Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence (Paperback)
Language: English
Published by Shark Nail, 2026
- Softcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 25.89
Quantity: 1 available
Add to basketItem description from seller
Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …
Seller Inventory # 9781962116459
- Title
- Spatial Correlation and Poverty Prediction Index (PPI) Generation Using Artificial Intelligence (Paperback)
- Author
- Jenifer Lowan
- Publisher
- Shark Nail
- Publication year
- 2026
- Condition
- new
- Binding
- Paperback
- Language
- English
- ISBN 10
- 196211645X
- ISBN 13
- 9781962116459
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.
Grand Eagle Retail
Bensenville, IL, U.S.A.
AbeBooks seller since October 12, 2005
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