Geoprocessing with Python / Edition 1 available in Paperback, eBook
- ISBN-10:
- 1617292141
- ISBN-13:
- 9781617292149
- Pub. Date:
- 05/23/2016
- Publisher:
- Manning
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Overview
Geoprocessing with Python teaches you how to use the Python programming language, along with free and open source tools, to read, write, and process geospatial data.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
This book is about the science of reading, analyzing, and presenting geospatial data programmatically, using Python. Thanks to dozens of open source Python libraries and tools, you can take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo. The book shows you how.
About the Book
Geoprocessing with Python teaches you how to access available datasets to make maps or perform your own analyses using free tools like the GDAL, NumPy, and matplotlib Python modules. Through lots of hands-on examples, you’ll master core practices like handling multiple vector file formats, editing geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. The book also covers how to manipulate, resample, and analyze raster data, such as aerial photographs and digital elevation models.
What's Inside
- Geoprocessing from the ground up
- Read, write, process, and analyze raster data
- Visualize data with matplotlib
- Write custom geoprocessing tools
- Three additional appendixes available online
About the Reader
To read this book all you need is a basic knowledge of Python or a similar programming language.
About the Author
Chris Garrard works as a developer for Utah State University and teaches a graduate course on Python programming for GIS.
Table of Contents
- Introduction
- Python basics
- Reading and writing vector data
- Working with different vector file formats
- Filtering data with OGR
- Manipulating geometries with OGR
- Vector analysis with OGR
- Using spatial reference systems
- Reading and writing raster data
- Working with raster data
- Map algebra with NumPy and SciPy
- Map classification
- Visualizing data
Appendixes
- A - Installation
- B - References
- C - OGR - online only
- D - OSR - online only
- E - GDAL - online only
Product Details
| ISBN-13: | 9781617292149 |
|---|---|
| Publisher: | Manning |
| Publication date: | 05/23/2016 |
| Edition description: | 1st Edition |
| Pages: | 360 |
| Product dimensions: | 7.40(w) x 9.20(h) x 1.00(d) |
About the Author
Table of Contents
Preface xi
Acknowledgments xiii
About this book xiv
About the author xvii
About the cover illustration xviii
1 Introduction 1
1.1 Why use Python and open source? 2
1.2 Types of spatial data 3
1.3 What is geoprocessing? 7
1.4 Exploring your data 10
1.5 Summary 14
2 Python basics 15
2.1 Writing and executing code 16
2.2 Basic structure of a script 17
2.3 Variables 18
2.4 Data types 20
Booleans 20
Numeric types 20
Strings 22
Lists and tuples 24
Sets 26
Dictionaries 26
2.5 Control flow 27
If statements 27
While statements 29
For statements 29
Break, continue, and else 30
2.6 Functions 31
2.7 Classes 32
2.8 Summary 34
3 Reading and writing vector data 35
3.1 Introduction to vector data 36
3.2 Introduction to OGR 41
3.3 Reading vector data 44
Accessing specific features 47
Viewing your data 49
3.4 Getting metadata about the data 51
3.5 Writing vector data 54
Creating new data sources 59
Creating new fields 61
3.6 Updating existing data 63
Changing the layer definition 63
Adding, updating, and deleting features 64
3.7 Summary 66
4 Working with different vector file formats 67
4.1 Vector file formats 68
File-based formats such as shapefiles and geoJSON 68
Multi-user database formats such as PostGIS 71
4.2 Working with more data formats 71
SpatiaLite 72
PostGIS 73
Folders as data sources (shapefiles and CSV) 74
Esri file geodatabases 74
Web feature services 76
4.3 Testing format capabilities 84
4.4 Summary 87
5 Filtering data with OGR 88
5.1 Attribute filters 89
5.2 Spatial filters 93
5.3 Using SQL to create temporary layers 99
5.4 Taking advantage of filters 103
5.5 Summary 104
6 Manipulating geometries with OGR 105
6.1 Introduction to geometries 106
6.2 Working with points 107
Creating and editing single points 108
Creating and editing multipoints: multiple points as one geometry 110
6.3 Working with lines 112
Creating and editing single lines 114
Creating and editing multilines: multiple lines as one geometry 118
6.4 Working with polygons 120
Creating and editing single polygons 122
Creating and editing multipolygons: multiple polygons as one geometry 124
Creating and editing polygons with holes: donuts 126
6.5 Summary 128
7 Vector analysis with OGR 129
7.1 Overlay tools: what's on top of what? 130
7.2 Proximity tools: how far apart are things? 136
7.3 Example: locating areas suitable for wind farms 140
7.4 Example: animal tracking data 144
7.5 Summary 152
8 Using spatial reference systems 153
8.1 Introduction to spatial reference systems 154
8.2 Using spatial references with OSR 159
Spatial reference objects 159
Creating spatial reference objects 161
Assigning an SRS to data 163
Reprojecting geometries 164
Reprojecting an entire layer 167
8.3 Using spatial references with pyproj 168
Transforming coordinates between spatial reference systems 169
Great-circle calculations 171
8.4 Summary 172
9 Reading and writing raster data 173
9.1 Introduction to raster data 174
9.2 Introduction to GDAL 181
9.3 Reading partial datasets 187
Using real-world coordinates 193
Resampling data 196
9.4 Byte sequences 200
9.5 Subdatasets 203
9.6 Web map services 204
9.7 Summary 207
10 Working with raster data 208
10.1 Ground control points 209
10.2 Converting pixel coordinates to another image 213
10.3 Color tables 215
Transparency 217
10.4 Histograms 218
10.5 Attribute tables 221
10.6 Virtual raster format 223
Subsetting 225
Creating troublesome formats 227
Reprojecting images 228
10.7 Callback functions 230
10.8 Exceptions and error handlers 232
10.9 Summary 236
11 Map algebra with NumPy and SciPy 237
11.1 Introduction to NumPy 238
11.2 Map algebra 242
Local analyses 243
Focal analyses 247
Zonal analyses 258
Global analyses 263
11.3 Resampling data 267
11.4 Summary 275
12 Map classification 276
12.1 Unsupervised classification 278
12.2 Supervised classification 280
Accuracy assessments 284
12.3 Summary 286
13 Visualizing data 287
13.1 Matplotlib 288
Plotting vector data 288
Plotting raster data 300
Plotting 3D data 305
13.2 Mapnik 307
Drawing vector data 308
Storing information as XML 314
Drawing raster data 316
Summary 318
Appendix A Installation 319
Appendix B References 327
Index 331