Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering DOI Creative Commons
Ibrahim Mutambik

Land, Год журнала: 2024, Номер 13(11), С. 1744 - 1744

Опубликована: Окт. 24, 2024

Today, urban areas across the world are increasingly vulnerable to emergencies due expanding populations and impact of climate change. This paper presents a data-driven method for assessing susceptibility regions emergencies, using publicly available data clustering-based algorithm. The study incorporates both spatial temporal dynamics, capturing fluctuating nature infrastructure patterns human movement over time. By introducing notion Points Temporal Influence (PTIs) new “susceptibility level” parameter, proposed model offers an innovative approach understanding susceptibility. Experiments conducted in London, UK, demonstrated effectiveness Spatiotemporal K-means Clustering algorithm identifying with heightened time-sensitive findings highlight value incorporating enhance emergency response strategies optimize planning efforts. contributes literature on smart cities by providing scalable adaptable improving resilience face evolving challenges.

Язык: Английский

AI-based Classification Model for Low-Energy Buildings: Promoting Sustainable Economic Development of Smart Cities with Spherical Fuzzy Decision Algorithm DOI Creative Commons

Lin Yang

IEEE Access, Год журнала: 2025, Номер 13, С. 18386 - 18402

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A multiple criteria analysis approach for assessing regional and territorial progress toward achieving the Sustainable Development Goals in Italy DOI Creative Commons
Idiano D’Adamo, Massimo Gastaldi, Antonio Felice Uricchio

и другие.

Decision Analytics Journal, Год журнала: 2025, Номер unknown, С. 100559 - 100559

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Risk management in smart cities: influence analysis using DEMATEL DOI

Adalberto Freitas,

Carlos Rafael Silva de Oliveira, Pedro Fernandes de Oliveira Gomes

и другие.

Environmental Hazards, Год журнала: 2025, Номер unknown, С. 1 - 27

Опубликована: Март 6, 2025

Язык: Английский

Процитировано

0
A Hybrid MCDM Framework for Assessing Urban Competitiveness: A Case Study of European Cities DOI
Özcan Işık, Mohsin Shabir, Sarbast Moslem

и другие.

Socio-Economic Planning Sciences, Год журнала: 2024, Номер 96, С. 102109 - 102109

Опубликована: Ноя. 14, 2024

Язык: Английский

Процитировано

1
Research on site selection of emergency material reserve based on set pair analysis and TOPSIS integration method: a case study of Hebei Province, China DOI

Dekun Kong,

Won Sik Yang

The Journal of Supercomputing, Год журнала: 2024, Номер 81(1)

Опубликована: Дек. 28, 2024

Язык: Английский

Процитировано

1
Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering DOI Creative Commons
Ibrahim Mutambik

Land, Год журнала: 2024, Номер 13(11), С. 1744 - 1744

Опубликована: Окт. 24, 2024

Today, urban areas across the world are increasingly vulnerable to emergencies due expanding populations and impact of climate change. This paper presents a data-driven method for assessing susceptibility regions emergencies, using publicly available data clustering-based algorithm. The study incorporates both spatial temporal dynamics, capturing fluctuating nature infrastructure patterns human movement over time. By introducing notion Points Temporal Influence (PTIs) new “susceptibility level” parameter, proposed model offers an innovative approach understanding susceptibility. Experiments conducted in London, UK, demonstrated effectiveness Spatiotemporal K-means Clustering algorithm identifying with heightened time-sensitive findings highlight value incorporating enhance emergency response strategies optimize planning efforts. contributes literature on smart cities by providing scalable adaptable improving resilience face evolving challenges.

Язык: Английский

Процитировано

0