确定两个邮政编码之间的距离（替代mapdist） [英] Determining the distance between two ZIP codes (alternatives to mapdist)

问题描述

我想要计算大约的距离。 100,000个不同的邮政编码。我知道 ggmap 包中的 mapdist 函数

mapdist 完美无缺：

  library（ggmap）
 mapdist（'Washington'，'New York'，mode ='driving'）
 
＃从m km公里秒分钟小时
＃1华盛顿纽约366284 366.284 227.6089 13997 233.2833 3.888056 
 
 
 mapdist（'20001'，'10001'，mode ='driving'）
 
＃从m km km秒钟分钟
＃1 20001 10001 363119 363.119 225.6421 13713 228.55 3.809167 
  

然而， mapdist 依赖于Google Geocoding API，该API必须符合每天2500个地理位置请求的查询限制。

您是否知道替代r代码来计算使用具有较高请求限制的其他服务（例如诺基亚地图或）的两点之间的距离Bing）？

解决方案

taRifx.geo :: georoute 此处，直到我推出另一个更新，此时它可以通过 install.packages ）使用Bing Maps（支持我相信每天25k）并且可以返回距离。

  georoute（c（3817 Spruce St，Philadelphia，PA 19104，
9000 Rockville Pike，Bethesda，Maryland 20892），
 verbose = TRUE，returntype =time，
 service =bing）
  

您必须获取Bing Maps API密钥并将其设置在您的R全局选项中（理想位置在 .Rprofile 中），但密钥是免费的：

  options（BingMapsKey =whateverBingGivesYouForYourKey）
  

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)?

解决方案

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")

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