GIS and Machine Learning for Small Area Classifications in Developing Countries

Dr. Adegbola Ojo is Director of Teaching and Learning, Programme Leader, and Senior Lecturer in Urban Geography and Applications of Big Data at the School of Geography, University of Lincoln, UK. He received his PhD in Quantitative Human Geography from the University of Sheffield, his MSc in Geographic Information Science from the University College London, and his BSc in Geography and Planning Sciences from the University of Ado-Ekiti, Nigeria. His research interests are focused in understanding and representing social and spatial dynamics and intricacies of population behavior within a framework of Interdisciplinary Studies, Population Geography, Quantitative Social Science, and Computer Modeling. His research activities are grouped around the development and application of small area classifications, geographic information systems and geographic information science for informing public policy. Dr. Ojo has published many monographs and research articles with reputable journals. He has designed and delivered lectures, workshops, seminars, tutorials, practical labs, and assessments to a range of undergraduate and graduate students and working professionals.

• PART 1: BACKGROUND, CONCEPTS AND DEFINITIONS1. Introduction2. Origins and Concept of Social Area Classification3. Public Policy Prospects of Small Area Classifications for Developing Countries4. Reasons for Slow Proliferation of Area Classifications across Developing CountriesPART 2: UNDERLYING TECHNIQUES AND DEPLOYMENT APPROACHES5. Building Blocks: Spatial Data Preparation6. Machine Learning Methods for Building Small Area Classifications7. Visualizing Small Area Geodemographics Data and Information ProductsPART 3: ILLUSTRATIVE APPLICATIONS AND CONCLUSION8. The Grouping of Nigerian Local Government Areas9. Combining Continuous and Categorical Data to Segment Philippines Barangays10. Modeling Temporal Distribution and Seasonality of Infectious Diseases with Area Classifications11. Segmenting Gender Gaps in Levels of Educational Attainment12. Conclusion

A very comprehensive, powerful and insightful contribution to the field of Geoinformatics in the Social Sciences. Highly novel both in terms of the study regions it focuses on and in the state-of-the-art methods it engages with and applies.Dimitris Ballas, Professor of Economic Geography, University of Groningen. The NetherlandsThis book represents a clearly written and highly inventive attempt to enrich public policy analysis in developing countries by applying GIS and machine learning for small area classification. It offers both a technical introduction to the topic and a wide range of contemporary illustrative applications from both Nigeria and the Philippines. The book should be of interest to development scholars, methodologists and to human geographers interested in the application of new spatial technologies to Global South contexts.Roger Burrows, Professor of Cities, School of Architecture, Planning, and Landscape, Newcastle University, UK