MARC 主機 00000cam a2200493 i 4500
008 200716t20212021fluab b 001 0 eng d
020 9780367322441|q(hbk.)
020 |z9780429318344|q(ebk.)
040 YDX|beng|erda|cYDX|dUKMGB|dOCLCF|dOCLCO|dYDXIT|dEEM|dOCLCQ
|dOCLCO|dOL$|dNTNU
050 4 HB2043.A3|bO36 2021
082 04 304.80941|223
100 1 Ojo, Adegbola,|eauthor
245 10 GIS and machine learning for small area classifications in
developing countries /|cAdegbola Ojo
246 3 Geographic information system and machine learning for
small area classifications in developing countries
250 First edition
264 1 Boca Raton, FL :|bCRC Press, Taylor & Francis Group,|c2021
264 4 |c©2021
300 xxii, 246 pages :|billustrations (some color), maps (some
color) ;|c24 cm
336 text|btxt|2rdacontent
337 unmediated|bn|2rdamedia
338 volume|bnc|2rdacarrier
504 Includes bibliographical references and index
505 0 BACKGROUND, CONCEPTS AND DEFINITIONS. Introduction ;
Origins and Concept of Social Area Classification --
Public Policy Prospects of Small Area Classifications for
Developing Countries ; Reasons for Slow Proliferation of
Area Classifications across Developing Countries --
UNDERLYING TECHNIQUES AND DEPLOYMENT APPROACHES. Building
Blocks: Spatial Data Preparation ; Machine Learning
Methods for Building Small Area Classifications ;
Visualizing Small Area Geodemographics Data and
Information Products -- ILLUSTRATIVE APPLICATIONS AND
CONCLUSION. The Grouping of Nigerian Local Government
Areas ; Combining Continuous and Categorical Data to
Segment Philippines Barangays ; Modeling Temporal
Distribution and Seasonality of Infectious Diseases with
Area Classifications ; Segmenting Gender Gaps in Levels of
Educational Attainment ; Conclusion
520 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.--
|cPublisher's description
650 0 Geodemographics|zDeveloping countries
650 0 Geographic information systems
650 0 Machine learning
911 zenith
