Maintaining and sustaining situational awareness is regarded as a primary task in a wildfire incident response. Clearly, due to the complexity of wildfire incidents, there are a wide range of significant hazards and risks which need to be considered. To better conduct situational awareness during wildfire emergency, we established a multi-model forecasting system to predict the fire behavior, estimate the resource requirements and share multisource information based on weather prediction model, wildfire behavior prediction model, resource scheduling model and GIS. Multi-model forecasting system provides the emergency mangers periodic situational awareness for quick and efficient responses to a wildfire emergency.
{"title":"Effective situational awareness to wildfire emergency command based on multi-model forecasting system","authors":"Chuanjie Yang, Jianguo Chen, G. Su","doi":"10.1145/3017611.3017612","DOIUrl":"https://doi.org/10.1145/3017611.3017612","url":null,"abstract":"Maintaining and sustaining situational awareness is regarded as a primary task in a wildfire incident response. Clearly, due to the complexity of wildfire incidents, there are a wide range of significant hazards and risks which need to be considered. To better conduct situational awareness during wildfire emergency, we established a multi-model forecasting system to predict the fire behavior, estimate the resource requirements and share multisource information based on weather prediction model, wildfire behavior prediction model, resource scheduling model and GIS. Multi-model forecasting system provides the emergency mangers periodic situational awareness for quick and efficient responses to a wildfire emergency.","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":"134193874","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}
T. Mondal, Jaydeep Roy, Indrajit Bhattacharya, Sandip Chakraborty, Arka Saha, Subhanjan Saha
Small sized unmanned aerial vehicles (UAV) play major roles in variety of applications for aerial explorations and surveillance, transport, videography/photography and other areas. However, some other real life applications of UAV have also been studied. One of them is as a 'Disaster Response' component. In a post disaster situation, the UAVs can be used for search and rescue, damage assessment, rapid response and other emergency operations. However, in a disaster response situation it is very challenging to predict whether the climatic conditions are suitable to fly the UAV. Also it is necessary for an efficient dynamic path planning technique for effective damage assessment. In this paper, such dynamic path planning algorithms have been proposed for micro-jet, a small sized fixed wing UAV for data collection and dissemination in a post disaster situation. The proposed algorithms have been implemented on paparazziUAV simulator considering different environment simulators (wind speed, wind direction etc.) and calibration parameters of UAV like battery level, flight duration etc. The results have been obtained and compared with baseline algorithm used in paparazziUAV simulator for navigation. It has been observed that, the proposed navigation techniques work well in terms of different calibration parameters (flight duration, battery level) and can be effective not only for shelter point detection but also to reserve battery level, flight time for micro-jet in a post disaster scenario. The proposed techniques take approximately 20% less time and consume approximately 19% less battery power than baseline navigation technique. From analysis of produced results, it has been observed that the proposed work can be helpful for estimating the feasibility of flying UAV in a disaster response situation. Finally, the proposed path planning techniques have been carried out during field test using a micro-jet. It has been observed that, our proposed dynamic path planning algorithms give proximate results compare to simulation in terms of flight duration and battery level consumption.
{"title":"Smart navigation and dynamic path planning of a micro-jet in a post disaster scenario","authors":"T. Mondal, Jaydeep Roy, Indrajit Bhattacharya, Sandip Chakraborty, Arka Saha, Subhanjan Saha","doi":"10.1145/3017611.3017625","DOIUrl":"https://doi.org/10.1145/3017611.3017625","url":null,"abstract":"Small sized unmanned aerial vehicles (UAV) play major roles in variety of applications for aerial explorations and surveillance, transport, videography/photography and other areas. However, some other real life applications of UAV have also been studied. One of them is as a 'Disaster Response' component. In a post disaster situation, the UAVs can be used for search and rescue, damage assessment, rapid response and other emergency operations. However, in a disaster response situation it is very challenging to predict whether the climatic conditions are suitable to fly the UAV. Also it is necessary for an efficient dynamic path planning technique for effective damage assessment. In this paper, such dynamic path planning algorithms have been proposed for micro-jet, a small sized fixed wing UAV for data collection and dissemination in a post disaster situation. The proposed algorithms have been implemented on paparazziUAV simulator considering different environment simulators (wind speed, wind direction etc.) and calibration parameters of UAV like battery level, flight duration etc. The results have been obtained and compared with baseline algorithm used in paparazziUAV simulator for navigation. It has been observed that, the proposed navigation techniques work well in terms of different calibration parameters (flight duration, battery level) and can be effective not only for shelter point detection but also to reserve battery level, flight time for micro-jet in a post disaster scenario. The proposed techniques take approximately 20% less time and consume approximately 19% less battery power than baseline navigation technique. From analysis of produced results, it has been observed that the proposed work can be helpful for estimating the feasibility of flying UAV in a disaster response situation. Finally, the proposed path planning techniques have been carried out during field test using a micro-jet. It has been observed that, our proposed dynamic path planning algorithms give proximate results compare to simulation in terms of flight duration and battery level consumption.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"58 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":"116018861","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}
H. Hayashi, A. Asahara, Natsuko Sugaya, Yuichi Ogawa, H. Tomita
When a large-scale natural disaster occurs, it is necessary to quickly collect damage information so that disaster-relief operations and wide-area support in accordance with the scale of the natural disaster can be initiated. Previously, we proposed a fast spatio-temporal similarity search method (called the STSim method) that searches a database storing many scenarios of disaster simulation data represented by time-series grid data for scenarios similar to insufficient observed data sent from sensors. When the STSim method is naively applied for estimating disasters occurring at multiple locations, e.g., fire spreading after a large-scale earthquake, it must prepare a huge number of combinations consisting of scenarios that represent disasters at multiple locations. This paper presents a combination method of simulation data in order to apply the STSim method for estimating disasters occurring at multiple locations. This proposed method stores scenarios, each of which represents a disaster occurring at a single location, to a database; thus, reducing the number of scenarios. After a disaster occurs, it extracts and composes scenarios similar to observed data, resulting in efficient disaster estimation in any situation. We conducted performance evaluations under the assumption that an earthquake occurs below the Tokyo metropolitan region and estimating the spread of fire in the initial response. These results of the processing time for estimating the spread of fire show that the processing time is within 10 minutes, which is practical.
{"title":"Composition of simulation data for large-scale disaster estimation","authors":"H. Hayashi, A. Asahara, Natsuko Sugaya, Yuichi Ogawa, H. Tomita","doi":"10.1145/3017611.3017615","DOIUrl":"https://doi.org/10.1145/3017611.3017615","url":null,"abstract":"When a large-scale natural disaster occurs, it is necessary to quickly collect damage information so that disaster-relief operations and wide-area support in accordance with the scale of the natural disaster can be initiated. Previously, we proposed a fast spatio-temporal similarity search method (called the STSim method) that searches a database storing many scenarios of disaster simulation data represented by time-series grid data for scenarios similar to insufficient observed data sent from sensors. When the STSim method is naively applied for estimating disasters occurring at multiple locations, e.g., fire spreading after a large-scale earthquake, it must prepare a huge number of combinations consisting of scenarios that represent disasters at multiple locations. This paper presents a combination method of simulation data in order to apply the STSim method for estimating disasters occurring at multiple locations. This proposed method stores scenarios, each of which represents a disaster occurring at a single location, to a database; thus, reducing the number of scenarios. After a disaster occurs, it extracts and composes scenarios similar to observed data, resulting in efficient disaster estimation in any situation. We conducted performance evaluations under the assumption that an earthquake occurs below the Tokyo metropolitan region and estimating the spread of fire in the initial response. These results of the processing time for estimating the spread of fire show that the processing time is within 10 minutes, which is practical.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"40 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":"128998904","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}
Analysis on zoonotic infectious diseases is an important issue in emergency management as it significantly supports governmental and medical decision making when a zoonotic infectious disease outbreaks. To effectively prevent and control the diseases, it is necessary to explore the pathogenesis and identify correlative influence factors. However, limited by natural conditions and physical measurements, we can hardly obtain complete observations to precisely catch on to the actual pathogenesis of zoonotic infectious diseases. A feasible solution for analysis on the diseases is to measure correlations between environmental factors and incidences of the diseases, and then extract the pivotal factors. Many existing studies have provided qualitative analysis on zoonotic infectious disease. In this paper we consider a quantified method using regression models to measure effects that derive from environmental factors. Significant factors are extracted through a multiple backward stepwise logistic regression and compose a set of explanatory variables, which is exploited in the regression of the incidence of zoonotic infectious diseases. Furthermore, considering the variance among different areas and complex interactions between neighboring areas, we incorporate unobserved individual heterogeneity and neighborhood-based spatial effects into the regression model. Therefore, the model is updated with spatial structures. Several different estimators are involved to provide unbiased estimations for models without spatial structures and models with spatial structures. Then comparisons between different models are illustrated. The result shows that our quantified models are valid and the regression model performs better with individual heterogeneity and spatial effects allowed for.
{"title":"How to find environmental risk factors of zoonotic infectious disease quickly","authors":"Y. Zhu, Danhuai Guo, Deqiang Wang, Jianhui Li","doi":"10.1145/3017611.3017613","DOIUrl":"https://doi.org/10.1145/3017611.3017613","url":null,"abstract":"Analysis on zoonotic infectious diseases is an important issue in emergency management as it significantly supports governmental and medical decision making when a zoonotic infectious disease outbreaks. To effectively prevent and control the diseases, it is necessary to explore the pathogenesis and identify correlative influence factors. However, limited by natural conditions and physical measurements, we can hardly obtain complete observations to precisely catch on to the actual pathogenesis of zoonotic infectious diseases. A feasible solution for analysis on the diseases is to measure correlations between environmental factors and incidences of the diseases, and then extract the pivotal factors. Many existing studies have provided qualitative analysis on zoonotic infectious disease. In this paper we consider a quantified method using regression models to measure effects that derive from environmental factors. Significant factors are extracted through a multiple backward stepwise logistic regression and compose a set of explanatory variables, which is exploited in the regression of the incidence of zoonotic infectious diseases. Furthermore, considering the variance among different areas and complex interactions between neighboring areas, we incorporate unobserved individual heterogeneity and neighborhood-based spatial effects into the regression model. Therefore, the model is updated with spatial structures. Several different estimators are involved to provide unbiased estimations for models without spatial structures and models with spatial structures. Then comparisons between different models are illustrated. The result shows that our quantified models are valid and the regression model performs better with individual heterogeneity and spatial effects allowed for.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"8 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":"114567147","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}
P. Paul, Hridoy Sankar Dutta, B. Ghosh, K. Hazra, Sandip Chakraborty, Sujoy Saha, S. Nandi
Decision making after an emergency like those after a large-scale disaster (natural/man-made) is often impaired due the non-availability of crisis information from field. The key reason behind such hindrance in getting information off the field is due to disruption/breaking of conventional communication channel (manual or automatic) as an outcome of the crisis event. The post-crisis operations like evacuation, rescue-relief are affected at large due to poor decision making and lack of coordination among the field workers and officials in charge of the emergency management and mitigation. This leads to added suffering to the victims, increased death-toll, and mass agitation, anger and mistrust among all the stake-holders. Humanitarian organizations present crisis mapping services for shaping the rescue-relief activities. The crisis mapping systems collects crisis data from online social media, news feeds, etc., and portrays them through an online map server. However, in a situation when network is disrupted, such services become useless. In this work of ours, we would like to present an application that may run on Android-based mobile devices and could prepare 'localized' crisis map through 'offline' crowd-sourcing of situational data and a distributed processing of the collected data in seamless manner. To ensure that the generated localized crisis map hold the most important information, and that it contains information from almost every corner of the affected area, a novel data dissemination strategy is proposed. For better serving the affected community, the resulting crisis data is portrayed on a nice map interface generated locally, whenever possible. In addition to crisis data, mobility trails of other users, whenever available, are embedded on the same interface for the purpose of travel route suggestion for the users in a changing environment after the crisis.
{"title":"Offline crisis mapping by opportunistic dissemination of crisis data after large-scale disasters","authors":"P. Paul, Hridoy Sankar Dutta, B. Ghosh, K. Hazra, Sandip Chakraborty, Sujoy Saha, S. Nandi","doi":"10.1145/3017611.3017620","DOIUrl":"https://doi.org/10.1145/3017611.3017620","url":null,"abstract":"Decision making after an emergency like those after a large-scale disaster (natural/man-made) is often impaired due the non-availability of crisis information from field. The key reason behind such hindrance in getting information off the field is due to disruption/breaking of conventional communication channel (manual or automatic) as an outcome of the crisis event. The post-crisis operations like evacuation, rescue-relief are affected at large due to poor decision making and lack of coordination among the field workers and officials in charge of the emergency management and mitigation. This leads to added suffering to the victims, increased death-toll, and mass agitation, anger and mistrust among all the stake-holders. Humanitarian organizations present crisis mapping services for shaping the rescue-relief activities. The crisis mapping systems collects crisis data from online social media, news feeds, etc., and portrays them through an online map server. However, in a situation when network is disrupted, such services become useless. In this work of ours, we would like to present an application that may run on Android-based mobile devices and could prepare 'localized' crisis map through 'offline' crowd-sourcing of situational data and a distributed processing of the collected data in seamless manner. To ensure that the generated localized crisis map hold the most important information, and that it contains information from almost every corner of the affected area, a novel data dissemination strategy is proposed. For better serving the affected community, the resulting crisis data is portrayed on a nice map interface generated locally, whenever possible. In addition to crisis data, mobility trails of other users, whenever available, are embedded on the same interface for the purpose of travel route suggestion for the users in a changing environment after the crisis.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"37 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":"115477786","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}
Stock market is often affected by events, especially emergencies, such as natural disasters. Stock price prediction is significant to traders in this market as the references for the future to better invest and for market supervision. In this paper, the forecasting model combing topic models with data mining tools, namely event-driven prediction, is aimed to seek for more accurate predicting price results through extracting topics from news articles related to the stock as well as the historical price data. Our experiment is carried out in an famous agricultural products company in China and the empirical results show that the proper information extracted from news in popular portal website in previous day can be beneficial for the current price prediction.
{"title":"Event-driven data mining methods for large-scale market prediction: a case study of an agricultural products company","authors":"Donglai Niu, Mingming Wang, Hui Yuan, Wei Xu","doi":"10.1145/3017611.3017618","DOIUrl":"https://doi.org/10.1145/3017611.3017618","url":null,"abstract":"Stock market is often affected by events, especially emergencies, such as natural disasters. Stock price prediction is significant to traders in this market as the references for the future to better invest and for market supervision. In this paper, the forecasting model combing topic models with data mining tools, namely event-driven prediction, is aimed to seek for more accurate predicting price results through extracting topics from news articles related to the stock as well as the historical price data. Our experiment is carried out in an famous agricultural products company in China and the empirical results show that the proper information extracted from news in popular portal website in previous day can be beneficial for the current price prediction.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"34 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":"126837754","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}
Liu Cheng, Yuan Shengcheng, Qian Jing, Yu Shuiping, Zhang Hui, Liu Yi
In this paper, a scenario-based case representation model in spatio- temporal framework is developed. The term 'scenario' is defined formally in this paper, and formal representation of scenario is presented. The developed case representation model is introduced in detail, including the characteristics of the model and the process of developing the model. There are two main advantages of the case representation model: Firstly, it contributes to the similarity assessment for bridging the gap between qualitative description and formal representation of a scenario. Secondly, it helps emergency decision-makers with information of the scenario and its evolution as well as the response in the spatio-temporal framework.
{"title":"A scenario-based case representation model in spatio-temporal framework","authors":"Liu Cheng, Yuan Shengcheng, Qian Jing, Yu Shuiping, Zhang Hui, Liu Yi","doi":"10.1145/3017611.3017616","DOIUrl":"https://doi.org/10.1145/3017611.3017616","url":null,"abstract":"In this paper, a scenario-based case representation model in spatio- temporal framework is developed. The term 'scenario' is defined formally in this paper, and formal representation of scenario is presented. The developed case representation model is introduced in detail, including the characteristics of the model and the process of developing the model. There are two main advantages of the case representation model: Firstly, it contributes to the similarity assessment for bridging the gap between qualitative description and formal representation of a scenario. Secondly, it helps emergency decision-makers with information of the scenario and its evolution as well as the response in the spatio-temporal framework.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"22 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":"115451023","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}
S. Tavakkol, Hien To, S. H. Kim, P. Lynett, C. Shahabi
After a disaster, authorities need to efficiently collect and analyze data from the disaster area in order to increase their situational awareness and make informed decisions. The conventional data acquisition methods such as dispatching inspection teams are often time-consuming. With the widespread availability of mobile devices, crowdsourcing has become an effective alternative means for data acquisition. However, the large amount of crowdsourced data is often overwhelming and requires triage on the collected data. In this paper, we introduce a framework to crowdsource post-disaster data and a new prioritization strategy based on the expected value of the information contained in the collected data (entropy) and their significance. We propose a multi-objective problem to analyze a portion of the collected data such that the entropy retrieved from the disaster area and the significance of analyzed data are maximized. We solve this problem using Pareto optimization that strikes a balance between both objectives. We evaluate our framework by applying it on bridges inspection after the 2001 Nisqually earthquake as a case study. We also investigate the feasibility of sending the crowdsourced data to the crowd for reviewing. The results demonstrate the effectiveness and feasibility of the proposed framework.
{"title":"An entropy-based framework for efficient post-disaster assessment based on crowdsourced data","authors":"S. Tavakkol, Hien To, S. H. Kim, P. Lynett, C. Shahabi","doi":"10.1145/3017611.3017624","DOIUrl":"https://doi.org/10.1145/3017611.3017624","url":null,"abstract":"After a disaster, authorities need to efficiently collect and analyze data from the disaster area in order to increase their situational awareness and make informed decisions. The conventional data acquisition methods such as dispatching inspection teams are often time-consuming. With the widespread availability of mobile devices, crowdsourcing has become an effective alternative means for data acquisition. However, the large amount of crowdsourced data is often overwhelming and requires triage on the collected data. In this paper, we introduce a framework to crowdsource post-disaster data and a new prioritization strategy based on the expected value of the information contained in the collected data (entropy) and their significance. We propose a multi-objective problem to analyze a portion of the collected data such that the entropy retrieved from the disaster area and the significance of analyzed data are maximized. We solve this problem using Pareto optimization that strikes a balance between both objectives. We evaluate our framework by applying it on bridges inspection after the 2001 Nisqually earthquake as a case study. We also investigate the feasibility of sending the crowdsourced data to the crowd for reviewing. The results demonstrate the effectiveness and feasibility of the proposed framework.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"26 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":"124111998","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}
Ping Zhang, Rui Yang, Xiaodong Liu, Yi Liu, Hui Zhang
Seismic disaster can cause severe damage to the urban system like infrastructure breakdown, residential isolation and corresponding secondary disasters. Dealing with the impact of these damages at the urban area requires a better understanding of city vulnerability and corresponding emergency management. Meanwhile, accurate damage simulation integrated with geographic information system is able to improve emergency response level. In this paper, Chaoyang district, a densely and populated region of Beijing, is studied as a scenario area. Falling debris from tall buildings is considered as one of the most destructive elements to the traffic, which can cause severe road blockage. In particular, risk-rating scheme is depicted by incorporating building vulnerability and city fire hazard. In order to optimize emergency transportation system for post-earthquake, a vehicle routing problem is developed to decrease the total travelling route for dispatching commodities. According to the problem's property, the Tabu search method based on heuristic algorithm is used. Furthermore, rescue from the inner city and exterior zone are discussed. For promoting emergency response, this study aims to give a brief description of the circumstance that may be encountered during and after seismic disaster.
{"title":"A GIS-based urban vulnerability and emergency response research after an earthquake disaster","authors":"Ping Zhang, Rui Yang, Xiaodong Liu, Yi Liu, Hui Zhang","doi":"10.1145/3017611.3017622","DOIUrl":"https://doi.org/10.1145/3017611.3017622","url":null,"abstract":"Seismic disaster can cause severe damage to the urban system like infrastructure breakdown, residential isolation and corresponding secondary disasters. Dealing with the impact of these damages at the urban area requires a better understanding of city vulnerability and corresponding emergency management. Meanwhile, accurate damage simulation integrated with geographic information system is able to improve emergency response level. In this paper, Chaoyang district, a densely and populated region of Beijing, is studied as a scenario area. Falling debris from tall buildings is considered as one of the most destructive elements to the traffic, which can cause severe road blockage. In particular, risk-rating scheme is depicted by incorporating building vulnerability and city fire hazard. In order to optimize emergency transportation system for post-earthquake, a vehicle routing problem is developed to decrease the total travelling route for dispatching commodities. According to the problem's property, the Tabu search method based on heuristic algorithm is used. Furthermore, rescue from the inner city and exterior zone are discussed. For promoting emergency response, this study aims to give a brief description of the circumstance that may be encountered during and after seismic disaster.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"101 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":"122086667","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}
Cloud storage is a kind of external storage which can provide by unlimited storage space with high availability and low cost on maintenance. On the other side, the size of geospatial data is too large and is increasing dramatically which makes such data is hard to be stored in the local data warehouse. Hence following the benefits of Cloud storage, such geospatial data is suitable to be stored in Cloud storage and managed by local data warehouse. However, there is a gap between Cloud storages and data warehouses built on traditional infrastructures, such as the mostly adopted massive parallel processing (MPP) based data warehouse. Therefore, in this paper, we propose a middleware-like architecture to connect MPP data warehouse and Cloud storage. It supports traditional geospatial data retrieving while integrating the Cloud storage lineage by a set of technical designs. Based on the prototype system and practical data, we demonstrate the appreciable performance and the flexibility for other third parties' development. Another major contribution of this paper is that we implement the prototype on open-source data warehouse and we make it open-sourced to public.
{"title":"On storing and retrieving geospatial big-data in cloud","authors":"Kuien Liu, Haozhou Wang, Yandong Yao","doi":"10.1145/3017611.3017627","DOIUrl":"https://doi.org/10.1145/3017611.3017627","url":null,"abstract":"Cloud storage is a kind of external storage which can provide by unlimited storage space with high availability and low cost on maintenance. On the other side, the size of geospatial data is too large and is increasing dramatically which makes such data is hard to be stored in the local data warehouse. Hence following the benefits of Cloud storage, such geospatial data is suitable to be stored in Cloud storage and managed by local data warehouse. However, there is a gap between Cloud storages and data warehouses built on traditional infrastructures, such as the mostly adopted massive parallel processing (MPP) based data warehouse. Therefore, in this paper, we propose a middleware-like architecture to connect MPP data warehouse and Cloud storage. It supports traditional geospatial data retrieving while integrating the Cloud storage lineage by a set of technical designs. Based on the prototype system and practical data, we demonstrate the appreciable performance and the flexibility for other third parties' development. Another major contribution of this paper is that we implement the prototype on open-source data warehouse and we make it open-sourced to public.","PeriodicalId":159080,"journal":{"name":"Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management","volume":"42 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":"131449719","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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