Just came across this excellent flow map tool that takes a google sheet and turns it into an interactive flow map. It’s super-easy to use, here is a quick demo.
We are using the commuting flow data between census subdivisions from the 2016 census. First we load the required libraries
library(tidyverse) library(cancensus) #remotes::install_github("mountainmath/statcanXtabs") library(statcanXtabs) library(sf) library(googlesheets4)
Next we create the google sheet for our flow map. It comes with three sheets, one defining overall properties, one defining the locations and one for the flows.
my_properties <- c( "title"="Canadian animated commuter flow", "description"="Canadian animated commuter flow", "source.name"="Statistics Canada Census 2016", "source.url"="https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/dt-td/Rp-eng.cfm?LANG=E&APATH=3&DETAIL=0&DIM=0&FL=A&FREE=0&GC=0&GID=0&GK=0&GRP=1&PID=111332&PRID=10&PTYPE=109445&S=0&SHOWALL=0&SUB=0&Temporal=2017&THEME=125&VID=0&VNAMEE=&VNAMEF=", "createdBy.name"="Jens von Bergmann", "createdBy.email"="firstname.lastname@example.org", "createdBy.url"="https://doodles.mountainmath.ca/posts/2020-01-06-flow_maps" , "mapbox.mapStyle"=NA, "colors.scheme"="Default", "colors.darkMode"="yes", "animate.flows"="yes", "clustering"="yes" ) properties <- tibble(property=names(my_properties)) %>% mutate(value=my_properties[property])
For the locations data we use the centroids of the census CSDs, where we prettify the names and fine-tune some special locations like Electoral A in Vancouver, which we hide behind the
clean_location_data() function call.
locations <- get_census("CA16",regions=list(C="01"),level="CSD",geo_format = "sf") %>% st_centroid(of_largest_polygon = TRUE) %>% cbind(st_coordinates(.)) %>% st_set_geometry(NULL) %>% clean_location_data() %>% select(id=GeoUID,name=Name,lat=Y,lon=X)
For the flows we take the commute flow cross tabulation at the CSD level.
flows <- get_sqlite_xtab("98-400-X2016325","https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/dt-td/CompDataDownload.cfm?LANG=E&PID=111332&OFT=CSV") %>% select(origin=`GEO_CODE (POR)`,dest=`GEO_CODE (POW)`,count=`Dim: Sex (3): Member ID: : Total - Sex`) %>% collect()
Putting it together
The only thing left to do is to upload the data to google sheets.
#my_new_sheet <- sheets_create(name="Animated commuter flow",sheets=c("properties","locations","flows")) my_new_sheet <- sheets_get("13Q-xsfL59XXPw7-3ue_9G9xab9FtYiL3S9vOZp-yJyY") write_sheet(properties,my_new_sheet,"properties") write_sheet(locations,my_new_sheet,"locations") write_sheet(flows,my_new_sheet,"flows")
That’s it. We can read off the sheet id and embed our flow map.
It clusters locations by default, so at low zoom levels it only shows high-level flows. Zoom in to see finer commute flows for your region of interest.