000			01603nam a2200217 4500
005			20250507140841.0
008			250507b |||||||| |||| 00| 0 eng d
020			_a9780367652326
037			_cTextual
040			_aRTL _cRTL
084			_aD6,9(B) R1 _qRTL
100			_aOjo, Adegbola _9755399
245			_aGIS and machine learning for small area classfications in developing countries
260			_aBoca Raton _bCRC Press _c2021
300			_axxii, 246 p. ill. _bIncludes bibliographical references and index
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.
650			_aMachine Learning
650			_aGIS- Developing countries _9755400
650			_aData information _9755401
942			_2CC _n0 _cTEXL _hD6,9(B) R1
999			_c1320321 _d1320321