library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
library(data.table)
library(reshape2)
##
## Attaching package: 'reshape2'
## The following objects are masked from 'package:data.table':
##
## dcast, melt
library(sf)
## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::as_data_frame() masks tibble::as_data_frame(), igraph::as_data_frame()
## x dplyr::between() masks data.table::between()
## x purrr::compose() masks igraph::compose()
## x tidyr::crossing() masks igraph::crossing()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks data.table::first()
## x dplyr::groups() masks igraph::groups()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks data.table::last()
## x purrr::simplify() masks igraph::simplify()
## x purrr::transpose() masks data.table::transpose()
library(tmap)
Run_Communities <- function(g,isSimp,algorithmCom){
cc <- algorithmCom
f <- as.data.frame(cbind(V(g)$name,cc$membership))
colnames(f)<- c("names", "community")
f$type <- i
f$method <- algorithm(cc)
f$n_clusters <- length(cc)
f$modulary <- modularity(cc)
print(head(f))
write.table(f, "MOD.csv", row.names = FALSE, col.names = FALSE, append=TRUE, sep=",")
}
setwd("/Users/chaneumpark/Dropbox (GaTech)/2021-Fall/Urban Analytics/R lab session/Assignment4 CDR")
all2types <- read.csv("all2types.csv") #source=origin, target=destination, weight=# of movements !NOTE: not directed
CountyNames <- read.csv("CountyNames.csv")
OrigNames <- merge(all2types, CountyNames, by.x = 'source', by.y = 'FIPS')
OrigDestNames <- merge(OrigNames, CountyNames, by.x = 'target', by.y = 'FIPS')
all2types <- OrigDestNames
rm(OrigNames, OrigDestNames)
### Q1 & Q2 ###
filter(all2types, type =="Commutes") %>% filter(weight == max(weight))
## target source weight type Name.x Name.y
## 1 6037 6037 4181968 Commutes Los Angeles_California Los Angeles_California
filter(all2types, type =="Migrants") %>% filter(weight == max(weight))
## target source weight type Name.x Name.y
## 1 6037 6037 851215 Migrants Los Angeles_California Los Angeles_California
### Q3 & Q4 ###
filter(all2types, source != target, type =="Commutes") %>% filter(weight == max(weight))
## target source weight type Name.x Name.y
## 1 36061 36047 457281 Commutes Kings_New York New York_New York
filter(all2types, source != target, type =="Migrants") %>% filter(weight == max(weight))
## target source weight type Name.x Name.y
## 1 6059 6037 66604 Migrants Los Angeles_California Orange_California
### Q5 ###
if (file.exists("MOD.csv")) {file.remove("MOD.csv")}
## [1] TRUE
types <- unique(all2types$type)
###cluster_louvain
for (i in types){
net <- filter(all2types, type==i)
g <- graph_from_data_frame(net, directed = FALSE)
Run_Communities(g,0,cluster_louvain(g))
}
## names community type method n_clusters modulary
## 1 1001 1 Migrants multi level 28 0.8003269
## 2 1003 1 Migrants multi level 28 0.8003269
## 3 1005 1 Migrants multi level 28 0.8003269
## 4 1007 1 Migrants multi level 28 0.8003269
## 5 1009 1 Migrants multi level 28 0.8003269
## 6 1011 1 Migrants multi level 28 0.8003269
## names community type method n_clusters modulary
## 1 1001 1 Commutes multi level 73 0.9541115
## 2 1003 2 Commutes multi level 73 0.9541115
## 3 1005 1 Commutes multi level 73 0.9541115
## 4 1007 1 Commutes multi level 73 0.9541115
## 5 1009 1 Commutes multi level 73 0.9541115
## 6 1011 1 Commutes multi level 73 0.9541115
#cluster_fast_greedy
Run_Communities <- function(g,isSimp,algorithmCom){
cc <- algorithmCom
f <- as.data.frame(cbind(V(g)$name,cc$membership))
colnames(f)<- c("names", "community")
f$type <- i
f$method <- algorithm(cc)
f$n_clusters <- length(cc)
f$modulary <- modularity(cc)
print(head(f))
write.table(f, "MOD_fastgreedy.csv", row.names = FALSE, col.names = FALSE, append=TRUE, sep=",")
}
for (i in types){
net <- filter(all2types, type==i)
g <- graph_from_data_frame(net, directed = FALSE)
Run_Communities(g,0,cluster_fast_greedy(g))
}
## names community type method n_clusters modulary
## 1 1001 21 Migrants fast greedy 29 0.800249
## 2 1003 21 Migrants fast greedy 29 0.800249
## 3 1005 21 Migrants fast greedy 29 0.800249
## 4 1007 21 Migrants fast greedy 29 0.800249
## 5 1009 21 Migrants fast greedy 29 0.800249
## 6 1011 21 Migrants fast greedy 29 0.800249
## names community type method n_clusters modulary
## 1 1001 45 Commutes fast greedy 74 0.9541703
## 2 1003 21 Commutes fast greedy 74 0.9541703
## 3 1005 35 Commutes fast greedy 74 0.9541703
## 4 1007 45 Commutes fast greedy 74 0.9541703
## 5 1009 45 Commutes fast greedy 74 0.9541703
## 6 1011 45 Commutes fast greedy 74 0.9541703
#cluster_walktrap
Run_Communities <- function(g,isSimp,algorithmCom){
cc <- algorithmCom
f <- as.data.frame(cbind(V(g)$name,cc$membership))
colnames(f)<- c("names", "community")
f$type <- i
f$method <- algorithm(cc)
f$n_clusters <- length(cc)
f$modulary <- modularity(cc)
print(head(f))
write.table(f, "MOD_walktrap.csv", row.names = FALSE, col.names = FALSE, append=TRUE, sep=",")
}
for (i in types){
net <- filter(all2types, type==i)
g <- graph_from_data_frame(net, directed = FALSE)
Run_Communities(g,0,cluster_walktrap(g))
}
## names community type method n_clusters modulary
## 1 1001 13 Migrants walktrap 136 0.2451867
## 2 1003 13 Migrants walktrap 136 0.2451867
## 3 1005 13 Migrants walktrap 136 0.2451867
## 4 1007 13 Migrants walktrap 136 0.2451867
## 5 1009 13 Migrants walktrap 136 0.2451867
## 6 1011 13 Migrants walktrap 136 0.2451867
## names community type method n_clusters modulary
## 1 1001 452 Commutes walktrap 1351 0.2106047
## 2 1003 11 Commutes walktrap 1351 0.2106047
## 3 1005 469 Commutes walktrap 1351 0.2106047
## 4 1007 192 Commutes walktrap 1351 0.2106047
## 5 1009 17 Commutes walktrap 1351 0.2106047
## 6 1011 483 Commutes walktrap 1351 0.2106047
#cluster_infomap (takes a while, you may have to wait)
Run_Communities <- function(g,isSimp,algorithmCom){
cc <- algorithmCom
f <- as.data.frame(cbind(V(g)$name,cc$membership))
colnames(f)<- c("names", "community")
f$type <- i
f$method <- algorithm(cc)
f$n_clusters <- length(cc)
f$modulary <- modularity(cc)
print(head(f))
write.table(f, "MOD_infomap.csv", row.names = FALSE, col.names = FALSE, append=TRUE, sep=",")
}
for (i in types){
net <- filter(all2types, type==i)
g <- graph_from_data_frame(net, directed = FALSE)
Run_Communities(g,0,cluster_infomap(g))
}
## names community type method n_clusters modulary
## 1 1001 1 Migrants infomap 50 0.794581
## 2 1003 1 Migrants infomap 50 0.794581
## 3 1005 1 Migrants infomap 50 0.794581
## 4 1007 1 Migrants infomap 50 0.794581
## 5 1009 1 Migrants infomap 50 0.794581
## 6 1011 1 Migrants infomap 50 0.794581
## names community type method n_clusters modulary
## 1 1001 1 Commutes infomap 255 0.9449073
## 2 1003 2 Commutes infomap 255 0.9449073
## 3 1005 3 Commutes infomap 255 0.9449073
## 4 1007 4 Commutes infomap 255 0.9449073
## 5 1009 4 Commutes infomap 255 0.9449073
## 6 1011 3 Commutes infomap 255 0.9449073
MOD <- read.csv("MOD.csv", header=FALSE)
colnames(MOD) <- c("fips", "community", "type", "method", "n_regions", "modularity") ###community is just an id for it
MOD$fips <- as.numeric(MOD$fips)
counties <- st_read("counties.shp")
## Reading layer `counties' from data source
## `/Users/chaneumpark/Dropbox (GaTech)/2021-Fall/Urban Analytics/R lab session/Assignment4 CDR/counties.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 3142 features and 62 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -19839090 ymin: 2145730 xmax: -7454985 ymax: 11542620
## Projected CRS: WGS 84 / Pseudo-Mercator
st_crs(counties, crs= 42304)
## Coordinate Reference System:
## User input: WGS 84 / Pseudo-Mercator
## wkt:
## PROJCRS["WGS 84 / Pseudo-Mercator",
## BASEGEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]],
## CONVERSION["Popular Visualisation Pseudo-Mercator",
## METHOD["Popular Visualisation Pseudo Mercator",
## ID["EPSG",1024]],
## PARAMETER["Latitude of natural origin",0,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8801]],
## PARAMETER["Longitude of natural origin",0,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8802]],
## PARAMETER["False easting",0,
## LENGTHUNIT["metre",1],
## ID["EPSG",8806]],
## PARAMETER["False northing",0,
## LENGTHUNIT["metre",1],
## ID["EPSG",8807]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["metre",1]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["metre",1]],
## USAGE[
## SCOPE["Web mapping and visualisation."],
## AREA["World between 85.06°S and 85.06°N."],
## BBOX[-85.06,-180,85.06,180]],
## ID["EPSG",3857]]
counties <- counties[,1:2]
countycommunities <- merge(counties, MOD, by.x = "OBJECTID", by.y = "fips")
countycommunities <- countycommunities[,3:8]
countycommunities$community <- as.character(countycommunities$community)
states <- st_read('Extra shp/States.shp')
## Reading layer `States' from data source
## `/Users/chaneumpark/Dropbox (GaTech)/2021-Fall/Urban Analytics/R lab session/Assignment4 CDR/Extra shp/States.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 56 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -179.1473 ymin: -14.55255 xmax: 179.7785 ymax: 71.35256
## Geodetic CRS: NAD83
cities <- st_read('Extra shp/Cities.shp')
## Reading layer `Cities' from data source
## `/Users/chaneumpark/Dropbox (GaTech)/2021-Fall/Urban Analytics/R lab session/Assignment4 CDR/Extra shp/Cities.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 98 features and 44 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -157.8427 ymin: 21.33078 xmax: -69.77768 ymax: 61.2187
## Geodetic CRS: WGS 84
### map for 'Migrants' ###
tm_shape(filter(countycommunities, type=="Migrants")) + tm_fill(col = 'community') +
tm_shape(states) + tm_borders() +
tm_shape(cities) + tm_dots() + tm_text("NAME", just = 'center', remove.overlap = T, size=0.5) +
tm_legend(show=F)
### map for 'Commutes' ###
tm_shape(filter(countycommunities, type=="Commutes")) + tm_fill(col = 'community') +
tm_shape(states) + tm_borders() + tm_shape(cities) + tm_dots() + tm_text("NAME", just = 'center', remove.overlap = T, size=0.5) + tm_legend(show=F)
## Warning: Number of levels of the variable "community" is 73, which is
## larger than max.categories (which is 30), so levels are combined. Set
## tmap_options(max.categories = 73) in the layer function to show all levels.
