## Geocomputation, Cartography, and Spatial Analysis Techniques Using Python

[![Tweetcity](https://github.com/urschrei/CityEngine-Twitter/blob/master/london.png)](http://urschrei.github.io/CityEngine-Twitter/ "Tweet")

This is a repository of various geo/spatial analysis techniques using Python libraries, chiefly Numpy, Pandas, Shapely, Fiona, Descartes, Matplotlib, and Matplotlib-Basemap.

These tutorials, visualisations, and libraries are an occasional side effect of being embroiled in a PhD at the [Bartlett Centre for Advanced Spatial Analysis](http://www.bartlett.ucl.ac.uk/casa), at [UCL](http://www.ucl.ac.uk), and teaching on the undergraduate [Data Science and Visualisation](http://www.ucl.ac.uk/basc/prospective/core/qm2/#tabs-2) course.

[![CASA](data/casa_black.png)](http://www.bartlett.ucl.ac.uk/casa/programmes/postgraduate "Bloomsbury is lovely, you know.")

### The Notebooks

[![Hovertube](https://github.com/urschrei/hovertube/blob/master/tube.png)](https://github.com/urschrei/hovertube "Ghostly. vv ghostly").  
Process TfL tube and rail data, and display it using Mapbox GL JS

[![Inset](data/inset_gh.png)](isochrone.ipynb "Yo Dawg").  
TfL bike rental travel time isochrone calculation

[![Airbnb](https://github.com/urschrei/geopython/blob/master/rentals_gh.png)](https://github.com/urschrei/Geopython/blob/master/airbnb.ipynb "All that is solid melts into air")

[Airbnb](https://github.com/urschrei/Geopython/blob/master/airbnb.ipynb): mapping Airbnb rental data in London

[![Boroughs](https://github.com/urschrei/geopython/blob/master/boroughs_gh.png)](https://github.com/urschrei/Geopython/blob/master/airbnb.ipynb "Can I borough a feeling")

[GWR](https://github.com/urschrei/Geopython/blob/master/geographically_weighted_regression.ipynb): An introduction to Geographically Weighted Regression using PySAL, using a worked example.

[Convert](convert.ipynb): demonstrates point, choropleth, and hexbin mapping techniques using pandas and Matplotlib Basemap  

[Convert_Folium](convert_folium.ipynb): demonstrates the use of the [Folium](https://github.com/wrobstory/folium) library for creating web-based maps from Python data (pandas) using [Leaflet](http://leafletjs.com) to generate a choropleth map

[Router Comparison](https://github.com/urschrei/router_comparison): analyses route characteristics of journeys from the London Cycle Hire network centroid to 554 stations, calculated using [OSRM](https://mapzen.com/blog/osrm-sunset), Mapzen's new [Valhalla](https://mapzen.com/projects/valhalla/) router, and the [Google Maps Directions API](https://developers.google.com/maps/documentation/directions/intro). There's also some outlier identification using RANSAC, and hopefully useful wrappers for route retrieval in `helpers.py`.

[![Routers](https://github.com/urschrei/router_comparison/blob/master/combined_gh.png)](https://github.com/urschrei/router_comparison "IT'S CALLED VALHALLA. LIKE IN FURY ROAD. AAAAA. I LIVE. I DIE. I LIVE AGAIN!")

[WLS](https://github.com/urschrei/linalg/blob/master/notebooks/weighted_least_squares.ipynb): demonstrates the use of weighted least-squares estimation of similarity and affine transforms to calculate parameters for a transformation matrix which can be used to transform and align coordinates. This approach is useful for e.g. georeferencing raster data, and map feature alignment and correction.

[![weighted](https://raw.githubusercontent.com/urschrei/linalg/master/WLS.png)](https://github.com/urschrei/linalg/blob/master/notebooks/weighted_least_squares.ipynb "Weighty")

[Contour](contour.ipynb): demonstrates interpolation of irregularly-spaced point data (mean rainfall) into a regular grid, calculating a contour plot, and imposing it onto a basemap (see graphic below). Two approaches for calculating continuous surfaces are then compared – Delaunay Natural Neighbour (`matplotlib.griddata`), and refinement of a coarse Delaunay mesh using `matplotlib.UniformTriRefiner`, which uses recursive subdivision and cubic interpolation. High-res images are available in the [data](data) folder, all beginning with `rainfall_`. :umbrella::umbrella::umbrella:  
Finally, the map is partitioned into *clipped* Voronoi cells based on the sensor locations, and some plotting methods (more flexible than `scipy.spatial.voronoi2d`) are shown.  

[![Make it rain](data/rainfall_interpolation.gif)](contour.ipynb "Anigifs are the future of spatial analysis")

[![Cellular](data/voronoi_gh.png)](https://raw.githubusercontent.com/urschrei/Geopython/master/data/voronoi.png "Tesselate Everything")

[Bikepath](bikepath.ipynb): takes a subset of London bicycle hire stations, creates a DataFrame of all possible origin and destination pairs, and then uses MapZen's wonderful [open Valhalla endpoint](https://mapzen.com/projects/valhalla) to get valid bicycle routes between them. These routes are then plotted (it's just the first 400 – there are over 490k origin / destination pairs in total) on a map.

[![Bikepath](data/london_bike_routes_gh.png)](bikepath.ipynb "Unpleasantly vascular, no?")

[Isochrone](http://nbviewer.ipython.org/github/urschrei/Geopython/blob/master/isochrone.ipynb): an [isochrone](http://en.wikipedia.org/wiki/Isochrone_map) is computed for the complete London bike network from an origin at its centroid. Given a network of stations, [Single-Source Shortest Path Length](https://networkx.github.io/documentation/latest/reference/algorithms.shortest_paths.html), weighted by the travel time between the origin and destinations can be used to generate travel times – though this isn't necessary for a single origin.

[![Isochrone](data/isochrone_gh.gif)](isochrone.ipynb "The Burning Eye of Bike Hire")

[Plaques_Geopandas](plaques_geopandas.ipynb): demonstrates [Geopandas](http://geopandas.org) and its spatial join functionality, used to create a choropleth.

[![Choropleth](data/london_plaque_density_gh.png)](http://sensitivecities.com/so-youd-like-to-make-a-map-using-python-EN.html "Boropleth")
## Libraries
[Circles](circles.ipynb)  
Demonstrates drawing circles with correct distortion characteristics on a map (the dot-shaded circle is erroneously non-distorted). The helper library used to plot the circles is available [here](https://github.com/urschrei/Circles).

[Lonlat-BNG](https://github.com/urschrei/lonlat_bng/blob/master/rust_BNG.ipynb)  
A demonstration of writing a [Rust](http://www.rust-lang.org) library, and linking it to Python using FFI, in order to carry out fast Longitude and Latitude to British National Grid transformations, using multithreading. I've blogged about the process [here](http://sensitivecities.com/rust-python-ffi-bng-EN.html). The Python library is available [here](https://github.com/urschrei/convertbng).

[Pypolyline](https://github.com/urschrei/pypolyline)  
A Python library for encoding and decoding Google encoded polylines, using a Rust binary and FFI.

[RDP](https://github.com/urschrei/rdp)  
A Rust implementation of the Ramer–Douglas–Peucker line simplification algorithm. Includes an FFI wrapper.

[Polylabel-rs](https://github.com/urschrei/polylabel-rs)  
A Rust implementation of the Mapbox Polylabel algorithm, which determines optimum label placement on a polygon by calculating its pole of inaccessibility. The library has an FFI wrapper, and a sample Python script which demonstrates its use (I know Shapely also includes the algorithm, but it's a pure-Python implementation, and this one is…somewhat faster)

[![Circles](data/circles_gh.png)](https://github.com/urschrei/Circles "Borges's 'The Circular Ruins' is a good story. Also an apt title for my PhD.")

---
### Installation and usage

`uv sync` or the `pip` equivalent (in which case you should create and initialised a virtualenv first).  
The Rust libraries (though not their Python counterparts) require a Rust installation. For most platforms (macOS, Windows, *nix), using [rustup.rs](https://rustup.rs) is the easiest way to install and update a Rust installation.

### License
© Stephan Hügel 2017 - 2025 

Unless otherwise specified, the [wards.geojson](wards.geojson) file and any UK shapefiles are provided under
[Crown Copyright](http://www.nationalarchives.gov.uk/information-management/re-using-public-sector-information/copyright/crown-copyright/), and their use must be acknowledged in any output by reproducing the following notice:

`Contains Ordnance Survey data  
© Crown copyright and database right 2015`

Unless otherwise specified, all other files are provided under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](http://creativecommons.org/licenses/by-nc-sa/4.0/) license.  

![CC BY-NC-SA 4.0](https://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png)