With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a c...With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.展开更多
Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Locatio...Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Location choice is driven by diverse characteristics;including but not limited to environmental factors,access,services,and the socioeconomic status of a neighbourhood.This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a sevenfactor classification tool and an assessment of international property websites.Despite the availability of digital earth data,the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making.Based on a case study in Dublin,Ireland,we find that although neighbourhood digital earth data may be readily available to support decision making,the gap persists.We hypothesise that the reason is two-fold.Firstly,there is a technical challenge to transform location data into usable information.Secondly,the market may not wish to provide location information which can be perceived as negative.We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.展开更多
基金supported by the U.S.National Science Foundation under[grant numbers:1047916,1429699,1443080,1551492,and 1664119].
文摘With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.
基金Hamidreza Rabiei-Dastjerdi is a Marie Skłodowska-Curie Career-FIT Fellow at the UCD School of Computer Science and CeADAR(Ireland’s National Centre for Applied Data Analytics&AI)Career-FIT has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.713654.
文摘Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Location choice is driven by diverse characteristics;including but not limited to environmental factors,access,services,and the socioeconomic status of a neighbourhood.This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a sevenfactor classification tool and an assessment of international property websites.Despite the availability of digital earth data,the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making.Based on a case study in Dublin,Ireland,we find that although neighbourhood digital earth data may be readily available to support decision making,the gap persists.We hypothesise that the reason is two-fold.Firstly,there is a technical challenge to transform location data into usable information.Secondly,the market may not wish to provide location information which can be perceived as negative.We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.
