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001 9780429318344
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008 200724s2021 flua gob 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781000289398
_q(electronic bk.)
020 _a1000289397
_q(electronic bk.)
020 _a9780429318344
_q(electronic bk.)
020 _a0429318340
_q(electronic bk.)
020 _a9781000289381
_q(electronic bk. : Mobipocket)
020 _a1000289389
_q(electronic bk. : Mobipocket)
020 _a9781000289374
_q(electronic bk. : PDF)
020 _a1000289370
_q(electronic bk. : PDF)
020 _z0367322447
020 _z9780367322441
024 7 _a10.1201/9780429318344
_2doi
035 _a(OCoLC)1223026448
_z(OCoLC)1204343270
_z(OCoLC)1224187557
035 _a(OCoLC-P)1223026448
050 4 _aHB2160
072 7 _aSCI
_x030000
_2bisacsh
072 7 _aSCI
_x026000
_2bisacsh
072 7 _aRGC
_2bicssc
082 0 4 _a304.8091724
_223
100 1 _aOjo, Adegbola,
_eauthor.
245 1 0 _aGIS and Machine Learning for Small Area Classifications in Developing Countries /
_cAdegbola Ojo.
250 _aFirst edition.
264 1 _aBoca Raton :
_bCRC Press,
_c2021.
300 _a1 online resource :
_billustrations (black and white, and colour)
336 _atext
_2rdacontent
336 _astill image
_2rdacontent
337 _acomputer
_2rdamedia
338 _aonline resource
_2rdacarrier
520 _aSince 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.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aGeodemographics
_zDeveloping countries.
650 0 _aGeographic information systems.
650 0 _aMachine learning.
650 7 _aSCIENCE / Earth Sciences / Geography
_2bisacsh
650 7 _aSCIENCE / Environmental Science
_2bisacsh
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780429318344
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c5506
_d5506