دانلود کتاب Machine Learning on Geographical Data Using Python: Introduction into Geodata with Applications and Use Cases

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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