Details
| Title | Learning geospatial analysis with Python: perform GIS processing tasks and remote sensing data analysis using Python 3.7. — Third edition. |
|---|---|
| Creators | Lawhead Joel. |
| Imprint | Birmingham: Packt Publishing, 2019 |
| Collection | Электронные книги зарубежных издательств ; Общая коллекция |
| Subjects | Geospatial data. ; Python (Computer program language) ; EBSCO eBooks |
| Document type | Other |
| File type | |
| Language | English |
| Rights | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Record key | on1122450412 |
| Record create date | 10/19/2019 |
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- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Table of Contents
- Preface
- Section 1: The History and the Present of the Industry
- Chapter 1: Learning about Geospatial Analysis with Python
- Technical requirements
- Geospatial analysis and our world
- History of geospatial analysis
- GIS
- Remote sensing
- Elevation data
- Computer-aided drafting
- Geospatial analysis and computer programming
- Object-oriented programming for geospatial analysis
- The importance of geospatial analysis
- GIS concepts
- Thematic maps
- Spatial indexing
- Metadata
- Map projections
- Rendering
- Remote sensing concepts
- Images as data
- Remote sensing and color
- Common vector GIS concepts
- Data structures
- Geospatial rules about polygons
- Buffer
- Dissolve
- Generalize
- Intersection
- Union
- Join
- Data structures
- Common raster data concepts
- Band math
- Change detection
- Histogram
- Feature extraction
- Supervised and unsupervised classification
- Creating the simplest possible Python GIS
- Getting started with Python
- Building a SimpleGIS
- Setting up the data model
- Rendering the map
- Summary
- Further reading
- Chapter 2: Learning Geospatial Data
- Getting an overview of common data formats
- Understanding data structures
- Common traits
- Understanding spatial indexing
- Spatial indexing algorithms
- Quadtree index
- R-tree index
- Grids
- Spatial indexing algorithms
- What are overviews?
- What is metadata?
- Understanding the file structure
- Knowing the most widely used vector data types
- Shapefiles
- CAD files
- Tag-based and markup-based formats
- GeoJSON
- GeoPackage
- Understanding raster data types
- TIFF files
- JPEG, GIF, BMP, and PNG
- Compressed formats
- ASCII Grids
- World files
- What is point cloud data?
- LIDAR
- What are web services?
- Understanding geospatial databases
- Summary
- Further reading
- Chapter 3: The Geospatial Technology Landscape
- Technical requirements
- Understanding data access
- GDAL
- GDAL and raster data
- GDAL and vector data
- GDAL
- Understanding computational geometry
- The PROJ projection library
- CGAL
- JTS
- GEOS
- PostGIS
- Other spatially enabled databases
- Oracle Spatial and Graph
- ArcSDE
- Microsoft SQL Server
- MySQL
- SpatiaLite
- GeoPackage
- Routing
- Esri Network Analyst and Spatial Analyst
- pgRouting
- Understanding desktop tools (including visualization)
- Quantum GIS
- OpenEV
- GRASS GIS
- gvSIG
- OpenJUMP
- Google Earth
- NASA WorldWind
- ArcGIS
- Understanding metadata management
- Python's pycsw Library
- GeoNode
- GeoNetwork
- Summary
- Further reading
- Section 2: Geospatial Analysis Concepts
- Chapter 4: Geospatial Python Toolbox
- Technical requirements
- Installing third-party Python modules
- Python virtualenv
- Conda
- Installing GDAL
- Windows
- Linux
- macOS X
- Python networking libraries for acquiring data
- The Python urllib module
- The Python requests module
- FTP
- ZIP and TAR files
- Python markup and tag-based parsers
- The minidom module
- ElementTree
- Building XML using ElementTree and Minidom
- Well-Known Text (WKT)
- Python JSON libraries
- The json module
- The geojson module
- OGR
- PyShp
- dbfpy
- Shapely
- Fiona
- ESRI shapefile
- GDAL
- NumPy
- PIL
- PNGCanvas
- GeoPandas
- PyMySQL
- PyFPDF
- Geospatial PDF
- Rasterio
- OSMnx
- Jupyter
- Conda
- Summary
- Further reading
- Chapter 5: Python and Geographic Information Systems
- Technical requirements
- Measuring distance
- Using the Pythagorean theorem
- Using the haversine formula
- Using the Vincenty formula
- Calculating line direction
- Understanding coordinate conversion
- Understanding reprojection
- Understanding coordinate format conversion
- Calculating the area of a polygon
- 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
- The point-in-polygon formula
- Bounding box selections
- Attribute selections
- Accessing the shapefile
- Aggregating geometry
- Creating images for visualization
- Dot density calculations
- Choropleth maps
- Using spreadsheets
- Creating heat maps
- Using GPS data
- Geocoding
- Multiprocessing
- Summary
- Chapter 6: Python and Remote Sensing
- Technical requirements
- Swapping image bands
- Creating histograms
- Performing a histogram stretch
- Clipping images
- Classifying images
- Extracting features from images
- Understanding change detection
- Summary
- Further reading
- Chapter 7: Python and Elevation Data
- Accessing ASCII Grid files
- Reading grids
- Writing grids
- Creating a shaded relief
- Creating elevation contours
- Working with LIDAR data
- Creating a grid from the LIDAR data
- Using PIL to visualize LIDAR data
- Creating a triangulated irregular network
- Summary
- Further reading
- Accessing ASCII Grid files
- Section 3: Practical Geospatial Processing Techniques
- Chapter 8: Advanced Geospatial Python Modeling
- Technical requirements
- 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
- Predicting flood inundation
- Creating a color hillshade
- Performing least cost path analysis
- 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
- Converting the route to a shapefile
- Routing along streets
- Geolocating photos
- Calculating satellite image cloud cover
- Summary
- Chapter 9: Real-Time Data
- Technical requirements
- Limitations of real-time data
- Using 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
- Technical requirements
- Understanding a typical GPS report
- Building a GPS reporting tool
- Initial setup
- Working with utility functions
- Parsing the GPX
- Getting the bounding box
- Downloading map and elevation images
- Creating the hillshade
- Creating maps
- Locating the photo
- Measuring elevation
- Measuring distance
- Retrieving weather data
- Summary
- Further reading
- Other Books You May Enjoy
- Index
