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E-raamat: Geoprocessing with Python

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  • Formaat: 360 pages
  • Ilmumisaeg: 05-May-2016
  • Kirjastus: Manning Publications
  • Keel: eng
  • ISBN-13: 9781638353140
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Geoprocessing with PythonSuurem pilt
  • Formaat: 360 pages
  • Ilmumisaeg: 05-May-2016
  • Kirjastus: Manning Publications
  • Keel: eng
  • ISBN-13: 9781638353140
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Geospatial data is hard to ignore. Nearly every car, phone, or camera has a GPS sensor, and aerial photos, satellite imagery, and data representing political boundaries, roads, rivers, and streams are available for free download from many websites. Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. The Python language, along with dozens of open source libraries and tools, makes it possible to take on professional geoprocessing tasks without investing in expensive proprietary packages like ArcGIS and MapInfo.

Geoprocessing with Python teaches how to use the Python programming language along with free and open source tools to read, write, and process geospatial data. It shows how to access available data sets to make maps or perform analyses using free and open source tools like the GDAL, Shapely, and Fiona Python modules. Readers will master core practices like handling multiple vector file formats, editing and manipulating geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. They'll also learn how to create geospatial data, rather than just consuming it. The book also covers how to manipulate and analyze raster data, such as aerial photographs, satellite images, and digital elevation models.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Preface xi
Acknowledgments xiii
About this book xiv
About the author xvii
About the cover illustration xviii
1 Introduction
1(14)
1.1 Why use Python and open source?
2(1)
1.2 Types of spatial data
3(4)
1.3 What is geoprocessing?
7(3)
1.4 Exploring your data
10(4)
1.5 Summary
14(1)
2 Python basics
15(20)
2.1 Writing and executing code
16(1)
2.2 Basic structure of a script
17(1)
2.3 Variables
18(2)
2.4 Data types
20(7)
Booleans
20(1)
Numeric types
20(2)
Strings
22(2)
Lists and tuples
24(2)
Sets
26(1)
Dictionaries
26(1)
2.5 Control flow
27(4)
If statements
27(2)
While statements
29(1)
For statements
29(1)
break, continue, and else
30(1)
2.6 Functions
31(1)
2.7 Classes
32(2)
2.8 Summary
34(1)
3 Reading and writing vector data
35(32)
3.1 Introduction to vector data
36(5)
3.2 Introduction to OGR
41(3)
3.3 Reading vector data
44(7)
Accessing specific features
47(2)
Viewing your data
49(2)
3.4 Getting metadata about the data
51(3)
3.5 Writing vector data
54(9)
Creating new data sources
59(2)
Creating new fields
61(2)
3.6 Updating existing data
63(3)
Changing the layer definition
63(1)
Adding, updating, and deleting features
64(2)
3.7 Summary
66(1)
4 Working with different vector file formats
67(21)
4.1 Vector file formats
68(3)
File-based formats such as shapefiles and geoJSON
68(3)
Multi-user database formats such as PostGIS
71(1)
4.2 Working with more data formats
71(13)
SpatiaLite
72(1)
PostGIS
73(1)
Folders as data sources (shapefiles and CSV)
74(1)
Esri file geodatabases
74(2)
Web feature services
76(8)
4.3 Testing format capabilities
84(3)
4.4 Summary
87(1)
5 Filtering data with OGR
88(17)
5.1 Attribute filters
89(4)
5.2 Spatial filters
93(6)
5.3 Using SQL to create temporary layers
99(4)
5.4 Taking advantage of filters
103(1)
5.5 Summary
104(1)
6 Manipulating geometries with OGR
105(24)
6.1 Introduction to geometries
106(1)
6.2 Working with points
107(5)
Creating and editing single points
108(2)
Creating and editing multipoints: multiple points as one geometry
110(2)
6.3 Working with lines
112(8)
Creating and editing single lines
114(4)
Creating and editing multilines: multiple lines as one geometry
118(2)
6.4 Working with polygons
120(8)
Creating and editing single polygons
122(2)
Creating and editing multipolygons: multiple polygons as one geometry
124(2)
Creating and editing polygons with holes: donuts
126(2)
6.5 Summary
128(1)
7 Vector analysis with OGR
129(24)
7.1 Overlay tools: what's on top of what?
130(6)
7.2 Proximity tools: how far apart are things?
136(4)
7.3 Example: locating areas suitable for wind farms
140(4)
7.4 Example: animal tracking data
144(8)
7.5 Summary
152(1)
8 Using spatial reference systems
153(20)
8.1 Introduction to spatial reference systems
154(5)
8.2 Using spatial references with OSR
159(9)
Spatial reference objects
159(2)
Creating spatial reference objects
161(2)
Assigning an SRS to data
163(1)
Reprojecting geometries
164(3)
Reprojecting an entire layer
167(1)
8.3 Using spatial references with pyproj
168(4)
Transforming coordinates between spatial reference systems
169(2)
Great-circle calculations
171(1)
8.4 Summary
172(1)
9 Reading and writing raster data
173(35)
9.1 Introduction to raster data
174(7)
9.2 Introduction to GDAL
181(6)
9.3 Reading partial datasets
187(13)
Using real-world coordinates
193(3)
Resampling data
196(4)
9.4 Byte sequences
200(3)
9.5 Subdatasets
203(1)
9.6 Web map services
204(3)
9.7 Summary
207(1)
10 Working with raster data
208(29)
10.1 Ground control points
209(4)
10.2 Converting pixel coordinates to another image
213(2)
10.3 Color tables
215(3)
Transparency
217(1)
10.4 Histograms
218(3)
10.5 Attribute tables
221(2)
10.6 Virtual raster format
223(7)
Subsetting
225(2)
Creating troublesome formats
227(1)
Reprojecting images
228(2)
10.7 Callback functions
230(2)
10.8 Exceptions and error handlers
232(4)
10.9 Summary
236(1)
11 Map algebra with NumPy and SciPy
237(39)
11.1 Introduction to NumPy
238(4)
11.2 Map algebra
242(25)
Local analyses
243(4)
Focal analyses
247(11)
Zonal analyses
258(5)
Global analyses
263(4)
11.3 Resampling data
267(8)
11.4 Summary
275(1)
12 Map classification
276(11)
12.1 Unsupervised classification
278(2)
12.2 Supervised classification
280(6)
Accuracy assessments
284(2)
12.3 Summary
286(1)
13 Visualizing data
287(32)
13.1 Matplotlib
288(19)
Plotting vector data
288(12)
Plotting raster data
300(5)
Plotting 3D data
305(2)
13.2 Mapnik
307(11)
Drawing vector data
308(6)
Storing information as XML
314(2)
Drawing raster data
316(2)
13.3 Summary
318(1)
Appendix A Installation 319(8)
Appendix B References 327(4)
Index 331
Chris Garrard has worked as a developer for the Remote Sensing / GIS Laboratory at Utah State University for over a decade. She teaches a graduate level course on Python programming for GIS and enjoys helping students see the power that comes with writing their own code.
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