Spatiotemporal Intelligence in Smart Cities

Editors


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Guest Editor
National Innovative Institute of Defense Technology, Beijing 100091, China
Interests: urban computing; spatio-temporal data; intelligent transportation; computational social science

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Guest Editor
Data Science and Analytics Thrust, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
Interests: spatio-temporal data mining; time series; urban computing; foundation model

Special Issue Information

Dear Colleagues,

We invite scholars to contribute to this Special Issue exploring the frontier of urban Spatiotemporal Intelligence (STI). While smart cities generate massive data, translating it into actionable insights remains fundamentally challenged by complex spatiotemporal dynamics. Furthermore, overcoming data scarcity through few-shot learning and ensuring rigorous data privacy in decentralized systems are critical bottlenecks. Recently, the integration of Large Language Models (LLMs) has opened unprecedented opportunities for complex urban reasoning and decision-making. We are particularly interested in how these advanced methodologies can transform critical urban subsystems, such as intelligent transportation and rail transit scheduling, crowd dynamics and pedestrian flow management, as well as the spatiotemporal optimization of communication networks. We welcome original research addressing these core challenges, spanning innovative algorithms, privacy-preserving frameworks, and LLM-driven urban applications, to collaboratively shape resilient and intelligent urban futures.

Dr. Guangyin Jin
Dr. Yuxuan Liang
Guest Editors

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Keywords

  • spatiotemporal intelligence
  • urban computing
  • urban dynamics
  • big data mining

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Published Papers (1 paper)

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Research

21 pages, 5138 KB  
Article
Urban Spatiotemporal Intelligence for Predicting Recorded ECU911 Incident Volume and Local Hotspot Dynamics in Intermediate Territories
by Wilman Merino-Vivanco, Xavier Merino-Vivanco, Yasmany García-Ramírez and Fabián Díaz-Muñoz
Smart Cities 2026, 9(9), 138; https://doi.org/10.3390/smartcities9090138 - 26 Aug 2026
Viewed by 322
Abstract
Administrative emergency records can support spatiotemporal analysis in territories that are rarely represented in urban-computing research, but recorded volume is not equivalent to population risk or underlying incidence. This study develops an integrated framework for characterizing and predicting ECU911 recorded incident volume in [...] Read more.
Administrative emergency records can support spatiotemporal analysis in territories that are rarely represented in urban-computing research, but recorded volume is not equivalent to population risk or underlying incidence. This study develops an integrated framework for characterizing and predicting ECU911 recorded incident volume in Loja and Zamora Chinchipe, Ecuador, from January 2015 to 30 June 2026. The analysis used 925,000 administrative rows; 905,038 fell within the prespecified study period, and 899,232 were retained for spatial analysis after date and coordinate control. Annual spatial analysis used a 1 km grid with 3414 active cells; k = 10 nearest neighbours, including self-neighbours; row-standardized weights; conditional permutations; and Benjamini–Hochberg adjustment. The main analysis used 999 permutations; additional sensitivity analyses used 199 permutations. Global Moran’s I ranged from approximately 0.461 to 0.524 (permutation p = 0.001). Local Moran’s I produced no significant clusters after adjustment, whereas Gi* identified a small number of local hotspots. Across the complete years 2015–2025, no cell was identified as a hotspot in at least 50% of years; temporal classes comprised 18 occasional, 7 consecutive, 4 sporadic, 1 new, and 3384 cells, with no pattern detected. Leakage-free random forest regression achieved MAE = 0.552, RMSE = 2.230, RMSLE = 0.285, and R2 = 0.982 in the temporal test, but for pooled spatial block cross-validation, these rates declined to MAE = 0.797, RMSE = 10.423, and R2 = 0.661. A high-count classifier using the upper quintile of positive monthly cell counts achieved F1 = 0.883, ROC–AUC = 0.998, and PR–AUC = 0.966 in the temporal test. The contribution lies in the dataset, the understudied territory, and the methodological integration rather than in algorithmic novelty. Full article
(This article belongs to the Special Issue Spatiotemporal Intelligence in Smart Cities)
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