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Abstract: Two approaches to clustering and blockmodeling of temporal networks are presented: the first is based on an adaptation of the clustering of symbolic data described by modal values and the second is based on clustering with relational constraints. Different options for describing a temporal block model are discussed.
Keywords: social networks, network analysis, blockmodeling, symbolic data analysis, clustering with relational constraints
Published in DiRROS: 08.04.2024; Views: 87; Downloads: 44
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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.
Keywords: urban fire risk, fire risk management, big data technologies, data governance, socio-economic factors, city-wide analysis
Published in DiRROS: 09.01.2024; Views: 170; Downloads: 35
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Abstract: The analysis of the current state of fire statistics and data collection in Europe and other countries is needed to increase awareness of how fire incidents affect buildings and to support pan-European fire prevention and fire mitigation mea- sures. The terminology and data collected regarding fire incidents in buildings in the EU Member States were mapped to obtain meaningful datasets to determine common terminology, collection methodology, and data interpretation system. An extensive literature review showed that fire data collection systems have been instrumental in informing firefighting strategies, evidence-based planning, prevention, and educational programmes. Differences and similarities between fire data collection systems were also investigated. The amount and quality of the information in fire statistical recording systems appear to be influenced by the complexity and structure with which the data are collected. The analysis also examined the existing fire statistics in the EU Member States and a few other countries. Finally, a detailed investigation of the number of fires, fire deaths, and injuries from 2009 to 2018 in several countries was examined based on data from a report by CTIF. The trends showed differences attributable to the existing fire statistical practices in terms of terminology and data collection, and interpretation. Part II proposes a common terminology for selected fire statistical variables. The results provide relevant information regarding fire safety at the European level and should be used to guide the development of more uniform fire statistics across Europe.
Keywords: fire statistics, fire incidents, fire statistical variable, terminology, data collection, data interpretation
Published in DiRROS: 13.11.2023; Views: 374; Downloads: 109
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Abstract: Since 1963, the International Heat Flow Commission has been fostering the compilation of the Global Heat Flow Database to provide reliable heat-flow data. Over time, techniques and methodologies evolved, calling for a reorganization of the database structure and for a reassessment of stored heat-flow data. Here, we provide the results of a collaborative, community-driven approach to set-up a new, quality-approved global heat-flow database. We present background information on how heat-flow is determined and how this important thermal parameter could be systematically evaluated. The latter requires appropriate documentation of metadata to allow the application of a consistent evaluation scheme. The knowledge of basic data (name and coordinates of the site, depth range of temperature measurements, etc.), details on temperature and thermal-conductivity data and possible perturbing effects need to be given. The proposed heat-flow quality evaluation scheme can discriminate between different quality aspects affecting heat flow: numerical uncertainties, methodological uncertainties, and environmental effects. The resulting quality codes allow the evaluation of every stored heat-flow data entry. If mandatory basic data are missing, the entry is marked accordingly. In cases where more than one heat-flow determination is presented for one specific site, and all of them are considered for the site, the poorest evaluation score is inherited to the site level. The required data and the proposed scheme are presented in this paper. Due to the requirements of the newly developed evaluation scheme, the database structure as presented in 2021 has been updated and is available in the appendix of this paper. The new quality scheme will allow a comprehensible evaluation of the stored heat-flow data for the first time.
Keywords: heat-flow density, quality scheme, thermal geophysics, global heat flow database (GHFD), thermal parameter, data information system, International Heat Flow Commission (IHFC)
Published in DiRROS: 09.08.2023; Views: 329; Downloads: 127
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Abstract: In this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.).
Keywords: tomographic 3D/4D imaging data, image processing and analysis, open source software, Python
Published in DiRROS: 28.04.2023; Views: 481; Downloads: 153
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