推进时空预测的人工智能：扩展地理与时间加权神经网络（GTWNN）框架以适应不同地理与时间情境
Advancing Spatiotemporal Prediction using Artificial Intelligence: Extending the Framework of Geographically and Temporally Weighted Neural Network (GTWNN) for Differing Geographical and Temporal Contexts
AI总结：
本文通过扩展GTWNN框架提出半解析GTWR求解法及三个数学扩展，组合成五个新ANN，在伦敦和底特律犯罪数据上验证了高精度，并指出历史依赖模块优于冗余扩展，为时空预测提供更情境化、准确且稳健的AI方法。
摘要：
本文旨在通过扩展针对一般时空问题定制的人工神经网络（ANN）的数学框架并适当应用它们，来改进预测性犯罪模型。地理空间-时间建模领域的最新进展集中于在其深度学习模型中纳入地理加权，以处理空间数据中常见的非平稳性。我们提出了一种新颖的半解析方法来解决地理与时间加权回归（GTWR），并将其应用于伦敦犯罪数据。结果产生了高精度的预测评估分数，证实了该方法中假设和近似的有效性。本文对地理与时间加权神经网络（GTWNN）框架提出了数学上的进展，为该领域提供了新颖的贡献。来自以往文献的见解与假设和近似和谐地结合，产生了对GTWNN框架的三个数学扩展。这些扩展的组合产生了五个新颖的ANN，应用于伦敦和底特律数据集。结果表明，其中一个扩展是冗余的，并且通常被另一个我们称为历史依赖模块的扩展所超越。其余扩展形成了三个新颖的ANN设计，这些设计可能带来GTWNN的改进。我们在伦敦和底特律犯罪数据集中评估了各种模型的有效性，强调了在选择建模策略以改善模型适用性时考虑特定地理和时间特征的重要性。总的来说，所提出的方法为在时空建模中实现更具情境意识、更准确和更稳健的ANN方法奠定了基础。
英文摘要：
This paper aims at improving predictive crime models by extending the mathematical framework of Artificial Neural Networks (ANNs) tailored to general spatiotemporal problems and appropriately applying them. Recent advancements in the geospatial-temporal modelling field have focused on the inclusion of geographical weighting in their deep learning models to account for nonspatial stationarity, which is often apparent in spatial data. We formulate a novel semi-analytical approach to solving Geographically and Temporally Weighted Regression (GTWR), and applying it to London crime data. The results produce high-accuracy predictive evaluation scores that affirm the validity of the assumptions and approximations in the approach. This paper presents mathematical advances to the Geographically and Temporally Weighted Neural Network (GTWNN) framework, which offers a novel contribution to the field. Insights from past literature are harmoniously employed with the assumptions and approximations to generate three mathematical extensions to GTWNN's framework. Combinations of these extensions produce five novel ANNs, applied to the London and Detroit datasets. The results suggest that one of the extensions is redundant and is generally surpassed by another extension, which we term the history-dependent module. The remaining extensions form three novel ANN designs that pose potential GTWNN improvements. We evaluated the efficacy of various models in both the London and Detroit crime datasets, highlighting the importance of accounting for specific geographic and temporal characteristics when selecting modelling strategies to improve model suitability. In general, the proposed methods provide the foundations for a more context-aware, accurate, and robust ANN approach in spatio-temporal modelling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
