Introductory Geocoding

Obtain coordinates for: street addresses, intersections, place names, or zip codes

Enables

• Mapping of addresses
• Linking with other geospatial data
• Spatial calculations, such as distances

In general, these are the steps in a geocoding project

• Identify needs
• Choose geocoder
• Preprocess data
• Geocode
• Verify output
• Postprocess data

Think about

• Cost
• Geographic scope
• Output quality
• Speed
• Scale

Two primary options: local and remote

In some cases, you may want or need to use a local geocoder

• Data is confidential or restricted use
• Too many addresses for remote

Software options: ArcGIS, Postgres/PostGIS

Many options

• Google Maps Geocoding API
• SmartyStreets
• ArcGIS
• Nominatim
• DSTK
• Photon
• geopy

Vary based on

• Usage limits
• Methods of use
• Output quality
• Coverage

Google Maps Geocoding API has high accuracy but has usage limits:

• 2,500 free requests per day, 10 per second
• $0.50/1000 requests, up to 100,000 daily

/street2coordinates

Pass in the address as a parameter

https://maps.googleapis.com/maps/api/geocode/json?address=<_your address_>

/street2coordinates

Pass in the address as a parameter

https://maps.googleapis.com/maps/api/geocode/json?address=
1600 Amphitheatre Pkwy, Mountain View, CA

{ "results" : ...
"geometry" : {
            "location" : {
               "lat" : 37.422245,
               "lng" : -122.0840084
            },
            "location_type" : "ROOFTOP",
            "viewport" : {
               "northeast" : {
                  "lat" : 37.42359398029149,
                  "lng" : -122.0826594197085
               },
               "southwest" : {
                  "lat" : 37.4208960197085,
                  "lng" : -122.0853573802915
               }
            }
         }...
}

https://maps.googleapis.com/maps/api/geocode/json?address=<_your address_>

What happens when

• State is omitted
• Zip code is omitted
• Commas are removed
• Mix case

Photon is free and open source

• Uses OpenStreetMap data
• Worldwide coverage
• Multilingual search
• Typo tolerance
• Fast & scalable

However, "extensive usage will be throttled"

Search

photon.komoot.de/api/?q=berkeley

Limit number of results

photon.komoot.de/api/?q=berkeley&limit=1

Preferred language

photon.komoot.de/api/?q=berkeley&lang=fr

Pass in the address as a parameter

photon.komoot.de/api/?q=
1600 Amphitheatre Pkwy, Mountain View, CA

{"features": [{
  "properties": {
    "osm_key":"office",
    "street":"Amphitheatre Parkway",
    "name":"Google Headquaters",
    "osm_id":2192620021,
    "osm_type":"N",
    "osm_value":"commercial",
  },
  "type":"Feature",
  "geometry": {
    "type":"Point",
    "coordinates":[-122.0850862,37.4228139]
  }
}],
"type":"FeatureCollection"}

Full output

Geocoding with Python

Access to many geocoding services

• OpenStreetMap Nominatim
• ESRI ArcGIS
• Google Geocoding API
• Baidu Maps
• Bing Maps

geopy on GitHub

Example from the docs

$ pip install geopy

>>> import geopy
>>> from geopy.geocoders import Nominatim
>>> geolocator = Nominatim()
>>> location = geolocator.geocode("1600 Amphitheatre Pkwy, Mountain View, CA")
>>> print((location.latitude, location.longitude))
(37.4228139, -122.0850862)

You can also reverse geocode, calculate distances, and more

Check out the geopy documentation

JavaScript Object Notation

• Format typically used to send data between a server and web app

Convert JSON to CSV

• Write a script in Python, R, etc.
• Use other modules, e.g., pandas

Go to Photon API linkand paste output in command below:

import json

json_str = '[output from Photon API]' 
python_obj = json.loads(json_obj)

# navigating dict by key 
type(python_obj)
python_obj['features']

# navigating dict by list 
type(python_obj['features'])
python_obj['features'][0]

Varies over services (remote & local)

• Incorrect street ranges, inaccurate or low quality features
• Inaccurate feature attributes

Year matching between geocoded and reference data

• Missing streets
• Address changes

Ways to assess quality

• Compare input street name to street_name
• Count missing values
• Test against sub-sample with known or high-quality coordinates

Because results are based on an underlying database or interpolation method, there will be variation in coordinate quality. In cases where the results are not good enough, consider using another service for those addresses.

To GeoJSON

Link to Census Blocks

http://data.fcc.gov/api/block/find?latitude=<_latitude_>&longitude=<_longitude_>

Block FIPS="060855046011175"

The first two characters (06) indicate the state (CA), the next three (085) indicate the county (Alameda), the next 6 indicate the census tract (5046.01) and the last four characters indicates the census block group and block number (1175). The first digit of the block identifies the block group.

Several options

• Leaflet
• geojson.io
• CartoDB
• ArcGIS/QGIS
• GeoCanvas
• Python

Preprocess data

• Formatting
• Components

Sample and test

Use multiple sources

Map results to verify

Clone the repo or download the zip file from:

https://github.com/dlab-geo/geocoding-geopy

Navigate to the directory and start an IPython notebook instance

$ ipython notebook

Let's get to work

We'll create a map of the 44 BART stations in the Bay Area