Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence
Language: English
Published by SHARK NAIL, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 43.12
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence examines computational approaches for analyzing spatial relationships and generating poverty prediction indicators through artificial intelligence. The book introduces spatial correlation, socioeconomic data analysis, predictive modeling, machine learning, and geographic data analysis. It considers how spatial patterns and related variables can be examined to identify relationships associated with poverty conditions and support prediction. Attention is given to data preparation, feature representation, spatial analysis, model development, prediction, and evaluation. The discussion connects artificial intelligence with poverty measurement and analytics, providing a foundation for socioeconomic assessment. The book is intended for students, researchers, data scientists, economists, and scientists interested in artificial intelligence, machine learning, spatial analysis, socioeconomic modeling.…
Seller Inventory # 9781962116459
- Title
- Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence
- Author
- Jenifer Lowan
- Publisher
- SHARK NAIL
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 196211645X
- ISBN 13
- 9781962116459
- Item weight
- 223 grams
- Dimensions
- 229x152x8 mm
Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence examines computational approaches for analyzing spatial relationships and generating poverty prediction indicators through artificial intelligence. The book introduces spatial correlation, socioeconomic data analysis, predictive modeling, machine learning, and geographic data analysis. It considers how spatial patterns and related variables can be examined to identify relationships associated with poverty conditions and support prediction. Attention is given to data preparation, feature representation, spatial analysis, model development, prediction, and evaluation. The discussion connects artificial intelligence with poverty measurement and analytics, providing a foundation for socioeconomic assessment. The book is intended for students, researchers, data scientists, economists, and scientists interested in artificial intelligence, machine learning, spatial analysis, socioeconomic modeling.
"Synopsis" may belong to another edition of this title.
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