GIS and Machine Learning for Small Area Classifications in Developing Countries

Ojo, Adegbola (2020) GIS and Machine Learning for Small Area Classifications in Developing Countries. Routledge, New York. ISBN 9780367322441

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Since the emergence of contemporary area classifications, population geography has witnessed a renaissance in the area of policy related spatial analysis. Area classifications subsume geodemographic systems which often 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. Outputs developed from the grouping of small geographical areas on the basis of multi- dimensional data have proved beneficial particularly for decision-making in the commercial sectors of a vast number of countries in the northern hemisphere. 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.

Keywords:Geography, Built Environment, Development Studies, Engineering & Technology, Environment, Urban Studies, Computer Science
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G760 Machine Learning
L Social studies > L700 Human and Social Geography
Divisions:College of Science > School of Geography
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ID Code:43144
Deposited On:18 Dec 2020 10:28

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