There are several powerful Python libraries for reading, processing, analyzing, and viewing geospatial data. There are also a number of websites that provide high-quality geospatial data, which you can use freely in your own projects. This data will often be the basis for your analysis, providing the shapes of countries, the positions of cities, the outlines of roads, and so on. Using this data in conjunction with the available geospatial libraries gives you a powerful toolkit for performing your own geospatial analysis using Python.
Chapter 1, Geospatial Analysis and Techniques, walks the reader through the process of downloading sample geospatial data, before writing a simple Python program to read and analyze that sample data.
Chapter 2, Geospatial Data, focuses on the data used for geospatial analysis: how to obtain it, why good data is important, the different formats that geospatial data can come in, and how to generate your own spatial datasets.
Chapter 3, Spatial Databases, provides a brief introduction to creating geospatial databases, how to store data in a spatially-enabled database, and how to perform efficient queries against that data.
Chapter 4, Creating Maps, looks at how to use the Mapnik library to produce great-looking maps.
Chapter 5, Analyzing Geospatial Data, guides the reader through the process of writing spatial analysis programs using Python. Based on the datasets downloaded in Chapter 2, Geospatial Data, and using the major Python libraries for geospatial analysis, this chapter uses a recipe-like format to solve a range of typical spatial analysis problems.
Chapter 6, Building a Complete Geospatial Analysis System, uses all the various libraries and techniques covered in the earlier chapters to build a complete geospatial analysis system.
The code examples in this book use Python 2 to analyze geospatial data. Any reasonably powerful computer running Windows, Mac OS X, or Linux will be suitable. You will need to download and install the following software onto your computer:
Python version 2.7 or later, excluding Python 3.x
GDAL/OGR version 1.11 or later
GEOS version 3.4.2 or later
Shapely version 1.5.7 or later
PostgreSQL version 9.3 or later
PostGIS version 2.1.4 or later
psycopg2 version 2.5 or later
Mapnik version 2.2 or later
PROJ version 4.0 or later
PyProj version 1.9.4 or later
NetworkX version 1.9.1 or later
Full instructions for downloading, installing, and using these various tools and libraries are included in this book.
If you are an experienced Python developer wishing to come up to speed with geospatial programming, or have specific spatial programming needs, then this book is for you. While familiarity with installing third-party Python libraries will be an advantage, no prior knowledge of geospatial programming concepts or techniques is required.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Once you have installed it, you can check that it's working by firing up your Python interpreter and typing import osgeo.gdal and then import osgeo.ogr."
A block of code is set as follows:
import osgeo.ogr
shapefile = osgeo.ogr.Open("TM_WORLD_BORDERS-0.3.shp")
layer = shapefile.GetLayer(0)
for i in range(layer.GetFeatureCount()):
feature = layer.GetFeature(i)
feature_name = feature.GetField("NAME")
geometry = feature.GetGeometryRef()
geometry_type = geometry.GetGeometryName()
print i, feature_name, geometry_typeWhen we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
from osgeo import ogr
driver = ogr.GetDriverByName("ESRI Shapefile")
dstFile = driver.CreateDataSource("test-shapefile")Any command-line input or output is written as follows:
% python readRaster.py -500 53081919 -84 1 -83 8 -82 9 -81 17 ... 5241 1 5295 1 5300 1 5443 1
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking on the Next button moves you to the next screen."
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