal_Temperature_Range_2000	2016	51	0.86	9.32	0.97	6.82	8.63	9.21	9.97	12.19	▁▇▇▃▁
Diurnal_Temperature_Range_2000	2021	0	1.00	9.22	0.91	7.06	8.59	9.21	9.83	12.03	▂▇▇▃▁
Diurnal_Temperature_Range_2005	1997	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2005	2008	154	0.87	9.29	0.90	6.82	8.64	9.21	9.91	12.19	▁▆▇▃▁
Diurnal_Temperature_Range_2005	2011	43	0.84	9.11	0.92	6.82	8.53	8.96	9.90	12.19	▁▇▆▂▁
Diurnal_Temperature_Range_2005	2016	51	0.86	9.32	0.97	6.82	8.63	9.21	9.97	12.19	▁▇▇▃▁
Diurnal_Temperature_Range_2005	2021	0	1.00	9.22	0.91	7.06	8.59	9.21	9.83	12.03	▂▇▇▃▁
Diurnal_Temperature_Range_2010	1997	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2010	2008	154	0.87	9.29	0.90	6.82	8.64	9.21	9.91	12.19	▁▆▇▃▁
Diurnal_Temperature_Range_2010	2011	43	0.84	9.11	0.92	6.82	8.53	8.96	9.90	12.19	▁▇▆▂▁
Diurnal_Temperature_Range_2010	2016	51	0.86	9.32	0.97	6.82	8.63	9.21	9.97	12.19	▁▇▇▃▁
Diurnal_Temperature_Range_2010	2021	0	1.00	9.22	0.91	7.06	8.59	9.21	9.83	12.03	▂▇▇▃▁
Diurnal_Temperature_Range_2015	1997	28	0.90	9.10	0.76	6.82	8.60	9.13	9.43	11.95	▁▆▇▂▁
Diurnal_Temperature_Range_2015	2008	154	0.87	9.29	0.90	6.82	8.64	9.21	9.91	12.19	▁▆▇▃▁
Diurnal_Temperature_Range_2015	2011	43	0.84	9.11	0.92	6.82	8.53	8.96	9.90	12.19	▁▇▆▂▁
Diurnal_Temperature_Range_2015	2016	51	0.86	9.32	0.97	6.82	8.63	9.21	9.97	12.19	▁▇▇▃▁
Diurnal_Temperature_Range_2015	2021	0	1.00	9.23	0.91	7.06	8.59	9.23	9.83	12.03	▂▇▇▃▁
Drought_Episodes	1997	139	0.48	4.95	2.91	1.00	2.00	5.00	8.00	10.00	▇▁▇▂▅
Drought_Episodes	2008	464	0.61	4.81	2.89	1.00	2.00	5.00	8.00	10.00	▇▁▆▂▅
Drought_Episodes	2011	80	0.70	4.81	2.64	1.00	2.00	5.00	8.00	10.00	▇▁▇▃▃
Drought_Episodes	2016	121	0.66	4.68	2.73	1.00	2.00	5.00	7.00	10.00	▇▁▇▂▃
Drought_Episodes	2021	254	0.61	4.74	2.82	1.00	2.00	5.00	7.00	10.00	▇▁▆▂▃
Enhanced_Vegetation_Index_1985	1997	3	0.99	2784.42	924.10	144.00	2148.74	2501.60	3292.59	5015.00	▁▃▇▂▃
Enhanced_Vegetation_Index_1985	2008	36	0.97	2690.00	867.01	144.00	2179.11	2491.15	3150.11	4811.82	▁▁▇▂▂
Enhanced_Vegetation_Index_1985	2011	8	0.97	2539.08	904.57	144.00	2108.17	2312.50	3004.17	4903.00	▁▂▇▂▂
Enhanced_Vegetation_Index_1985	2016	12	0.97	2802.55	863.49	170.00	2221.35	2552.90	3300.61	5006.90	▁▂▇▃▂
Enhanced_Vegetation_Index_1985	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_1990	1997	3	0.99	2810.96	999.52	144.00	2096.75	2479.80	3387.27	5532.00	▁▇▇▃▂
Enhanced_Vegetation_Index_1990	2008	36	0.97	2719.89	932.43	144.00	2154.83	2489.55	3288.53	5841.00	▁▇▆▃▁
Enhanced_Vegetation_Index_1990	2011	8	0.97	2566.21	988.16	144.00	2074.83	2291.00	3013.00	5841.00	▁▇▅▂▁
Enhanced_Vegetation_Index_1990	2016	12	0.97	2841.34	933.08	151.00	2212.49	2525.89	3524.07	5841.00	▁▇▇▅▁
Enhanced_Vegetation_Index_1990	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_1995	1997	3	0.99	2888.93	985.29	141.00	2195.50	2597.86	3470.80	5208.00	▁▃▇▂▃
Enhanced_Vegetation_Index_1995	2008	36	0.97	2809.16	915.75	141.00	2261.93	2575.68	3347.85	5216.00	▁▂▇▃▂
Enhanced_Vegetation_Index_1995	2011	8	0.97	2650.52	949.16	141.00	2172.96	2412.00	3079.59	5208.00	▁▃▇▂▂
Enhanced_Vegetation_Index_1995	2016	12	0.97	2931.62	904.85	175.00	2317.59	2617.50	3600.80	5208.00	▁▂▇▂▂
Enhanced_Vegetation_Index_1995	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_2000	1997	3	0.99	2930.20	969.60	123.00	2235.00	2647.80	3529.59	5156.00	▁▃▇▂▃
Enhanced_Vegetation_Index_2000	2008	36	0.97	2845.17	905.37	123.00	2281.92	2605.40	3403.00	4996.09	▁▁▇▃▂
Enhanced_Vegetation_Index_2000	2011	8	0.97	2723.79	922.99	123.00	2265.25	2496.00	3231.21	4904.00	▁▁▇▂▂
Enhanced_Vegetation_Index_2000	2016	12	0.97	2971.28	895.48	271.00	2358.85	2658.46	3587.32	5144.80	▁▂▇▃▂
Enhanced_Vegetation_Index_2000	2021	0	1.00	0.29	0.10	0.16	0.21	0.25	0.35	0.58	▇▃▂▂▁
Enhanced_Vegetation_Index_2005	1997	3	0.99	3192.23	1197.40	95.00	2322.00	2794.29	4044.61	5861.50	▁▅▇▂▃
Enhanced_Vegetation_Index_2005	2008	36	0.97	3076.57	1090.95	95.00	2405.95	2747.72	3669.65	5762.00	▁▃▇▂▂
Enhanced_Vegetation_Index_2005	2011	8	0.97	2955.72	1097.46	95.00	2365.99	2644.75	3450.56	5572.00	▁▂▇▂▂
Enhanced_Vegetation_Index_2005	2016	12	0.97	3236.25	1106.09	315.00	2488.50	2780.95	4081.80	5821.20	▁▅▇▃▃
Enhanced_Vegetation_Index_2005	2021	0	1.00	0.31	0.11	0.17	0.23	0.27	0.37	0.60	▇▅▂▂▂
Enhanced_Vegetation_Index_2010	1997	3	0.99	3111.48	1166.01	113.00	2213.00	2773.92	3959.77	5738.00	▁▅▇▂▃
Enhanced_Vegetation_Index_2010	2008	36	0.97	3017.47	1050.68	113.00	2402.09	2728.09	3616.48	5664.75	▁▃▇▂▂
Enhanced_Vegetation_Index_2010	2011	8	0.97	2904.92	1050.98	113.00	2368.57	2644.36	3360.38	5533.00	▁▂▇▂▂
Enhanced_Vegetation_Index_2010	2016	12	0.97	3173.84	1052.14	301.00	2463.65	2790.19	3910.98	5780.00	▁▅▇▃▂
Enhanced_Vegetation_Index_2010	2021	0	1.00	0.30	0.11	0.14	0.22	0.26	0.36	0.59	▆▇▃▂▂
Enhanced_Vegetation_Index_2015	1997	3	0.99	3046.41	1195.82	113.00	2112.94	2667.18	3904.76	5616.56	▁▆▇▂▃
Enhanced_Vegetation_Index_2015	2008	36	0.97	2948.94	1073.58	113.00	2310.62	2634.11	3529.60	5718.83	▁▅▇▂▂
Enhanced_Vegetation_Index_2015	2011	8	0.97	2852.48	1058.12	113.00	2306.07	2564.30	3239.80	5423.00	▁▂▇▂▂
Enhanced_Vegetation_Index_2015	2016	12	0.97	3118.83	1079.47	336.00	2362.85	2675.09	3905.67	5709.36	▁▆▇▃▃
Enhanced_Vegetation_Index_2015	2021	0	1.00	0.30	0.10	0.15	0.22	0.26	0.36	0.55	▇▇▃▂▂
Frost_Days_2000	1997	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2000	2008	154	0.87	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2000	2011	43	0.84	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
Frost_Days_2000	2016	51	0.86	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2000	2021	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
Frost_Days_2005	1997	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2005	2008	154	0.87	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2005	2011	43	0.84	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2005	2016	51	0.86	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2005	2021	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
Frost_Days_2010	1997	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2010	2008	154	0.87	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2010	2011	43	0.84	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2010	2016	51	0.86	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2010	2021	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
Frost_Days_2015	1997	28	0.90	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Frost_Days_2015	2008	154	0.87	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Frost_Days_2015	2011	43	0.84	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Frost_Days_2015	2016	51	0.86	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Frost_Days_2015	2021	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
Global_Human_Footprint	1997	2	0.99	39.36	19.79	11.08	26.28	30.81	46.44	87.00	▅▇▁▁▂
Global_Human_Footprint	2008	20	0.98	34.01	16.47	4.82	24.37	29.37	35.53	86.06	▂▇▁▁▁
Global_Human_Footprint	2011	4	0.99	32.78	12.27	13.16	24.75	29.25	35.30	72.00	▅▇▂▁▁
Global_Human_Footprint	2016	1	1.00	30.07	9.67	9.39	23.68	28.46	33.65	68.88	▂▇▂▁▁
Global_Human_Footprint	2021	1	1.00	32.45	14.59	4.55	23.52	28.90	35.20	74.70	▁▇▃▁▁
Gross_Cell_Production	1997	2	0.99	840.38	118.15	682.18	742.02	809.05	993.11	1009.43	▇▅▃▁▇
Gross_Cell_Production	2008	30	0.97	833.44	108.91	546.56	744.66	812.32	967.83	1009.43	▁▂▇▂▅
Gross_Cell_Production	2011	9	0.97	815.45	92.52	666.97	744.66	792.01	841.05	1009.43	▂▇▆▁▃
Gross_Cell_Production	2016	7	0.98	814.69	111.11	0.00	741.36	801.61	849.58	1009.43	▁▁▁▇▇
Gross_Cell_Production	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Growing_Season_Length	1997	10	0.96	11.21	2.85	4.00	9.00	11.00	14.00	15.00	▂▃▅▆▇
Growing_Season_Length	2008	52	0.96	10.89	2.91	4.00	9.00	11.00	14.00	15.00	▂▃▅▅▇
Growing_Season_Length	2011	15	0.94	11.23	2.86	4.00	9.00	11.00	14.00	15.00	▁▂▅▅▇
Growing_Season_Length	2016	10	0.97	10.97	3.04	4.00	9.00	11.00	14.00	15.00	▁▃▅▃▇
Growing_Season_Length	2021	1	1.00	10.86	2.97	4.00	9.00	11.00	14.00	15.00	▂▅▅▅▇
Irrigation	1997	8	0.97	7.68	9.49	0.00	0.05	3.10	16.02	58.66	▇▂▁▁▁
Irrigation	2008	46	0.96	6.02	8.67	0.00	0.00	2.32	7.45	58.66	▇▁▁▁▁
Irrigation	2011	11	0.96	4.01	6.36	0.00	0.00	1.49	5.22	43.68	▇▁▁▁▁
Irrigation	2016	8	0.98	4.50	6.91	0.00	0.00	1.63	6.15	45.80	▇▁▁▁▁
Irrigation	2021	0	1.00	5.80	8.88	0.00	0.00	2.00	6.95	60.51	▇▁▁▁▁
ITN_Coverage_2005	1997	5	0.98	0.11	0.11	0.00	0.00	0.10	0.21	0.31	▇▂▃▂▃
ITN_Coverage_2005	2008	44	0.96	0.11	0.10	0.00	0.00	0.10	0.18	0.33	▇▃▃▂▂
ITN_Coverage_2005	2011	8	0.97	0.10	0.10	0.00	0.00	0.06	0.17	0.33	▇▂▃▁▂
ITN_Coverage_2005	2016	8	0.98	0.12	0.10	0.00	0.01	0.12	0.19	0.33	▇▃▅▂▂
ITN_Coverage_2005	2021	0	1.00	0.09	0.03	0.00	0.07	0.08	0.11	0.15	▁▃▇▆▂
ITN_Coverage_2010	1997	5	0.98	0.55	0.26	0.15	0.23	0.66	0.78	0.87	▆▁▁▃▇
ITN_Coverage_2010	2008	44	0.96	0.58	0.24	0.15	0.33	0.67	0.77	0.91	▆▂▂▆▇
ITN_Coverage_2010	2011	8	0.97	0.59	0.21	0.17	0.45	0.64	0.77	0.87	▅▂▅▇▇
ITN_Coverage_2010	2016	8	0.98	0.62	0.21	0.17	0.50	0.71	0.78	0.91	▃▂▂▇▇
ITN_Coverage_2010	2021	0	1.00	0.59	0.26	0.00	0.32	0.71	0.79	0.88	▂▂▁▃▇
ITN_Coverage_2015	1997	5	0.98	0.76	0.29	0.30	0.43	0.97	1.00	1.00	▃▁▁▁▇
ITN_Coverage_2015	2008	44	0.96	0.80	0.26	0.30	0.53	0.96	1.00	1.00	▂▁▁▁▇
ITN_Coverage_2015	2011	8	0.97	0.83	0.23	0.34	0.68	0.98	1.00	1.00	▂▁▁▁▇
ITN_Coverage_2015	2016	8	0.98	0.85	0.22	0.34	0.72	0.98	1.00	1.00	▂▁▁▁▇
ITN_Coverage_2015	2021	0	1.00	0.66	0.23	0.00	0.46	0.75	0.83	0.94	▁▃▁▃▇
Land_Surface_Temperature_2000	1997	3	0.99	22.16	3.28	16.45	19.88	20.87	25.48	28.43	▂▇▃▂▅
Land_Surface_Temperature_2000	2008	36	0.97	22.71	3.29	15.49	20.04	21.82	25.98	29.12	▁▇▅▃▅
Land_Surface_Temperature_2000	2011	8	0.97	23.35	3.51	15.35	20.18	23.29	26.83	29.12	▂▆▅▃▇
Land_Surface_Temperature_2000	2016	12	0.97	23.08	3.14	16.40	20.51	22.59	26.10	29.12	▂▇▅▅▆
Land_Surface_Temperature_2000	2021	0	1.00	22.71	3.26	15.31	19.94	21.97	25.74	29.07	▁▇▅▅▅
Land_Surface_Temperature_2005	1997	3	0.99	22.22	3.05	17.16	20.14	21.04	25.12	28.58	▃▇▂▂▃
Land_Surface_Temperature_2005	2008	36	0.97	22.71	3.08	15.58	20.14	22.03	25.71	28.91	▁▇▅▅▅
Land_Surface_Temperature_2005	2011	8	0.97	23.11	3.12	15.84	20.26	23.14	25.84	28.35	▂▇▆▇▇
Land_Surface_Temperature_2005	2016	12	0.97	23.03	2.91	17.19	20.72	22.47	25.57	28.35	▃▇▆▆▆
Land_Surface_Temperature_2005	2021	0	1.00	22.73	3.04	15.51	20.19	22.17	25.46	28.83	▁▇▆▅▅
Land_Surface_Temperature_2010	1997	3	0.99	23.05	3.06	17.79	20.99	21.84	26.16	29.09	▂▇▂▁▅
Land_Surface_Temperature_2010	2008	36	0.97	23.53	3.07	16.66	21.03	22.95	26.70	29.57	▁▇▆▃▅
Land_Surface_Temperature_2010	2011	8	0.97	24.12	3.30	16.38	21.18	24.30	27.32	29.57	▁▆▅▅▇
Land_Surface_Temperature_2010	2016	12	0.97	23.87	2.95	17.87	21.51	23.37	26.83	29.57	▃▇▆▅▆
Land_Surface_Temperature_2010	2021	0	1.00	23.54	3.05	16.11	21.06	23.01	26.40	29.52	▁▇▇▅▆
Land_Surface_Temperature_2015	1997	3	0.99	23.05	2.94	17.86	21.13	21.90	26.02	29.32	▂▇▃▂▃
Land_Surface_Temperature_2015	2008	36	0.97	23.46	3.01	17.18	21.00	22.87	26.55	30.16	▂▇▅▅▂
Land_Surface_Temperature_2015	2011	8	0.97	23.96	3.14	17.22	21.20	24.02	26.97	30.16	▃▆▃▇▂
Land_Surface_Temperature_2015	2016	12	0.97	23.76	2.87	18.08	21.46	23.25	26.68	30.16	▃▇▅▆▂
Land_Surface_Temperature_2015	2021	0	1.00	23.47	2.95	16.20	21.16	22.98	26.42	29.91	▁▇▆▅▃
Livestock_Cattle	1997	1	1.00	18.02	17.55	1.13	7.35	14.14	23.52	189.52	▇▁▁▁▁
Livestock_Cattle	2008	18	0.98	17.70	14.63	0.81	7.86	14.00	22.84	156.30	▇▁▁▁▁
Livestock_Cattle	2011	1	1.00	23.31	20.61	0.22	10.10	18.15	31.55	204.29	▇▁▁▁▁
Livestock_Cattle	2016	0	1.00	18.74	15.69	0.77	7.88	14.84	24.04	127.06	▇▂▁▁▁
Livestock_Cattle	2021	0	1.00	21.85	32.43	0.78	7.79	16.05	24.54	432.75	▇▁▁▁▁
Livestock_Chickens	1997	1	1.00	280.77	484.46	0.79	30.81	73.53	225.77	2216.75	▇▁▁▁▁
Livestock_Chickens	2008	18	0.98	150.55	305.36	0.86	19.21	49.25	125.84	2829.74	▇▁▁▁▁
Livestock_Chickens	2011	1	1.00	112.41	183.89	0.80	16.47	41.26	118.63	901.09	▇▁▁▁▁
Livestock_Chickens	2016	0	1.00	91.93	140.47	0.00	18.38	44.35	101.85	850.54	▇▁▁▁▁
Livestock_Chickens	2021	0	1.00	160.91	321.93	0.89	18.05	50.82	123.84	2229.32	▇▁▁▁▁
Livestock_Goats	1997	1	1.00	5.83	73.74	0.00	0.00	0.01	0.48	1206.19	▇▁▁▁▁
Livestock_Goats	2008	18	0.98	1.43	4.71	0.00	0.00	0.01	0.43	34.33	▇▁▁▁▁
Livestock_Goats	2011	1	1.00	3.80	7.71	0.00	0.00	0.06	3.92	34.75	▇▁▁▁▁
Livestock_Goats	2016	0	1.00	4.56	51.25	0.00	0.00	0.01	0.39	965.83	▇▁▁▁▁
Livestock_Goats	2021	0	1.00	6.34	65.25	0.00	0.00	0.00	0.55	1350.23	▇▁▁▁▁
Livestock_Pigs	1997	1	1.00	6.34	8.71	0.00	0.71	2.29	9.85	53.50	▇▂▁▁▁
Livestock_Pigs	2008	18	0.98	4.89	7.11	0.00	0.52	1.80	5.93	58.08	▇▁▁▁▁
Livestock_Pigs	2011	1	1.00	4.69	7.88	0.00	0.34	1.06	4.66	38.16	▇▁▁▁▁
Livestock_Pigs	2016	0	1.00	4.31	6.90	0.00	0.44	1.37	4.86	42.74	▇▁▁▁▁
Livestock_Pigs	2021	0	1.00	5.80	14.14	0.00	0.55	2.02	6.80	310.23	▇▁▁▁▁
Livestock_Sheep	1997	1	1.00	2.98	29.88	0.00	0.00	0.19	1.09	487.91	▇▁▁▁▁
Livestock_Sheep	2008	18	0.98	1.27	4.03	0.00	0.00	0.08	0.78	57.99	▇▁▁▁▁
Livestock_Sheep	2011	1	1.00	2.66	5.94	0.00	0.00	0.39	2.13	38.89	▇▁▁▁▁
Livestock_Sheep	2016	0	1.00	2.44	21.02	0.00	0.00	0.03	0.60	390.45	▇▁▁▁▁
Livestock_Sheep	2021	0	1.00	3.40	26.38	0.00	0.00	0.06	0.93	537.92	▇▁▁▁▁
Malaria_Incidence_2000	1997	5	0.98	0.23	0.15	0.02	0.06	0.29	0.34	0.48	▇▁▂▇▂
Malaria_Incidence_2000	2008	44	0.96	0.24	0.14	0.00	0.10	0.29	0.34	0.50	▆▂▅▇▂
Malaria_Incidence_2000	2011	8	0.97	0.26	0.13	0.00	0.14	0.31	0.35	0.52	▃▂▃▇▁
Malaria_Incidence_2000	2016	8	0.98	0.27	0.13	0.00	0.18	0.31	0.37	0.50	▃▂▃▇▂
Malaria_Incidence_2000	2021	0	1.00	0.23	0.13	0.00	0.10	0.26	0.33	0.50	▇▃▇▇▂
Malaria_Incidence_2005	1997	5	0.98	0.24	0.10	0.09	0.13	0.25	0.31	0.44	▇▂▆▅▂
Malaria_Incidence_2005	2008	44	0.96	0.24	0.09	0.00	0.16	0.25	0.31	0.47	▁▇▇▇▂
Malaria_Incidence_2005	2011	8	0.97	0.25	0.08	0.00	0.17	0.27	0.31	0.46	▁▅▅▇▁
Malaria_Incidence_2005	2016	8	0.98	0.26	0.09	0.00	0.19	0.28	0.32	0.47	▁▆▇▇▂
Malaria_Incidence_2005	2021	5	0.99	0.22	0.10	0.00	0.13	0.22	0.29	0.50	▂▆▇▃▁
Malaria_Incidence_2010	1997	5	0.98	0.09	0.05	0.02	0.05	0.07	0.12	0.29	▇▅▃▁▁
Malaria_Incidence_2010	2008	44	0.96	0.09	0.05	0.00	0.06	0.07	0.12	0.32	▇▇▃▁▁
Malaria_Incidence_2010	2011	8	0.97	0.08	0.05	0.00	0.05	0.06	0.10	0.34	▇▅▂▁▁
Malaria_Incidence_2010	2016	8	0.98	0.10	0.05	0.00	0.06	0.08	0.13	0.33	▇▇▃▁▁
Malaria_Incidence_2010	2021	5	0.99	0.08	0.05	0.00	0.05	0.07	0.11	0.30	▇▇▂▁▁
Malaria_Incidence_2015	1997	5	0.98	0.07	0.04	0.02	0.03	0.06	0.09	0.24	▇▃▂▁▁
Malaria_Incidence_2015	2008	44	0.96	0.08	0.05	0.00	0.04	0.06	0.10	0.32	▇▅▂▁▁
Malaria_Incidence_2015	2011	8	0.97	0.07	0.05	0.00	0.04	0.05	0.09	0.22	▇▅▂▂▁
Malaria_Incidence_2015	2016	8	0.98	0.08	0.05	0.00	0.04	0.07	0.10	0.27	▇▇▃▂▁
Malaria_Incidence_2015	2021	5	0.99	0.09	0.05	0.00	0.05	0.08	0.12	0.29	▆▇▂▂▁
Malaria_Prevalence_2000	1997	5	0.98	0.19	0.14	0.01	0.03	0.23	0.29	0.47	▇▁▆▃▂
Malaria_Prevalence_2000	2008	44	0.96	0.19	0.13	0.00	0.05	0.23	0.29	0.49	▇▃▇▅▁
Malaria_Prevalence_2000	2011	8	0.97	0.21	0.12	0.00	0.08	0.26	0.29	0.51	▅▂▇▂▁
Malaria_Prevalence_2000	2016	8	0.98	0.22	0.13	0.00	0.11	0.26	0.31	0.49	▆▃▇▆▂
Malaria_Prevalence_2000	2021	5	0.99	0.19	0.13	0.00	0.05	0.21	0.29	0.51	▇▅▇▃▂
Malaria_Prevalence_2005	1997	5	0.98	0.19	0.10	0.05	0.08	0.20	0.26	0.45	▇▃▆▂▂
Malaria_Prevalence_2005	2008	44	0.96	0.19	0.09	0.00	0.11	0.19	0.25	0.47	▅▆▇▂▁
Malaria_Prevalence_2005	2011	8	0.97	0.20	0.09	0.00	0.11	0.21	0.25	0.45	▂▅▇▂▁
Malaria_Prevalence_2005	2016	8	0.98	0.21	0.10	0.00	0.14	0.22	0.27	0.49	▂▆▇▂▁
Malaria_Prevalence_2005	2021	5	0.99	0.18	0.11	0.00	0.09	0.17	0.24	0.58	▆▇▅▁▁
Malaria_Prevalence_2010	1997	5	0.98	0.06	0.04	0.01	0.03	0.04	0.07	0.25	▇▃▁▁▁
Malaria_Prevalence_2010	2008	44	0.96	0.06	0.04	0.00	0.03	0.04	0.07	0.30	▇▃▁▁▁
Malaria_Prevalence_2010	2011	8	0.97	0.05	0.04	0.00	0.03	0.03	0.05	0.32	▇▂▁▁▁
Malaria_Prevalence_2010	2016	8	0.98	0.06	0.04	0.00	0.03	0.04	0.08	0.31	▇▃▁▁▁
Malaria_Prevalence_2010	2021	5	0.99	0.05	0.04	0.00	0.03	0.04	0.06	0.25	▇▃▁▁▁
Malaria_Prevalence_2015	1997	5	0.98	0.04	0.03	0.01	0.02	0.03	0.05	0.18	▇▂▁▁▁
Malaria_Prevalence_2015	2008	44	0.96	0.05	0.04	0.00	0.02	0.03	0.06	0.27	▇▂▁▁▁
Malaria_Prevalence_2015	2011	8	0.97	0.04	0.03	0.00	0.02	0.02	0.05	0.16	▇▃▁▁▁
Malaria_Prevalence_2015	2016	8	0.98	0.05	0.04	0.00	0.02	0.03	0.06	0.21	▇▃▂▁▁
Malaria_Prevalence_2015	2021	5	0.99	0.06	0.04	0.00	0.03	0.04	0.07	0.24	▇▃▁▁▁
Maximum_Temperature_2000	1997	28	0.90	25.79	2.86	21.08	23.08	25.63	27.66	31.38	▇▅▅▆▃
Maximum_Temperature_2000	2008	154	0.87	26.11	2.79	21.08	23.88	26.35	28.00	31.20	▇▆▇▆▆
Maximum_Temperature_2000	2011	43	0.84	26.33	2.56	21.83	24.04	26.94	27.87	31.11	▅▇▆▇▃
Maximum_Temperature_2000	2016	51	0.86	26.52	2.54	21.08	24.36	26.88	28.15	31.20	▃▇▇▇▅
Maximum_Temperature_2000	2021	0	1.00	26.46	2.73	21.31	24.09	26.58	28.29	31.52	▆▅▇▅▆
Maximum_Temperature_2005	1997	28	0.90	26.04	2.84	21.33	23.44	25.95	27.92	31.74	▂▇▅▃▃
Maximum_Temperature_2005	2008	154	0.87	26.32	2.78	21.33	24.04	26.38	27.98	31.61	▃▇▇▃▅
Maximum_Temperature_2005	2011	43	0.84	26.47	2.51	22.08	24.37	27.16	27.93	31.48	▅▆▇▅▃
Maximum_Temperature_2005	2016	51	0.86	26.72	2.54	21.33	24.53	27.03	28.17	31.61	▂▆▇▅▃
Maximum_Temperature_2005	2021	0	1.00	26.67	2.73	21.56	24.25	26.73	28.47	31.90	▅▅▇▃▅
Maximum_Temperature_2010	1997	28	0.90	26.48	2.81	21.77	23.86	26.37	28.38	32.03	▂▇▃▅▃
Maximum_Temperature_2010	2008	154	0.87	26.76	2.75	21.77	24.56	26.88	28.43	31.79	▃▇▆▅▅
Maximum_Temperature_2010	2011	43	0.84	26.95	2.49	22.53	24.84	27.65	28.38	31.75	▅▇▇▆▃
Maximum_Temperature_2010	2016	51	0.86	27.17	2.50	21.77	25.07	27.62	28.79	31.79	▂▇▆▇▅
Maximum_Temperature_2010	2021	0	1.00	27.11	2.69	22.00	24.73	27.20	28.92	32.13	▆▅▇▅▆
Maximum_Temperature_2015	1997	28	0.90	26.37	2.81	21.66	23.78	26.30	28.27	31.94	▂▇▅▃▃
Maximum_Temperature_2015	2008	154	0.87	26.64	2.74	21.66	24.43	26.70	28.32	31.74	▃▇▇▅▅
Maximum_Temperature_2015	2011	43	0.84	26.80	2.48	22.42	24.72	27.51	28.27	31.68	▅▆▇▅▃
Maximum_Temperature_2015	2016	51	0.86	27.04	2.50	21.66	24.91	27.46	28.63	31.74	▃▇▇▇▆
Maximum_Temperature_2015	2021	0	1.00	26.99	2.69	21.89	24.59	27.04	28.83	32.09	▆▅▇▅▆
Mean_Temperature_2000	1997	28	0.90	21.21	2.80	16.79	18.45	21.14	23.48	26.54	▇▅▃▇▃
Mean_Temperature_2000	2008	154	0.87	21.43	2.68	16.79	18.92	21.41	23.48	26.48	▇▆▇▇▆
Mean_Temperature_2000	2011	43	0.84	21.75	2.50	17.25	19.46	22.64	23.51	26.35	▅▅▃▇▂
Mean_Temperature_2000	2016	51	0.86	21.83	2.46	16.79	19.57	21.94	23.55	26.48	▃▅▅▇▃
Mean_Temperature_2000	2021	0	1.00	21.82	2.66	16.94	19.31	22.04	23.72	26.53	▇▆▆▇▆
Mean_Temperature_2005	1997	28	0.90	21.47	2.78	17.03	18.81	21.44	23.63	26.91	▇▅▃▇▃
Mean_Temperature_2005	2008	154	0.87	21.65	2.67	17.03	19.23	21.54	23.58	26.89	▇▆▇▇▅
Mean_Temperature_2005	2011	43	0.84	21.89	2.45	17.53	19.55	22.60	23.57	26.71	▆▅▅▇▂
Mean_Temperature_2005	2016	51	0.86	22.04	2.46	17.03	19.89	22.29	23.63	26.89	▃▆▆▇▃
Mean_Temperature_2005	2021	0	1.00	22.03	2.66	17.19	19.48	22.24	23.99	26.91	▇▆▇▇▆
Mean_Temperature_2010	1997	28	0.90	21.90	2.76	17.48	19.23	21.88	24.19	27.19	▇▅▃▇▃
Mean_Temperature_2010	2008	154	0.87	22.09	2.64	17.48	19.64	22.03	24.12	27.06	▇▆▇▇▅
Mean_Temperature_2010	2011	43	0.84	22.37	2.44	17.95	20.08	23.12	24.02	27.02	▅▃▃▇▂
Mean_Temperature_2010	2016	51	0.86	22.48	2.43	17.48	20.27	22.72	24.21	27.06	▃▅▅▇▃
Mean_Temperature_2010	2021	0	1.00	22.47	2.63	17.63	19.95	22.73	24.40	27.14	▆▆▆▇▆
Mean_Temperature_2015	1997	28	0.90	21.79	2.75	17.37	19.15	21.81	24.05	27.11	▇▅▃▇▃
Mean_Temperature_2015	2008	154	0.87	21.97	2.63	17.37	19.54	21.88	23.97	26.98	▇▇▇▇▅
Mean_Temperature_2015	2011	43	0.84	22.22	2.43	17.85	19.91	22.98	23.88	26.95	▆▃▅▇▂
Mean_Temperature_2015	2016	51	0.86	22.35	2.43	17.37	20.15	22.65	24.05	26.98	▃▆▆▇▃
Mean_Temperature_2015	2021	0	1.00	22.34	2.63	17.52	19.83	22.60	24.27	27.10	▆▆▆▇▆
Minimum_Temperature_2000	1997	28	0.90	16.69	2.78	12.57	13.88	16.55	19.02	21.75	▇▃▂▇▃
Minimum_Temperature_2000	2008	154	0.87	16.81	2.64	12.57	13.98	16.67	19.02	21.81	▇▅▅▇▃
Minimum_Temperature_2000	2011	43	0.84	17.22	2.53	12.72	14.59	18.08	19.37	21.66	▅▃▂▇▂
Minimum_Temperature_2000	2016	51	0.86	17.20	2.48	12.57	14.74	17.73	19.15	21.81	▅▅▃▇▂
Minimum_Temperature_2000	2021	0	1.00	17.23	2.67	12.63	14.64	17.56	19.49	22.02	▆▅▃▇▃
Minimum_Temperature_2005	1997	28	0.90	16.94	2.77	12.82	14.23	16.77	19.32	22.12	▇▃▃▆▃
Minimum_Temperature_2005	2008	154	0.87	17.03	2.63	12.82	14.30	16.82	19.32	22.22	▇▅▅▇▃
Minimum_Temperature_2005	2011	43	0.84	17.36	2.48	13.00	14.82	18.07	19.36	22.02	▆▃▅▇▂
Minimum_Temperature_2005	2016	51	0.86	17.40	2.48	12.82	14.98	17.80	19.36	22.22	▅▅▅▇▂
Minimum_Temperature_2005	2021	0	1.00	17.44	2.67	12.88	14.81	17.70	19.52	22.41	▇▆▅▇▅
Minimum_Temperature_2010	1997	28	0.90	17.38	2.75	13.27	14.65	17.19	19.78	22.40	▇▃▂▇▃
Minimum_Temperature_2010	2008	154	0.87	17.47	2.61	13.27	14.71	17.35	19.74	22.38	▇▅▅▇▃
Minimum_Temperature_2010	2011	43	0.84	17.84	2.48	13.42	15.22	18.62	19.85	22.33	▅▃▃▇▂
Minimum_Temperature_2010	2016	51	0.86	17.85	2.46	13.27	15.42	18.43	19.85	22.38	▅▅▃▇▂
Minimum_Temperature_2010	2021	0	1.00	17.88	2.65	13.33	15.30	18.19	20.08	22.66	▆▅▅▇▅
Minimum_Temperature_2015	1997	28	0.90	17.27	2.75	13.15	14.58	17.12	19.68	22.32	▇▃▂▆▃
Minimum_Temperature_2015	2008	154	0.87	17.35	2.60	13.15	14.61	17.20	19.57	22.31	▇▅▅▇▃
Minimum_Temperature_2015	2011	43	0.84	17.69	2.47	13.32	15.10	18.45	19.75	22.26	▆▂▅▇▂
Minimum_Temperature_2015	2016	51	0.86	17.72	2.47	13.15	15.33	18.25	19.72	22.31	▅▅▅▇▂
Minimum_Temperature_2015	2021	0	1.00	17.76	2.64	13.21	15.16	18.05	19.90	22.61	▇▅▅▇▅
Nightlights_Composite	1997	1	1.00	1.73	3.27	0.00	0.00	0.00	1.43	11.27	▇▁▁▁▁
Nightlights_Composite	2008	18	0.98	0.85	2.25	0.00	0.00	0.00	0.06	11.63	▇▁▁▁▁
Nightlights_Composite	2011	1	1.00	0.55	1.40	0.00	0.00	0.00	0.22	9.94	▇▁▁▁▁
Nightlights_Composite	2016	0	1.00	0.24	0.90	0.00	0.00	0.00	0.01	9.17	▇▁▁▁▁
Nightlights_Composite	2021	0	1.00	0.63	1.89	0.00	0.00	0.00	0.02	9.41	▇▁▁▁▁
Night_Land_Surface_Temp2010	1997	3	0.99	17.08	3.23	10.10	14.41	16.78	19.43	23.41	▂▇▅▅▅
Night_Land_Surface_Temp2010	2008	36	0.97	17.46	3.16	10.51	14.45	17.63	19.80	23.45	▂▇▇▇▆
Night_Land_Surface_Temp2010	2011	8	0.97	17.84	3.04	11.27	14.88	18.72	19.60	23.45	▂▅▃▇▃
Night_Land_Surface_Temp2010	2016	12	0.97	17.81	2.84	11.73	15.49	18.01	19.59	23.45	▃▅▇▆▅
Night_Land_Surface_Temp2010	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp2015	1997	3	0.99	17.08	3.23	9.83	14.56	16.68	19.29	23.54	▂▇▆▇▅
Night_Land_Surface_Temp2015	2008	36	0.97	17.37	3.20	10.02	14.56	17.51	19.76	24.03	▂▇▆▆▅
Night_Land_Surface_Temp2015	2011	8	0.97	17.67	3.02	11.36	14.78	18.50	19.42	24.03	▃▇▇▇▃
Night_Land_Surface_Temp2015	2016	12	0.97	17.69	2.90	11.78	15.15	17.86	19.48	24.03	▃▅▇▃▂
Night_Land_Surface_Temp2015	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp_2000	1997	3	0.99	15.62	3.53	8.80	12.83	15.51	18.56	22.17	▃▇▅▆▅
Night_Land_Surface_Temp_2000	2008	36	0.97	16.11	3.43	8.74	12.86	16.40	18.98	22.60	▂▇▆▆▆
Night_Land_Surface_Temp_2000	2011	8	0.97	16.57	3.29	9.85	13.55	17.29	18.90	22.60	▅▃▅▇▃
Night_Land_Surface_Temp_2000	2016	12	0.97	16.56	3.03	10.21	14.03	16.82	18.83	22.60	▅▅▇▆▃
Night_Land_Surface_Temp_2000	2021	0	1.00	16.17	3.41	7.98	12.92	16.48	18.99	22.25	▂▇▆▇▇
Night_Land_Surface_Temp_2005	1997	3	0.99	16.47	3.20	9.50	14.01	16.16	18.50	22.98	▂▇▅▅▃
Night_Land_Surface_Temp_2005	2008	36	0.97	16.79	3.16	9.56	14.01	16.91	19.22	22.98	▂▇▇▆▅
Night_Land_Surface_Temp_2005	2011	8	0.97	17.11	3.01	10.76	14.20	17.83	18.88	23.10	▃▆▆▇▃
Night_Land_Surface_Temp_2005	2016	12	0.97	17.11	2.85	11.13	14.78	17.23	18.87	22.98	▃▅▇▅▃
Night_Land_Surface_Temp_2005	2021	0	1.00	16.87	3.13	8.46	14.37	16.94	19.14	22.88	▁▆▇▇▆
Night_Land_Surface_Temp_2010	1997	3	0.99	17.08	3.23	10.10	14.41	16.78	19.43	23.41	▂▇▅▅▅
Night_Land_Surface_Temp_2010	2008	36	0.97	17.46	3.16	10.51	14.45	17.63	19.80	23.45	▂▇▇▇▆
Night_Land_Surface_Temp_2010	2011	8	0.97	17.84	3.04	11.27	14.88	18.72	19.60	23.45	▂▅▃▇▃
Night_Land_Surface_Temp_2010	2016	12	0.97	17.81	2.84	11.73	15.49	18.01	19.59	23.45	▃▅▇▆▅
Night_Land_Surface_Temp_2010	2021	0	1.00	17.52	3.12	9.40	14.69	17.64	19.77	23.35	▁▇▆▇▆
Night_Land_Surface_Temp_2015	1997	3	0.99	17.08	3.23	9.83	14.56	16.68	19.29	23.54	▂▇▆▇▅
Night_Land_Surface_Temp_2015	2008	36	0.97	17.37	3.20	10.02	14.56	17.51	19.76	24.03	▂▇▆▆▅
Night_Land_Surface_Temp_2015	2011	8	0.97	17.67	3.02	11.36	14.78	18.50	19.42	24.03	▃▇▇▇▃
Night_Land_Surface_Temp_2015	2016	12	0.97	17.69	2.90	11.78	15.15	17.86	19.48	24.03	▃▅▇▃▂
Night_Land_Surface_Temp_2015	2021	0	1.00	17.44	3.16	7.96	14.87	17.56	19.62	23.72	▁▅▇▇▅
PET_2000	1997	28	0.90	3.20	0.45	2.73	2.87	3.08	3.47	4.59	▇▃▂▁▁
PET_2000	2008	154	0.87	3.23	0.42	2.73	2.87	3.12	3.51	4.59	▇▅▂▂▁
PET_2000	2011	43	0.84	3.28	0.42	2.73	2.95	3.16	3.58	4.59	▇▃▆▁▁
PET_2000	2016	51	0.86	3.27	0.40	2.73	2.97	3.16	3.58	4.59	▇▆▃▂▁
PET_2000	2021	0	1.00	3.29	0.42	2.73	2.91	3.18	3.59	4.59	▇▅▃▂▁
PET_2005	1997	28	0.90	3.27	0.46	2.79	2.92	3.15	3.54	4.62	▇▃▂▂▁
PET_2005	2008	154	0.87	3.30	0.42	2.79	2.92	3.22	3.62	4.62	▇▅▃▂▁
PET_2005	2011	43	0.84	3.36	0.43	2.79	3.00	3.26	3.68	4.62	▇▅▆▁▁
PET_2005	2016	51	0.86	3.34	0.41	2.79	3.00	3.26	3.67	4.62	▇▇▅▂▁
PET_2005	2021	0	1.00	3.36	0.43	2.79	2.97	3.25	3.68	4.62	▇▆▃▂▁
PET_2010	1997	28	0.90	3.38	0.47	2.85	3.00	3.27	3.66	4.78	▇▅▂▂▁
PET_2010	2008	154	0.87	3.40	0.43	2.85	3.00	3.33	3.69	4.78	▇▆▃▂▁
PET_2010	2011	43	0.84	3.45	0.43	2.85	3.08	3.39	3.76	4.78	▇▆▇▁▁
PET_2010	2016	51	0.86	3.44	0.41	2.85	3.10	3.38	3.75	4.78	▇▇▅▂▁
PET_2010	2021	0	1.00	3.46	0.43	2.86	3.06	3.38	3.76	4.77	▇▇▅▂▁
PET_2015	1997	28	0.90	3.39	0.47	2.85	3.00	3.28	3.60	4.73	▇▅▂▁▁
PET_2015	2008	154	0.87	3.41	0.44	2.85	3.00	3.33	3.68	4.73	▇▇▃▂▁
PET_2015	2011	43	0.84	3.46	0.43	2.85	3.11	3.42	3.74	4.73	▇▆▆▁▁
PET_2015	2016	51	0.86	3.46	0.41	2.85	3.12	3.40	3.74	4.73	▆▇▃▂▁
PET_2015	2021	0	1.00	3.47	0.44	2.86	3.07	3.40	3.76	4.73	▇▇▅▂▁
Proximity_to_National_Borders	1997	1	1.00	88932.78	67270.55	316.62	22396.25	87766.24	159230.69	230387.86	▇▃▃▆▁
Proximity_to_National_Borders	2008	18	0.98	92047.87	70568.86	8.36	20419.18	94761.31	158545.52	249730.14	▇▂▃▅▁
Proximity_to_National_Borders	2011	1	1.00	74232.51	67199.61	56.68	9236.96	61339.21	125469.14	245241.37	▇▃▃▂▁
Proximity_to_National_Borders	2016	0	1.00	85109.13	73400.08	80.11	15240.33	69524.68	145556.92	238304.40	▇▂▃▃▂
Proximity_to_National_Borders	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Proximity_to_Protected_Areas	1997	1	1.00	56700.81	37271.58	0.00	26160.14	56481.23	79314.84	169207.51	▇▃▇▂▁
Proximity_to_Protected_Areas	2008	18	0.98	57359.19	34641.21	997.91	29211.68	57399.78	79269.59	173615.53	▇▇▆▂▁
Proximity_to_Protected_Areas	2011	1	1.00	52552.42	29975.18	0.00	28260.25	52424.43	72077.91	171829.16	▇▇▆▁▁
Proximity_to_Protected_Areas	2016	0	1.00	54804.17	35741.81	0.00	27051.89	49564.93	74083.60	175733.15	▇▇▅▂▁
Proximity_to_Protected_Areas	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Proximity_to_Water	1997	1	1.00	54440.89	39802.56	0.00	16925.04	61911.76	74937.83	180764.07	▇▆▅▂▁
Proximity_to_Water	2008	18	0.98	57718.30	44976.61	74.19	15608.71	56918.01	89357.11	189429.43	▇▅▃▂▁
Proximity_to_Water	2011	1	1.00	50598.76	44446.78	0.00	7626.95	37376.33	86032.63	155653.61	▇▃▃▃▁
Proximity_to_Water	2016	0	1.00	54793.57	46841.58	9.55	12007.74	40321.99	97804.05	187408.40	▇▃▂▂▁
Proximity_to_Water	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_1985	1997	7	0.97	1798.34	689.09	227.00	1553.00	1702.00	2155.33	3977.00	▂▇▅▂▁
Rainfall_1985	2008	46	0.96	1717.72	703.74	227.00	1384.89	1700.40	2029.84	3892.00	▃▇▇▂▁
Rainfall_1985	2011	15	0.94	1483.35	814.91	227.00	661.04	1608.67	1867.83	3770.00	▇▅▇▃▁
Rainfall_1985	2016	12	0.97	1756.62	784.62	227.00	1329.75	1716.61	2199.78	4008.00	▅▇▇▃▁
Rainfall_1985	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_1990	1997	7	0.97	1356.16	572.44	314.30	1039.00	1164.50	1665.90	3521.00	▂▇▂▁▁
Rainfall_1990	2008	46	0.96	1316.14	566.39	321.00	1002.40	1182.42	1538.97	3480.00	▃▇▂▁▁
Rainfall_1990	2011	15	0.94	1175.86	589.99	298.11	659.67	1093.53	1359.33	3253.20	▆▇▂▂▁
Rainfall_1990	2016	12	0.97	1371.44	623.56	321.00	987.58	1242.91	1745.33	3457.44	▃▇▃▂▁
Rainfall_1990	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_1995	1997	7	0.97	1633.00	605.79	278.00	1368.00	1510.35	1946.44	3848.00	▂▇▃▂▁
Rainfall_1995	2008	46	0.96	1552.92	601.42	278.00	1237.08	1494.00	1814.67	3806.00	▃▇▅▂▁
Rainfall_1995	2011	15	0.94	1347.72	690.10	277.56	660.18	1418.51	1751.75	3331.70	▇▆▇▃▁
Rainfall_1995	2016	12	0.97	1586.91	656.39	278.00	1164.13	1598.40	1957.28	3495.00	▅▇▇▃▁
Rainfall_1995	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_2000	1997	7	0.97	1489.11	523.24	393.00	1259.00	1378.00	1745.64	3161.00	▂▇▃▂▁
Rainfall_2000	2008	46	0.96	1473.27	527.39	361.00	1220.97	1379.25	1724.99	3295.67	▂▇▃▁▁
Rainfall_2000	2011	15	0.94	1272.75	615.78	303.00	744.31	1317.50	1521.73	3156.00	▆▇▅▂▁
Rainfall_2000	2016	12	0.97	1510.92	584.37	352.91	1155.54	1463.14	1796.86	3295.67	▃▇▆▂▁
Rainfall_2000	2021	3	1.00	1485.19	521.86	360.10	1219.94	1392.79	1746.27	3233.52	▂▇▅▂▁
Rainfall_2005	1997	7	0.97	1581.27	559.80	351.00	1290.05	1464.00	1848.91	3380.00	▂▇▃▂▁
Rainfall_2005	2008	46	0.96	1533.98	585.46	351.00	1176.10	1457.00	1832.88	3380.00	▂▇▅▂▁
Rainfall_2005	2011	15	0.94	1382.54	638.89	351.00	889.45	1353.26	1646.46	3380.00	▆▇▅▂▁
Rainfall_2005	2016	12	0.97	1573.87	646.64	351.00	1070.80	1475.47	1946.35	3380.00	▃▇▅▃▁
Rainfall_2005	2021	3	1.00	1542.61	579.97	420.51	1162.93	1459.38	1838.64	3348.00	▃▇▅▂▁
Rainfall_2010	1997	7	0.97	1506.61	626.63	235.00	1175.00	1294.06	1781.44	3483.00	▂▇▃▂▁
Rainfall_2010	2008	46	0.96	1469.21	645.65	235.00	1125.65	1329.90	1745.68	3446.67	▃▇▃▂▁
Rainfall_2010	2011	15	0.94	1301.42	684.96	234.33	725.92	1263.93	1639.33	3203.00	▆▇▃▂▂
Rainfall_2010	2016	12	0.97	1521.32	705.02	235.00	1043.89	1395.06	1813.38	3458.00	▃▇▃▂▁
Rainfall_2010	2021	3	1.00	1474.15	632.68	254.60	1124.85	1348.20	1748.54	3436.88	▃▇▃▂▁
Rainfall_2015	1997	7	0.97	1710.23	617.71	414.00	1479.75	1561.05	1983.10	4471.27	▂▇▂▁▁
Rainfall_2015	2008	46	0.96	1656.73	588.75	381.89	1423.35	1575.33	1894.69	4291.33	▂▇▃▁▁
Rainfall_2015	2011	15	0.94	1440.19	701.41	320.44	733.95	1536.68	1769.76	4428.40	▆▇▃▁▁
Rainfall_2015	2016	12	0.97	1685.29	650.48	381.55	1353.00	1627.50	2067.33	4316.30	▃▇▃▁▁
Rainfall_2015	2021	3	1.00	1669.85	591.51	432.44	1423.00	1601.67	1965.96	4518.49	▃▇▂▁▁
Slope	1997	1	1.00	1.32	1.14	0.03	0.45	0.83	1.95	5.30	▇▃▂▁▁
Slope	2008	20	0.98	1.46	1.28	0.02	0.57	1.01	2.10	8.65	▇▃▁▁▁
Slope	2011	2	0.99	1.38	1.24	0.02	0.54	0.93	1.84	6.96	▇▃▁▁▁
Slope	2016	0	1.00	1.46	1.16	0.03	0.63	1.12	2.07	7.06	▇▃▁▁▁
Slope	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
SMOD_Population_1990	1997	1	1.00	0.90	1.14	0.00	0.00	0.00	1.00	3.00	▇▃▁▁▃
SMOD_Population_1990	2008	18	0.98	0.61	0.87	0.00	0.00	0.00	1.00	3.00	▇▅▁▁▁
SMOD_Population_1990	2011	1	1.00	0.46	0.68	0.00	0.00	0.00	1.00	3.00	▇▃▁▁▁
SMOD_Population_1990	2016	0	1.00	0.40	0.58	0.00	0.00	0.00	1.00	2.00	▇▁▃▁▁
SMOD_Population_1990	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
SMOD_Population_2000	1997	1	1.00	0.89	1.19	0.00	0.00	0.00	2.00	3.00	▇▂▁▁▃
SMOD_Population_2000	2008	18	0.98	0.60	0.94	0.00	0.00	0.00	1.00	3.00	▇▃▁▁▁
SMOD_Population_2000	2011	1	1.00	0.47	0.81	0.00	0.00	0.00	1.00	3.00	▇▂▁▁▁
SMOD_Population_2000	2016	0	1.00	0.36	0.62	0.00	0.00	0.00	1.00	3.00	▇▃▁▁▁
SMOD_Population_2000	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
SMOD_Population_2015	1997	1	1.00	0.91	1.24	0.00	0.00	0.00	2.00	3.00	▇▂▁▁▃
SMOD_Population_2015	2008	18	0.98	0.64	1.01	0.00	0.00	0.00	1.00	3.00	▇▂▁▁▂
SMOD_Population_2015	2011	1	1.00	0.56	0.97	0.00	0.00	0.00	1.00	3.00	▇▂▁▁▁
SMOD_Population_2015	2016	0	1.00	0.40	0.76	0.00	0.00	0.00	1.00	3.00	▇▂▁▁▁
SMOD_Population_2015	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Temperature_April	1997	8	0.97	22.89	3.09	16.19	20.06	23.86	25.31	28.36	▂▇▂▇▅
Temperature_April	2008	46	0.96	23.13	3.04	16.05	20.09	23.83	25.46	28.49	▂▅▅▇▅
Temperature_April	2011	11	0.96	23.42	2.80	16.71	20.91	24.53	25.34	28.49	▂▃▂▇▂
Temperature_April	2016	8	0.98	23.63	2.77	17.15	21.67	24.25	25.53	28.49	▂▃▅▇▃
Temperature_April	2021	3	1.00	23.25	3.02	14.54	20.17	23.84	25.60	28.41	▁▅▅▇▆
Temperature_August	1997	8	0.97	18.76	3.58	12.10	15.44	19.61	21.22	25.99	▂▇▅▆▃
Temperature_August	2008	46	0.96	19.16	3.53	11.68	15.64	19.82	21.41	26.20	▃▇▇▇▅
Temperature_August	2011	11	0.96	19.21	3.08	12.16	16.62	20.15	20.84	26.20	▃▅▇▆▂
Temperature_August	2016	8	0.98	19.72	3.19	12.56	17.44	20.14	21.34	26.20	▂▃▇▃▃
Temperature_August	2021	3	1.00	19.31	3.50	10.78	15.80	19.84	21.57	26.09	▂▆▆▇▅
Temperature_December	1997	8	0.97	23.98	2.96	17.23	21.16	24.83	26.51	28.88	▂▇▂▇▆
Temperature_December	2008	46	0.96	24.27	2.89	17.48	21.22	24.80	26.64	29.06	▂▆▅▇▆
Temperature_December	2011	11	0.96	24.70	2.80	18.10	22.17	25.70	27.06	29.06	▂▅▃▇▇
Temperature_December	2016	8	0.98	24.77	2.61	18.58	22.97	25.33	26.87	29.06	▂▃▅▇▅
Temperature_December	2021	3	1.00	24.38	2.84	15.94	21.36	24.92	26.76	28.97	▁▅▅▇▇
Temperature_February	1997	8	0.97	24.16	2.84	17.27	21.51	25.04	26.75	28.16	▁▆▂▃▇
Temperature_February	2008	46	0.96	24.37	2.76	17.44	21.51	25.00	26.80	28.34	▁▆▃▅▇
Temperature_February	2011	11	0.96	24.88	2.74	18.28	22.32	26.18	27.15	28.34	▁▃▂▂▇
Temperature_February	2016	8	0.98	24.87	2.52	18.79	22.87	25.92	26.89	28.41	▂▃▃▆▇
Temperature_February	2021	3	1.00	24.47	2.72	16.00	21.61	25.12	26.82	28.45	▁▂▅▅▇
Temperature_January	1997	8	0.97	24.16	2.83	17.46	21.48	25.09	26.69	28.09	▁▆▂▃▇
Temperature_January	2008	46	0.96	24.41	2.76	17.63	21.48	24.97	26.78	28.34	▁▅▃▅▇
Temperature_January	2011	11	0.96	24.93	2.75	18.46	22.43	26.25	27.19	28.16	▁▂▂▂▇
Temperature_January	2016	8	0.98	24.91	2.52	18.89	22.94	25.97	26.92	28.42	▂▃▃▇▇
Temperature_January	2021	3	1.00	24.50	2.72	15.94	21.56	25.13	26.77	28.45	▁▂▅▅▇
Temperature_July	1997	8	0.97	18.26	3.56	11.49	15.04	18.56	20.87	25.48	▂▇▅▅▃
Temperature_July	2008	46	0.96	18.52	3.50	11.11	15.10	18.96	20.86	25.66	▂▇▇▅▃
Temperature_July	2011	11	0.96	18.53	3.07	11.53	15.91	19.20	20.42	25.53	▂▅▇▅▂
Temperature_July	2016	8	0.98	19.02	3.20	11.98	16.58	19.24	20.88	25.55	▂▅▇▃▃
Temperature_July	2021	3	1.00	18.66	3.49	10.18	15.19	18.96	21.17	25.59	▂▇▇▇▆
Temperature_June	1997	8	0.97	19.09	3.47	12.05	15.97	19.36	21.67	25.82	▂▇▅▇▃
Temperature_June	2008	46	0.96	19.28	3.38	11.94	15.99	19.67	21.58	25.97	▃▇▇▇▅
Temperature_June	2011	11	0.96	19.28	2.97	12.25	17.05	19.76	21.19	25.86	▂▅▇▆▂
Temperature_June	2016	8	0.98	19.78	3.10	12.75	17.70	19.94	21.64	25.92	▂▅▇▅▃
Temperature_June	2021	3	1.00	19.44	3.38	10.83	16.04	19.67	21.84	25.94	▂▆▇▇▆
Temperature_March	1997	8	0.97	23.84	2.93	17.23	21.12	24.66	26.29	28.47	▂▇▃▇▇
Temperature_March	2008	46	0.96	24.07	2.87	17.30	21.12	24.76	26.45	28.59	▂▆▅▇▇
Temperature_March	2011	11	0.96	24.52	2.77	17.98	21.73	25.79	26.85	28.59	▂▅▃▇▇
Temperature_March	2016	8	0.98	24.57	2.61	18.44	22.67	25.28	26.63	28.59	▂▃▅▇▆
Temperature_March	2021	3	1.00	24.18	2.83	15.89	21.22	24.69	26.57	28.52	▁▃▅▇▇
Temperature_May	1997	8	0.97	20.93	3.31	13.92	18.04	21.23	23.36	27.29	▂▇▃▇▃
Temperature_May	2008	46	0.96	21.11	3.25	13.77	18.04	21.55	23.39	27.40	▂▆▇▇▅
Temperature_May	2011	11	0.96	21.16	2.87	14.26	18.85	21.69	22.93	27.31	▂▆▇▇▃
Temperature_May	2016	8	0.98	21.57	2.97	14.78	19.62	21.78	23.45	27.38	▂▃▇▅▃
Temperature_May	2021	3	1.00	21.24	3.23	12.26	18.10	21.44	23.61	27.36	▁▆▆▇▅
Temperature_November	1997	8	0.97	23.40	3.07	16.94	20.70	23.87	25.87	29.64	▂▇▆▆▃
Temperature_November	2008	46	0.96	23.75	3.05	17.12	20.70	24.19	26.09	29.83	▃▆▇▆▅
Temperature_November	2011	11	0.96	24.02	2.83	17.57	21.46	24.50	26.21	29.83	▂▅▆▇▂
Temperature_November	2016	8	0.98	24.20	2.77	18.02	22.30	24.40	26.16	29.83	▂▃▇▅▃
Temperature_November	2021	3	1.00	23.86	3.01	15.83	20.79	24.23	26.16	29.73	▁▆▅▇▅
Temperature_October	1997	8	0.97	21.94	3.23	15.57	18.97	22.19	24.41	28.90	▂▇▅▅▃
Temperature_October	2008	46	0.96	22.40	3.21	15.65	19.22	22.77	24.78	29.19	▂▇▇▆▃
Temperature_October	2011	11	0.96	22.56	2.87	16.37	19.98	23.06	24.75	29.19	▃▆▇▇▂
Temperature_October	2016	8	0.98	22.87	2.90	16.65	20.76	23.06	24.80	29.19	▃▃▇▅▂
Temperature_October	2021	3	1.00	22.52	3.18	14.79	19.46	22.84	24.77	29.04	▂▆▇▆▅
Temperature_September	1997	8	0.97	20.06	3.47	13.65	16.75	20.45	22.43	27.11	▂▇▅▅▃
Temperature_September	2008	46	0.96	20.54	3.43	13.46	17.11	21.00	22.78	27.47	▃▇▇▅▅
Temperature_September	2011	11	0.96	20.65	3.01	13.99	18.13	21.42	22.57	27.47	▃▅▇▆▂
Temperature_September	2016	8	0.98	21.08	3.09	14.33	18.73	21.34	22.90	27.47	▃▃▇▃▃
Temperature_September	2021	3	1.00	20.68	3.39	12.84	17.46	21.05	22.98	27.38	▂▇▇▇▅
Travel_Times_2000	1997	1	1.00	180.15	180.01	1.44	42.29	147.08	252.44	1288.22	▇▂▁▁▁
Travel_Times_2000	2008	18	0.98	202.38	151.24	0.90	108.31	184.99	273.58	1313.80	▇▃▁▁▁
Travel_Times_2000	2011	1	1.00	181.62	136.41	1.47	86.05	164.89	239.37	945.02	▇▅▁▁▁
Travel_Times_2000	2016	0	1.00	214.25	143.37	1.92	132.53	188.79	262.99	1137.39	▇▃▁▁▁
Travel_Times_2000	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
Travel_Times_2015	1997	1	1.00	280.19	308.16	0.00	12.52	156.13	461.26	1167.04	▇▂▁▂▁
Travel_Times_2015	2008	18	0.98	296.57	289.50	0.00	80.53	209.55	424.04	1247.77	▇▃▂▁▁
Travel_Times_2015	2011	1	1.00	273.74	274.12	0.00	73.50	164.09	391.00	1259.09	▇▃▁▁▁
Travel_Times_2015	2016	0	1.00	314.64	272.29	0.00	112.99	227.72	434.94	1083.41	▇▅▂▂▁
Travel_Times_2015	2021	650	0.00	NaN	NA	NA	NA	NA	NA	NA
U5_Population_2000	1997	1	1.00	340.19	644.19	0.06	3.52	8.75	323.07	2734.98	▇▁▁▁▁
U5_Population_2000	2008	18	0.98	200.21	517.93	0.05	2.83	6.58	25.83	2754.93	▇▁▁▁▁
U5_Population_2000	2011	1	1.00	169.08	457.81	0.42	2.74	7.17	30.87	2893.00	▇▁▁▁▁
U5_Population_2000	2016	0	1.00	81.88	301.65	0.11	2.66	5.45	13.75	2413.05	▇▁▁▁▁
U5_Population_2000	2021	0	1.00	1337.67	4003.72	1.33	52.14	168.08	453.40	31558.26	▇▁▁▁▁
U5_Population_2005	1997	1	1.00	395.19	748.34	0.07	4.09	10.17	375.31	3177.16	▇▁▁▁▁
U5_Population_2005	2008	18	0.98	232.58	601.67	0.05	3.29	7.64	30.00	3200.34	▇▁▁▁▁
U5_Population_2005	2011	1	1.00	196.41	531.82	0.49	3.18	8.33	35.86	3360.73	▇▁▁▁▁
U5_Population_2005	2016	0	1.00	95.12	350.42	0.13	3.09	6.33	15.97	2803.19	▇▁▁▁▁
U5_Population_2005	2021	0	1.00	1548.44	4595.43	1.00	61.28	202.81	545.80	36839.40	▇▁▁▁▁
U5_Population_2010	1997	1	1.00	455.45	862.45	0.09	4.71	11.72	432.54	3661.65	▇▁▁▁▁
U5_Population_2010	2008	18	0.98	268.04	693.42	0.06	3.79	8.81	34.58	3688.37	▇▁▁▁▁
U5_Population_2010	2011	1	1.00	226.36	612.92	0.56	3.67	9.60	41.33	3873.22	▇▁▁▁▁
U5_Population_2010	2016	0	1.00	109.63	403.86	0.14	3.56	7.29	18.40	3230.65	▇▁▁▁▁
U5_Population_2010	2021	0	1.00	1772.94	5190.20	0.96	75.70	214.39	550.07	41661.78	▇▁▁▁▁
U5_Population_2015	1997	1	1.00	523.64	991.57	0.10	5.42	13.47	497.30	4209.84	▇▁▁▁▁
U5_Population_2015	2008	18	0.98	308.17	797.23	0.07	4.36	10.13	39.75	4240.56	▇▁▁▁▁
U5_Population_2015	2011	1	1.00	260.25	704.68	0.65	4.22	11.04	47.52	4453.08	▇▁▁▁▁
U5_Population_2015	2016	0	1.00	126.04	464.32	0.17	4.09	8.38	21.16	3714.32	▇▁▁▁▁
U5_Population_2015	2021	0	1.00	2064.37	5979.00	2.43	84.27	231.59	577.07	47435.15	▇▁▁▁▁
UN_Population_Count_2000	1997	1	1.00	37664.54	101121.60	53.69	2589.59	11530.82	43719.60	889610.56	▇▁▁▁▁
UN_Population_Count_2000	2008	18	0.98	26910.26	84280.94	52.49	2722.88	8084.97	22328.27	1027879.38	▇▁▁▁▁
UN_Population_Count_2000	2011	1	1.00	42572.82	147614.00	79.44	2846.53	6032.06	17370.75	994352.38	▇▁▁▁▁
UN_Population_Count_2000	2016	0	1.00	27318.68	95208.18	79.44	2631.21	7187.05	17644.85	973558.12	▇▁▁▁▁
UN_Population_Count_2000	2021	0	1.00	8563.06	27866.85	36.62	235.87	721.69	2013.11	216954.31	▇▁▁▁▁
UN_Population_Count_2005	1997	1	1.00	45759.48	127106.19	74.21	3210.11	13711.80	48352.12	1114080.00	▇▁▁▁▁
UN_Population_Count_2005	2008	18	0.98	32530.75	106139.75	80.48	3447.74	9501.35	24911.19	1271597.25	▇▁▁▁▁
UN_Population_Count_2005	2011	1	1.00	52978.47	186083.39	91.87	3691.11	7137.31	20400.26	1242592.38	▇▁▁▁▁
UN_Population_Count_2005	2016	0	1.00	33186.56	120678.28	91.88	3268.33	9055.07	20030.09	1206462.88	▇▁▁▁▁
UN_Population_Count_2005	2021	0	1.00	10600.08	34196.78	48.61	280.74	838.24	2223.54	264199.72	▇▁▁▁▁
UN_Population_Count_2010	1997	1	1.00	54981.20	157797.71	86.27	3928.02	15695.22	49745.27	1374629.50	▇▁▁▁▁
UN_Population_Count_2010	2008	18	0.98	39015.93	132240.17	95.90	4269.06	10894.86	29097.11	1547702.50	▇▁▁▁▁
UN_Population_Count_2010	2011	1	1.00	65326.85	231622.52	98.59	4538.44	8766.48	23338.87	1529369.25	▇▁▁▁▁
UN_Population_Count_2010	2016	0	1.00	40016.60	151346.11	98.60	3833.93	10479.72	21905.89	1470694.50	▇▁▁▁▁
UN_Population_Count_2010	2021	0	1.00	12959.74	41333.67	54.77	340.51	960.85	2561.64	315955.12	▇▁▁▁▁
UN_Population_Count_2015	1997	1	1.00	65692.14	194627.93	91.03	4894.93	18500.81	51867.09	1680597.75	▇▁▁▁▁
UN_Population_Count_2015	2008	18	0.98	46675.19	163930.59	109.59	5008.26	12462.25	30944.45	1863661.75	▇▁▁▁▁
UN_Population_Count_2015	2011	1	1.00	80238.42	286357.31	104.04	5439.60	10589.36	27952.77	1864373.75	▇▁▁▁▁
UN_Population_Count_2015	2016	0	1.00	48153.53	188876.47	104.05	4554.25	11986.20	24107.24	1773171.00	▇▁▁▁▁
UN_Population_Count_2015	2021	0	1.00	15739.82	49542.49	54.79	432.42	1032.73	2794.87	373142.50	▇▁▁▁▁
UN_Population_Density_2000	1997	1	1.00	1099.30	2210.79	2.54	23.86	68.99	266.12	10035.58	▇▁▁▁▁
UN_Population_Density_2000	2008	18	0.98	515.78	1558.75	1.71	16.74	46.46	126.96	11423.42	▇▁▁▁▁
UN_Population_Density_2000	2011	1	1.00	195.71	478.29	2.86	17.95	52.75	149.35	3216.20	▇▁▁▁▁
UN_Population_Density_2000	2016	0	1.00	118.29	315.87	1.70	15.30	39.20	82.98	3131.24	▇▁▁▁▁
UN_Population_Density_2000	2021	0	1.00	428.11	1371.43	2.02	12.45	36.95	102.63	10656.12	▇▁▁▁▁
UN_Population_Density_2005	1997	1	1.00	1351.01	2707.36	2.64	28.19	79.42	335.49	12247.07	▇▁▁▁▁
UN_Population_Density_2005	2008	18	0.98	632.32	1900.63	2.62	20.27	55.35	138.72	13717.51	▇▁▁▁▁
UN_Population_Density_2005	2011	1	1.00	242.05	602.30	3.68	22.33	58.44	158.87	4018.12	▇▁▁▁▁
UN_Population_Density_2005	2016	0	1.00	144.36	400.72	2.70	18.36	44.88	94.56	3879.70	▇▁▁▁▁
UN_Population_Density_2005	2021	0	1.00	530.31	1683.24	2.63	15.41	43.35	116.72	12976.67	▇▁▁▁▁
UN_Population_Density_2010	1997	1	1.00	1635.41	3266.47	2.69	33.04	83.86	413.77	14843.68	▇▁▁▁▁
UN_Population_Density_2010	2008	18	0.98	763.92	2279.66	3.23	23.93	61.86	145.63	16120.43	▇▁▁▁▁
UN_Population_Density_2010	2011	1	1.00	296.25	748.93	4.64	27.26	69.24	166.42	4944.16	▇▁▁▁▁
UN_Population_Density_2010	2016	0	1.00	174.61	502.91	2.77	22.35	52.23	106.89	4728.65	▇▁▁▁▁
UN_Population_Density_2010	2021	0	1.00	648.80	2035.00	2.67	18.14	49.01	131.18	15518.74	▇▁▁▁▁
UN_Population_Density_2015	1997	1	1.00	1961.27	3910.89	2.69	37.38	92.24	505.47	17814.64	▇▁▁▁▁
UN_Population_Density_2015	2008	18	0.98	914.65	2707.73	3.69	27.32	70.21	154.97	18629.06	▇▁▁▁▁
UN_Population_Density_2015	2011	1	1.00	360.64	924.88	5.75	32.82	78.06	195.07	6025.54	▇▁▁▁▁
UN_Population_Density_2015	2016	0	1.00	210.41	627.79	2.80	25.75	58.85	116.77	5700.31	▇▁▁▁▁
UN_Population_Density_2015	2021	0	1.00	788.52	2439.88	2.67	22.25	54.54	140.47	18327.61	▇▁▁▁▁
Wet_Days_2000	1997	28	0.90	13.34	2.85	6.65	11.84	13.05	14.49	20.15	▁▃▇▂▂
Wet_Days_2000	2008	154	0.87	12.61	2.62	6.65	11.11	12.38	13.55	20.17	▁▇▇▂▂
Wet_Days_2000	2011	43	0.84	12.20	2.92	6.65	9.83	11.84	13.55	18.88	▂▇▇▂▃
Wet_Days_2000	2016	51	0.86	12.67	2.87	6.65	10.94	12.08	14.07	20.15	▁▇▆▂▂
Wet_Days_2000	2021	0	1.00	12.50	2.66	6.07	10.84	12.41	13.53	20.16	▁▆▇▂▁
Wet_Days_2005	1997	28	0.90	14.03	3.76	4.91	11.98	13.28	16.43	22.42	▁▃▇▂▂
Wet_Days_2005	2008	154	0.87	13.18	3.61	4.85	10.76	13.28	15.64	22.42	▂▅▇▃▂
Wet_Days_2005	2011	43	0.84	13.31	3.61	4.91	11.31	13.24	15.34	20.97	▁▃▇▂▂
Wet_Days_2005	2016	51	0.86	13.28	3.86	4.91	10.70	13.04	16.00	22.42	▂▇▇▅▃
Wet_Days_2005	2021	0	1.00	13.16	3.60	4.80	10.77	12.85	15.61	22.50	▂▅▇▃▂
Wet_Days_2010	1997	28	0.90	12.42	4.23	4.38	10.22	10.84	14.34	21.56	▁▇▃▁▂
Wet_Days_2010	2008	154	0.87	11.57	3.85	4.06	9.17	10.43	13.54	21.56	▂▇▃▂▂
Wet_Days_2010	2011	43	0.84	11.88	3.94	4.62	9.31	10.84	13.54	21.46	▂▇▅▁▂
Wet_Days_2010	2016	51	0.86	11.89	4.21	4.06	8.90	10.72	14.28	21.56	▂▇▃▂▂
Wet_Days_2010	2021	0	1.00	11.70	3.85	4.25	9.20	10.74	13.75	21.50	▂▇▅▂▂
Wet_Days_2015	1997	28	0.90	12.11	4.02	4.38	9.93	10.64	13.71	23.67	▁▇▃▁▁
Wet_Days_2015	2008	154	0.87	11.36	3.78	4.35	9.02	10.33	13.43	23.67	▃▇▃▁▁
Wet_Days_2015	2011	43	0.84	11.90	4.00	5.12	9.31	10.78	13.39	23.67	▃▇▃▁▁
Wet_Days_2015	2016	51	0.86	11.69	4.24	4.35	8.72	10.67	13.71	23.67	▅▇▅▁▂
Wet_Days_2015	2021	0	1.00	11.40	3.69	4.39	9.20	10.67	13.41	22.22	▂▇▅▂▁
All_Population_Count_2000	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2000	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2000	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2000	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2000	2021	0	1.00	9824.48	30747.85	7.65	283.35	897.30	2367.30	220572.09	▇▁▁▁▁
All_Population_Count_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
All_Population_Count_2020	2021	0	1.00	20599.82	62013.53	18.64	616.12	1636.76	4001.32	484648.25	▇▁▁▁▁
Aridity_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Aridity_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Aridity_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Aridity_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Aridity_2020	2021	0	1.00	36.35	13.48	9.30	26.56	35.03	42.83	68.01	▃▇▇▃▃
Day_Land_Surface_Temp_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Day_Land_Surface_Temp_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Day_Land_Surface_Temp_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Day_Land_Surface_Temp_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Day_Land_Surface_Temp_2020	2021	0	1.00	29.85	3.74	21.24	26.93	28.65	33.20	39.30	▁▇▃▅▂
Diurnal_Temperature_Range_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Diurnal_Temperature_Range_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Diurnal_Temperature_Range_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Diurnal_Temperature_Range_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Diurnal_Temperature_Range_2020	2021	0	1.00	9.22	0.91	7.06	8.59	9.21	9.83	12.03	▂▇▇▃▁
Elevation	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Elevation	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Elevation	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Elevation	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Elevation	2021	0	1.00	601.94	541.87	1.60	61.39	465.64	1179.53	2016.89	▇▂▂▃▁
Enhanced_Vegetation_Index_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Enhanced_Vegetation_Index_2020	2021	0	1.00	0.29	0.11	0.14	0.21	0.25	0.36	0.58	▇▇▃▂▂
Frost_Days_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Frost_Days_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Frost_Days_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Frost_Days_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Frost_Days_2020	2021	0	1.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
ITN_Coverage_2000	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2000	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2000	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2000	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2000	2021	0	1.00	0.04	0.02	0.00	0.03	0.04	0.05	0.09	▁▇▆▃▂
ITN_Coverage_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
ITN_Coverage_2020	2021	0	1.00	0.55	0.09	0.00	0.49	0.55	0.64	0.70	▁▁▁▇▇
Land_Surface_Temperature_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Land_Surface_Temperature_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Land_Surface_Temperature_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Land_Surface_Temperature_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Land_Surface_Temperature_2020	2021	0	1.00	23.60	3.28	16.21	21.02	22.66	26.77	30.17	▁▇▅▃▅
Malaria_Incidence_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Incidence_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Incidence_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Incidence_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Incidence_2020	2021	5	0.99	0.10	0.03	0.00	0.08	0.10	0.12	0.23	▁▇▇▂▁
Malaria_Prevalence_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Prevalence_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Prevalence_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Prevalence_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Malaria_Prevalence_2020	2021	5	0.99	0.06	0.03	0.00	0.04	0.06	0.07	0.19	▂▇▂▁▁
Maximum_Temperature_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Maximum_Temperature_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Maximum_Temperature_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Maximum_Temperature_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Maximum_Temperature_2020	2021	0	1.00	26.85	2.75	21.69	24.38	26.86	28.83	32.15	▅▅▇▃▅
Mean_Temperature_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Mean_Temperature_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Mean_Temperature_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Mean_Temperature_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Mean_Temperature_2020	2021	0	1.00	22.21	2.70	17.31	19.65	22.41	24.18	27.16	▇▆▇▇▆
Minimum_Temperature_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Minimum_Temperature_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Minimum_Temperature_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Minimum_Temperature_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Minimum_Temperature_2020	2021	0	1.00	17.63	2.71	13.01	14.94	17.86	19.67	22.89	▇▆▆▇▅
Night_Land_Surface_Temp_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Night_Land_Surface_Temp_2020	2021	0	1.00	17.34	3.30	9.24	14.36	17.35	19.81	23.61	▁▇▇▆▆
PET_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
PET_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
PET_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
PET_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
PET_2020	2021	0	1.00	3.40	0.41	2.87	3.05	3.29	3.69	4.71	▇▅▃▂▁
Precipitation_2000	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2000	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2000	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2000	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2000	2021	0	1.00	116.10	34.60	30.78	93.74	116.22	137.65	205.92	▁▅▇▃▁
Precipitation_2005	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2005	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2005	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2005	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2005	2021	0	1.00	127.49	39.52	37.50	100.64	122.12	160.29	213.44	▂▅▇▅▃
Precipitation_2010	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2010	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2010	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2010	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2010	2021	0	1.00	115.55	43.74	20.34	86.09	100.72	140.27	219.26	▁▇▅▂▂
Precipitation_2015	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2015	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2015	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2015	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2015	2021	0	1.00	115.01	31.94	38.42	95.37	110.32	133.32	212.18	▂▇▇▂▁
Precipitation_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Precipitation_2020	2021	0	1.00	120.78	40.36	35.73	95.76	115.61	139.68	218.69	▂▇▇▂▃
Rainfall_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Rainfall_2020	2021	0	1.00	1362.89	619.66	187.49	1015.12	1306.97	1695.35	4422.22	▃▇▂▁▁
Travel_Times	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Travel_Times	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Travel_Times	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Travel_Times	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Travel_Times	2021	0	1.00	313.64	298.64	0.00	80.79	220.36	459.97	1750.43	▇▂▂▁▁
U5_Population_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
U5_Population_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
U5_Population_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
U5_Population_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
U5_Population_2020	2021	0	1.00	2485.29	7150.45	2.56	96.74	247.61	657.53	55940.81	▇▁▁▁▁
UN_Population_Count_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Count_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Count_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Count_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Count_2020	2021	0	1.00	18997.53	58990.10	53.86	491.73	1210.60	2979.85	435338.28	▇▁▁▁▁
UN_Population_Density_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Density_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Density_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Density_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
UN_Population_Density_2020	2021	0	1.00	952.35	2906.28	2.63	25.02	61.70	152.60	21382.47	▇▁▁▁▁
Wet_Days_2020	1997	269	0.00	NaN	NA	NA	NA	NA	NA	NA
Wet_Days_2020	2008	1188	0.00	NaN	NA	NA	NA	NA	NA	NA
Wet_Days_2020	2011	267	0.00	NaN	NA	NA	NA	NA	NA	NA
Wet_Days_2020	2016	358	0.00	NaN	NA	NA	NA	NA	NA	NA
Wet_Days_2020	2021	0	1.00	13.60	3.61	5.16	11.19	13.73	15.34	22.50	▂▆▇▃▂

Conclusion

The DHS harmonization package provides methods to read in flat DHS data files, GPS data files, and GPS covariate files. These functions are used to provide an initial targets pipeline that reads in all of the relevant data for Madagascar DHS surveys.

To get a harmonized dataset, reach out to Tinashe on Github and provide a data request, and we can work on harmonizing the datasets for your use case. The harmonized dataset will be made available through a targets endpoint in the package that you can load into your R session.