Learning Geospatial Analysis with Python -- Table of Contents -- Learning Geospatial Analysis with Python -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 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 -- Rendering -- Raster 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 -- 2. Geospatial Data -- Data structures -- Common traits -- Geo-location -- Subject information -- Spatial indexing -- Indexing algorithms -- Quad-Tree index -- R-Tree index -- Grids -- Overviews -- Metadata -- File structure -- Vector data -- Shapefiles -- CAD files -- Tag and markup-based formats -- GeoJSON -- Raster data -- TIFF files.

JPEG, GIF, BMP, and PNG -- Compressed formats -- ASCII GRIDS -- World files -- Point cloud data -- Summary -- 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 Earth -- NASA World Wind -- ArcGIS -- Metadata management -- GeoNetwork -- CatMDEdit -- Summary -- 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 -- 5. Python and Geographic Information Systems -- Measuring distance -- Pythagorean 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 -- 6. Python and Remote Sensing -- Swapping image bands -- Creating histograms -- Performing a histogram stretch -- Clipping images.

Classifying images -- Extracting features from images -- Change detection -- Summary -- 7. Python and Elevation Data -- ASCII Grid files -- Reading grids -- Writing grids -- Creating a shaded relief -- Creating elevation contours -- Working with LIDAR -- Creating a grid from LIDAR -- Using PIL to visualize LIDAR -- Creating a Triangulated Irregular Network (TIN) -- Summary -- 8. Advanced Geospatial Python Modelling -- Creating an NDVI -- Setting up the framework -- Loading the data -- Rasterizing the shapefile -- Clipping the bands -- Using the NDVI formula -- Classifying the NDVI -- Additional functions -- Loading the NDVI -- Creating classes -- Creating a flood inundation model -- The flood fill function -- Making a flood -- Least cost path analysis -- Setting up the test grid -- The simple A* algorithm -- Generating the test path -- Viewing the test output -- The real-world example -- Loading the grid -- Defining the helper functions -- The real-world A* algorithm -- Generating a real-world path -- Summary -- 9. Real-Time Data -- Tracking vehicles -- Nextbus agency list -- Nextbus route list -- Nextbus vehicle locations -- Mapping Nextbus locations -- Storm chasing -- Summary -- 10. Putting It All Together -- A typical GPS report -- Working with GPX-Reporter.py -- Stepping through the program -- Initial setup -- Working with utility functions -- Parsing the GPX -- Getting the bounding box -- Downloading OpenStreetMap images -- Creating the hillshade -- Creating maps -- Measuring elevation -- Measuring distance -- Retrieving weather data -- Summary -- Index.