GIS for Python People


Sara Safavi   / @sarasomewhere

What is GIS?

Technically, Geographic Information Systems is
computer systems, infrastructure, software, an entire industry dedicated to "spatial data"... blah, blah, blah, blah


Really, GIS is mostly about "data used to make maps"

Spatial isn't Special

Spatial data is just regular data, with location info attached.

For Example...

Regular data:

Table of firestations in Austin - via


Table of firestations in Austin with lat/lon coordinates - via

Which can make:

Map of firestations in Austin - via

That's great...
so what?

Injecting spatial into your everyday data munging is an easy way to punch up the impact of your products:
don't just show what, show where.


(Plus, maps just look cool.)

Types of GIS Data

Vector vs. Raster

Spatial data comes in two flavors: vector and raster Source: Penn
	State's 'Nature of Geographic Information'

Most likely, most of the data you'll work with (or create) will be vector data...

...But let's talk about rasters for a minute anyway.

Raster GIS data in
2 minutes or less

Rasters store data in pixel grids:
each pixel (or cell) stores a single value.*

*Trivia: None is still a value.

Rasters can be useful for:

Aerial imagery

Elevation models

Remote sensing

Raster GIS data is the same as "regular" digital images, just with location data attached.

What rasters are good for

Rasters are really good at representing information that uniformly covers a broad area (e.g., elevation).


Not so good for "discrete" entities (e.g., firestation locations, congressional districts).

For everything else, there's...

Vector GIS data

Points, lines, and polygons.

Not pixels.

Inherently spatial

Just like geometric points are defined by an (x,y) pair:
geospatial points can be defined by (lat/lon) coordinates.

What's it good for?

  • Points of interest (e.g., firestations)
  • Roads, rivers, boundary lines
  • Lakes, building footprints, census areas
  • etc.

Working With GIS Data

Step 0: look at the big picture

You've found some GIS data. Now what?

You can use QGIS to visualize spatial data:

QGIS. Free & open-source - also offers a whole lot more than visual previews. Edit, query, analyze. Extend existing functionality via Python.

Storing data & GIS formats

Mostly, GIS data is stored like any other data, just with some added "spatial" components.


Options: Use a database, serialize your data, or download a shapefile and immediately convert it into something else.

Option 1: Use a database*

*Relational. Usually.


GIS data is traditionally stored in a spatial database.


That's just a regular database, plus extra spatial-enabled features:


  • Geometry: store coordinates alongside attributes
  • Spatial queries: i.e., "find all the pubs within 1 mile of me"
  • Spatial indexing: spatial queries need optimization, too

(Use this)

PostGIS. The PostgreSQL you know & love, plus spatial goodness. Robust, reliable, well-supported.


Spatialite. Spatially-enabled SQLite. Great for sharing data or development, probably not for production.

Option 2: Serialize All The Things

(Vector) GIS data can also be stored in a serialized format:
just add location data to existing formats.

(Use this)

GeoJSON. Regular JSON, but with coordinates. e.g.,

	  "type": "Feature",
	  "geometry": {
 			"type": "Point",
 			"coordinates": [-97.740323, 30.274656]
	  "properties": {
		"name": "Texas Capitol Building",
		"address": "1100 Congress Ave, Austin, TX 78701"


If you can tolerate XML:
KML. Google's XML flavor. Import into Google Earth.

But there's also...

Option 3: shapefiles


The most common format of GIS data right now:
most ready-made GIS vector data from public data sources will be in shapefile format.

This is "a shapefile."

Yes, really.


With Python, reading a shapefile & writing it to a better format is easy.

Playing with data

Reading & writing GIS data

Programmatic data munging

Suggestion: if you're stuck with shapefiles, start with pyshp.


Pure Python, reads (and writes) shapefile data. That's all.

import shapefile

shp = shapefile.Reader(my_shapefile_path)
fields = shp.fields[1:]  # shp.fields[0] == deletion flag
field_names = [field[0] for field in fields]
records = shp.shapeRecords()

For more complicated things

GDAL/OGR: Massive library of raster & vector GIS tools.



  • Free & open source
  • Does everything... then cooks you dinner



  • (Can be) a pain in the #%! to install/config
  • Docs & features can be overwhelming
  • Chaining commands gets cumbersome
  • Native Python bindings are nasty

Good news: We speak Python


Caveat: Many Python libs still require GDAL/OGR installations and headers. Worth it, but can be a bumpy ride the first time around.

GIS I/O + Python

(Use THIS - not GDAL/OGR's vanilla APIs)


Fiona. Elegant Python API for OGR (vectors) - plus fio, a commandline tool

>>> with'path/to/my_awesome_shapefile.shp', 'r') as collection:
>>>    print(len(collection)) 
>>>    # collections contain one object for each feature in input GIS data
>>>    print(collection[1]) 
>>>    # collections of features are iterable, GeoJSON-ready strings

$ fio info my_awesome_shapefile.shp
# json-ified data summary (e.g., feature count, bounding box coords, etc)

Supports every format OGR supports (not just shapefiles), makes reading/munging/writing GIS data a breeze.

For Rasters...

Rasterio. By Fiona's author: a GDAL-based library for raster I/O -- with bonus numpy interop.


And yes, there's a command line tool rio.

The Fun Stuff

Shapely. After creating or extracting useful data, use this Python library for more complex manipulation & analysis.

  • Create buffers around vector elements
  • Visualize things like convex hulls, unions, intersections, centroids...
  • Find (geometric) differences between elements

Combine Fiona + Shapely to chain reading, converting, and analysing GIS data:

>>> with'city_parks.shp', 'r') as collection:
>>>     parks = [shapely.geometry.shape(c['geometry']) for c in collection]

>>> park = parks[0] # grab the first park just for demo purposes

>>> park.type

>>> park.area

>>> park.buffer(10.0).area

>>> (park.centroid.x, park.centroid.y)
(3100374.119480808, 10106879.690095564)

Useful ONLINE Tools


  • Quickly validate & visualize your GeoJSON
  • Make giant datasets tiny, fast.


Geocoding services

  • Free+$; basic, clean & simple. US addresses only
  • Mapquest API: Free+$$$; robust, less flashy. International

What to do with your spatial data?

Make maps.

Web mapping for fun & profit

Option 1: DIY Web Mapping Applications



Regular Django... but with maps.

  1. Include django.contrib.gis, stick PostGIS behind everything
  2. Create spatial models & use spatial-ized queries
  3. Your app is now magic

Option 2: Web Mapping for lazy busy developers

GitHub + GeoJSON

Maps in 2 minutes or less.

  1. Push a GeoJSON file to GitHub.
  2. View your GeoJSON file on GitHub.
  3. There is no step three.


Spatial isn't all that special
Python makes things a lot easier
Maps are a great party trick