Summary

This document describes the steps for preparing the pathogen pollution and pathogen pollution trend data layers for the 2021 global assessment.

The percentage of the population with access to improved sanitation facilities (World Health Organization and United Nations Children’s Fund, Joint Monitoring Programme, 2011) was used in combination with measurements of coastal population as a proxy for pathogens in coastal waters. Access to improved sanitation facilities is defined as the percentage of the population in a country with at least adequate access to disposal facilities that can effectively prevent human, animal, and insect contact with excreta. These data are a country-wide average (not specific to the coastal region).

Updates from previous assessment

Data source (WHO-UNICEF) now reports sanitation data for three different sectors: households, schools, and health care facilities. We decided to use the household data as it is most likely to include the greatest number of citizens of each region, and be most comparable with previous datasets.

WHO-UNICEF also changed how they report the percentages of the population with access to basic sanitation. In previous years, some regions were reported with 100% of the population having basic access, which are now denoted as >99 in the raw data. We converted this value to 99.5% as there were other regions with 99%. The data now cover years 2000-2020, vs. last year’s assessment which had data for 2000-2017.

Updates on our end:

We changed the reporting of the Caribbean Netherlands regions to be at a higher resolution, as historical sanitation data for them have been backfilled by WHO-UNICEF as of this year.
We also changed the way uninhabited regions (which we assign perfect scores in this layer) are identified. The code was changed to match the methods for other goals (AO, etc), which use the low_pop() function in common.R and filter out low population/uninhabited regions.

Consider for future assesments: make the “Safely managed” data more complete, and reconsider whether these data would be better to use.

Definition of each variable according to the data source: “At least basic”: Use of improved facilities that are not shared with other households.

“Safely managed”: Use of improved facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite

Data Source

Reference: https://washdata.org/data Updated October 2021

Downloaded: Downloaded 10/01/2021

Description: Percentage of the National population that has access to improved facilities that are not shared with other households (National, At a basic level).

Access to improved sanitation facilities is defined as the percentage of the population within a country with at least adequate access to excreta disposal facilities that can effectively prevent human, animal, and insect contact with excreta.

Native data resolution: country-wide average (not specific to the coastal region)

Time range: 2000 - 2020

Format: csv

Methods

Percentage of people without sanitation was multiplied by the coastal population (within 25 miles of the coast) to estimate the number of people located along the coast without sanitation. This value was rescaled between 0 and 1 by dividing by the 99th quantile across all regions from years 2000 to 2009.

Setup

library(ohicore)  #devtools::install_github('ohi-science/ohicore@dev')
library(zoo)     # for na.locf: Last Observation Carried Forward
library(tidyverse)
library(here)
library(plotly)

source('https://raw.githubusercontent.com/OHI-Science/ohiprep_v2021/gh-pages/workflow/R/common.R')

Import raw data

# Load data from Mazu
sani_raw <- read.csv(file.path(dir_M, "git-annex/globalprep/_raw_data/WHO_UNICEFF/d2021/JMP_2021_WLD.csv"), header=FALSE, sep = ",", na.strings = c(NA, ''), stringsAsFactors = FALSE, strip.white = TRUE)

Methods

Data wrangling

Selecting and naming the columns of interest. Scale the percentage of population with access to improved sanitation to proportion (from 0-1). Transform population and percentage into a numeric variable.

sani <- sani_raw %>%
  dplyr::slice(-1) %>%                # cut first row with column names
  dplyr::select(country = V1,
         year = V3,
         pop = V4,
         basic_pct = V6) %>%
  dplyr::mutate(basic_pct = ifelse(basic_pct=="-", NA, basic_pct)) %>% 
  dplyr::mutate(pop = stringr::str_remove_all(pop, pattern = " ")) %>%
  dplyr::mutate(basic_pct = ifelse(stringr::str_detect(basic_pct,">99"), 99.5, basic_pct)) %>%
  dplyr::mutate_at(.vars = c("pop", "basic_pct", "year"), .funs = as.numeric)%>%
  dplyr::mutate(pop = pop * 1000,
          basic_prop = basic_pct/100) %>%
  dplyr::filter(!is.na(year))

Change names of regions to match names in ohicore and filter out regions that are not part of the OHI assesment or do not have data.

If after running ‘name_2_rgn’ (see next r chunk), there are some coastal regions that are not identified by name_2rgn function. They must be checked to determine how to best include them (or not include them).

sani_improved <- sani %>%
   dplyr::mutate(country = ifelse(stringr::str_detect(country,"Cura\\Sao"), "Curacao", country))%>% 
   dplyr::mutate(country = ifelse(stringr::str_detect(country,"R\\Sunion"), "Reunion", country)) %>% 
   dplyr::mutate(country = ifelse(stringr::str_detect(country, "C\\Ste d\\SIvoire"), "Ivory Coast", country)) %>% 
   dplyr::mutate(country=ifelse(stringr::str_detect(country,"Hong Kong"), "Hong Kong", country)) %>% 
   dplyr::mutate(country=ifelse(stringr::str_detect(country,"Macao"), "Macao", country)) %>%
  dplyr::mutate(country=ifelse(stringr::str_detect(country, "Saint Martin"), "Northern Saint-Martin", country))

## v2021: Reporting Caribbean Netherlands regions (Bonaire, Sint Eustatius, and Saba) at a higher resolution:

CN <- filter(sani_improved, country=="Caribbean Netherlands") %>%
   rename(country_old = country)

CN_subregions <- data.frame(country_old = "Caribbean Netherlands",
                            country = c("Bonaire", "Sint Eustatius", "Saba")) %>%
  left_join(CN) %>%
  select(-country_old)

sani_improved <- sani_improved %>%
   filter(country != "Caribbean Netherlands") %>%
   rbind(CN_subregions)  

# Channel Islands correspond to OHI regions of Guernsey and Jersey. Here the data reported for Channel Islands is used for these two regions.

CI <- dplyr::filter(sani_improved, country=="Channel Islands") %>%
  dplyr::rename(country_old = country)
CI_subregions <- data.frame(country_old = "Channel Islands",
                            country = c("Guernsey", "Jersey")) %>%
  dplyr::left_join(CI) %>%
  dplyr::select(-country_old)

sani_improved <- sani_improved %>%
  dplyr::filter(country != "Channel Islands") %>%
  rbind(CI_subregions)

Add rgn_id and merge duplicate regions using a mean weighted by population.

rgn_sani <- name_2_rgn(sani_improved, 
                        fld_name     = 'country',
                        flds_unique  = c('country','year')) 

## Check list in warning to make sure all countries removed are not of interest for the OHI global assesment. 
# v2019 non-matched regions: Eswatini (landlocked), Isle of Man (not reported in OHI), North Macedonia (landlocked), State of Palestine (disputed), Saint Barthelemy (not reported in OHI)

##Check for duplicate regions.
sort(table(rgn_sani$rgn_id)) #for this analysis, regions which are duplicated are: rgn_id = 209, 140, 116, 13

## Each region has 18 years of data in this case. If there are regions with more than 18 values is because in this database they are reported in a higher resolution than the OHI global assesment (eg: China, Hong Kong and Macao are just one region for the OHI global). For these regions a weighted average will be calculated as its final score.


## Calculating weighted means for the duplicated regions.

sani_final <- rgn_sani %>% 
  dplyr::group_by(rgn_id, rgn_name, year) %>% 
  dplyr::summarise(basic_sani_prop= weighted.mean(basic_prop, pop, na.rm = TRUE))

##Check for duplicate regions. At this point, all regions should have the same sample size.
sort(table(sani_final$rgn_id))

Gapfilling

First step is to get an idea of what needs to be gapfilled.

sani_gf <- sani_final %>% 
  dplyr::group_by(rgn_id, rgn_name) %>%
  dplyr::mutate(gf_count = sum(is.na(basic_sani_prop))) %>% # create column to count # of NAs (# of data points to gapfill)
  ungroup()

summary(sani_gf) # 161 NAs in v2019
# 212 NAs in v2021; makes sense, there are more years of data

## list of regions that need gapfilling
dplyr::filter(sani_gf, gf_count>0) %>% 
  dplyr::select(rgn_id, rgn_name, gf_count) %>% 
  unique() %>%
  data.frame()

##Some regions have no data - we will filter them out to be gapfilled later.

##Define number of years of data
years_data <- mean(table(sani_gf$rgn_id))

sani_gf <- sani_gf %>%
  dplyr::filter(gf_count != years_data)

# Note: there are regions in this data frame that do not need to be gapfilled.

Gapfilling 1: Linear model

Use a linear model within country data to estimate missing years.

sani_gf_lm <- sani_gf %>%
  dplyr::group_by(rgn_id, rgn_name) %>%
  dplyr::do({
    mod <- lm(basic_sani_prop ~ year, data = .)
    gf_lm <- predict(mod, newdata = .[c('year')])
    data.frame(., gf_lm)
  }) %>%
  dplyr::ungroup()


sani_gf_lm <- sani_gf_lm %>%
  mutate(gf_lm = ifelse(gf_lm > 1, 1, gf_lm)) %>% # constrain predictions to <=1 
  mutate(method = ifelse(is.na(basic_sani_prop), "lm prediction based on year", NA)) %>%
  mutate(basic_sani_prop = ifelse(is.na(basic_sani_prop), gf_lm, basic_sani_prop))

Gapfilling 2: Georegional averages

Georegional gapfilling for regions that do not have data.

UNgeorgn()
UNgeorgn <- UNgeorgn %>%
  dplyr::select(rgn_id, rgn_label, r1=r1_label, r2=r2_label)


year <- min(sani_gf_lm$year):max(sani_gf_lm$year) #defines the year range

sani_georgn_gf <- UNgeorgn %>%
  expand(year, UNgeorgn) %>%
  dplyr::left_join(sani_gf_lm, by = c('rgn_id', 'year'))


##Calculate two different gapfill columns using r2 and r1 UN geopolitical classification
sani_georgn_gf <- sani_georgn_gf %>%
  dplyr::group_by(year, r2) %>%
  dplyr::mutate(basic_sani_r2 = mean(basic_sani_prop, na.rm=TRUE)) %>%
  dplyr::ungroup() %>%
  dplyr::group_by(year, r1) %>%
  dplyr::mutate(basic_sani_r1 = mean(basic_sani_prop, na.rm=TRUE)) %>%
  dplyr::ungroup()%>%
  dplyr::arrange(rgn_id, year)


##First gapfill with r2, if no value available use r1; create column indicating whether value was gapfilled and if so, by what method. Give NA to inhabited regions
sani_georgn_gf <- sani_georgn_gf %>%
  dplyr::mutate(method = ifelse(is.na(basic_sani_prop) & !is.na(basic_sani_r2), "UN georegion avg. (r2)", method)) %>%
  dplyr::mutate(method = ifelse(is.na(basic_sani_prop) & is.na(basic_sani_r2) & !is.na(basic_sani_r1), "UN georegion avg (r1)", method))%>%
  dplyr::mutate(basic_sani_prop = ifelse(is.na(basic_sani_prop) & !is.na(basic_sani_r2), basic_sani_r2, basic_sani_prop)) %>%
  dplyr::mutate(basic_sani_prop = ifelse(is.na(basic_sani_prop) & !is.na(basic_sani_r1), basic_sani_r1, basic_sani_prop)) %>%
  dplyr::select(rgn_id, rgn_label, year, basic_sani_prop, method)

##See regions that have not been gapfilled. 
dplyr::filter(sani_georgn_gf, is.na(basic_sani_prop)) %>% 
  dplyr::select(rgn_id, basic_sani_prop) %>% 
  unique() %>%
  data.frame() #NA values for uninhabitated regions.

Gapfilling 3: Replace scores for uninhabited regions

Uninhabited regions get a perfect score.

## Identify uninhabited regions 

low_pop() # requires common.R to be loaded 
low_pop <- low_pop %>%
  dplyr::filter(est_population < 100 | is.na(est_population)) 



#Fill in all inhabited in rgn_inhab with perfect sanitation prop
sani_complete <- sani_georgn_gf %>% 
  dplyr::mutate(basic_sani_prop = ifelse(rgn_id %in% low_pop$rgn_id, 1, basic_sani_prop)) %>% 
  dplyr::mutate(gapfill = ifelse(is.na(method), 0, 1)) %>%
  dplyr::mutate(gapfill = ifelse(rgn_id %in% low_pop$rgn_id, 0, gapfill)) %>%
  dplyr::mutate(method = ifelse(rgn_id %in% low_pop$rgn_id, "No est. human population", method)) %>%
  dplyr::select(rgn_id, rgn_name = rgn_label, year, basic_sani_prop, gapfill, method)

summary(sani_complete) # should be no more NAs 
table(sani_complete$gapfill, sani_complete$method) # no pop is not considered gapfilled, should be N years * 20 regions

Save gapfilled data records

write_csv(sani_complete, here("globalprep/prs_cw_pathogen/v2021/intermediate/sani_complete.csv"))


# Quick check with v2019 data
sani_complete_old <- read_csv(here("globalprep/prs_cw_pathogen/v2019/intermediate/sani_complete.csv")) %>% 
  rename(basic_sani_prop_2019 = basic_sani_prop) %>%
  rename(gapfill_2019 = gapfill) %>% 
  rename(method_2019 = method) %>% 
  left_join(sani_complete, by=c("rgn_id", "year", "rgn_name")) %>%
  filter(year == 2017)

ggplotly(ggplot(sani_complete_old, aes(y = basic_sani_prop, x = basic_sani_prop_2019, labels = rgn_id)) +
  geom_point() +
  geom_abline(slope = 1, intercept = 0, color = "red"))

# save gapfilling info
gf_data <- sani_complete %>%
  dplyr::select(rgn_id, year, gapfill, method)

write_csv(gf_data, here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi_gf.csv"))

gf_data_trend <- sani_complete %>%
  dplyr::arrange(rgn_id, year) %>%
  dplyr::group_by(rgn_id) %>%
  dplyr::mutate(gapfill_5year = rollsum(gapfill, 5, align="right", fill=NA)) %>%
  dplyr::mutate(method = paste(na.exclude(unique(method)), collapse = ", ")) %>%
  dplyr::mutate(gapfill = gapfill_5year/5) %>%
  dplyr::select(rgn_id, year, gapfill, method)

write_csv(gf_data, here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi_trend_gf.csv"))

Standarizing sanitation data by population density

First calculate coastal population density (people/km²) is calculated by dividing the population within 25 miles of the coast by km² within the 25 mile inland coastal area (yes! This is confusing because area is in km^2, despite the boundary being 25 miles inland).

## Population within 25 miles of coastline - v2021; hasn't been udpated for 2021, so use 2020 data
population <- read_csv(here("globalprep/mar_prs_population/v2020/output/mar_pop_25mi.csv")) %>%
  dplyr::arrange(rgn_id, year)

#Read area 25mi inland to calculate population density
# (NOTE: this is confusing because it calculates the area in km2 for the 25 mile inland area)
area <- read_csv(here("globalprep/mar_prs_population/v2020/int/area_km2_25mi.csv"))

# People per km2 (for the 25 mile inland area)
pop_density <- population %>% 
  dplyr::left_join(area, by = 'rgn_id') %>% 
  dplyr::mutate(pop_per_km2 = popsum/area_km2) %>% 
  dplyr::select(rgn_id, year, pop_per_km2)

##Save population density data
write_csv(pop_density, here("globalprep/prs_cw_pathogen/v2021/intermediate/pathogen_pop_density_25mi.csv"))

These data are transformed to a pressure, with a zero score indicating no pressure and 1 indicating the highest possible pressure. Given this we want to determine the number of people without access.

The number of people per km^2 without access to sanitation is calculated by:

converting proportion with access to sanitation to proportion without access to sanitation (i.e., 1 - proportion_with_access).
The proportion without access is multiplied by the coastal population density.
Number of people without access are log transformed (ln(x+1))

unsani_pop <- sani_complete %>%  
    dplyr::select(rgn_id, rgn_name, year, basic_sani_prop) %>%
    dplyr::left_join(pop_density, 
              by=c('rgn_id', 'year')) %>%
    dplyr::mutate(propWO_x_pop     = (1 - basic_sani_prop) * pop_per_km2, # this calculates the population density of people without access (WO)
           propWO_x_pop_log = log(propWO_x_pop + 1)) # log is important because the skew was high otherwise

hist(unsani_pop$propWO_x_pop, main = "people without access")

hist(unsani_pop$propWO_x_pop_log, main = "log of people without access")

Pressure Score

The reference point is the 99th quantile across all countries and years 2000-2009 as a reference point.

##Calculate reference point
ref_calc <- unsani_pop %>% 
  dplyr::filter(year %in% 2000:2009) %>% #years of reference
  ##summarise(ref= max(propWO_x_pop_log, na.rm = TRUE)*1.1) %>%  # old method
  dplyr::summarise(ref= quantile(propWO_x_pop_log, probs=c(0.99), na.rm = TRUE)) %>% 
  .$ref

ref_calc
## save to the master reference point list - new folder might need to be created for assessment year if this file does not already exist.
master_refs <- read.csv(here("globalprep/supplementary_information/v2021/reference_points_pressures.csv"), stringsAsFactors = FALSE)

master_refs$ref_point[master_refs$pressure == "Sanitation"] <- ref_calc

write.csv(master_refs, here("globalprep/supplementary_information/v2021/reference_points_pressures.csv"), row.names=FALSE)

master_refs <- read.csv(here("globalprep/supplementary_information/v2021/reference_points_pressures.csv")) 
ref_value <- as.numeric(as.character(master_refs$ref_point[master_refs$pressure == "Sanitation"])) 
ref_value #7.10

unsani_prs <- unsani_pop %>%
  dplyr::mutate(pressure_score = propWO_x_pop_log / ref_value) %>% 
  dplyr::mutate(pressure_score = ifelse(pressure_score>1, 1, pressure_score)) %>% #limits pressure scores not to be higher than 1
  dplyr::select(rgn_id, year, pressure_score) 

summary(unsani_prs)

#Save data pressure scores 
write_csv(unsani_prs, here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi.csv"))

# Compare to v2018 data

unsani_prs_old <- read_csv(here("globalprep/prs_cw_pathogen/v2019/output/po_pathogen_popdensity25mi.csv")) %>%
  rename(pressure_score_2019 = pressure_score) %>% 
  left_join(unsani_prs, by=c("rgn_id", "year")) %>%
  filter(year == 2015)

filter(unsani_prs_old, rgn_id %in% c(185, 208))

  ggplotly(ggplot(unsani_prs_old, aes(y = pressure_score, x = pressure_score_2019, labels = rgn_id)) +
  geom_point() +
  geom_abline(slope = 1, intercept = 0, color = "red"))

Model Trend

Using CalculateTrend function form the ohicore, trend is calculated by applying a linear regression model to the pressuere scores using a window of 5 years of data. The solope of the linear regression (annual change in pressure) is then divided by the earliest year to get proportional change and then multiplied by 5 to get estimate trend on pressure in the next five years.

##Calculate trend using CalculateTrend()

##Define relevant years: Min and max year of data to calculate trend
first_assess_year <- 2012 
current_assess_year <- 2021
current_data_year <- max(unsani_prs$year) ##max year of data
first_data_year <- first_assess_year - (current_assess_year - current_data_year)

trend_data <- data.frame() #create a data.frame to save trend socores


##For loop: calculates trend for all assess years within the corresponding 5 year window.
#focal_year is the year for which the trend is being calculated.
for(focal_year in first_data_year:current_data_year){ #focal_year = 2017 

  trend_years <- (focal_year-4):focal_year #defines the 5 years window to calculate trend
  
  data_new <- unsani_prs %>% #format data to work in CalculateTrend()
    select(rgn_id, year, status=pressure_score)
  
trend_data_focal_year <- CalculateTrend(data_new, trend_years)

trend_data_focal_year <- trend_data_focal_year %>%
  mutate(year = focal_year) %>%
  select(rgn_id = region_id, year, trend=score) %>%
  data.frame()

trend_data <- rbind(trend_data, trend_data_focal_year) #add trend calculation to dataframe crearted outside the loop
}
summary(trend_data)

##Save trend data
write_csv(trend_data, here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi_trend.csv"))

Compare to previous years

Trend data

new <- read_csv(here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi_trend.csv"))

compare <- read_csv(here("globalprep/prs_cw_pathogen/v2019/output/po_pathogen_popdensity25mi_trend.csv")) %>%
  select(rgn_id, year, trend_2019 = trend) %>%
  left_join(new, by=c('rgn_id', 'year')) %>%
  filter(year == 2017)


ggplotly(ggplot(compare, aes(y = trend, x = trend_2019, labels = rgn_id)) +
  geom_point() +
  geom_abline(slope = 1, intercept = 0, color = "red"))

# A few regions have large variation in trend between v2021 and v2019
# positive outliers: rgn_id 161, rgn_id 154
# negative outliers: 48, 8 


# Outlier investigation 

data_old <- read_csv(here("globalprep/prs_cw_pathogen/v2019/output/po_pathogen_popdensity25mi.csv")) %>% 
  rename(old_pressure_score=pressure_score)

data_new <- read_csv(here("globalprep/prs_cw_pathogen/v2021/output/po_pathogen_popdensity25mi.csv"))

outlier <- data_new %>% 
  left_join(data_old, by=c("rgn_id", "year")) %>% 
  filter(rgn_id == 161)

plot(outlier$pressure_score, outlier$old_pressure_score)
abline(0,1, col="red")

Compare results

We checked the main discrepancies and these were due to changes in source data. Fairly small changes in access can lead to fairly large changes in pressure scores, depending on the population. There are slightly higher pressure scores this year (indicated by points tending to be above the 1-1 red line) due to modifications of reference point calculations.

Outlier exploration

### Comparison of basic access to sanitation scores 
sani_raw <- read.csv(file.path(dir_M, "git-annex/globalprep/_raw_data/WHO_UNICEFF/d2021/JMP_2021_WLD.csv"), header=FALSE, sep = ",", na.strings = c(NA, ''), stringsAsFactors = FALSE, strip.white = TRUE)

sani_raw_old <- read.csv(file.path(dir_M, "git-annex/globalprep/_raw_data/WHO_UNICEFF/d2019/JMP_2019_WLD.csv"), header=FALSE, sep = ",", na.strings = c(NA, ''), stringsAsFactors = FALSE, strip.white = TRUE)

sani_old <- sani_raw_old %>%
  dplyr::slice(-1) %>%                # cut first row with column names
  dplyr::select(country = V1,
         year = V3,
         pop = V4,
         basic_pct = V6) %>%
  dplyr::mutate(basic_pct = ifelse(basic_pct=="-", NA, basic_pct)) %>% 
  dplyr::mutate(pop = stringr::str_remove_all(pop, pattern = " ")) %>%
  dplyr::mutate(basic_pct = ifelse(stringr::str_detect(basic_pct,">99"), 99.5, basic_pct)) %>%
  dplyr::mutate_at(.vars = c("pop", "basic_pct", "year"), .funs = as.numeric)%>%
  dplyr::mutate(pop = pop * 1000,
          basic_prop = basic_pct/100) %>%
  dplyr::filter(!is.na(year))

sani_old_outliers <- sani_old %>% 
  filter(country == "Palau" | country == "Oman" | country == "Niue" | country == "Wallis and Futuna") %>% 
  rename(pop_2019 = pop, basic_pct_2019 = basic_pct, basic_prop_2019 = basic_prop)
  
sani_compare <- sani %>%
  filter(country == "Palau" | country == "Oman" | country == "Niue" | country == "Wallis and Futuna") %>% 
  left_join(sani_old_outliers, by=c("country", "year")) %>% 
  select(country, year, pop, pop_2019, basic_pct, basic_pct_2019)  # yep, trends changes in raw data


### Comparison of pressure scores

unsani_prs_old <- read_csv(here("globalprep/prs_cw_pathogen/v2019/output/po_pathogen_popdensity25mi.csv")) %>%
  rename(pressure_score_2019 = pressure_score) %>% 
  left_join(unsani_prs, by=c("rgn_id", "year")) %>% 
  filter(rgn_id %in% c(8, 48, 154, 161))

unsani_prs_old_all <- read_csv(here("globalprep/prs_cw_pathogen/v2019/output/po_pathogen_popdensity25mi.csv")) %>%
  rename(pressure_score_2019 = pressure_score) %>% 
  left_join(unsani_prs, by=c("rgn_id", "year")) %>% 
  mutate(diff = pressure_score-pressure_score_2019)

OHI 2021 - Clean Water - Pathogen Pollution: Preparing Sanitation data

Compiled on Fri Nov 12 13:03:08 2021 by sgclawson

Summary

Updates from previous assessment

Data Source

Methods

Setup

Import raw data

Methods

Data wrangling

Gapfilling

Gapfilling 1: Linear model

Gapfilling 2: Georegional averages

Gapfilling 3: Replace scores for uninhabited regions

Save gapfilled data records

Standarizing sanitation data by population density

Pressure Score

Model Trend

Compare to previous years

Trend data

Compare results

Outlier exploration