Learning Geospatial Analysis with Python : master GIS and remote sensing analysis using Python with these easy to follow tutorials / Joel Lawhead.

Preface -- Chapter 1: Learning Geospatial Analysis with Python -- Chapter 2: Geospatial Data -- Chapter 3: The Geospatial Technology Landscape -- Chapter 4: Geospatial Python Toolbox -- Chapter 5: Python and Geographic Information Systems -- Chapter 6: Python and Remote Sensing -- Chapter 7: Python and elevation data -- Chapter 8: Advances Python geospatial modelling -- Chapter 9: Real-time data -- Chapter 10: Putting it all together.

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Learning Geospatial Analysis with Python; Geospatial analysis and our world; Beyond politics; History of geospatial analysis; Geographic Information Systems; Remote sensing; Elevation data; Computer-aided drafting; Geospatial analysis and computer programming; Object-oriented programming for geospatial analysis; Importance of geospatial analysis; Geographic Information System concepts; Thematic maps; Spatial databases; Spatial Indexing; Metadata; Map projections.

RenderingRaster data concepts; Images as data; Remote sensing and color; Common vector GIS concepts; Data structures; Buffer; Dissolve; Generalize; Intersection; Merge; Point in polygon; Union; Join; Geospatial rules about polygons; Common raster data concepts; Band math; Change detection; Histogram; Feature extraction; Supervised classification; Unsupervised classification; Creating the simplest possible Python GIS; Getting started with Python; Building SimpleGIS; Summary; Chapter 2: Geospatial Data; Data structures; Common traits; Geo-location; Subject information; Spatial indexing.

MetadataFile structure; Vector data; Shapefiles; CAD files; Tag and markup-based formats; GeoJSON; Raster data; TIFF files; JPEG, GIF, BMP, PNG; Compressed formats; ASCII GRIDS; World files; Point cloud data; Summary; Chapter 3: The Geospatial Technology Landscape; Data access; GDAL; OGR; Computational geometry; PROJ. 4; CGAL; JTS; GEOS; PostGIS; Other spatially-enabled databases; Oracle spatial and graph; ArcSDE; Microsoft SQL Server; MySQL; SpatiaLite; Routing; Esri Network Analyst and Spatial Analyst; pgRouting; Desktop tools; Quantum GIS; OpenEV; GRASS GIS; uDig; gvSIG; OpenJUMP.

Google EarthNASA World Wind; ArcGIS; Metadata management; GeoNetwork; CatMDEdit; Summary; Chapter 4: Geospatial Python Toolbox; Installing third-party Python modules; Installing GDAL; Windows; Linux; Mac OS X; Python networking libraries for acquiring data; Python urllib module; FTP; ZIP and TAR files; Python markup and tag-based parsers; The minidom module; ElementTree; Building XML; WKT; Python JSON libraries; json module; geojson module; OGR; PyShp; dbfpy; Shapely; GDAL; NumPy; PIL; PNGCanvas; PyFPDF; Spectral Python; Summary; Chapter 5: Python and Geographic Information Systems.

Measuring distancePythagorean theorem; Haversine formula; Vincenty formula; Coordinate conversion; Reprojection; Editing shapefiles; Accessing the shapefile; Reading shapefile attributes; Reading shapefile geometry; Changing a shapefile; Adding fields; Merging shapefiles; Splitting shapefiles; Subsetting spatially; Performing selections; Point in polygon formula; Attribute selections; Creating images for visualization; Dot density calculations; Choropleth maps; Using spreadsheets; Using GPS data; Summary; Chapter 6: Python and Remote Sensing; Swapping image bands; Creating histograms.

"This is a tutorial-style book that helps you to perform Geospatial and GIS analysis with Python and its tools/libraries. This book will first introduce various Python-related tools/packages in the initial chapters before moving towards practical usage, examples, and implementation in specialized kinds of Geospatial data analysis. This book is for anyone who wants to understand digital mapping and analysis and who uses Python or another scripting language for automation or crunching data manually. This book primarily targets Python developers, researchers, and analysts who want to perform Geospatial, modeling, and GIS analysis with Python."--Provided by publisher.

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