GIS and Machine Learning for Small Area Classifications in Developing Countries

Population geography has witnessed a renaissance in policy-related spatial analysis due to the emergence of area classifications. These systems use data mining techniques and machine learning algorithms to simplify large and complex bodies of information about people and the places in which they live, work, and undertake other social activities. This book argues that small area classifications offer countries in the Global South a distinct opportunity to address human population policy-related challenges in novel ways using area-based initiatives and evidence-based methods. It provides a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples.

Format: Paperback / softback
Length: 246 pages
Publication date: 31 May 2023
Publisher: Taylor & Francis Ltd

Since the advent of contemporary area classifications, population geography has experienced a remarkable resurgence in the field of policy-related spatial analysis. Area classifications encompass geodemographic systems, which frequently employ data mining techniques and machine learning algorithms to streamline vast and intricate datasets concerning people and the places they inhabit, work, and engage in various social activities. The outputs derived from the grouping of small geographical areas based on multi-dimensional data have proven invaluable, particularly for decision-making in the commercial sectors of numerous countries in the northern hemisphere. This book posits that small area classifications present countries in the Global South with a unique opportunity to address human population policy-related challenges in innovative ways through area-based initiatives and evidence-based methodologies.

This book serves as a comprehensive guide for researchers, practitioners, and students seeking to understand, interpret, and visualize the configuration, dynamics, and correlates of development policy challenges at small spatial scales. It presents strategic and operational responses to these challenges in cost-effective manners. By utilizing two developing countries as case studies, the book establishes a connection between new transdisciplinary ways of thinking about social and spatial inequalities from a scientific perspective and GIS (Geographic Information System) and Data Science. This offers all stakeholders a framework for engaging in practical dialogue on development policy within urban and rural settings, based on real-world examples.

Key Features:

The first book to comprehensively address the immense potential of small area segmentation for sustainable development, combining explanations of concepts, a diverse range of techniques, and current applications.

In-depth coverage of small area segmentation techniques, including spatial clustering, principal component analysis, and spatial autocorrelation analysis.

Practical examples and case studies from developing countries, illustrating the application of small area segmentation in understanding and addressing development policy challenges.

Integration of GIS and Data Science tools to enhance the analysis and visualization of small area data, facilitating informed decision-making and policy interventions.

A valuable resource for researchers, practitioners, policymakers, and students interested in advancing the field of population geography and sustainable development.

Weight: 500g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367652326