In annecori/mRIIDSprocessData: Epidemiological forcasting using real-time data

library(dplyr)
library(stringr)
library(ggplot2)

For some countries we want to deal at ADM1 level while for others we want to work at ADM2 level.

If we choose to work at ADM2 level in DRC then we should work at ADM1 in all other countries (if we try to match populations). If, on the other hand, we work at ADM1 level in DRC then the closest population match is achieved by working at the following levels.

|ADM0 |level | |:--------------------------------|:-----| |Angola |ADM1 | |Burundi |ADM0 | |Central African Republic |ADM0 | |Democratic Republic of the Congo |ADM1 | |Republic of Congo |ADM0 | |Rwanda |ADM0 | |South Sudan |ADM0 | |Tanzania |ADM1 | |Uganda |ADM1 | |Zambia |ADM0 |

For ease of analysis, pull out the meta-data and put them together.

Read in ADM2 data

#adm2_neighbors <- c("Democratic Republic of the Congo")

adm1_neighbors <- c("Angola", "Burundi", 
                   "Uganda", "Republic of Congo",
                   "Central African Republic",
                   "South Sudan",
                   "Rwanda",
                   "Zambia")


outfile <- here::here("data/Geography/GravityModel/processed",
                      "drc_neighbors_pops.csv")

Working to match spatial area

adm1_neighbors <- c("Uganda",
                    "Angola",
                    "Central African Republic",
                    "Republic of Congo",
                    "South Sudan",
                    "Zambia")
##                    "Tanzania")

adm0_neighbors <- c("Burundi", 
                    "Rwanda")

outfile <- here::here("data/Geography/GravityModel/processed",
                      "drc_neighbors_area.csv")

Stack them together

centroids_adm2 <- here::here("data/Geography/GravityModel/raw",
                             "adm2-fixed.txt") %>%
    readr::read_tsv(.) %>%
    filter(ADM0 %in% adm2_neighbors)


centroids_adm1 <- here::here("data/Geography/GravityModel/raw",
                             "adm1_centroids_fixed.tsv") %>%
    readr::read_tsv(.) %>%
    filter(ADM0 %in% adm1_neighbors)

centroids_adm0 <- here::here("data/Geography/GravityModel/raw",
                             "adm0.txt") %>%
    readr::read_tsv(.) %>%
    filter(ADM0 %in% adm0_neighbors) %>%
    select(ADM0,
           Centroid_Lon,
           Centroid_Lat,
           Pop)    

centroids_adm0$district <- centroids_adm0$ADM0

centroids <- select(centroids_adm2,
                    ADM0,
                    district = ADM2,
                    Centroid_Lon,
                    Centroid_Lat,
                    Pop)

centroids <- select(centroids_adm1,
                    ADM0,
                    district = ADM1,
                    Centroid_Lon,
                    Centroid_Lat,
                    Pop) #%>% rbind(centroids)

centroids <- rbind(centroids, centroids_adm0)

Affix DRC Centroids.

drc_metadata <- here::here("data/Geography/GravityModel/processed",
                           "drc_metadata.csv") %>%
    readr::read_csv() %>%
    select(ADM0,
           district = adm2,
           Centroid_Lon,
           Centroid_Lat,
           Pop = pop)

centroids <- rbind(centroids, drc_metadata)

Write out processed file

readr::write_csv(centroids,
                 path = outfile)

annecori/mRIIDSprocessData documentation built on May 29, 2019, 1:16 p.m.