HARCI-EU, a harmonized gridded dataset of critical infrastructures in Europe for large-scale risk assessments

Silva, Filipe Batista e; Forzieri, Giovanni; Herrera, Mario A. Marin; Bianchi, Alessandra; Lavalle, Carlo; Feyen, Luc

doi:10.1038/s41597-019-0135-1

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…Hence, it can be classified as CI all infrastructures related to transport, energy generation and distribution, water, industry, education, and health [21], among other systems, and services that are critical to ensure the health, wealth, and security of a country [17,22]. Therefore, its essential role in society becomes clear when a situation of disaster or crisis occurs, when CI failure is more probable and, consequently, the disruption of daily functioning affects society [23].…”

Section: Critical Infrastructurementioning

confidence: 99%

“…In order to facilitate exposure analysis of CI, several authors have provided different CI spatial harmonized gridded datasets [7,21]. Some studies address the infrastructure exposure by developing different metrics to associate the number of users to each individual infrastructure asset, and conducting a hotspot analysis, by applying the Kernel density estimator [33,38].…”

Section: Risk Analysismentioning

confidence: 99%

“…For the CI exposure assessment, the "HARCI-EU harmonized gridded dataset of critical infrastructures in Europe" [21,46] has been used, which represents major CI in the energy, industry, transport, and social sectors, at a 1 km 2 resolution, expressed in sector-specific, economically relevant units. We have only considered those infrastructure types with a nodal structure for the study.…”

Section: Data 221 CI Datasetsmentioning

confidence: 99%

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Are We Underestimating the Risk of Coastal Flooding in Europe? The Relevance of Critical Infrastructure

Barrios-Crespo,

Torres-Ortega,

Díaz-Simal

2023

JMSE

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The effects of climate change imply an increase in the risk levels due to coastal flooding. These areas present a high concentration of population and economic activity. The failure of critical infrastructures (CI) serving these communities can have significant impacts on these sectors. This study aims to analyze the risk and its future evolution due to climate change in coastal areas, considering the increased impact that the population would experience if the CI (of the energy or water and waste treatment sectors) were affected by coastal flooding. The methodology used has made it possible to analyze the risk of the Functional Urban Areas (FUA) of the European coasts, identifying the areas in which the level of risk to the population is significantly underestimated. In some of these areas and scenarios, the population affected by coastal flooding if the failure of the CI occurs would be up to 4.5 times higher than what is estimated if only their direct impact is considered.

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Section: Critical Infrastructurementioning

confidence: 99%

Section: Risk Analysismentioning

confidence: 99%

Section: Data 221 CI Datasetsmentioning

confidence: 99%

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Are We Underestimating the Risk of Coastal Flooding in Europe? The Relevance of Critical Infrastructure

Barrios-Crespo,

Torres-Ortega,

Díaz-Simal

2023

JMSE

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“…More advanced methodologies have incorporated data on interdependencies and user demand to identify CI hotspots 8 , 9 , 26 . At the larger European scale, the development of a harmonized dataset of CI 27 has aided the assessment of multi-hazard risk 15 , and recently the very first estimates of multi-hazard risk to transport infrastructure were calculated at the global scale 6 .…”

Section: Background and Summarymentioning

confidence: 99%

A spatially-explicit harmonized global dataset of critical infrastructure

et al. 2022

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Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural and human-made threats, however, pose a major risk to CI. Therefore, geospatial data on the location of CI are fundamental for in-depth risk analyses, which are required to inform policy decisions aiming to reduce risk. We present a first-of-its-kind globally harmonized spatial dataset for the representation of CI. In this study, we: (1) collect and harmonize detailed geospatial data of the world’s main CI systems into a single geospatial database; and (2) develop the Critical Infrastructure Spatial Index (CISI) to express the global spatial intensity of CI. The CISI aggregates high-resolution geospatial OpenStreetMap (OSM) data of 39 CI types that are categorized under seven overarching CI systems. The detailed geospatial data are rasterized into a harmonized and consistent dataset with a resolution of 0.10 × 0.10 and 0.25 × 0.25 degrees. The dataset can be applied to explore the current landscape of CI, identify CI hotspots, and as exposure input for large-scale risk assessments.

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“…European governments also provided extensive supportive datasets. The HARmonized grids of Critical Infrastructures in EUrope (HARCI-EU) dataset contains data on harmonized grids of critical infrastructures (CIs) within the European Union [101]. CIs are defined as infrastructures essential to the safety and well-being of people.…”

Section: Supporting Datasets and Public Apismentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

et al. 2021

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The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs—maintained by government organizations and train operating companies (TOCs)—that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.

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HARCI-EU, a harmonized gridded dataset of critical infrastructures in Europe for large-scale risk assessments

Cited by 13 publications

References 23 publications

Are We Underestimating the Risk of Coastal Flooding in Europe? The Relevance of Critical Infrastructure

Are We Underestimating the Risk of Coastal Flooding in Europe? The Relevance of Critical Infrastructure

A spatially-explicit harmonized global dataset of critical infrastructure

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

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