Human factors are important causes of hazardous chemical storage accidents, and clarifying the relationship between human factors can help to identify the logical chain between unsafe behaviors and influential factors in accidents. Therefore, the human factor relationship of hazardous chemical storage accidents was studied in this paper. First, the human factors analysis and classification system (HFACS), which originated from accident analysis in the aviation field, was introduced. Since some items were designed for aviation accident analysis, such as the item “Crew Resource Management”, it is not fully applicable to the analysis of hazardous chemical storage accidents. Therefore, this article introduced some modifications and changes to make the HFACS model suitable for the analysis of hazardous chemical storage accidents. Based on the improved HFACS model, 42 hazardous chemicals storage accidents were analyzed, and the causes were classified. After analysis, we found that under the HFACS framework, the most frequent cause of accidents is resource management, followed by violations and inadequate supervision, and finally the organizational process and technological environment. Finally, according to the statistical results for the various causes of accidents obtained from the improved HFACS analysis, the chi-square test and odds ratio analysis were used to further explore the relevance of human factors in hazardous chemical storage accidents. The 16 groups of significant causal relationships among the four levels of factors include resource management and inadequate supervision, planned inappropriate operations and technological environment, inadequate supervision and physical/mental limitations, and technological environment and skill-based errors, among others.
Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction’s historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment.
Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way.
The emergency rescue process of road transportation leakage accidents involving hazardous chemicals is complex and includes various emergency activities. A quantitative study of human errors in emergency activities is conducive to seeking the focus of the emergency rescue process. To quantitatively analyze human error in emergency activities during the emergency rescue process of road transportation leakage accidents of hazardous chemicals, sequentially timed events plotting (STEP) and the cognitive reliability and error analysis method (CREAM), were used. First, STEP was used to analyze six laws, regulations and standards, as well as 54 accident cases, to derive 24 emergency activities in the emergency rescue process. Then, CREAM was used to analyze and obtain the probability of human error for each emergency activity. Two high error level emergency activities, five medium error level emergency activities, and seventeen low error level emergency activities were identified after the human error levels of the emergency activities were classified. The results show that two emergency activities, the initial handling of the accident, and cleanup of the leakage site, should be prioritized in the emergency rescue process of road transportation leakage accidents of hazardous chemicals.
Compared with other types of transportation, hazardous chemical transportation is more dangerous and more likely to cause accidents, such as combustion and explosion. To better study the advantages of different accident analysis models and realize the sustainable development of the accident analysis, this paper compares the 24Model and the cognitive reliability and error analysis method in their analyses of causes of hazardous chemical transportation accidents. Regarding their analyses of the causes of hazardous chemical transportation accidents, the causal factors of hazardous chemical transportation accidents are obtained. Then the analysis results of the two models are compared on three aspects: the object of accident influence, the module of accident analysis, and the number of accident causes. Gray correlation analysis and regression analysis are used to quantitatively compare and verify the focus of the two models on the cause of the accident. The results show that the 24Model emphasizes the safety culture of the enterprise, the cognitive reliability and error analysis method emphasizes the technology of the enterprise, and the two accident analysis models provide different emphases on preventing accidents to better achieve the goal of sustainable development.
In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.
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