Ali Kalantari Oskouei; Mahdi Saber Khoshemehr
Abstract
Extended Abstract Introduction Data sharing is one of the key issues in the success of Spatial Data Infrastructure (SDI). Data sharing can prevent the repeated production of spatial data by various organizations and institutions, and provide the reduction of the costs, prevention of the resource losses, ...
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Extended Abstract Introduction Data sharing is one of the key issues in the success of Spatial Data Infrastructure (SDI). Data sharing can prevent the repeated production of spatial data by various organizations and institutions, and provide the reduction of the costs, prevention of the resource losses, helping the economic development and using the capabilities of spatial data in processes of decision-making. But, evidences suggest that the realization of the spatial data sharing has always faced numerous challenges and problems, and that the SDI development goals will not be achieved without solving them. Therefore, the main objective of this research is to provide a strategy for identifying and prioritizing the sharing of spatial data in the country. Materials & Methods The framework of the research is based on surveying, the concept of risk, and the use of a fuzzy inference system. In order to objectify this framework, a case study was conducted with the participation of 19 organizations in East Azarbaijan province. At first, various sources were reviewed and the 25 probabilistic sharing challenges were identified. Then, with the help of a questionnaire, experts’ viewpoints regarding the probability of the occurrence and severity of the impacts of the challenges were investigated. The questionnaire consists of two parts. The first part focuses on the demographic information of the experts such as affiliation, years of experience, and academic degree, which were used to determine the experts’ importance weight. The second section measures the probability and the impact intensity of each identified challenge. To measure the factors of probability and impact intensity, a five level verbal rating scale including, very high, high, medium, low and very low (as verbal terms) was used to maintain the balance between simplicity and comprehensiveness. In the next stage, a fuzzy inference system, with two inputs and one output, 25 fuzzy rules, Guassian membership functions and the field inference engine were developed to process the views of the experts and to calculate the fuzzy scores of each of the challenges using MatlabR software. Having determined the sharing challenges scores, a cluster analysis was carried out to divide them based on the score related to the groups (clusters), so that, the challenges inside a cluster are very similar (but not identical) to one another but very different from the challenges in other clusters. Since there is no need to specify the numbers of clusters in hierarchical methods in advance, the hierarchical method was used as a clustering technique to group the challenges. Then, the results were evaluated by a number of knowledgeable experts. Results & Discussion According to the findings, the most important challenges which had the highest scores and were also in the same cluster, include: lack of a Geoportal for searching, access and evaluation, lost problems or metadata, lack of coordination among different organizations for spatial data sharing, fear of disclosing of organizational spatial data and information, the lack of up-to-date spatial data and information, the tendency to parallel work (the lack of investigation of other organizations for spatial data and information needed by the organization) and the lack of specialist in spatial data and sharing the information. Moreover, 25 challenges of spatial data were categorized into five homogenous groups (clusters) by applying a hierarchical cluster analysis. Based on the results, the overall geometric mean value of the 25 challenges of the spatial data and information sharing was calculated as 62.76% that shows the existence of the important challenges in the realization and implementation of the spatial data sharing and SDI initiatives in organizations. Analyzing the results with regard to the two types of sharing challenges revealed that the organizational challenges with a geometric mean of 55/56% were more important than the technical challenges with a geometric mean of 44/44%. These results may mean that, in order to overcome the organizational challenges, more time and efforts have to be taken into consideration in the planning and development of SDI compared to the technical challenges. Conclusion It seems that there is no accurate and complete picture of the concept of the spatial data sharing in the majority of the organizations, and it is often interpreted as putting spatial data and information of the organization in the hands of others. However, a significant part of the sharing issue is associated with the metadata sharing that prevents the repeated works and spending unnecessary credits of the organizations, and make the available data to be accessible with spatial services in different formats after an agreement between data providers and consumers, and to be used in decision making processes. The information gap in this regard is very tangible in the organizations, nevertheless, it would be possible to change the views and behaviors of individuals and organizations by creating capacity, and eventually to be hopeful that the willingness of organizations to participate in data sharing improve. Finally, the following recommendations were suggested in order to improve the status of the data sharing: individual and organizational attitude changes towards the issue of data sharing, increasing technical knowledge and empowering organizations in spatial technologies and clarifying the benefits of spatial data sharing and its socioeconomic roles in society, specifying the leader organization and forcing organizations to create standard spatial databases and metadata.
Navid Houshangi; Ali Asghar Alesheikh
Abstract
Extended Abstract Introduction Disasters such as earthquakes have always been a serious threat to human life in urban environments. People have always sought to reduce the financial and human damages caused by such disasters. Large scale earthquakes and rapid changes in the environment make the people ...
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Extended Abstract Introduction Disasters such as earthquakes have always been a serious threat to human life in urban environments. People have always sought to reduce the financial and human damages caused by such disasters. Large scale earthquakes and rapid changes in the environment make the people unable to deliver the optimal solution to save lives and minimize damages. The earthquake has destroyed streets, roads and other infrastructures, and also causes fire. Fires which are started by earthquake, destroy homes in the city. During different periods of time, earthquake damages on Iranian society in social and economic fields are clear, therefore, dealing with the crisis in the form of a proper management and optimization is absolutely necessary. Complicated access to the earthquake-stricken areas, is always along with the difficulty of the rescue operations. Management structures can only overcome the prevailing political situation in these difficult circumstances that the acceptable level of cohesion and flexibility are paramount. The importance of intelligent systems that can help rescue the human species is very obvious. Materials & Methods Agent-based Modeling (ABM) is a new approach to the development of simulation tools for complex phenomena in different areas such as natural disasters, biological studies, and earthquake rescue. This paper presents a simulation system for the search and rescue (SAR) operation using Geospatial Information System (GIS), multi-agent systems (MAS) and the concept of integration for dynamic task allocation. Due to the flexibility of the agent based systems and the possibility of combining space and time, MASs can be a powerful tool in the simulation of rescue operations and strategic management issues. These systems can simulate all factors in earthquakes such as people, robots, helicopters, and vehicles to communicate and cooperate with each other to solve the distributed problems. In several studies, the high capability of using agent-based Modeling structures to model human behavior as a part of an environment to coordinate the rescue operation is referenced. The use of the agent-based Modeling and the possibility of combining flexibility with respect to location and time of the simulation can be a powerful tool in their search and rescue operations and strategic management issues. There are many reasons to use multi-agent systems to manage the crisis. Multi agent systems make it possible to simulate the demolition of buildings and homes, the fire, firefighters' activities, urban infrastructure damages, injured and displaced, and the victims, so they can find optimal strategies for search and rescue operations in large-scale accidents and crisis management performance in which multi-factor systems are used. Multi-agent systems are allowed to participate in environment to cooperate or compete with the environment. Multi-agent systems are targeted complex systems with an emphasis on the interaction between agents. They can break complex systems into sub-systems and other simple factors in environments. Various studies have stated that MASs possess high potentials for natural disaster management, from rescue operation to locating the positions of injured persons. The main object of this paper is to use multi-agent systems to simulate activities and increase the efficiency of rescue groups. This research tries to offer a way to find relationship between the number of search agents and rescue agents with regard to the maximum number of surviving people. This system can be used for managing and decision-making before the earthquake. Results & Discussion Evaluation of the developed system took place in a part of Region 3 in Tehran. The proposed system consists of three parts: 1) Modeling environment and working groups with the use of GIS, analysis of the search operation by multi-factor system and visualization of the results. Therefore, environment has been modelled using spatial data, and the amount of space that each agent must search, is assigned for each agent. 2) Then, each of the search agents uses the ant colony algorithm for sequencing tasks in order to find a near-optimal solutions to look for environment. 3) The injuries that are found by search agents are assigned to rescue agents through net contract and then operation is executed. Conclusion The Result of this research is shown in the form of a diagram which highlights the relationship between the number of search agents and release agents (according to the number of survivors, and is done). The result offers a model in finding the number of people needed for rescue operations in different parts of the city.
Seyyed Ali Ebadinejad; Hamid Panahi
Volume 18, Issue 70 , August 2009, , Pages 30-33
Abstract
Once man left his house for the first time in search of food, he needed a way to return home. The marking of rocks in the round trip, the use of coastline and celestial bodies such as the sun, the moon, and the stars were the first solutions that were less accurate and time-consuming. Later, with the ...
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Once man left his house for the first time in search of food, he needed a way to return home. The marking of rocks in the round trip, the use of coastline and celestial bodies such as the sun, the moon, and the stars were the first solutions that were less accurate and time-consuming. Later, with the development of technology of radio systems, and then the positioning satellites, routing became faster and more precise. The Global Positioning System was first created by the US Army in 1983, with an expense of $ 12 billion, and with launching the first satellite into space. This system has a variety of basic, manual and car models that have higher accuracy and cost, respectively. The system consists of three spatial, ground and user controls. The receivers will compare the time of sending the signal from the satellite with its receiving time and determine from the time difference the receiver's distance from the satellite. It is necessary to receive information from four satellites in order to find 3D coordinates. Availability in all hours of the day, any kind of weather conditions and ease of use are among the benefits of the system. Variants of this system include TRANSIT, GLONASS, SRARFIX and DORIS. System error sources include: user’s calculation errors and decrease in geometric accuracy.
