Seismic resilience of urban networks: dataset for infrastructure visualization and vulnerability assessment.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-03 DOI:10.1038/s41597-025-05903-y

Marco Civera, Fabrizio Aloschi, Galilea Margherita Di Maio, Juan Pablo Fierro Carrasco, Andrea Miano, Bernardino Chiaia, Andrea Prota

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引用次数: 0

Abstract

We provide geographic information system (GIS) data and a multimodal dataset from a systematic infrastructure vulnerability assessment in the urban road networks of Turin and Naples, Italy. The seismic typologies of the relevant structural objects (SOs), including bridges, buildings, and roads, were evaluated using digital elevation models (DEMs) and satellite data. The presented GIS data are essential for visualizing and spatially interconnecting SOs; this enables network modeling as a complex system within the Spatial Data Infrastructure (SDI) portfolio of interest. The dataset also includes landslide characteristics from Geoportale Piemonte and the GeoNetwork catalog. Potential applications include resilience analysis, seismic risk assessment, emergency response planning, and post-disaster recovery estimations. Moreover, the dataset helps investigate the interplay between structural vulnerability and geohazards like landslides, heavy rainfall, and earthquakes. Notably, it is particularly relevant for research on urban networks as complex systems, where SDIs assess transportation efficiency and functionality in both pre- and post-event scenarios.

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城市网络的地震恢复力：基础设施可视化和脆弱性评估数据集。

我们提供了地理信息系统（GIS）数据和来自意大利都灵和那不勒斯城市道路网络系统基础设施脆弱性评估的多模式数据集。使用数字高程模型（dem）和卫星数据评估了相关结构对象（so）的地震类型，包括桥梁、建筑物和道路。所提供的GIS数据对于可视化和空间互连SOs至关重要；这使得网络建模成为空间数据基础设施（SDI）投资组合中的一个复杂系统。该数据集还包括来自皮埃蒙特地质公园和GeoNetwork目录的滑坡特征。潜在的应用包括复原力分析、地震风险评估、应急响应计划和灾后恢复评估。此外，该数据集还有助于调查结构脆弱性与山体滑坡、强降雨和地震等地质灾害之间的相互作用。值得注意的是，它与城市网络作为复杂系统的研究特别相关，其中sdi评估事前和事后情景下的交通效率和功能。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.