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Arxiv
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Advancing Spatiotemporal Prediction using Artificial Intelligence: Extending the Framework of Geographically and Temporally Weighted Neural Network (GTWNN) for Differing Geographical and Temporal Contexts
- This paper proposes an extension to the Geographically and Temporally Weighted Neural Network (GTWNN) framework for spatio-temporal prediction.
- The authors formulate a novel semi-analytical approach to solving Geographically and Temporally Weighted Regression (GTWR) and apply it to London crime data.
- The results demonstrate high-accuracy predictive evaluation scores, validating the assumptions and approximations in the approach.
- The study highlights the importance of considering specific geographic and temporal characteristics when selecting modeling strategies for improved accuracy and suitability.
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