Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano

Renner, Kathrin; Schneiderbauer, Stefan; Pruß, Fabio; Kofler, Christian; Martin, David; Cockings, Samantha

doi:10.1016/j.ijdrr.2017.11.011

Cited by 18 publications

(15 citation statements)

References 21 publications

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“…Location of South Tyrol (Autonomous Province of Bozen/Bolzano). The meteorological stations used, the main catchments and the mean resident population of the year 2015 ( km 1/ 2 ) are shown; the population was modelled from data provided by the Department for Monitoring of the Labour Market of the provincial government33 . Map realised with QGIS Geographic Information System, Open Source Geospatial Foundation Project (http://qgis.osgeo.org).…”

mentioning

confidence: 99%

Changes in climate patterns and their association to natural hazard distribution in South Tyrol (Eastern Italian Alps)

Schlögel

Kofler

Gariano

et al. 2020

Sci Rep

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In Alpine regions changes in seasonal climatic parameters, such as temperature, rainfall, and snow amount have already been observed. Specifically, in the South Tyrol area, meteorological observations indicate that temperatures are increasing and the number of snow days has generally diminished over time with perennial snow line now observed at higher elevations. Changes in rainfall have also been observed with more events associated with higher temperatures in the summer season. Natural hazards-mainly debris and mud flows, landslides, avalanches, rock falls, and (flash) floods-that affect this area every year, damaging population and infrastructures, are either weather or cryosphere-related. While these events have been recorded sporadically since the beginning of the 20th century, a systematic approach of their inventory has been done by local authorities since the 1990s. So far, Earth observation data has not been exploited to complete or complement existing inventories nor have they been used to investigate the influence of climate perturbation on potentially dangerous natural phenomena. The research presented here thus has three objectives: (i) analyse long time series of climate data and hazard occurrence in South Tyrol to examine if these records exhibit a coherent response of hazards to changes in climate; (ii) measure the spatio-temporal evolution of climatic and natural hazard events recorded, and (iii) explore potential relations between meteorological conditions and the hazard occurrence. In this context, in-situ and satellite-based climate data are exploited to study natural hazard triggers while the potential of Earth observation data is evaluated as a complement to the existing historical records of natural hazards. Specifically, Copernicus Sentinel-1 images are used to detect the spatio-temporal distribution of slow earth surface deformations and the results used for checking the completeness of the actual slow-moving landslide inventories. Hazard-related changes in the South tyrolian landscape have also been analysed in relation to particular meteorological events at a regional scale, assessing trends and anomalies. Results show that: (i) satellite data are very useful to complement the existing natural hazard inventories; (ii) in-situ and satellite-based climate records show similar patterns but differ due to regional versus local variability; (iii) even in a data-rich region such as the analysed area, the overall response of natural hazard occurrence, magnitude, and frequency to change in climate variables is difficult to decipher due to the presence of multiple triggers and locally driven ground responses. However, an increase in the average annual duration of rainfall events and debris flow occurrence can be observed. Mountains have been acknowledged as "sentinels of environmental change" because they have physical dynamics that are readily identifiable and respond more rapidly than other geographical entities to environmental change 1 .

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confidence: 99%

Changes in climate patterns and their association to natural hazard distribution in South Tyrol (Eastern Italian Alps)

Schlögel

Kofler

Gariano

et al. 2020

Sci Rep

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“…The results of calculating the amount of generated and absorbed trips indicated an abundance of daytime taxi trips, which illustrated that people take taxis more during the day than at night. The results of modeling the Spatial-temporal distribution of the population in the research of Ma et al [21] and Renner et al [11] indicated that the day and night populations are not the same. Moreover, the population increasing during the day from the central areas to the suburbs.…”

Section: Conclusion and Recommendationsmentioning

confidence: 98%

“…The results of the mentioned study showed that there is a difference in the population distribution between the day and the night. Renner et al [11] addressed spatial-temporal population modeling in order to improve the risk assessment in Bolzano. They used statistical data and a field survey for the population modeling.…”

Section: Introductionmentioning

confidence: 99%

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

et al. 2021

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During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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“…Population distribution data are human mobility data that are derived, analyzed, or simulated on a grid basis using datasets representing a population, such as census data. They tend to be combined with other grid data, such as hazard maps (Liu et al, 2021; Renner et al, 2018) and digital elevation maps (Khakpour and Rød, 2016). Population distribution analysis is better suited for detecting large spatiotemporal scale patterns and changes, such as daily, hourly, or seasonal fluctuations (Chen et al, 2020; Freire and Aubrecht, 2012; Renner et al, 2018).…”

Section: Human Mobility Data and Analytical Approachesmentioning

confidence: 99%

Human mobility data and analysis for urban resilience: A systematic review

Haraguchi

Nishino

Kodaka

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.

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Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano

Cited by 18 publications

References 21 publications

Changes in climate patterns and their association to natural hazard distribution in South Tyrol (Eastern Italian Alps)

Changes in climate patterns and their association to natural hazard distribution in South Tyrol (Eastern Italian Alps)

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Human mobility data and analysis for urban resilience: A systematic review

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