PDF: gis and machine learning for small area classifications in developing countries 1st edition
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"GIS and Machine Learning for Small Area Classifications in Developing Countries" is a comprehensive work that explores the integration of Geographic Information Systems (GIS) and machine learning techniques to classify small areas, specifically within the context of developing nations. The book provides insights into the methodologies and technologies that can be employed to accurately analyze spatial data, identifying patterns and classifications that are crucial for effective planning and resource allocation in these regions.
The authors of the book incorporate a blend of theoretical frameworks and practical case studies, illustrating the application of GIS and machine learning in real-world scenarios. By detailing specific algorithms and tools, they empower researchers and practitioners to harness the capabilities of these technologies. The book emphasizes the importance of high-quality, localized data and addresses issues related to data scarcity and accessibility often faced in developing countries.
With ISBN 978-0367336999, this first edition is published by Taylor & Francis. The authors, who have expertise in the fields of geography, data science, and development studies, are committed to promoting innovative solutions that can aid decision-makers in addressing the complex challenges faced in developing regions.
In summary, "GIS and Machine Learning for Small Area Classifications in Developing Countries" stands as a critical resource for anyone interested in leveraging advanced technological approaches to improve socio-economic conditions. It aims to inspire and equip a new generation of researchers and policy-makers to utilize GIS and machine learning for informed decision-making in the pursuit of sustainable development.
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