Python geospatial development develop sophisticated mapping applications from scratch using Python 3 tools for geospatial development

Cover
Copyright
Credits
About the Author
About the Reviewer
www.PacktPub.com
Table of Contents
Preface
Chapter 1: Geospatial Development Using Python
	Python
		Python 3
	Geospatial development
	Applications of geospatial development
		Analysing geospatial data
		Visualizing geospatial data
		Creating a geospatial mash-up
	Recent developments
	Summary
Chapter 2: GIS
	Core GIS concepts
		Location
		Distance
		Units
		Projections
			Cylindrical projections
			Conic projections
			Azimuthal projections
			The nature of map projections
		Coordinate systems
		Datums
		Shapes
	GIS data formats
	Working with GIS data manually
		Obtaining the data
		Installing GDAL
			Installing GDAL on Linux
			Installing GDAL on Mac OS X
			Installing GDAL on MS Windows
			Testing your GDAL installation
			Examining the Downloaded Shapefile
	Summary
Chapter 3: Python Libraries for Geospatial Development
	Reading and writing geospatial data
		GDAL/OGR
		Installing GDAL/OGR
		Understanding GDAL
		GDAL example code
		Understanding OGR
		OGR example code
		GDAL/OGR documentation
	Dealing with projections
		pyproj
		Installing pyproj
		Understanding pyproj
			Proj
			Geod
		Example code
		Documentation
	Analyzing and manipulating Geospatial data
		Shapely
		Installing Shapely
		Understanding Shapely
		Shapely example code
		Shapely documentation
	Visualizing geospatial data
		Mapnik
		Installing Mapnik
		Understanding Mapnik
		Mapnik example code
		Mapnik documentation
	Summary
Chapter 4: Sources of Geospatial Data
	Sources of geospatial data in vector format
		OpenStreetMap
			The OpenStreetMap data format
			Obtaining and using OpenStreetMap data
		TIGER
			The TIGER data format
			Obtaining and using TIGER data
		Natural Earth
			The Natural Earth data format
			Obtaining and using Natural Earth vector data
		The Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG)
			The GSHHG data format
			Obtaining the GSHHG database
		The World Borders Dataset
			The World Borders Dataset data format
			Obtaining the World Borders Dataset
	Sources of geospatial data in raster format
		Landsat
			The Landsat data format
			Obtaining Landsat imagery
		Natural Earth
			The Natural Earth data format
			Obtaining and using Natural Earth raster data
		Global Land One-kilometer Base Elevation (GLOBE)
			The GLOBE data format
			Obtaining and using GLOBE data
		The National Elevation Dataset (NED)
			The NED data format
			Obtaining and using NED data
	Sources of other types of geospatial data
		The GEOnet Names Server
			The GEOnet Names Server data format
			Obtaining and using GEOnet Names Server data
		The Geographic Names Information System (GNIS)
			The GNIS data format
			Obtaining and using GNIS data
	Choosing your geospatial data source
	Summary
Chapter 5: Working with Geospatial Data in Python
	Pre-requisites
	Working with geospatial data
		Task – calculate the bounding box for each country in the world
		Task – calculate the border between Thailand and Myanmar
		Task – analyze elevations using a digital elevation map
	Changing datums and projections
		Task – changing projections to combine shapefiles using geographic and UTM coordinates
		Task – changing the datums to allow older and newer TIGER data to be combined
	Performing geospatial calculations
		Task – identifying parks in or near urban areas
	Converting and standardizing units of geometry and distance
		Task – calculating the length of the Thai-Myanmar border
		Task – finding a point 132.7 kilometers west of Shoshone, California
	Exercises
	Summary
Chapter 6: Spatial Databases
	Spatially-enabled databases
	Spatial indexes
	Introducing PostGIS
		Installing PostgreSQL
		Installing PostGIS
		Installing psycopg2
	Setting up a database
		Creating a Postgres user account
		Creating a database
		Allowing the user to access the database
		Spatially enable the database
	Using PostGIS
		PostGIS documentation
		Advanced PostGIS features
	Recommended best practices
		Best practice: use the database to keep track of spatial references
		Best practice: use the appropriate spatial reference for your data
			Option 1: Using GEOGRAPHY fields
			Option 2: Transforming features as required
			Option 3: Transforming features from the outset
			When to use unprojected coordinates
		Best practice: avoid on-the-fly transformations within a query
		Best practice: don't create geometries within a query
		Best practice: use spatial indexes appropriately
		Best practice: know the limits of your database's query optimizer
	Summary
Chapter 7: Using Python and Mapnik to Generate Maps
	Introducing Mapnik
	Creating an example map
	Mapnik concepts
		Data sources
			Shapefile
			PostGIS
			Gdal
			MemoryDatasource
		Rules, filters, and styles
			Filters
			"Else" rules
			Styles
		Symbolizers
			Drawing points
			Drawing lines
			Drawing polygons
			Drawing text
			Drawing raster images
		Maps and layers
		Map rendering
	Summary
Chapter 8: Working with Spatial Data
	About DISTAL
	Designing and building the database
	Downloading and importing the data
		The World Borders Dataset
		The GSHHG shoreline database
		US place names
		Non-US place names
	Implementing the DISTAL application
		The "select country" script
		The "select area" script
			Calculating the bounding box
			Calculating the map's dimensions
			Rendering the map image
		The "show results" script
			Identifying the clicked-on point
			Identifying matching place names
			Displaying the results
	Using DISTAL
	Summary
Chapter 9: Improving the DISTAL Application
	Dealing with the anti-meridian line
	Dealing with the scale problem
	Performance
		Finding the problem
		Improving performance
			Calculating the tiled shorelines
		Using the tiled shorelines
		Analyzing the performance improvement
	Summary
Chapter 10: Tools for Web-Based Geospatial Development
	Tools and techniques for geospatial web development
		Web applications
			A bare-bones approach
			Web application stacks
			Web application frameworks
			User interface libraries
		Web services
			An example web service
			Map rendering using a web service
			Tile caching
		The "slippy map" stack
		Geospatial web protocols
	A closer look at three specific tools and techniques
		The Tile Map Service protocol
		OpenLayers
		GeoDjango
			Learning Django
			GeoDjango
	Summary
Chapter 11: Putting It All Together – a Complete Mapping System
	About the ShapeEditor
	Designing the ShapeEditor
		Importing a shapefile
		Selecting a feature
		Editing a feature
		Exporting a shapefile
	Prerequisites
	Setting up the database
	Setting up the ShapeEditor project
	Defining the ShapeEditor's applications
	Creating the shared application
	Defining the data models
		The Shapefile object
		The Attribute object
		The Feature object
		The AttributeValue object
		The models.py file
	Playing with the admin system
	Summary
Chapter 12: ShapeEditor – Importing and Exporting Shapefiles
	Implementing the shapefile list view
	Importing shapefiles
		The Import Shapefile form
		Extracting the uploaded shapefile
		Importing the shapefile's contents
			Opening the shapefile
			Adding the Shapefile object to the database
			Defining the shapefile's attributes
			Storing the shapefile's features
			Storing the shapefile's attributes
		Cleaning up
	Exporting shapefiles
		Define the OGR shapefile
		Saving the features into the shapefile
		Saving the attributes into the shapefile
		Compressing the shapefile
		Deleting temporary files
		Returning the ZIP archive to the user
	Summary
Chapter 13: ShapeEditor – Selecting and Editing Features
	Selecting the feature to edit
		Implementing the Tile Map Server
			Setting up the base map
			Tile rendering
			Completing the Tile Map Server
		Using OpenLayers to display the map
		Intercepting mouse clicks
		Implementing the "Find Feature" view
	Editing features
	Adding features
	Deleting features
	Deleting shapefiles
	Using the ShapeEditor
	Further improvements and enhancements
	Summary
Index