Learning Geospatial Analysis with Python Unleash the Power of Python 3 with Practical Techniques for Learning GIS and Remote Sensing
Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, en...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2023]
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| Edición: | Fourth edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009785407106719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Dedication
- Contributors
- Acknowledgments
- Table of Contents
- Preface
- Part 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
- Evolution of Geographic Information Systems (GISs)
- Remote sensing
- Point cloud 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 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
- 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
- Summary
- Questions
- Further reading
- Chapter 2: Learning about Geospatial Data
- Technical requirements
- Overview of common data formats
- Understanding data structures
- Common traits
- Understanding spatial indexing
- Spatial indexing algorithms
- What are overviews?
- What is metadata?
- Understanding the file structure
- Knowing about 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.
- More realistic geospatial models with 3D data
- What are web services?
- Understanding geospatial databases
- Sharing data with interchange formats
- Introducing spatiotemporal data
- Summary
- Questions
- Further reading
- Chapter 3: The Geospatial Technology Landscape
- Technical requirements
- Understanding data access
- GDAL
- PDAL
- Understanding computational geometry
- The PROJ projection library
- CGAL
- JTS
- GEOS
- PostGIS
- Other spatially enabled databases
- Routing
- Understanding desktop tools (including visualization)
- Quantum GIS
- GRASS GIS
- gvSIG
- OpenJUMP
- Google Earth
- NASA WorldWind
- ArcGIS
- Leaflet and OpenLayers
- Understanding metadata management
- Python's pycsw library
- GeoNode
- GeoNetwork
- A quick look at artificial intelligence
- Summary
- Questions
- Further reading
- Part 2: Geospatial Analysis Concepts
- Chapter 4: Geospatial Python Toolbox
- Technical requirements
- Using QGIS
- Installing third-party Python modules
- Anaconda
- Jupyter
- PyPI and pip
- The Python virtualenv module
- Python networking libraries for acquiring data
- The Python urllib module
- The Python requests module
- FTP
- Bundling and compressing files
- Python markup and tag-based parsers
- The minidom module
- The ElementTree module
- Building XML using ElementTree and minidom
- Well-Known Text (WKT)
- Python JSON libraries
- The json module
- The geojson module
- OGR
- PyShp
- Shapely
- Fiona
- GDAL
- NumPy
- PIL
- PNGCanvas
- GeoPandas
- PyFPDF
- PyMySQL
- Rasterio
- OSMnx
- Folium
- Summary
- Questions
- Further reading
- Chapter 5: Python and Geospatial Algorithms
- Technical requirements
- Measuring distance
- Using the Pythagorean theorem to measure distance
- 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
- Using ChatGPT to measure a polygon perimeter
- Summary
- Questions
- Further reading
- Chapter 6: Creating and Editing GIS Data
- Technical requirements
- Editing shapefiles
- Accessing the shapefile
- Changing a shapefile
- Adding fields
- Merging shapefiles
- Splitting shapefiles
- Performing selections
- Aggregating geometry
- Extracting geometry
- Connecting polygon faces to the nearest line point
- Creating images for visualization
- Dot density calculations
- Choropleth maps
- Using spreadsheets
- Creating heat maps
- Using GPS data
- Turning addresses into points with geocoding
- Performing GIS analysis faster with multiprocessing
- Summary
- Questions
- Further reading
- Chapter 7: Python and Remote Sensing
- Technical requirements
- Examining raster data properties
- Swapping image bands
- Creating image histograms
- Performing a histogram stretch
- Clipping images
- Classifying images
- Extracting features from images
- Understanding change detection
- Extracting image footprints using ChatGPT
- Summary
- Questions
- Further reading
- Chapter 8: Python and Elevation Data
- Technical requirements
- 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
- Colorizing LiDAR with aerial images
- Classifying LiDAR
- Working with bathymetry
- Summary
- Questions
- Further reading
- Part 3: Practical Geospatial Processing Techniques
- Chapter 9: Advanced Geospatial Modeling
- Technical requirements.
- Creating a normalized difference vegetation index (NDVI)
- Setting up the framework
- Loading the data
- Rasterizing the shapefile
- Clipping the bands
- Using the NDVI formula
- Classifying the NDVI
- Creating a flood inundation model
- The flood fill function
- Creating a color hillshade
- Performing least cost path analysis
- The real-world example
- Converting the route to a shapefile
- Routing along streets
- Geolocating photos
- Calculating satellite image cloud cover
- Summary
- Questions
- Further reading
- Chapter 10: Working with Real-Time Data
- Technical requirements
- Limitations of real-time data
- Using real-time data
- Tracking vehicles
- Getting a vehicle location
- Mapping a vehicle location
- Storm chasing
- Gathering reports from the field
- Summary
- Questions
- Further reading
- Chapter 11: Putting It All Together
- Technical requirements
- Understanding a typical GPS report
- Building a GPS reporting tool
- Importing libraries
- Setting up logging
- Helper functions
- Program variables
- Parsing the GPX file
- Downloading the basemap and elevation data
- Hillshading the elevation data
- Creating a map
- Adding a photo marker
- Creating an elevation profile chart
- Creating a weather report
- Generating a PDF report
- Summary
- Questions
- Further reading
- Assessments
- Chapter 1 - Learning about Geospatial Analysis with Python
- Chapter 2 - Learning about Geospatial Data
- Chapter 3 - The Geospatial Technology Landscape
- Chapter 4 - Geospatial Python Toolbox
- Chapter 5 - Python and Geospatial Algorithms
- Chapter 6 - Creating and Editing GIS Data
- Chapter 7 - Python and Remote Sensing
- Chapter 8 - Python and Elevation Data
- Chapter 9 - Advanced Geospatial Modeling
- Chapter 10 - Working with Real-Time Data
- Chapter 11 - Putting It All Together
- Index.
- About Packt
- Other Books You May Enjoy.
