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Python Geospatial Analysis Cookbook

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Python Geospatial Analysis Cookbook

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Python Geospatial Analysis Cookbook
Nov, 2015 | 310 pages
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Table of Contents (20 chapters)
Python Geospatial Analysis Cookbook
Python Geospatial Analysis Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Setting Up Your Geospatial Python Environment
Setting Up Your Geospatial Python Environment
Introduction
Installing virtualenv and virtualenvwrapper
Installing pyproj and NumPy
Installing shapely, matplotlib, and descartes
Installing pyshp, geojson, and pandas
Installing SciPy, PySAL, and IPython
Installing GDAL and OGR
Installing GeoDjango and PostgreSQL with PostGIS
2
Working with Projections
Working with Projections
Introduction
Discovering projection(s) of a Shapefile or GeoJSON dataset
Listing projection(s) from a WMS server
Creating a projection definition for a Shapefile if it does not exist
Batch setting the projection definition of a folder full of Shapefiles
Reprojecting a Shapefile from one projection to another
3
Moving Spatial Data from One Format to Another
Moving Spatial Data from One Format to Another
Introduction
Converting a Shapefile to a PostGIS table using ogr2ogr
Batch importing a folder of Shapefiles into PostGIS using ogr2ogr
Batch exporting a list of tables from PostGIS to Shapefiles
Converting an OpenStreetMap (OSM) XML to a Shapefile
Converting a Shapefile (vector) to a GeoTiff (raster)
Converting a raster (GeoTiff) to a vector (Shapefile) using GDAL
Creating a Shapefile from point data stored in Microsoft Excel
Converting an ESRI ASCII DEM to an image height map
4
Working with PostGIS
Working with PostGIS
Introduction
Executing a PostGIS ST_Buffer analysis query and exporting it to GeoJSON
Finding out whether a point is inside a polygon
Splitting LineStrings at intersections using ST_Node
Checking the validity of LineStrings
Executing a spatial join and assigning point attributes to a polygon
Conducting a complex spatial analysis query using ST_Distance()
5
Vector Analysis
Vector Analysis
Introduction
Clipping LineStrings to an area of interest
Splitting polygons with lines
Finding the location of a point on a line using linear referencing
Snapping a point to the nearest line
Calculating 3D ground distance and total elevation gain
6
Overlay Analysis
Overlay Analysis
Introduction
Punching holes in polygons with a symmetric difference operation
Union polygons without merging
Union polygons with merging (dissolving)
Performing an identity function (difference + intersection)
7
Raster Analysis
Raster Analysis
Introduction
Loading a DEM USGS ACSII CDED into PostGIS
Creating an elevation profile
Creating a hillshade raster from your DEM with ogr
Generating slope and aspect images from your DEM
Merging rasters to generate a color relief map
8
Network Routing Analysis
Network Routing Analysis
Introduction
Finding the Dijkstra shortest path with pgRouting
Finding the Dijkstra shortest path with NetworkX in pure Python
Generating evacuation polygons based on an indoor shortest path
Creating centerlines from polygons
Building an indoor routing system in 3D
Calculating indoor route walk time
9
Topology Checking and Data Validation
Topology Checking and Data Validation
Introduction
Creating a rule – only one point inside a polygon
A point must be on the starting and ending nodes of a line only
LineStrings must not overlap
A LineString must not have dangles
A polygon centroid must be within a specific distance of a line
10
Visualizing Your Analysis
Visualizing Your Analysis
Introduction
Generating a leaflet web map with Folium
Setting up TileStache to serve tiles
Visualizing DEM data with Three.js
Draping an orthophoto over a DEM
11
Web Analysis with GeoDjango
Web Analysis with GeoDjango
Introduction
Setting up a GeoDjango web application
Creating an indoor web routing service
Visualizing an indoor routing service
Creating an indoor route-type service
Creating an indoor route from room to room
Other Geospatial Python Libraries
Other Geospatial Python Libraries
Mapping Icon Libraries
Mapping Icon Libraries
Index
Index

Introduction

Raster analysis works similar to vector analysis but the spatial relation is determined by the position of the raster cell. Most of our raster data is collected through diverse remote sensing techniques. In this chapter, the goals are quite simple and focused on working with and around a digital elevation model (DEM). The DEM we are using is from Whistler, BC, Canada, home to the 2010 Winter Olympics. Our DEM is in the form of the USGS ASCII CDED (.dem) format. The DEM is our source data that is used to derive several new raster datasets. As with other chapters, we will leverage Python as our glue to run scripts to enable a processing pipeline for raster data. The visualization of our data will play out with matplotlib along with the QGIS desktop GIS.

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