slow way to tease out distinct areas from overlapping areas

make_areas_distinct.R

### =delete overlapping buffers= ###

options("scipen"=100)

require(varhandle)
require(data.table)
require(tidyr)
require(tidyverse)
require(dplyr)
require(DBI)
require(RSQLite)
require(sp)
require(gmt)
require(stringr)
require(geosphere)

db = dbConnect(RSQLite::SQLite(), "~/cities.sqlite")
dbListTables(db)
  geocities  = dbReadTable(db,"cities")
    setDT(geocities)
dbDisconnect(db)

geocities = geocities[order(X30s_02_mean)]

# split up data into regions
splitdt = split(geocities, geocities$c)

# function to get the total number of regions in the list
NROW(splitdt)

# function to get the total number of rows in a given region
NROW(splitdt[[1]])

## setup
dat=geocities[FALSE,][]
region_pointer=1

while (region_pointer <= NROW(splitdt)){
  ## setup new region
  origin_pointer=1
  region_data = as.data.frame(splitdt[[region_pointer]])
  region_data$remove = FALSE
  setDT(region_data)

  ## process a region
  while (origin_pointer <= NROW(region_data)) {
    # choose which row to use
    # as the first part of the distance formula
    origin_data = region_data[,c("longitude","latitude")] %>% slice(origin_pointer) ## set LeadingRow city
    setDT(origin_data)
    
    # calculate the distance from the specific row chosen 
    # and only keep ones which are further than 5.4km
    region_data = region_data %>% rowwise() %>% mutate(remove = ifelse(distHaversine(c(longitude,latitude), origin_data) < 5400, 
                                                                       TRUE, region_data$remove) ) # keep origin_data city
    
    # remove matched cities
    region_data = region_data[region_data$remove!=TRUE,]
  
    print(origin_pointer/NROW(splitdt[[region_pointer]])*100)
    print(NROW(region_data)/NROW(splitdt[[region_pointer]])*100)
    origin_pointer = origin_pointer+1
  }
  region_pointer = region_pointer+1
  
  print('#####################################')
  print(region_pointer/NROW(splitdt)*100)
  print('#####################################')
  
  # save results
  region_data = region_data[region_data$remove!=TRUE,]
  dat = rbind(dat, region_data, fill=TRUE)
}

fwrite(dat, file = '~/data/cities_processed.csv')