Identifying Nighttime Vitality Through Multisource Geodata: An Explainable AI Perspective DOI
Sheng Li, Xiaojin Liang, Jie Yu

et al.

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(3)

Published: May 1, 2025

ABSTRACT Nighttime vitality serves as a crucial criterion for assessing the attractiveness and competitiveness of urban areas. The varying multifaceted characteristics nighttime across neighborhoods reflect residents' preferences, social demands, localized economies. objective this study is to comprehensively assess by integrating multiple geospatial data sources, including mobile phone data, remote sensing small catering businesses data. Furthermore, within an explainable artificial intelligence (XAI) framework, aims quantitatively analyze nonlinear relationships between influencing factors vitality. To enhance interpretability, we employ SHapley Additive exPlanations (SHAP) visual model interpretation. This can further theory vitality, deepen understanding relationships, facilitate implementation AI in studies. findings effectively provide guidance planning decision‐making, improve quality life, promote sustainable development.

Language: Английский

Identifying Nighttime Vitality Through Multisource Geodata: An Explainable AI Perspective DOI
Sheng Li, Xiaojin Liang, Jie Yu

et al.

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(3)

Published: May 1, 2025

ABSTRACT Nighttime vitality serves as a crucial criterion for assessing the attractiveness and competitiveness of urban areas. The varying multifaceted characteristics nighttime across neighborhoods reflect residents' preferences, social demands, localized economies. objective this study is to comprehensively assess by integrating multiple geospatial data sources, including mobile phone data, remote sensing small catering businesses data. Furthermore, within an explainable artificial intelligence (XAI) framework, aims quantitatively analyze nonlinear relationships between influencing factors vitality. To enhance interpretability, we employ SHapley Additive exPlanations (SHAP) visual model interpretation. This can further theory vitality, deepen understanding relationships, facilitate implementation AI in studies. findings effectively provide guidance planning decision‐making, improve quality life, promote sustainable development.

Language: Английский

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