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I want to calculate the distance between approx. 100,000 different ZIP codes. I know about the mapdist function in the ggmap package
mapdist works perfectly:
library(ggmap)
mapdist('Washington', 'New York', mode = 'driving')
# from to m km miles seconds minutes hours
# 1 Washington New York 366284 366.284 227.6089 13997 233.2833 3.888056
mapdist('20001', '10001', mode = 'driving')
# from to m km miles seconds minutes hours
# 1 20001 10001 363119 363.119 225.6421 13713 228.55 3.809167
However, mapdist relies on the Google Geocoding API which is subject to a query limit of 2,500 geolocation requests per day.
Are you aware of any alternative r code to calculate the distance between two points using another service which has a higher request limit (such as Nokia Maps or Bing)?
820
As of 1963, zip codes' numbers are determined by a few factors: the area, the regional postal facility and the local zone. The first number of the five-digit code signifies the region which the address is located in, a number that grows from the east coast to the west.
You can map any kind of address data ranging from latitude and longitude coordinates to countries, states (provinces or regions), cities, zip codes, and/or specific addresses. Create a customized Google map by copying and pasting your excel or other spreadsheet data to make a map.
ZIP codes can and do cross state lines (rarely, but just enough to cause some problems and confusion), county lines (about 10% of ZIPs are in more than one county), political jurisdictions (cities, congressional districts), metro areas, etc.
taRifx.geo::georoute (only available here until I push out another update, at which point it will be available via install.packages) can use Bing Maps (which supports I believe 25k per day) and can return a distance.
georoute( c("3817 Spruce St, Philadelphia, PA 19104",
"9000 Rockville Pike, Bethesda, Maryland 20892"),
verbose=TRUE, returntype="time",
service="bing" )
You'll have to get a Bing Maps API key and set it in your R global options (ideal placement is in .Rprofile), but the key is free:
options(BingMapsKey="whateverBingGivesYouForYourKey")
This might be trivial, but one completely free option is to use Census ZCTA geography data to get co-ordinates for each zip code, and then calculate Haversine distances (or some similar distance metric) between coordinates.
45
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