Cargando…
APPLIED GEOSPATIAL DATA SCIENCE WITH PYTHON leverage geospatial data analysis and modeling to find unique solutions to environmental problems /
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into tradit...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd.,
[2023]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Contributors
- Table of Contents
- Preface
- Part 1: The Essentials of Geospatial Data Science
- Chapter 1: Introducing Geographic Information Systems and Geospatial Data Science
- What is GIS?
- What is data science?
- Mathematics
- Computer science
- Industry and domain knowledge
- Soft skills
- What is geospatial data science?
- Summary
- Chapter 2: What Is Geospatial Data and Where Can I Find It?
- Static and dynamic geospatial data
- Geospatial file formats
- Vector data
- Raster data
- Introducing geospatial databases and storage
- PostgreSQL and PostGIS
- ArcGIS geodatabase
- Exploring open geospatial data assets
- Human geography
- Physical geography
- Country- and area-specific data
- Summary
- Chapter 3: Working with Geographic and Projected Coordinate Systems
- Technical requirements
- Exploring geographic coordinate systems
- Understanding GCS versions
- Understanding projected coordinate systems
- Common types of projected coordinate systems
- Working with GCS and PCS in Python
- PyProj
- GeoPandas
- Summary
- Chapter 4: Exploring Geospatial Data Science Packages
- Technical requirements
- Packages for working with geospatial data
- GeoPandas
- GDAL
- Shapely
- Fiona
- Rasterio
- Packages enabling spatial analysis and modeling
- PySAL
- Packages for producing production-quality spatial visualizations
- ipyLeaflet
- Folium
- geoplot
- GeoViews
- Datashader
- Reviewing foundational data science packages
- pandas
- scikit-learn
- Summary
- Part 2: Exploratory Spatial Data Analysis
- Chapter 5: Exploratory Data Visualization
- Technical requirements
- The fundamentals of ESDA
- Example
- New York City Airbnb listings
- Conducting EDA
- ESDA
- Summary
- Chapter 6: Hypothesis Testing and Spatial Randomness
- Technical requirements
- Constructing a spatial hypothesis test
- Understanding spatial weights and spatial lags
- Global spatial autocorrelation
- Local spatial autocorrelation
- Point pattern analysis
- Ripley's alphabet functions
- Summary
- Chapter 7: Spatial Feature Engineering
- Technical requirements
- Defining spatial feature engineering
- Performing a bit of geospatial magic
- Engineering summary spatial features