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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

By : Michael Diener
4.4 (5)
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Python Geospatial Analysis Cookbook

Python Geospatial Analysis Cookbook

4.4 (5)
By: Michael 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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12
A. Other Geospatial Python Libraries
13
B. Mapping Icon Libraries
14
Index

Loading a DEM USGS ACSII CDED into PostGIS


Importing and working with a DEM in PostGIS is what this recipe is all about. We begin our journey with a text file that's full of points and is stored in the USGS ASCII CDED format (to read more about the details of this format, feel free to look at the documentation page at http://www.gdal.org/frmt_usgsdem.html). The ASCII format is well known and accepted by many desktop GIS applications as a direct data source. Feel free to simply open up your ASCII file with QGIS to view the files and see the resulting raster representation that it creates for you. Our task at hand is to import this DEM file into a PostGIS database, creating a new PostGIS raster dataset within PostGIS We perform this task by using a command-line tool called raster2pgsql, which is installed along with your PostGIS installation. The raster2pgsql tool is located on Windows under C:\Program Files\PostgreSQL\9.3\bin\ if you are running PostgreSQL 9.

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Python Geospatial Analysis Cookbook
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