Machine learning on geographical data using Python : introduction into geodata with applications and use cases

Machine learning on geographical data using Python introduction into geodata with applications and use cases

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of...

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Detalles Bibliográficos
Otros Autores: Korstanje, Joos, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: New York, New York : Apress [2022]
Materias:
Machine learning.
Python (Computer program language)
Geodatabases.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009671496706719
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Author
  • About the Technical Reviewer
  • Introduction
  • Part I: General Introduction
  • Chapter 1: Introduction to Geodata
  • Reading Guide for This Book
  • Geodata Definitions
  • Cartesian Coordinates
  • Polar Coordinates and Degrees
  • The Difference with Reality
  • Geographic Information Systems and Common Tools
  • What Are Geographic Information Systems
  • ArcGIS
  • QGIS and Other Open Source ArcGIS Alternatives
  • Python/R Programming
  • Standard Formats of Geodata
  • Shapefile
  • Google KML File
  • GeoJSON
  • TIFF/JPEG/PNG
  • CSV/TXT/Excel
  • Overview of Python Tools for Geodata
  • Key Takeaways
  • Chapter 2: Coordinate Systems and Projections
  • Coordinate Systems
  • Geographic Coordinate Systems
  • Latitude and Longitude
  • WGS 1984 Geographic Coordinate System
  • Other Geographic Coordinate Systems
  • Projected Coordinate Systems
  • X and Y Coordinates
  • Four Types of Projected Coordinate Systems
  • Equal Area Projections
  • Example 1: Mollweide Projection
  • Example 2: Albers Equal Area Conic
  • Conformal Projections
  • Mercator
  • Lambert Conformal Conic
  • Equidistant Projections
  • Azimuthal Equidistant Projection
  • Equidistant Conic Projection
  • True Direction or Azimuthal Projections
  • Lambert Equal Area Azimuthal
  • Two-Point Equidistant Projection
  • Local Coordinate Systems
  • Which Coordinate System to Choose
  • Playing Around with Some Maps
  • Example: Working with Own Data
  • Step 1: Make Your Own Dataset on Google My Maps
  • Step 2: Add Some Features on Your Map
  • Step 3: Export Your Map As a .KML
  • Step 4: Import the .KML in Python
  • Step 5: Plot the Map
  • Step 6: Change the Coordinate System
  • Step 7: Plot the Map Again
  • Key Takeaways
  • Chapter 3: Geodata Data Types
  • Vector vs. Raster Data
  • Dealing with Attributes in Vector and Raster
  • Points.
  • Definition of a Point
  • Importing an Example Point Dataset in Python
  • Some Basic Operations with Points
  • Filter Morning vs. Afternoon
  • Lines
  • Definition of a Line
  • An Example Line Dataset in Python
  • Polygons
  • Definition of a Polygon
  • An Example Polygon Dataset in Python
  • Some Simple Operations with Polygons
  • Rasters/Grids
  • Definition of a Grid or Raster
  • Importing a Raster Dataset in Python
  • Key Takeaways
  • Chapter 4: Creating Maps
  • Mapping Using Geopandas and Matplotlib
  • Getting a Dataset into Python
  • Making a Basic Plot
  • Plot Title
  • Plot Legend
  • Mapping a Point Dataset with Geopandas and Matplotlib
  • Concluding on Mapping with Geopandas and Matplotlib
  • Making a Map with Cartopy
  • Concluding on Mapping with Cartopy
  • Making a Map with Plotly
  • Concluding on Mapping with Plotly
  • Making a Map with Folium
  • Concluding on Mapping with Folium
  • Key Takeaways
  • Part II: GIS Operations
  • Chapter 5: Clipping and Intersecting
  • What Is Clipping?
  • A Schematic Example of Clipping
  • What Happens in Practice When Clipping?
  • Clipping in Python
  • What Is Intersecting?
  • What Happens in Practice When Intersecting?
  • Conceptual Examples of Intersecting Geodata
  • Intersecting in Python
  • Difference Between Clipping and Intersecting
  • Key Takeaways
  • Chapter 6: Buffers
  • What Are Buffers?
  • A Schematic Example of Buffering
  • What Happens in Practice When Buffering?
  • Buffers for Point Data
  • Buffers for Line Data
  • Buffers for Polygon Data
  • Creating Buffers in Python
  • Creating Buffers Around Points in Python
  • Creating Buffers Around Lines in Python
  • Creating Buffers Around Polygons in Python
  • Combining Buffers and Set Operations
  • Key Takeaways
  • Chapter 7: Merge and Dissolve
  • The Merge Operation
  • What Is a Merge?
  • A Schematic Example of Merging
  • Different Definitions of Merging.
  • Merging in Python
  • Row-Wise Merging in Python
  • Attribute Join in Python
  • Spatial Join in Python
  • The Dissolve Operation
  • What Is the Dissolve Operation?
  • Schematic Overview of the Dissolve Operation
  • The Dissolve Operation in Python
  • Key Takeaways
  • Chapter 8: Erase
  • The Erase Operation
  • Schematic Overview of Spatially Erasing Points
  • Schematic Overview of Spatially Erasing Lines
  • Schematic Overview of Spatially Erasing Polygons
  • Erase vs. Other Operations
  • Erase vs. Deleting a Feature
  • Erase vs. Clip
  • Erase vs. Overlay
  • Erasing in Python
  • Erasing Portugal from Iberia to Obtain Spain
  • Erasing Points in Portugal from the Dataset
  • Cutting Lines to Be Only in Spain
  • Key Takeaways
  • Part III: Machine Learning and Mathematics
  • Chapter 9: Interpolation
  • What Is Interpolation?
  • Different Types of Interpolation
  • Linear Interpolation
  • Polynomial Interpolation
  • Piecewise Polynomial or Spline
  • Nearest Neighbor Interpolation
  • From One-Dimensional to Spatial Interpolation
  • Spatial Interpolation in Python
  • Linear Interpolation Using Scipy Interp2d
  • Kriging
  • Linear Ordinary Kriging
  • Gaussian Ordinary Kriging
  • Exponential Ordinary Kriging
  • Conclusion on Interpolation Methods
  • Key Takeaways
  • Chapter 10: Classification
  • Quick Intro to Machine Learning
  • Quick Intro to Classification
  • Spatial Classification Use Case
  • Feature Engineering with Additional Data
  • Importing and Inspecting the Data
  • Spatial Operations for Feature Engineering
  • Reorganizing and Standardizing the Data
  • Modeling
  • Model Benchmarking
  • Key Takeaways
  • Chapter 11: Regression
  • Introduction to Regression
  • Spatial Regression Use Case
  • Importing and Preparing Data
  • Iteration 1 of Data Exploration
  • Iteration 1 of the Model
  • Interpretation of Iteration 1 Model
  • Iteration 2 of Data Exploration.
  • Iteration 2 of the Model
  • Iteration 3 of the Model
  • Iteration 4 of the Model
  • Interpretation of Iteration 4 Model
  • Key Takeaways
  • Chapter 12: Clustering
  • Introduction to Unsupervised Modeling
  • Introduction to Clustering
  • Different Clustering Models
  • Spatial Clustering Use Case
  • Importing and Inspecting the Data
  • Cluster Model for One Person
  • Tuning the Clustering Model
  • Applying the Model to All Data
  • Key Takeaways
  • Chapter 13: Conclusion
  • What You Should Remember from This Book
  • Recap of Chapter 1 - Introduction to Geodata
  • Recap of Chapter 2 - Coordinate Systems and Projections
  • Recap of Chapter 3 - Geodata Data Types
  • Recap of Chapter 4 - Creating Maps
  • Recap of Chapter 5 - Clipping and Intersecting
  • Recap of Chapter 6 - Buffers
  • Recap of Chapter 7 - Merge and Dissolve
  • Recap of Chapter 8 - Erase
  • Recap of Chapter 9 - Interpolation
  • Recap of Chapter 10 - Classification
  • Recap of Chapter 11 - Regression
  • Recap of Chapter 12 - Clustering
  • Further Learning Path
  • Going into Specialized GIS
  • Specializing in Machine Learning
  • Remote Sensing and Image Treatment
  • Other Specialties
  • Key Takeaways
  • Index.

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