In the last decades, there have been much more public health crises in the world such as H1N1, H7N9 and Ebola out-break. In the same time, it has been proved that our world has come into the time when public crisis accidents number was growing fast. Sometimes, crisis response to these public emergency accidents is involved in a complex system consisting of cyber, physics and society domains (CPS Model). In order to collect and analyze these accidents with higher efficiency, we need to design and adopt some new tools and models. In this paper, we used CPS Model based Online Opinion Governance system which constructed on cellphone APP for data collection and decision making in the back end. Based on the online opinion data we collected, we also proposed the graded risk classification. By the risk classification method, we have built an efficient CPS Model based simulated emergency accident replying and handling system. It has been proved useful in some real accidents in China in recent years.
{"title":"CPS model based online opinion governance modeling and evaluation of emergency accidents","authors":"Xiaolong Deng, Yingtong Dou, Yihua Huang","doi":"10.1145/3017611.3017619","DOIUrl":"https://doi.org/10.1145/3017611.3017619","url":null,"abstract":"In the last decades, there have been much more public health crises in the world such as H1N1, H7N9 and Ebola out-break. In the same time, it has been proved that our world has come into the time when public crisis accidents number was growing fast. Sometimes, crisis response to these public emergency accidents is involved in a complex system consisting of cyber, physics and society domains (CPS Model). In order to collect and analyze these accidents with higher efficiency, we need to design and adopt some new tools and models. In this paper, we used CPS Model based Online Opinion Governance system which constructed on cellphone APP for data collection and decision making in the back end. Based on the online opinion data we collected, we also proposed the graded risk classification. By the risk classification method, we have built an efficient CPS Model based simulated emergency accident replying and handling system. It has been proved useful in some real accidents in China in recent years.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133184446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qing Deng, Yi Liu, Lihua Li, Xiaolong Deng, Hui Zhang
Risk communication is an effective means of emergency management. Online information plays an important role in risk communication, especially in the Big Data Era. Citizens' psychology to send and receive information determines their online behavior when they face risk. Using the Tianjin Port Explosions as an example, multiple linear regression analysis is used to untangle the relationship between online attention and psychology behavior under risk. Citizens' online attention is estimated by social media data collection from the Sina Weibo. Psychology behavior is quantified by psychological distance which consists of four dimensions: spatial distance, temporal distance, social distance and probability. The regression model is built via SPSS 20.0 and the obtained result is matched with actual situation. It indicates that online attention is negatively correlated to spatial distance, temporal distance and social distance while positively correlated to probability of the event. It also shows that citizens' online attention under risk is positively correlated to their online attention under normal circumstance. Based on the regression model, citizens' attention and response to emergency are easy to be assessed.
{"title":"Emergency online attention and psychological distance under risk","authors":"Qing Deng, Yi Liu, Lihua Li, Xiaolong Deng, Hui Zhang","doi":"10.1145/3017611.3017614","DOIUrl":"https://doi.org/10.1145/3017611.3017614","url":null,"abstract":"Risk communication is an effective means of emergency management. Online information plays an important role in risk communication, especially in the Big Data Era. Citizens' psychology to send and receive information determines their online behavior when they face risk. Using the Tianjin Port Explosions as an example, multiple linear regression analysis is used to untangle the relationship between online attention and psychology behavior under risk. Citizens' online attention is estimated by social media data collection from the Sina Weibo. Psychology behavior is quantified by psychological distance which consists of four dimensions: spatial distance, temporal distance, social distance and probability. The regression model is built via SPSS 20.0 and the obtained result is matched with actual situation. It indicates that online attention is negatively correlated to spatial distance, temporal distance and social distance while positively correlated to probability of the event. It also shows that citizens' online attention under risk is positively correlated to their online attention under normal circumstance. Based on the regression model, citizens' attention and response to emergency are easy to be assessed.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131032337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chuan Ai, Bin Chen, Lingnan He, Yichong Bai, Xingbing Li, Zhichao Song
WeChat is a widely used online social media application in China for its popularity. Based on the analysis of big HTML5 data from WeChat, the Geography Interaction Activity Network (GIAN) is acquired first in this paper. Then we analyze the geographical characteristics of WeChat network through the community detection in GIAN. It is concluded that the cities in the same community stay close geographically usually, and the WeChat networks can be partitioned into communities in which there are five communities that are stable and contains the majority of cities in China.
{"title":"The phenomenon of geolocation aggregation of city nodes community partition in network of WeChat","authors":"Chuan Ai, Bin Chen, Lingnan He, Yichong Bai, Xingbing Li, Zhichao Song","doi":"10.1145/3017611.3017626","DOIUrl":"https://doi.org/10.1145/3017611.3017626","url":null,"abstract":"WeChat is a widely used online social media application in China for its popularity. Based on the analysis of big HTML5 data from WeChat, the Geography Interaction Activity Network (GIAN) is acquired first in this paper. Then we analyze the geographical characteristics of WeChat network through the community detection in GIAN. It is concluded that the cities in the same community stay close geographically usually, and the WeChat networks can be partitioned into communities in which there are five communities that are stable and contains the majority of cities in China.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133975163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiaojiao Wang, Zhidong Cao, D. Zeng, Quanyi Wang, Xiaoli Wang
Hand-foot-mouth disease (HFMD) outbreak greatly threatened Beijing city, the capital city of China, in 2008. The control prevention of HFMD has become an urgent mission for Beijing Center for Disease Control and Prevention and a focus problem for the citizens. Medical, social and environmental situations account for much of HFMD morbidity. The spatial driving forces of HFMD occurrence vary across geographical regions, whereas the factors that play a significant role in HFMD prevalence may be concealed by global statistics analysis. This study aims at the identification of the association between the spatial driving forces and HFMD morbidity across the study area and the epidemiological explanation of the results. HFMD spatial driving forces are represented by 6 factors which was obtained by Pearson Correlation analysis and Stepwise Regression method. Compared to Classical Linear Regression Model (CLRM), Geographically weighted regression (GWR) techniques were implemented to predict HFMD morbidity and examine the nonstationary of HFMD spatial driving forces. Informative maps of estimated HFMD morbidity and statistically significant spatial driving forces were generated and rigorously evaluated in quantitative terms. Prediction accuracy by GWR was higher than that by CLRM. The residual led to by CLRM suggested a significant degree of spatial dependence, while that by GWR indicated no significant spatial dependence. In the three regions plotted by Beijing city Ring Roads, HFMD morbidity was found to have significantly positive or negative association with the 6 kinds of spatial driving forces. GWR model can effectively represent the spatial heterogeneity of HFMD driving forces, significantly improve the prediction accuracy and greatly decrease the spatial dependence. The results improve current explanation of HFMD spread in the study area and provide valuable information for adequate disease intervention measures.
{"title":"Assessment for spatial driving forces of HFMD prevalence in Beijing, China","authors":"Jiaojiao Wang, Zhidong Cao, D. Zeng, Quanyi Wang, Xiaoli Wang","doi":"10.1145/3017611.3017617","DOIUrl":"https://doi.org/10.1145/3017611.3017617","url":null,"abstract":"Hand-foot-mouth disease (HFMD) outbreak greatly threatened Beijing city, the capital city of China, in 2008. The control prevention of HFMD has become an urgent mission for Beijing Center for Disease Control and Prevention and a focus problem for the citizens. Medical, social and environmental situations account for much of HFMD morbidity. The spatial driving forces of HFMD occurrence vary across geographical regions, whereas the factors that play a significant role in HFMD prevalence may be concealed by global statistics analysis. This study aims at the identification of the association between the spatial driving forces and HFMD morbidity across the study area and the epidemiological explanation of the results. HFMD spatial driving forces are represented by 6 factors which was obtained by Pearson Correlation analysis and Stepwise Regression method. Compared to Classical Linear Regression Model (CLRM), Geographically weighted regression (GWR) techniques were implemented to predict HFMD morbidity and examine the nonstationary of HFMD spatial driving forces. Informative maps of estimated HFMD morbidity and statistically significant spatial driving forces were generated and rigorously evaluated in quantitative terms. Prediction accuracy by GWR was higher than that by CLRM. The residual led to by CLRM suggested a significant degree of spatial dependence, while that by GWR indicated no significant spatial dependence. In the three regions plotted by Beijing city Ring Roads, HFMD morbidity was found to have significantly positive or negative association with the 6 kinds of spatial driving forces. GWR model can effectively represent the spatial heterogeneity of HFMD driving forces, significantly improve the prediction accuracy and greatly decrease the spatial dependence. The results improve current explanation of HFMD spread in the study area and provide valuable information for adequate disease intervention measures.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115400561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiang Zeyu, Wang Gangqiao, Xing Han, Wang Yong, Zhang Xiangpeng, Liu Yi
This paper discussed the possibility of using trajectory data by cell phone in calibration of car-following model. Experimental study of car following behaviors is performed with both GNSS devices and cell phones used for trajectory data collection. GM model is applied in model calibration. The results of this paper indicate possibility of rapid modeling using cell phone data and proved the feasibility of "real-time modeling".
{"title":"Calibrating car-following model with trajectory data by cell phone","authors":"Jiang Zeyu, Wang Gangqiao, Xing Han, Wang Yong, Zhang Xiangpeng, Liu Yi","doi":"10.1145/3017611.3017623","DOIUrl":"https://doi.org/10.1145/3017611.3017623","url":null,"abstract":"This paper discussed the possibility of using trajectory data by cell phone in calibration of car-following model. Experimental study of car following behaviors is performed with both GNSS devices and cell phones used for trajectory data collection. GM model is applied in model calibration. The results of this paper indicate possibility of rapid modeling using cell phone data and proved the feasibility of \"real-time modeling\".","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121433234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research of hot-spots is an important subject in public safety field as well as GIS system application, it reveals the spatial distribution law of crime and help develop targeted security preventive strategy, providing safeguards for the whole society. The mass incident is a special kind of public safety incident, but there are little research on its spatial distribution rules from the perspective of geography. This article analyzes the spatial distribution rules of mass incidents in China from the macro point of view and based on the spatial autocorrelation and Kernel density estimation, then finds hot-spots and calculates the degree of spatial agglomeration to express the spatial distribution law of crime.
{"title":"Research of spatial distribution rules of mass incidents based on GIS","authors":"Hu Jun, Shu Xueming, Shen Shifei, Tang Shiyang","doi":"10.1145/3017611.3017621","DOIUrl":"https://doi.org/10.1145/3017611.3017621","url":null,"abstract":"The research of hot-spots is an important subject in public safety field as well as GIS system application, it reveals the spatial distribution law of crime and help develop targeted security preventive strategy, providing safeguards for the whole society. The mass incident is a special kind of public safety incident, but there are little research on its spatial distribution rules from the perspective of geography. This article analyzes the spatial distribution rules of mass incidents in China from the macro point of view and based on the spatial autocorrelation and Kernel density estimation, then finds hot-spots and calculates the degree of spatial agglomeration to express the spatial distribution law of crime.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121947513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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