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Books > Earth & environment > Geography > Human geography
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GIS and Machine Learning for Small Area Classifications in Developing Countries (Hardcover)
Loot Price: R3,701
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GIS and Machine Learning for Small Area Classifications in Developing Countries (Hardcover)
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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. This book exposes researchers, practitioners, and students
to small area segmentation techniques for understanding,
interpreting, and visualizing 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 ways. Using two developing countries
as case studies, the book connects new transdisciplinary ways of
thinking about social and spatial inequalities from a scientific
perspective with GIS 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. Features: The first book to address the huge potential of
small area segmentation for sustainable development, combining
explanations of concepts, a range of techniques, and current
applications. Includes case studies focused on core challenges that
confront developing countries and provides thorough analytical
appraisal of issues that resonate with audiences from the Global
South. Combines GIS and machine learning methods for studying
interrelated disciplines such as Demography, Urban Science,
Sociology, Statistics, Sustainable Development and Public Policy.
Uses a multi-method approach and analytical techniques of primary
and secondary data. Embraces a balanced, chronological, and well
sequenced presentation of information, which is very practical for
readers.
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