Socio-environmental modifiers of heat-related mortality in eight Swiss cities: A case time series analysis

Wicki, Benedikt; Flückiger, Benjamin; Vienneau, Danielle; de Hoogh, Kees; Röösli, Martin; Ragettli, Martina S.

doi:10.1016/j.envres.2024.118116

Cited by 1 publication

(2 citation statements)

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“…Focuses on the dynamic fabric of civic life within the Municipality of Patras, Greece. Aligns with the existing literature [4,[6][7][8][9][10][11][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], which highlights the significance of temporal analysis of urban data in informing planning and decision-making.…”

Section: Discussionmentioning

confidence: 58%

“…The findings emphasize the need for localized investigations to inform population adaptation strategies in the face of rising temperatures and climate change. Utilizing time series analysis, the research [28] investigates how UHIs impact temperature-related mortality in eight Swiss cities. Focusing on UHIs, residential greenness, and neighborhood socio-economic factors, the study examines individual death records from 2003 to 2016.…”

Section: Related Workmentioning

confidence: 99%

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Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

Gkontzis,

Kotsiantis,

Feretzakis

et al. 2024

BDCC

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In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges.

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