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Learning GeoSpatial analysis with Python : an effective guide to geographic information system and remote sensing analysis using Python 3 /
An effective guide to geographic information systems and remote sensing analysis using Python 3About This Book Construct applications for GIS development by exploiting Python This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution systemno compil...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2015.
|
| Edición: | Second edition. |
| Colección: | Community experience distilled.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Machine generated contents note: 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
- Note continued: Building SimpleGIS
- Step by step
- Summary
- 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
- 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
- gySIG
- OpenJUMP
- Note continued: Google Earth
- NASA World Wind
- ArcG IS
- Metadata management
- GeoNetwork
- CatMDEdit
- Summary
- 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
- 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
- Note continued: 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
- Swapping image bands
- Creating histograms
- Performing a histogram stretch
- Clipping images
- Classifying images
- Extracting features from images
- Change detection
- Summary
- 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
- Creating a Normalized Difference Vegetative Index
- Setting up the framework
- Loading the data
- Rasterizing the shapefile
- Clipping the bands
- Using the NDVI formula
- Note continued: 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
- Tracking vehicles
- The NextBus agency list
- The NextBus route list
- NextBus vehicle locations
- Mapping NextBus locations
- Storm chasing
- Reports from the field
- Summary
- 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
- Note continued: Downloading map and elevation images
- Creating the hillshade
- Creating maps
- Measuring the elevation
- Measuring the distance
- Retrieving weather data
- Summary.