GIS and Machine Learning for Small Area Classifications in Developing Countries
- Submitting institution
-
University of Lincoln
- Unit of assessment
- 14 - Geography and Environmental Studies
- Output identifier
- 43144
- Type
- A - Authored book
- DOI
-
10.1201/9780429318344
- Publisher
- Routledge
- ISBN
- 9780367322441
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
-
- Proposed double-weighted
- Yes
- Double-weighted statement
- This 268-page book is the first academic resource to exclusively address the huge potential of small area segmentation for sustainable development. There is significant interest in the development and adaptation of small area problem-solving approaches across developed countries. However, there has been no detailed account of how public policy stakeholders within developing nations might tailor these tools to enhance their decision-making protocols. The book combines explanations of key concepts and constructs used within the discipline, brings together a range of techniques and methodological approaches including spatial analysis and machine learning, and focuses on current applications drawing on two developing countries.
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
