Hot answers tagged geocoding
API conditions constantly change, but this should work right now. OSM: devtools::install_github("hrbrmstr/nominatim") library(nominatim) b1 <- osm_geocode("Berlin, Germany") b1[c("lat", "lon")] Yahoo: devtools::install_github("trestletech/rydn") library(rydn) options(RYDN_KEY="yourAPIkey", RYDN_SECRET="yourSecret") b2 <- find_place("Berlin, ...
Ooo, time to pull out the data science, baby. I'll use Texas A&M and Mapquest as examples but you can use whatever you want... Basically, assuming you have clean data, we have to look at a couple things. 1) Can you find a pattern? (Say the difference between Texas A&M and Mapquest is usually around 2 miles. Look up Exploratory Data Analysis) 2) ...
Merge your old and new feature classes into one, then, using the Points to Line tool, input your merged table, and use the common field as the "line field"
The Pelias Geocoder from mapzen runs on elasticsearch, and uses OSM data by default, though it can use any data source. The importers are split into separate modules, so even if your not interested in using the pelias geocoder, you may still find the OSM importer useful. On another note: Shapefiles of OSM data are probably not what you want for source ...
Take a look at https://github.com/kiselev-dv/gazetteer/tree/develop/Gazetteer It will create you a json index for osm file. And you could use https://github.com/kiselev-dv/gazetteer/tree/develop/GazetteerWeb as an example of geocoder based on ElasticSearch
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