Areal Interpolation in Python using Tobler
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Contents
1
Disclaimer
2
Introduction
3
Getting Started
3.1
Install Required Software
3.2
Create A Project Folder
3.3
Download Required Files
3.4
Create Environment
3.5
Launch JupyterLab
4
Tutorial
4.1
Import Packages
4.2
Part 1: Areal Interpolation of Population Data
4.2.1
Read the data
4.2.2
Manage Projections
4.2.3
Perform Areal Interpolation
4.2.3.1
Area Weighted Interpolation
4.2.3.2
Dasymetric Interpolation
4.2.3.3
Model-based Interpolation
4.2.3.4
Pycnophylatic Interpolation
4.2.4
Assess the Results
4.2.4.1
Visual Assessment
4.2.4.2
Statistical Assessment
4.3
Part 2: Areal Interpolation of Synthetic Population Data
4.3.1
Generate the Synthetic Population Data
4.3.2
Perform Areal Interpolation
4.3.2.1
Area Weighted Interpolation
4.3.2.2
Dasymetric Interpolation
4.3.2.3
Model-based Interpolation
4.3.2.4
Pycnophylatic Interpolation
4.3.3
Assess the Results
4.3.3.1
Visual Assessment
4.3.3.2
Statistical Assessment
Disclaimer
Introduction
Getting Started
Install Required Software
Create A Project Folder
Download Required Files
Create Environment
Launch JupyterLab
Tutorial
Import Packages
Part 1: Areal Interpolation of Population Data
Read the data
Manage Projections
Perform Areal Interpolation
Area Weighted Interpolation
Dasymetric Interpolation
Model-based Interpolation
Pycnophylatic Interpolation
Assess the Results
Visual Assessment
Statistical Assessment
Part 2: Areal Interpolation of Synthetic Population Data
Generate the Synthetic Population Data
Perform Areal Interpolation
Area Weighted Interpolation
Dasymetric Interpolation
Model-based Interpolation
Pycnophylatic Interpolation
Assess the Results
Visual Assessment
Statistical Assessment
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