| Title: | Effects of governmental data governance on urban fire risk : a city-wide analysis in China |
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| Authors: | ID Liu, Zhao-Ge (Author)
ID Li, Xiang-Yang (Author)
ID Jomaas, Grunde (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S2212420922003570?via%3Dihub
 PDF - Presentation file, download (1,20 MB)
MD5: 181F5495D08FEA5D019C37816F5D0F72
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  ZAG - Slovenian National Building and Civil Engineering Institute
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| Abstract: | The effects of data governance (as a means to maximize big data value creation in fire risk management) performance on fire risk was analyzed based on multi-source statistical data of 105 cities in China from 2016 to 2018. Specifically, data governance was first quantified with ten detailed indicators, which were then selected for explaining urban fire risk through correlation analysis. Next, the sample cities were clustered in terms of major socio-economic characteristics, and then the effects of data governance were examined by constructing multivariate regression models for each city cluster with ordinary least squares (OLS). The results showed that the constructed regression models produced good interpretation of fire risk in different types of cities, with coefficient of determination (R2) in each model exceeding 0.65. Among the indicators, the development of infrastructures (e.g. data collection devices and data analysis platforms), the level of data use, and the updating of fire risk related data were proved to produce significant effects on the reduction of fire frequency and fire consequence. Moreover, the organizational maturity of data governance was proved to be helpful in reducing fire frequency. For the cities with large population, the cross-department sharing of high-value data was found to be another important determinant of urban fire frequency. In comparison with existing statistical models which interpreted fire risk with general social factors (with the highest R2 = 0.60), these new regression models presented a better statistical performance (with the average R2 = 0.72). These findings are expected to provide decision support for the local governments of China and other jurisdictions to facilitate big data projects in improving fire risk management. |
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| Keywords: | urban fire risk, fire risk management, big data technologies, data governance, socio-economic factors, city-wide analysis |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 28.06.2022 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2022 |
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| Number of pages: | Str. 1-17 |
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| Numbering: | Vol. 78, [article no.] 103138 |
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| PID: | 20.500.12556/DiRROS-17674  |
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| UDC: | 614.84 |
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| ISSN on article: | 2212-4209 |
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| DOI: | 10.1016/j.ijdrr.2022.103138 |
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| COBISS.SI-ID: | 143554563 |
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| Copyright: | © 2022 Elsevier Ltd. All rights reserved |
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| Publication date in DiRROS: | 09.01.2024 |
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| Views: | 500 |
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| Downloads: | 201 |
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