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After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

Achieving a good urban form has been a problem since the formation of the earliest cities. The tendency of human populations toward living in urban environments and urbanization has made the quality of life more prominent. This article aimed to calculate the quality of life in an objective way. For this purpose, the technique for order preferences by similarity to ideal solution (TOPSIS), vlseKriterijumsk optimizacija kompromisno resenje (VIKOR), simple additive weighted (SAW), and elimination and choice expressing reality (ELECTRE) have been utilized. Quality of life was assessed at three spatial levels. In this regard, socioeconomic, environmental, and accessibility dimensions were considered. As a result, in the first level of comparison, sub-districts in District 6 were ranked higher than that of District 13. On the second level, for District 6, vicinity sub-districts had higher rankings than the center, and for District 13, sub-districts near the center of the city had higher rankings. In the third level, District 6 had a higher quality of life. The results of the comparison between research methods showed that the SAW method performs better in terms of stability. Based on the results of correlation tables, there was a strong and direct relationship between each pair of methods at three spatial levels. In addition, as the study area became smaller, the similarity between the methods increased.

No abstract

Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.