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This paper examines physical vulnerability of District No. 6 of Tehran to blast damages and blast loads. The main objectives are to enhance planners and decision makers' awareness of the extent of vulnerability of buildings in the study area (under different risk scenarios) and to provide guidance on how to enhance preparedness against potential risks. Physical vulnerability of District No. 6 of Tehran was evaluated under three different blast loading scenarios. An expert survey, involving specialists with expertise in urban development, passive defense, construction, and architecture was carried out to extract fourteen vulnerability criteria. Fuzzy criteria maps corresponding to each of these criteria were produced in Geospatial Information Systems (GIS) environment. Fuzzy analytic hierarchy processing was used to weigh the criteria. Criterion maps were combined using the fuzzy weighted linear combination operator. Finally, Ordered Weighted Averaging (OWA) operator was applied to generate final vulnerability maps. Results show that, for all scenarios, almost 70% of the buildings in the district are of high vulnerability. Sensitivity analysis confirms the reliability of the model.

The uncertainty of task allocation in disaster environments is challenging both practically and theoretically. In real environments, agents encounter uncertain factors for the selection and execution of tasks. The present methods for task allocation seem inadequate in such environments. This paper aims to provide an efficient approach to improving the task allocation, despite the uncertainty in disaster environments. Therefore, after deduction of the major uncertainties in disaster environments, we propose a method for the agents’ decision-making about the task allocation. The allocation procedure includes four phases of ordering the tasks, choosing the coordinating agent, implementing the auction by considering the uncertainties, performing tasks and observing the real environment. The main innovation of this research is using the concepts of interval uncertainty in the task ordering as well as in the auction implementing. The results were obtained by comparing the proposed method with the contract net protocol (CNP) at three scales. In addition, the results were evaluated in the presence of uncertainties at different ranges. On average, the proposed method was better than the CNP in terms of search and rescue (SAR) operation time (124 min), the number of dead people (8) and the number of incorrect allocations (180 tasks). The uncertainties range in the tasks’ decision-making procedure affected SAR operation time by more than 26%. Therefore, considering that uncertainty in task allocation can be a great advantage in the disaster environment, and considering these factors in the decision-making procedure, we have improved confidence in allocating tasks with fewer errors.

A mutual interaction between citizens and government has become an important requirement for every successful urban decision making process. As cities develop, such interactions play a greater role in citizen satisfaction. Through these interactions, governments can recognize the needs of citizens and try to address their challenges. In these interactions, the role of the people is central; in fact, it is the people who express their needs to the service-providing organizations through different communication channels. A common channel that has been established in Iran is the ‘137 center,’ a municipal, phone-based request/response system. In this article, information from Municipal District 1 in the city of Bojnourd was gathered, analyzed to prioritize urban needs and assessed for citizen satisfaction. Forty-three citizen needs were identified and categorized based on K-means clustering, the Fuzzy Clustering Method (FCM) and the Imperialist Competitive Algorithm (ICA). The three algorithms were also evaluated. RFM (recency, frequency, monetary) analysis was performed for classification. The clustering methods were then assessed and compared using three parameters: execution time, accuracy and simplicity. The results of the FCM and ICA clustering were similar, however, the execution time for FCM was less than for ICA. Considering the similarity of the results and the flexibility of FCM, it was concluded that, if the execution time was of primary importance, then the use of FCM was more appropriate. In contrast, if accuracy was a priority, ICA was preferred. Our results also showed that if simplicity and speed were required, the K-means algorithm was the best choice. Finally, subjects such as the quality of the asphalt, garbage collection and park development were of primary importance to Bojnourd citizens, therefore the municipality should pay special attention to these subjects.

Reverse geocoding is a process that maps coordinates to a set of location identifiers such as addresses or toponyms. What makes the reverse geocoding process challenging is the uncertainty of the position being asked and the point features used to represent places. In recent years, due to advances in locating technologies, large amounts of location-based data have been produced in location-based social networks such as the Yelp, Foursquare, and Swarm. These data are a rich source of information about the patterns of people's behaviors in different places. In this paper, with the help of these data, the enhancement of spatial distance-only reverse geocoding has been attempted. The main purpose of this paper is to develop and validate an algorithm for matching categories in the Yelp and Swarm services. In this way, the data from the Yelp were used for generating temporal behavior data and the data from Swarm were used for collecting check-in data. Since the data from Yelp and Swarm services have different categorization structures, integrating these two structures was one of the main challenges of our study. After matching the categories of Yelp and Swarm services, the obtained temporal behavior data for all data sets of Yelp were used in the process of reverse geocoding for Swarm check-in data. In our study, linear, rational and sinusoidal functions were used for distorting the spatial distance with temporal check-in probability in the process of reverse geocoding. In addition, two sets of data include training and test data were used for determining the parameters of the model and validating the results. In this way, it was found that by combining a linear model with temporal behavior data, the results of spatial distance-only reverse geocoding can be improved by 29.96% for the Mean Reciprocal Rank index (a statistical measure for evaluating any process that produces a list of responses, ordered by probability of correctness) and 105.73% for the First Position index (which counts the number of correctly identified POIs). The findings of our study confirmed that the extended set of temporal probabilities of POI categories obtained from Yelp and Swarm gives better results than previous studies. The strengths of our method was demonstrated by validating it against a spatial distance only baseline by the Mean Reciprocal Rank and the First Position indices.

Increasing urbanization has been one of the most significant concerns of urban managers. The role of non-motorized transportation in sustainable urban development is vitally important for reducing overweight and obesity among citizens. These issues have led to numerous studies of the association between environmental characteristics, walkability index and levels of human health. The encouragement of public walking and cycling requires measures of walkability indices. One of the common challenges in measuring a walkability index is the complexity of the connection between the subjective indices resulting from public opinion and objective measures of geographic data. The scientific novelty of this paper lies in two aspects: First we developed and evaluated several artificial neural network (ANN) configurations for predicting subjective measures of walkability index from objective measures. Second, we introduced an index for two distinctive modes of walkability: daily shopping and recreation purposes that ranges from 1 to 10. The parameters of land-use diversity, population density, intersection density, network density, access to public transportation, green spaces and commercial places were utilized to calculate the objective value of the walkability index. The determination of subjective value of the walkability index was achieved using fieldwork reports. The resulting index was tested in districts 1 and 3 of Region 18 in the city of Tehran. The quantities used to evaluate the results included RMSE, MAE, MBE, and R. Network training was performed using the Levenberg-Marquardt algorithm. A 10-fold cross validation was used to evaluate and compare the performance of different network configurations. Our findings indicated that the best walkability index for purchasing can be estimated using Levenberg-Marquardt algorithm with one hidden layer and seven neurons. This configuration resulted in a correlation coefficient and an RMSE of 93.79% and 0.1368 respectively. To predict the walkability index for recreational purpose, the best result was obtained using Levenberg-Marquardt algorithm, representing a combination of one layer with four neurons for which the correlation coefficient and RMSE are 90.71% and 0.1602 respectively.

Land use planning is a potentially demanding search and optimization task that has been challenged by numerous researchers in the field of spatial planning. Agent and multi-agent systems are examples of the modern concepts, which have been gaining more attention in challenging spatial issues recently. Although the efficiency of belief, desire, and intention (BDI) architecture of agents is validated in varieties of sciences, its uses in Geospatial Information Systems (GIS) and specifically among spatial planners is still burgeoning. In this paper, we attempted to integrate the concepts of BDI agent architecture into spatial issues; as a result, a novel spatial agent model is designed and implemented to analyze the urban land use planning. The proposed approach was checked in urban land use planning problems using a case study in a municipal area. The result of implementation showed the effects of spatial agents' behaviors such as intention, commitment, and interaction on their decision.

Urban land-use development is a problematic phenomenon in developing countries. Modeling this phenomenon is of considerable interest to urban planners and city managers. Several methods have been developed to simulate the dynamics of land-use changes. However, the complexity of urban growth is considered a factor that impedes the usefulness of such simulation methods. Among the available methods, those considered “agent-based models” have found popularity in simulating land-use development and urban sprawl modeling. These methods use a dynamic bottom-up approach with the actors in land-use development as their basic components. In this paper, a new agent-based model is introduced. This model is equipped with new methods for modeling the movements of agents and competition among agents. The model is used to simulate urban land-use development in the Qazvin province of Iran, which covers an area of 36 × 45 km. The model is first calibrated with existing data and is then used to predict future land-use development. To test development policies, four scenarios are defined. The first scenario reflects the current pattern of development, which is evaluated using the calibrated model. The second and third scenarios examine different policies, including those that act as “incentive” strategies and those that are “punitive.” The fourth scenario focuses on changes to the demographic population of agents. The results reveal that the current trend in urban growth tends to be dispersed in the study area. However, different policies tend to produce different results: in areas in which an incentive policy is in place, 140 clusters of development were detected, while in areas in which a punitive policy is in place, 180 clusters were detected. The incentive strategy is concluded to be more successful than the punitive strategy in reducing the dispersion of development. Change in the population demography is observed to be more efficient in areas of development than in those of dispersion.

Indicator Kriging (IK) is a geostatistical method that uses observation points to quantify the probabilities at which a set of thresholds are exceeded at unmeasured points. To improve IK accuracy, the interpolation process should consider its uncertainty sources. By doing this, we also maintain its ability to provide the conditional cumulative distribution function (ccdf), which is a reliable measure of local uncertainty. This study modeled two IK uncertainty sources, i.e., measurement errors attached to observation points and subjective threshold choices. Soft Indicator Kriging (SIK), which uses a soft transformation for observation points, considers the measurement errors of these two sources. To select the thresholds objectively, a genetic algorithm (GA) was performed to obtain the optimum set of thresholds related to an objective function, which minimized the mean absolute error (MAE). The data used was a collection of 1889 gravitational acceleration points from Kordagh, Golestan, Iran. We used 95 points from those points to calculate the MAE values (jackknife). After applying GA to SIK and reaching the Genetic optimized Soft Indicator Kriging method (GSIK), our results showed a decrease in MAE (6.5925) compared to those of SIK and IK (8.4364 and 8.4366, respectively). Moreover, the coefficient of determination ( R 2 ) was used as another criterion to compare the methods. A more reliable method has a higher R 2 value; in this study, this value was 0.8683 for GSIK compared to those of SIK and IK (0.8423 and 0.8421, respectively). GSIK can improve the accuracy of the basic IK method.

Earthquakes have a greater effect on society than most people think. These effects range from structural damages to economic impacts and fatalities. An earthquake only lasts for a few seconds and the aftershocks may continue for days, but the damage does continue for years. Residential site safety and earthquake damage assessment studies play a crucial role in developing reliable rehabilitation and development programs, improving preparedness and mitigating losses in urbanized areas. The extremely densely populated metropolis of Tehran, which totals of 7,768,561 for 22 districts (according to the 2006 population census), coupled with the fragility of houses and infrastructure, highlight the necessity of a reliable earthquake damage assessment based on essential datasets, such as building resistance attributes, building population, soil structures, streets network and hazardous facilities. This paper presents a GIS-based model for earthquake loss estimation for a district in Tehran, Iran. Damages to buildings were calculated only for the ground shaking effect of one of the region's most active faults, the Mosha Fault in a likely earthquake scenario. Earthquake intensity for each building location was estimated based on attenuation relation and the ratio of damage was obtained from customized fragility curves. Human casualties and street blockages caused by collapsed buildings were taken into account in this study, as well. Finally, accessibility verification found locations without clear passages for temporary settlements by buildings via open streets. The model was validated using the 2003 Bam earthquake damages. The proposed model enables the decision-makers to make more reliable decisions based on various spatial datasets before and after an earthquake occurs. The results of the earthquake application showed total losses as follows: structural damages reaching 64% of the building stock, a death rate of 33% of all the residents, a severe injury rate reaching 27% of the population and street closures upwards of 22% due to building collapse.

A significant portion of the Mazandaran Province in Iran is prone to landslides due to climatic conditions, excessive rain, geology, and geomorphologic characteristics. These landslides cause damage to property and pose a threat to human lives. Numerous solutions have been proposed to assess landslide susceptibility over regions such as this one. This study proposes an indirect assessment strategy that shares in the advantages of quantitative and qualitative assessment methods. It employs a fuzzy inference system (FIS) to model expert knowledge, and an artificial neural network (ANN) to identify non-linear behavior and generalize historical data to the entire region. The results of the FIS are averaged with the intensity values of existing landslides, and then used as outputs to train the ANN. The input patterns include both physical landscape characteristics (criterion maps) and landslide inventory maps. The ANN is trained with a modified back-propagation algorithm. As part of this study, the strategy is implemented as a GIS extension using ArcGIS(R). This tool was used to create a four-domain landslide susceptibility map of the Mazandaran province. The overall accuracy of the LSM is estimated at 90.5%.

The availability of reliable and ubiquitous urban services data is a key component of successful city management. The inability of traditional bureaucratic public administration to meet citizens’ needs effectively has led to a search for new methods that grant more efficient, effective and reliable provision of services to citizens. Because there are many users with distinct computing platforms and preferences, urban services data must be accessible using interoperable solutions. Numerous solutions for providing interoperability are proposed by the Geospatial Information (GI) community, as well as the Information Technology (IT) community. The most widely used technologies for providing interoperability in the GI community are OGC services framework. In the IT community, Web services technologies provide full potential interoperability among heterogeneous computing platforms. However, these two kinds of services are not directly compatible. To provide an interoperable solution, OGC services and Web services should be integrated efficiently. The underlying issue in integrating OGC services and Web services is the gap between the predefined functionality inherent in the OGC services, and the lack of any predefined functionality in the Web services technologies. This paper identifies the implementation issues in integrating OGC services and Web services and then proposes standards-based approaches to overcome these integration issues. Afterwards, the paper explains the design and development of Standards-based Interoperable Services for Accessing Urban Services Data (SISAUSD).