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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

By : Diener
4.4 (5)
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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

4.4 (5)
By: Diener

Overview of this book

Geospatial development links your data to places on the Earth’s surface. Its analysis is used in almost every industry to answer location type questions. Combined with the power of the Python programming language, which is becoming the de facto spatial scripting choice for developers and analysts worldwide, this technology will help you to solve real-world spatial problems. This book begins by tackling the installation of the necessary software dependencies and libraries needed to perform spatial analysis with Python. From there, the next logical step is to prepare our data for analysis; we will do this by building up our tool box to deal with data preparation, transformations, and projections. Now that our data is ready for analysis, we will tackle the most common analysis methods for vector and raster data. To check or validate our results, we will explore how to use topology checks to ensure top-quality results. This is followed with network routing analysis focused on constructing indoor routes within buildings, over different levels. Finally, we put several recipes together in a GeoDjango web application that demonstrates a working indoor routing spatial analysis application. The round trip will provide you all the pieces you need to accomplish your own spatial analysis application to suit your requirements.
Table of Contents (15 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Setting Up Your Geospatial Python Environment
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1. 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
2. Working with Projections
2. 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
3. Moving Spatial Data from One Format to Another
3. 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
4. Working with PostGIS
4. 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
5. Vector Analysis
5. 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
6. Overlay Analysis
6. 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
7. Raster Analysis
7. 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
8. Network Routing Analysis
8. 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
9. Topology Checking and Data Validation
9. 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
10. Visualizing Your Analysis
10. 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
11. Web Analysis with GeoDjango
11. 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
12
A. Other Geospatial Python Libraries
A. Other Geospatial Python Libraries
13
B. Mapping Icon Libraries
B. Mapping Icon Libraries
14
Index
Index

Introduction

This chapter will get the grunt work done for you so that you can freely and actively complete all the recipes in this book. We will start off by installing, each of the libraries you will be using, one by one. Once each step is completed, we will test each library installation to make sure it works. Since this book is directed toward those of you already working with spatial data, you can skip this chapter if you have it installed already. If not, you will find the installation instructions here useful as a reference.

The choice of Python libraries is based on industry-proven reliability and functionality. The plethora of functions in Python libraries has led to a flourishing GIS support on many top desktop GIS systems, such as QGIS and ESRI ArcGIS.

Also included in this book is an installer.sh bash file. The installer.sh file can be used to install the Python libraries that are available for your virtual environment from pip and other dependencies via the apt-get command. The installer.sh bash file is executed from the command line and installs almost everything in one go, so please take a look at it. For those of you who are starting with Python for the first time, follow the instructions in this chapter and your machine will be set up to complete different recipes.

Installations can sometimes be tricky even for advanced users, so you will find some of the most common pitfalls and hook-ups described in this chapter.

The development of these recipes was completed on a fresh Linux/Ubuntu 14.04 machine. Therefore, the code examples, if not otherwise specified, are Linux/Ubuntu-specific with Windows notes wherever necessary, unless otherwise specified.

End of Section 2
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