如何使用 Data Science Toolbox 对简单地址进行地理编码

我厌倦了谷歌的地理编码，并决定尝试替代方案。数据科学工具包（http://www.datasciencetoolkit.org http://www.datasciencetoolkit.org）允许您对无限数量的地址进行地理编码。 R 有一个优秀的包，可以作为其函数的包装器（CRAN：RDSTK）。该包有一个函数叫做street2coordinates()与数据科学工具包的地理编码实用程序接口。

然而，RDSTK 函数street2coordinates()如果您尝试对简单的东西进行地理编码，例如城市，国家。在下面的示例中，我将尝试使用该函数来获取凤凰城的纬度和经度：

> require("RDSTK")
> street2coordinates("Phoenix+Arizona+United+States")
[1] full.address
<0 rows> (or 0-length row.names)

数据科学工具包中的实用程序运行良好。这是给出答案的 URL 请求：

我对多个地址进行地理编码（完整的地址和城市名称）感兴趣。我知道数据科学工具包 URL 会很好用。

如何与 URL 交互并将多个纬度和经度放入包含地址的数据框中？

这是一个示例数据集：

dff <- data.frame(address=c(
  "Birmingham, Alabama, United States",
  "Mobile, Alabama, United States",
  "Phoenix, Arizona, United States",
  "Tucson, Arizona, United States",
  "Little Rock, Arkansas, United States",
  "Berkeley, California, United States",
  "Duarte, California, United States",
  "Encinitas, California, United States",
  "La Jolla, California, United States",
  "Los Angeles, California, United States",
  "Orange, California, United States",
  "Redwood City, California, United States",
  "Sacramento, California, United States",
  "San Francisco, California, United States",
  "Stanford, California, United States",
  "Hartford, Connecticut, United States",
  "New Haven, Connecticut, United States"
  ))

像这样：

library(httr)
library(rjson)

data <- paste0("[",paste(paste0("\"",dff$address,"\""),collapse=","),"]")
url  <- "http://www.datasciencetoolkit.org/street2coordinates"
response <- POST(url,body=data)
json     <- fromJSON(content(response,type="text"))
geocode  <- do.call(rbind,sapply(json,
                                 function(x) c(long=x$longitude,lat=x$latitude)))
geocode
#                                                long      lat
# San Francisco, California, United States -117.88536 35.18713
# Mobile, Alabama, United States            -88.10318 30.70114
# La Jolla, California, United States      -117.87645 33.85751
# Duarte, California, United States        -118.29866 33.78659
# Little Rock, Arkansas, United States      -91.20736 33.60892
# Tucson, Arizona, United States           -110.97087 32.21798
# Redwood City, California, United States  -117.88536 35.18713
# New Haven, Connecticut, United States     -72.92751 41.36571
# Berkeley, California, United States      -122.29673 37.86058
# Hartford, Connecticut, United States      -72.76356 41.78516
# Sacramento, California, United States    -121.55541 38.38046
# Encinitas, California, United States     -116.84605 33.01693
# Birmingham, Alabama, United States        -86.80190 33.45641
# Stanford, California, United States      -122.16750 37.42509
# Orange, California, United States        -117.85311 33.78780
# Los Angeles, California, United States   -117.88536 35.18713

这利用了 street2coordinates API 的 POST 接口（记录在这里 http://www.datasciencetoolkit.org/developerdocs#street2coordinates），它在 1 个请求中返回所有结果，而不是使用多个 GET 请求。

Phoenix 的缺失似乎是 street2coordinates API 中的一个错误。如果你去API演示页面 http://www.datasciencetoolkit.org/并尝试“美国亚利桑那州菲尼克斯”，您会得到空响应。但是，正如您的示例所示，使用他们的“Google-style Geocoder”does给凤凰城一个结果。因此，这里有一个使用重复 GET 请求的解决方案。请注意，这会运行慢得多.

geo.dsk <- function(addr){ # single address geocode with data sciences toolkit
  require(httr)
  require(rjson)
  url      <- "http://www.datasciencetoolkit.org/maps/api/geocode/json"
  response <- GET(url,query=list(sensor="FALSE",address=addr))
  json <- fromJSON(content(response,type="text"))
  loc  <- json['results'][[1]][[1]]$geometry$location
  return(c(address=addr,long=loc$lng, lat= loc$lat))
}
result <- do.call(rbind,lapply(as.character(dff$address),geo.dsk))
result <- data.frame(result)
result
#                                     address         long        lat
# 1        Birmingham, Alabama, United States   -86.801904  33.456412
# 2            Mobile, Alabama, United States   -88.103184  30.701142
# 3           Phoenix, Arizona, United States -112.0733333 33.4483333
# 4            Tucson, Arizona, United States  -110.970869  32.217975
# 5      Little Rock, Arkansas, United States   -91.207356  33.608922
# 6       Berkeley, California, United States   -122.29673  37.860576
# 7         Duarte, California, United States  -118.298662  33.786594
# 8      Encinitas, California, United States  -116.846046  33.016928
# 9       La Jolla, California, United States  -117.876447  33.857515
# 10   Los Angeles, California, United States  -117.885359  35.187133
# 11        Orange, California, United States  -117.853112  33.787795
# 12  Redwood City, California, United States  -117.885359  35.187133
# 13    Sacramento, California, United States  -121.555406  38.380456
# 14 San Francisco, California, United States  -117.885359  35.187133
# 15      Stanford, California, United States    -122.1675   37.42509
# 16     Hartford, Connecticut, United States   -72.763564   41.78516
# 17    New Haven, Connecticut, United States   -72.927507  41.365709