Applied geospatial data science with Python : take control of implementing, analyzing, and visualizing geospatial and spatial data with geopandas and more

Applied geospatial data science with Python take control of implementing, analyzing, and visualizing geospatial and spatial data with geopandas and more

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...

Full description

Bibliographic Details
Other Authors: Jordan, David Silas, author (author)
Format: eBook
Language:Inglés
Published: Birmingham ; Mumbai : Packt Publishing [2023]
Edition:1st ed
Subjects:
Geographic information systems > Data processing.
Data mining.
Python (Computer program language)
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009724838906719
Table of Contents:
  • 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
  • Summary spatial features using one dataset
  • Summary spatial features using two datasets
  • Engineering proximity spatial features
  • Proximity spatial features - NYC attractions
  • Summary
  • Part 3: Geospatial Modeling Case Studies
  • Chapter 8: Spatial Clustering and Regionalization
  • Technical requirements
  • Collecting geodemographic data for modeling
  • Extracting data using the Census API
  • Cleaning the extracted data
  • Conducting EDA and ESDA
  • Developing geodemographic clusters
  • K-means geodemographic clustering
  • Agglomerative hierarchical geodemographic clustering
  • Spatially constrained agglomerative hierarchical geodemographic clustering
  • Measuring model performance
  • Summary
  • Chapter 9: Developing Spatial Regression Models
  • Technical requirements
  • A refresher on regression models
  • Constructing an initial regression model
  • Exploring unmodeled spatial relationships
  • Teaching the model to think spatially
  • Incorporating spatial fixed effects within the model
  • Introduction to GWR models
  • Fitting a GWR model to predict nightly Airbnb prices
  • Introduction to Multiscale Geographically Weighted Regression
  • Fitting an MGWR model to predict nightly Airbnb prices
  • How do I choose between these models?
  • Summary
  • Chapter 10: Developing Solutions for Spatial Optimization Problems
  • Technical requirements
  • Exploring the Location Set Covering Problem (LSCP).
  • Understanding the math behind the LSCP
  • Solving LSCPs
  • Exploring route-based combinatorial optimization problems
  • Understanding the math behind the TSP
  • Setting up the Google Maps API
  • Solving the TSP
  • Exploring a single-vehicle Vehicle Routing Problem (VRP)
  • Exploring a Capacitated Vehicle Routing Problem (CVRP)
  • Summary
  • Chapter 11: Advanced Topics in Spatial Data Science
  • Technical requirements
  • Efficient operations with spatial indexing
  • Implementing R-tree indexing in GeoPandas
  • Introducing the H3 spatial index
  • Estimating unknowns with spatial interpolation
  • Applying Inverse Distance Weighted (IDW) interpolation
  • Introduction to Kriging-based interpolation
  • Ethical spatial data science
  • Example 1 - Sharpiegate
  • Example 2 - Human mobility: The New York Times investigative report
  • Example 3 - COVID-19 contact tracing
  • Example 4 - United States Census Bureau disclosure avoidance system
  • Summary
  • Index
  • Other Books You May Enjoy.

Similar Items