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Python geospatial analysis essentials : process, analyze, and display geospatial data using Python libraries and related tools

Author: Erik Westra
Publisher: Birmingham, UK : Packt Publishing, 2015.
Series: Community experience distilled.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
If you are an experienced Python developer and wish to get up-to-speed with geospatial programming, then this book is for you. While familiarity with installing third-party Python libraries would be an advantage, no prior knowledge of geospatial programming is required.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Westra, Erik.
Python geospatial analysis essentials : process, analyze, and display geospatial data using Python libraries and related tools.
Birmingham, England : Packt Publishing Ltd, c2015
ix, 178 pages
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Erik Westra
ISBN: 9781783553891 1783553898 1782174516 9781782174516
OCLC Number: 913880040
Notes: Includes index.
Description: 1 online resource (1 volume) : illustrations.
Contents: Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Geospatial Analysis and Techniques; About geospatial analysis; Understanding geospatial data; Setting up your Python installation; Installing GDAL; Installing Shapely; Obtaining some geospatial data; Unlocking the shapefile; Analyzing the data; A program to identify neighboring countries; Summary; Chapter 2: Geospatial Data; Geospatial data quality; Types of geospatial data; Shapefiles; Well-known text; Well-known binary; Spatial databases; Geospatial microformats. GeoJSONGML; Digital elevation models; Raster basemaps; Multiband raster files; Sources of freely available geospatial data; Natural Earth Data; OpenStreetMap; US Census Bureau; World Borders Dataset; GLOBE; National Elevation Dataset; Reading and writing geospatial data using Python; Reading vector data; Writing vector data; Reading raster data; Writing raster data; Dealing with spatial reference systems; WGS84; Universal Transverse Mercator; Describing spatial reference systems; Transforming coordinates; Calculating lengths and areas; Geospatial data errors and how to fix them; Points. LineStringsLinear Rings; Polygons; MultiPolygons; Fixing invalid geometries; Summary; Chapter 3: Spatial Databases; Spatial database concepts; Installing a spatial database; Installing PostgreSQL; Installing PostGIS; Installing psycopg2; Accessing PostGIS from Python; Setting up a spatial database; Importing spatial data; Querying spatial data; Manipulating spatial data; Exporting spatial data; Summary; Chapter 4: Creating Maps; Introducing Mapnik; Installing Mapnik; A taste of Mapnik; Building a map; Styling a map; Learning Mapnik; Datasources; Symbolizers; PointSymbolizer; LineSymbolizer. PolygonSymbolizerTextSymbolizer; RasterSymbolizer; Map rendering; A working example; Next steps; Summary; Chapter 5: Analyzing Geospatial Data; Libraries for spatial analysis; PyProj; NetworkX; Spatial analysis recipes; Calculating and comparing coordinates; Calculating lengths; Calculating areas; Calculating shortest paths; Summary; Chapter 6: Building a Complete Geospatial Analysis System; Matching GPS data against a map; An overview of the GPS Heatmap system; Obtaining the necessary data; Obtaining GPS data; Downloading the road data; Implementing the GPS Heatmap system. Initializing the databaseImporting the road data; Splitting the road data into segments; Constructing a network of directed road segments; Implementing the map-matching algorithm; Generating the GPS Heatmap; Further improvements; Summary; Index.
Series Title: Community experience distilled.
Other Titles: Process, analyze, and display geospatial data using Python libraries and related tools
Responsibility: Erik Westra.

Abstract:

If you are an experienced Python developer and wish to get up-to-speed with geospatial programming, then this book is for you. While familiarity with installing third-party Python libraries would be an advantage, no prior knowledge of geospatial programming is required.

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