Package: local-geocode
Local-geocode :earth_americas:This is a very simple geocoding library which runs fully locally (without calling any APIs) and has therefore no limits in terms of processing. It runs very fast due to using an efficient in-memory datastructure calledFlashtext. It uses data fromhttp://www.geonames.org/.This project is mainly used in the context of decoding data from the "user.location" field of tweets but it can in principle be used on any address/location raw text field. Note that if you need very precise geographical information it is better to use one of the many available APIs. By default this repo only detects places with more than 30k inhabitants.I have compared the predictions by local-geocode with geopy for 500 Twitter user locations. Local-geocode performs signficantly better (85% accuracy) than geopy (64% accuracy) for this use case. Read more about the benchmarkhere.Installpip install local-geocodeExample usageLocal-geocode is able to parse arbitrary location names in many languages, as well as numerous alternative names of places and returns geographic information.fromgeocode.geocodeimportGeocodegc=Geocode()gc.load()# load geonames datamydata=['Tel Aviv','Mangalore 🇮🇳']forinput_textinmydata:locations=gc.decode(input_text)print(locations)[{"name":"Tel Aviv","official_name":"Tel Aviv","country_code":"IL","longitude":34.780570000000004,"latitude":32.08088,"geoname_id":"293397","location_type":"city","population":432892}][{"name":"Mangalore","official_name":"Mangalore","country_code":"IN","longitude":74.85603,"latitude":12.91723,"geoname_id":"1263780","location_type":"city","population":417387},{"name":"\ud83c\uddee\ud83c\uddf3","official_name":"Republic of India","country_code":"IN","longitude":79.0,"latitude":22.0,"geoname_id":"1269750","location_type":"country","population":1352617328}]UsageThe easiest way to integratelocal-geocodeto your project is to simply runpip install local-geocode. You can also simply clone this repository and copy the foldergeocodeinto your project.ConfigurationWhen installed with pip, local-geocode comes packaged with 2 pickle files which were generated using the default configuration. You can however change the configuration and then re-compute the pickle files for your needs.TheGeocode()initializer accepts the following arguments:min_population_cutoff(default: 30k): Places below this population size are excludedlarge_city_population_cutoff(default: 200k): Cities with a population size larger than this will be prioritized. Example: "Los Angeles, USA" will result in "Los Angeles" as the first result, and not "USA".location_types: Provide a list of location types which you would like to filter. By default it uses all location types (i.e.['city', 'place', 'country', 'admin1', 'admin2', 'admin3', 'admin4', 'admin5', 'admin6', 'admin_other', 'continent', 'region']).Example:fromgeocode.geocodeimportGeocodegc=Geocode(min_population_cutoff=100000)gc.load()# downloads geonames data (~1.2GB), parses data, generates pickle files in <package folder>/geocode/data for new configuration(This may take 1-2min to run)PrioritizationIf multiple locations are detected in an input string, local-geocode sorts the output by the following prioritization:Large cities (population size > large_city_population_cutoff)States/provinces (admin level 1)CountriesPlaces (population size <= large_city_population_cutoff)Counties (admin levels > 1)ContinentsRegionsParallelizedIf you have a large number of texts to decode, it might make sense to usedecode_parallelwhich runs decode in parallel:gc=Geocode()gc.load()# load geonames data# a large number of itemsmydata=['Tel Aviv',..,]num_cpus=None# By default use all CPUslocations=gc.decode_parallel(mydata,num_cpus=num_cpus)print(locations)ContactPlease open an issue, if you run into problems!