geolocChina

R package that takes Chinese location/address stings as input and returns geolocation data for each input string. This package does not rely on an external API service to perform geocoding (for info on how it works, see the How It Works section of the README below).

The functions are designed to work with business name strings and address strings. For each input string, the following values are returned: Province, City, County, Provincial geocode, City geocode, and County geocode. Values are returned as a tidy data frame.

The package is using geo substrings and associated geocodes from these sources:

Administrative-divisions-of-China on GitHub, put together by user modood.
http://www.stats.gov.cn

There are other R packages that offer similar functionality, namely baidumap and poster. For more info on geolocating Chinese strings, see this 2016 paper on the subject.

Please report issues, comments, or feature requests.

Installation

Install from GitHub

# install.packages("devtools")
devtools::install_github("ChrisMuir/geolocChina", INSTALL_opts="--no-staged-install")

Example Usage

library(geolocChina)
library(knitr)

terms <- c(
  "大连市甘井子区南关岭街道姚工街101号",
  "浙江省杭州市余杭区径山镇小古城村",
  "大连御洋食品有限公司",
  "徐州雅莲连锁超市有限公司", 
  "四川省南充市阆中市公园路63号", 
  "同德县鲜肉蔬菜配送行"
)

knitr::kable(
  geo_locate(terms)
)

location	province	city	county	province_code	city_code	county_code
大连市甘井子区南关岭街道姚工街101号	辽宁	大连	甘井子	21	2102	210211
浙江省杭州市余杭区径山镇小古城村	浙江	杭州	余杭	33	3301	330110
大连御洋食品有限公司	辽宁	大连	NA	21	2102	NA
徐州雅莲连锁超市有限公司	江苏	徐州	NA	32	3203	NA
四川省南充市阆中市公园路63号	四川	南充	阆中	51	5113	511381
同德县鲜肉蔬菜配送行	青海	海南藏族	同德	63	6325	632522

How It Works

The geolocation function geo_locate() works by using both substring matching and geocodes to validate child regions and infer parent regions. The package ships with internal data that contains region substrings and associated geocodes. Access to the package data is provided via function get_package_data(), which returns a tidy data frame.

df <- get_package_data()
head(df)
#>   region geocode region_type
#> 1   北京      11    province
#> 2   天津      12    province
#> 3   河北      13    province
#> 4   山西      14    province
#> 5 内蒙古      15    province
#> 6   辽宁      21    province

The variable region_type features four unique values.

# Province
head(df[df$region_type == "province", ], 1)
#>   region geocode region_type
#> 1   北京      11    province

# City
head(df[df$region_type == "city", ], 1)
#>    region geocode region_type
#> 32   辖区    1101        city

# County
head(df[df$region_type == "county", ], 1)
#>     region geocode region_type
#> 373   东城  110101      county

# County 2015
head(df[df$region_type == "county_2015", ], 1)
#>      region geocode region_type
#> 3351   密云  110228 county_2015

geo_locate() looks for matching substrings in this order: province, then city, then county. It operates largely on the premise that county geocodes contain a city geocode, and city geocodes contain a provincial geocode. So the county with geocode 330110 resides in the city with geocode 3301, which resides in the province with geocode 33.

Here are some different examples showing how geo_locate() works. To begin, we’ll start with a generic input location string.

base_str <- "工业路25号"

No matches

knitr::kable(geo_locate(base_str))

location	province	city	county	province_code	city_code	county_code
工业路25号	NA	NA	NA	NA	NA	NA

Province match

Add a province to the base_str.

x <- paste0("浙江省", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省工业路25号	浙江	NA	NA	33	NA	NA

Province and city match

Add a province and a city that exists within the province to the base_str.

x <- paste0("浙江省", "杭州市", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省杭州市工业路25号	浙江	杭州	NA	33	3301	NA

It’s important to note that, within the return data, the first two digits of the city_code match the province_code.

On the other hand, if we try to combine a province with a city that does NOT exist within the province, the city variables in the output will return NA.

x <- paste0("浙江省", "大连市", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省大连市工业路25号	浙江	NA	NA	33	NA	NA

This is because the functions attempt to establish a provincial match first, and then any city matches found must have an associated geocode that contain leading digits that match the provincial geocode. Here is the package data observation of the matching city substring.

df[df$region == "大连", ]
#>    region geocode region_type
#> 69   大连    2102        city

This same principle applies to the relationship between city matches/geocodes and county matches/geocodes, as we’ll see below.

Province, city, and county match

Add a logical province, city, and county to base_str.

x <- paste0("浙江省", "杭州市余", "余杭区", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省杭州市余余杭区工业路25号	浙江	杭州	余杭	33	3301	330110

Just like in the example above, if we try to combine a county with a city that does NOT exist within the city, the county variables will return NA

x <- paste0("浙江省", "杭州市余", "甘井子区", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省杭州市余甘井子区工业路25号	浙江	杭州	NA	33	3301	NA

# Print the pkg data observation related to county "甘井子"
df[df$region == "甘井子", ]
#>     region geocode region_type
#> 840 甘井子  210211      county

City match only

Add a city to base_str.

x <- x <- paste0("杭州市余", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
杭州市余工业路25号	浙江	杭州	NA	33	3301	NA

geo_locate() will return a matched province and city. This is because it will first look for a province match and return NA. It then looks for a city match (in this example, a city match is found). If a city match is found AND the province is NA, the province output values will be inferred from the city geocode. In this example, the matching city geocode is 3301, the first two digits are taken and assigned as the matching provincial geocode. Then that geocode 33 is used to fetch a corresponding matching province (浙江) from the package data.

County match only

Add a county to base_str

x <- paste0("余杭区", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
余杭区工业路25号	浙江	杭州	余杭	33	3301	330110

geo_locate() will return a matched province, city, and county. Just like in the last example, when the function cannot find a match for province and city, but a county match is found, the province and city output info will be inferred from the county geocode.

2015 county package data

As part of the internal package data, geolocChina ships with a list of counties (and corresponding geocodes) that existed in 2015 and do not currently exist, due to rezoning and county/district lines being redrawn. geo_locate() will only use the 2015 county data if there was no city match found. And if a match is found in the 2015 county data, the function will infer the output values for province and/or city, but will leave the county output values as NA (as opposed to returning county values that do not currently exist).

x <- paste0("北市区", base_str)
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
北市区工业路25号	河北	保定	NA	13	1306	NA

Two different regions of the same type

If there are multiple matches of the same region type in an input string (say, more than one provincial matches found), then geo_locate() will return the region that occupies the earliest character index within the input string (i.e. the substring that appears closest to the beginning of the input string). In the example below, the actual province is 浙江省, but the road name 辽宁 also matches to a different province in China.

x <- "浙江省杭州市辽宁路25号"
knitr::kable(geo_locate(x))

location	province	city	county	province_code	city_code	county_code
浙江省杭州市辽宁路25号	浙江	杭州	NA	33	3301	NA

The logic behind this comes from the fact that Chinese address/location strings typically have this structure

province-city-county-district-street-number

Reading left to right, the geographic resolution starts large and gets more and more fine grained.

Using 四川省南充市阆中市公园路63号 as an example, we have

knitr::kable(geo_locate("四川省南充市阆中市公园路63号"))

location	province	city	county	province_code	city_code	county_code
四川省南充市阆中市公园路63号	四川	南充	阆中	51	5113	511381

ChrisMuir/geolocChina

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