geopip 0.2

Reverse geocode a lng/lat coordinate within a geojson FeatureCollection.

# GEOPIP: Geojson Point in Polygon (PIP)

Reverse geocode a lng/lat coordinate within a geojson FeatureCollection and return information about the containing country (polygon).

Basically, you can use any geojson file (top level is a FeatureCollection) for reverse coding - set the environment variable REVERSE_GEOCODE_DATA to the geojson file. Only Polygon and MultiPolygon features will be used! If a point is found to be in a feature, the properties of that feature will be returned.

The default shape data (contained within the package) is from tematicmapping (the simple shapes). It contains polygons representing one country with the following meta-data (properties):

FIPS      String(2)         FIPS 10-4 Country Code
ISO2      String(2)         ISO 3166-1 Alpha-2 Country Code
ISO3      String(3)         ISO 3166-1 Alpha-3 Country Code
UN        Short Integer(3)  ISO 3166-1 Numeric-3 Country Code
NAME      String(50)        Name of country/area
AREA      Long Integer(7)   Land area, FAO Statistics (2002)
POP2005   Double(10,0)      Population, World Population Prospects (2005)
REGION    Short Integer(3)  Macro geographical (continental region), UN Statistics
SUBREGION Short Integer(3)  Geographical sub-region, UN Statistics
LON       FLOAT (7,3)       Longitude
LAT       FLOAT (6,3)       Latitude

Hence, you can use this package as an offline reverse geocoder on the country level (by default):

In [1]: import geopip
In [2]: geopip.search(lng=4.910248, lat=50.850981)
Out[2]:
{'AREA': 0,
 'FIPS': 'BE',
 'ISO2': 'BE',
 'ISO3': 'BEL',
 'LAT': 50.643,
 'LON': 4.664,
 'NAME': 'Belgium',
 'POP2005': 10398049,
 'REGION': 150,
 'SUBREGION': 155,
 'UN': 56}

NOTE: Since the polygons for each country are quite simple, reverse geocoding at the borders of two countrys is not exact. Use polygons with higher resolution for these use cases (see Data).

The shapely package will be used, if installed. Otherwise, a pure python implementation will be used (on the basis of winding numbers). See here, here and here for more informations and example implementations. Espacially for larger features, the shapely implementation might give performance improvements (default shape data and 2.6 GHz Intel Core i7, python3.6.2):

Pure:

In [1]: import geopip
In [2]: geopip.p_in_polygon?
Signature: geopip.p_in_polygon(p, shp)
Docstring:
Test, whether point `p` is in shape `shp`.

Use the pure python implementation for this.

Parameters:
    p: Tuple[float, float]  Point (lng, lat) in WGS84.
    shp: Dict[str, Any]     Prepared shape dictionary from `geopip._pure.prepare()`.

Returns:
    boolean: True, if p in shp, False otherwise
File:      ~/repositories/geopip/geopip/_pure.py
Type:      function
In [3]: %timeit geopip.search(4.910248, 50.850981)
64.4 µs ± 1.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Shapely:

In [1]: import geopip
In [2]: geopip.p_in_polygon?
Signature: geopip.p_in_polygon(p, shp)
Docstring:
Test, whether point `p` is in shape `shp`.

Use the shapely implementation for this.

Parameters:
    p: Tuple[float, float]  Point (lng, lat) in WGS84.
    shp: Dict[str, Any]     Prepared shape dictionary from `geopip._shapely.prepare()`.

Returns:
    boolean: True, if p in shp, False otherwise
File:      ~/repositories/geopip/geopip/_shapely.py
Type:      function
In [3]: %timeit geopip.search(4.910248, 50.850981)
87.1 µs ± 1.52 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Data

Other interesting shape data can be found at: - http://www.naturalearthdata.com/downloads/ : Different thematic shape files at 10m, 50m and 110m resolution. - http://www.gadm.org/version2 : Administrative area 0 or 1 contain contries or states, respectively. Attention to the license! - https://www2.census.gov/geo/tiger/: Various shape/gdb files and information for USA. - http://guides.library.upenn.edu/c.php?g=475518&p=3254770: Links to various geoinformation data. - http://thematicmapping.org/downloads/world_borders.php: Country borders and some interesting information. The default file is from here. There is also a higher resolution version.

NOTE: shapefiles / gdb databases have to be transformed into geojson. One way is to use fiona. Assuming the gdb files are in the directory /data/gdb:

fio insp /data/gdb
# a python shell opens
>>> import json
>>> features = []
>>> for feat in src:
...     features += [feat]
...
>>> f = open('/data/gdb.geo.json', 'w')
>>> json.dump(dict(type='FeatureCollection', features=features), f)
>>> f.close()

Then the gdb will be transformed into a geojson file gdb.geo.json.

Improvements:

• Unittesting!