Learning Geospatial Analysis with Python, 2nd Edition: An effective guide to geographic information systems and remote sensing analysis using Python 3

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 disasters
	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
			Remote sensing 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
			Step by step
	Summary
Chapter 2: Geospatial Data
	An overview of common data formats
	Data structures
		Common traits
			Geolocation
			Subject information
	Spatial indexing
		Indexing algorithms
			Quadtree index
			R-tree index
		Grids
	Overviews
	Metadata
	File structure
	Vector data
		Shapefiles
		CAD files
		Tag-based and markup-based formats
		GeoJSON
	Raster data
		TIFF files
		JPEG, GIF, BMP, and PNG
		Compressed formats
		ASCII grids
		World files
	Point cloud data
	Web services
	Summary
Chapter 3: The Geospatial Technology Landscape
	Data access
		GDAL
		OGR
	Computational geometry
		The PROJ.4 projection library
		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 (including visualization)
		Quantum GIS
		OpenEV
		GRASS GIS
		uDig
		gvSIG
		OpenJUMP
		Google Earth
		NASA 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
		The Python urllib module
		FTP
		ZIP and TAR files
	Python markup and tag-based parsers
		The minidom module
		ElementTree
			Building XML
		Well-known text (WKT)
	Python JSON libraries
		The json module
		The geojson module
	OGR
	PyShp
	dbfpy
	Shapely
	Fiona
	GDAL
	NumPy
	PIL
	PNGCanvas
	GeoPandas
	PyMySQL
	PyFPDF
	Spectral Python
	Summary
Chapter 5: Python and Geographic Information Systems
	Measuring distance
		Pythagorean theorem
		Haversine formula
		Vincenty\'s formula
	Calculating line direction
	Coordinate conversion
	Reprojection
	Editing shapefiles
		Accessing the shapefile
		Reading shapefile attributes
		Reading shapefile geometry
		Changing a shapefile
			Adding fields
		Merging shapefiles
			Merging shapefiles with dbfpy
		Splitting shapefiles
			Subsetting spatially
	Performing selections
		Point in polygon formula
			Bounding Box Selections
		Attribute selections
	Creating images for visualization
	Dot density calculations
	Choropleth maps
	Using spreadsheets
	Using GPS data
	Geocoding
	Summary
Chapter 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
Chapter 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
	Summary
Chapter 8: Advanced Geospatial Python Modeling
	Creating a Normalized Difference Vegetative Index
		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
			Preparing the NDVI
			Creating classes
	Creating a flood inundation model
		The flood fill function
		Making a flood
	Creating a color hillshade
	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
	Routing along streets
	Geolocating photos
	Summary
Chapter 9: Real-Time Data
	Tracking vehicles
	The NextBus agency list
	The NextBus route list
	NextBus vehicle locations
	Mapping NextBus locations
	Storm chasing
	Reports from the field
	Summary
Chapter 10: Putting It All Together
	A typical GPS report
	Working with GPX-Reporter.py
	Stepping through the program
	The initial setup
	Working with utility functions
	Parsing the GPX
	Getting the bounding box
	Downloading map and elevation images
	Creating the hillshade
	Creating maps
	Measuring the elevation
	Measuring the distance
	Retrieving weather data
	Summary
Index