Spatial analysis playground using data related to the 1854 cholera outbreak in Soho and Dr John Snow's 'cholera map that changed the world'.
Data are from several sources:
data/csdscontains data prepared by Center for Spatial Data Science.snow7/pumps.shp(vector) is points for each location of a pump
data/danicontains data prepared by Dani Arribas-Bel:polys.shp(vector) is building blocks (footprints) from the Ordnance Survey (OS data © Crown copyright and database right, 2015)Cholera_Deaths.shp(vector) is points for each location of at least one death (attribute value gives death count by location)
The data sets above are based in some way on data prepared by Robin Wilson and other sources.
- Relations.ipynb is a python notebook using the shapely package exploring some examples of spatial relations, specifically:
- distances (between points);
- within and contains (points and polygons);
- overlaps (of polygons);
- buffers (around points; technically an operation, but it makes sense to consider them here)
- Operations.ipynb is a python notebook using the shapely package exploring some examples of spatial operations, specifically:
- differences (between polygons);
- intersections (of polygons);
- unions (of polygons)
- Voronoi.ipynb is a python notebook providing overview of Voronoi Diagrams (Thiessen Polygons) using the pysal package
- SpatialJoins.ipynb is a python notebook that uses spatial joins to analyse data with geometries created in the Spatial Relations, Spatial Operations and Voronoi notebooks
- Arribas-Bel_etal_2017.ipynb re-implements python code from Arribas-Bel et al. (2017) for more recent versions of packages
RMarkdown files equivalent to python notebooks will be forthcoming
- OSullivanUnwin2003_Section7_4.pdf is an excerpt (section 7.4) from O'Sullivan and Unwin (2003), relevant to the notebook on Join Counts
- Johnson, S. (2006) The Ghost Map London: Penguin
- O'Sullivan, D. and Unwin, D.J. (2003) Geographical Information Analysis London: Wiley