Learning Geospatial Analysis with Python - Second Edition电子书

Learning Geospatial Analysis with Python Second Edition

Table of Contents

Learning Geospatial Analysis with Python Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

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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

Downloading the color images of this book

Errata

Piracy

Questions

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

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

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

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

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

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

Summary

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

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

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