README.md

In xiaojunlin/gaodemap: An R package using AMap Web Service API to convert between addresses and coordinates

amapR

An R package using AMap Web Service API.

Now, this packge has four functions: - geocoord: convert addresses into coordinates - geolocation: convert coordinates into addresses - transcoord: Transform the coordinates from other coordinate systems to amap coordinate system - getmap: get the map data at province, city, or district level

Latest Version: 0.2.9

Manual

Package features

• Parallel computing

Under the premise of not exceeding the query limit, the more CPU cores you use, the faster the functions in this package will be.

• Batch query

This package embeds the batch query feature of AMap Web Service API, which allows us to query a batch of addresses or coordinates in one time. For example, AMap API can convert 10 addresses into coordinates in one query. The upper query limit for personal certified developer is 3 million per day. That is, a developer with personal certification, theoratically, can convert 30 million addresses per day.

• Progress bar

This package provides a progress bar to show the query progress, which is very useful when the query time may be unpredictable.

Installation

devtools::install_github("xiaojunlin/amapR") 

Notes: - Before using the package, please make sure that you have applied the AMap Web Service API key from the official website. - set the amap.key options:

library(amapR)
options(amap.key = 'xxxxxxxx')

Usage

geocoord

convert addresses into coordinates

When the number of addresses is less than or equal to 200, geocoord only utilizes one processor, which makes it single-threaded.

• 200 addresses

test <- data.frame(n = 1:200, address = c("华中科技大学", "四川大学"))
system.time( result <- geocoord(data = test, address = "address") )

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%  
user     system    elapsed
0.690    0.045     10.736

Here is the returned result in data.table format.

result

       n   address          formatted_address longitude latitude
  1:   1  华中科技大学 湖北省武汉市洪山区华中科技大学  114.4345 30.51105
  2:   2     四川大学     四川省成都市武侯区四川大学  104.0837 30.63087
  3:   3 华中科技大学 湖北省武汉市洪山区华中科技大学  114.4345 30.51105
  4:   4     四川大学     四川省成都市武侯区四川大学  104.0837 30.63087
  5:   5 华中科技大学 湖北省武汉市洪山区华中科技大学  114.4345 30.51105
 ---                                                                   
196: 196     四川大学     四川省成都市武侯区四川大学  104.0837 30.63087
197: 197 华中科技大学 湖北省武汉市洪山区华中科技大学  114.4345 30.51105
198: 198     四川大学     四川省成都市武侯区四川大学  104.0837 30.63087
199: 199 华中科技大学 湖北省武汉市洪山区华中科技大学  114.4345 30.51105
200: 200     四川大学     四川省成都市武侯区四川大学  104.0837 30.63087

When the number of addresses is over 200, geocoord utilizes multiple processors and parallel computation. The following examples were run in the MacBook Pro (13-inch, 2016, Four Thunderbolt 3 Ports) with Intel Core i5 (2 cores, 4 threads).

• 10,000 addresses

# 10,000 addresses
test <- data.frame(n = 1:10000, address = c("华中科技大学", "四川大学"))
system.time( result <- geocoord(data = test, address = "address") )

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%
user     system    elapsed
1.044    0.344     64.697   

geolocation

Convert the coordinates into formatted addresses.

Examples:

• 200 coordinates

test <- data.frame(n = 1:200, lng = c(114.4345,104.0837), lat = c(30.51105, 30.63087))
system.time( results <- geolocation(data = test, longitude = "lng", latitude = "lat") )

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%
user    system  elapsed 
0.342   0.029   5.891 

• 10,000 coordinates

test <- data.frame(n = 1:10000, lng = c(114.4345,104.0837), lat = c(30.51105, 30.63087))
system.time( results <- geolocation(data = test, longitude = "lng", latitude = "lat") )

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%
user    system  elapsed 
0.604   0.224   40.198 

transcoord

Transform the coordinates from other coordinate systems to Amap system, including baidu, gps, and mapbar.

Examples:

• 200 coordinates from gps system

test <- data.frame(n = 1:200, lng = c(114.4345,104.0837), lat = c(30.51105, 30.63087))
results <- transcoord(data = test, longitude = "lng", latitude = "lat", coordsys = "gps")

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%
user    system  elapsed 
0.110   0.013   2.337 

• 10,000 coordinates from baidu system

test <- data.frame(n = 1:10000, lng = c(114.4345,104.0837), lat = c(30.51105, 30.63087))
system.time( results <- transcoord(data = test, longitude = "lng", latitude = "lat", coordsys = "baidu") )

|::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
Success rate:100% | Failure rate:0%
user    system  elapsed 
0.316   0.108   16.892 

getmap

Get the map data at province, city, or district level.

You can get the administrative division codes in the city lists provided by Amap.

Examples:

• China Map

library(ggplot2)
library(amapR)

map <- getmap(adcode = 100000, level = "province")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

map <- getmap(adcode = 100000, level = "city")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

map <- getmap(adcode = 100000, level = "district")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

• Sichuan Map

library(ggplot2)
library(amapR)

map <- getmap(adcode = 510000, level = "province")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

map <- getmap(adcode = 510000, level = "city")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

map <- getmap(adcode = 510000, level = "district")
ggplot(map) +
  geom_sf(fill = "white") +
  theme_bw()

References

[1] Amap Webservices API

[2] DATAV.GeoAtlas

xiaojunlin/gaodemap documentation built on Sept. 28, 2022, 2:25 a.m.