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Contemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks. [ABSTRACT FROM AUTHOR]

The location of highway signs has a significant impact on timely route-finding and also on avoiding potential driver confusion. This paper proposes an approach based on three-dimensional spatial analysis to determine the best attributes for signs and traffic flow concerning the visibility of signs. The position, height and perceived area of signs; maximum speed limit; and size of cars were utilized as indicators. Traffic flow was simulated using the Nagel-Schreckenberg model, while other vehicles were considered as dynamic obstacles. The performance of the proposed approach was evaluated in a case study by designing different scenarios. The results reveal that the proposed method can be used as an appropriate tool for evaluating the visibility of existing signs along highways and for determining the optimal place and height for new signs in three-dimensional, based on several parameters such as the maximum allowable speed, traffic flow, average density, and type of vehicles. [ABSTRACT FROM AUTHOR]

During the past years, air quality has become an important global issue, due to its impact on people's lives and the environment, and has caused severe problems for humans. As prevention to effectively control air pollution, forecasting models have been developed as a base for decision-makers and urban managers during the past decades. In general, these methods can be divided into three classes: statistical methods, machine learning methods and hybrid methods. This study's primary intent is to supply an overview of air pollution prediction techniques in urban areas and their advantages and disadvantages. A comparison has also been made between the methods in terms of error assessment and the use of geospatial information systems (GIS). In addition, several approaches were applied to actual data, and the findings were compared to those acquired from previous published literatures. The results showed that forecasting using machine learning and hybrid methods has provided better results. It has also been demonstrated that GIS can improve the results of the forecasting methods. [ABSTRACT FROM AUTHOR]

As complex man-made systems that are home to the majority of the world population, cities have always faced a wide range of risks such as earthquakes. As the backbone of urban systems, physical components, including buildings, transportation networks, communication networks, and open and green spaces, are also vulnerable to disasters. To enhance the capacity to deal with disaster risks, enhancing urban resilience has recently become an essential priority for cities. This study aims to develop and pilot test a framework to evaluate urban physical resilience based on resilience characteristics and associated physical indicators. Interpretive Structural Modelling (ISM) was used to determine the relationships between physical indicators, and Multi-Criteria Decision-Making methods were applied to determine the relative importance of the characteristics. The results showed that the 'Robustness of Building', 'Building Density', 'Aspect Ratio', and 'Street Width' are the most important among the twenty physical indicators considered in the proposed framework. Subsequently, the proposed framework was applied to one of the districts of Kerman, a major city located in the southwest, earthquake-prone part of Iran. Overall results indicate low levels of physical resilience. The findings of this study can provide urban planners and decision-makers with more transparent and practical insights into the physical resilience of cities. Results can also be used to design and implement policies and programs to improve the current conditions. [ABSTRACT FROM AUTHOR]

Using the 311 service, citizens can help the government gain a deeper understanding of citizens' needs by reporting the status of municipal services. This study proposes the STFTiS model to examine the level of citizens' satisfaction by investigating the spatio‐temporal patterns of 311 reports provided by citizens regarding municipal services. The main novelty of the proposed STFTiS model is its ability to integrate the spatial domain into the traditional FTiS (frequency, time interval, and satisfaction) model. For this purpose, Moran's I index was used to cluster the 311 reports from a spatial perspective. Afterward, the reports were clustered based on the spatial labels and the parameters of time interval and frequency. A fuzzy time series and an autoregressive moving average prediction model have been combined to find the best temporal window for association rule mining. The proposed model was applied to a database of the Boston 311 service. The results from interpreting clusters indicate that Boston's citizens are most interested in government responses to address Transportation and Inspection Service issues. Besides, the most important rule states a strong relationship between the Mayor's 24‐Hour Hotline and Inspection Service reports, with 86% support and 95.7% confidence. The evaluation shows that the STFTiS model has significantly improved the results of the non‐spatial FTiS model. The results can be used by policymakers to effectively manage citizens' needs and provide necessary on‐demand support by discovering the most critical needs of citizens. [ABSTRACT FROM AUTHOR]

In point‐of‐interest (POI) group recommender systems, suggesting the most suitable visit places for a heterogeneous group in which members are far from each other would be difficult in terms of finding a consensus among group members. In this article, we argue that the geographical proximity of POIs to users' locations has a notable influence on group decisions to visit the POI and their check‐in behavior. We develop a new geographical model based on the check‐in behavior of the group in location‐based social networks to improve group recommendations by introducing a new concept called users' spatial ratio in a group vectors and using a 2D kernel density estimation method. To evaluate the performance of the approach, experimental studies were conducted on a real dataset of users and group check‐in activities collected from Foursquare Swarm. In our experiments, the proposed method makes better recommendations compared with other existing approaches. Besides, analyzing group check‐ins shows that in a group visit, the distance between the most visited place by a group member alone and the places visited by the group follows a generalized gamma distribution. Also, the ratio of this distance for the group member to the distance of another member depends on the group size. [ABSTRACT FROM AUTHOR]

Considering the recent and projected increases in the frequency and intensity of hazards, many cities around the world are increasingly taking efforts to build on their resilience. This study focuses on the physical resilience of cities as a relatively under-studied branch of urban resilience. Due to the considerable complexity of the resilience concept, there is still no clear and applicable definition for urban physical resilience. To make this concept more tangible, this study elaborates on the determinant characteristics of physical resilience and explores their interactions. To this end, the characteristics were extracted and conceptualized from the review of previous research, and also using the Delphi method. The Interpretive Structural Modeling and MICMAC analysis were also used to partition the characteristics, determine the driving and dependent elements, and clarify the complex concepts of physical resilience. Then, the DEMATEL method was employed to quantitatively analyze the interrelationships and interactions among the characteristics and determine the causal relationships. The results showed that 'redundancy' and 'robustness' are two key characteristics that contribute to the physical resilience of cities. Also, the characteristics were partitioned into different levels based on driving and dependence power, and the cause and effect categories of characteristics were determined based on the interactions. The results from this research can be used for resilience-based urban and spatial planning to bridge the gap between the theory of physical resilience of cities and its practical approaches. Moreover, urban planners should pay special attention to and focus on the driving elements (i.e., causes), to provide the context for the proper realization of the dependent elements (i.e., effects). [ABSTRACT FROM AUTHOR]

Increasing population in urban areas and limitations of suitable lands for developing houses and urban infrastructure have led to the vertical development in cities. However, these developments are managed by a cadastral system which is mainly two-dimensional and cannot efficiently represent Rights, Restrictions, and Responsibilities (RRRs) in complex scenarios. In fact, a three-dimensional cadastre is required for efficiently registering and representing RRRs. In this paper, a 3D proximity analysis was proposed and implemented to determine RRRs and associated easement rights in non-topology-based data structures. This method can be used to investigate the surrounding spaces of a subject apartment unit or storage in a high-rise. The performance of the developed method was evaluated in a large complex high-rise in Tehran, Iran. The results confirmed that the proposed method can correctly identify the neighbor spaces in complex scenarios. [ABSTRACT FROM AUTHOR]

Gaining knowledge about the condition of quality of life (QoL) in different geographical regions has always been one of the concerns of the decision-makers. Dynamic and multi-dimensional characteristics of QoL ask for new data sources to update the relevant indicators and novel methods for QoL assessment. This research presents a comprehensive framework for assessing QoL in intra-national level. To address current challenges, the focus has been on integrating subjective and objective criteria, using the geotagged Tweets as user-generated geo-contents (UGGCs) to extract some subjective criteria, and utilizing a multi-criteria decision-making framework based on the cumulative belief degree (CBD) method to integrate and provide the final output. The proposed framework is implemented to assess the QoL in the provinces of Iran as the case study. Based on the obtained results, Tehran has a better QoL status, while Sistan-and-Balouchestan and Kermanshah have rather unsuitable conditions compared to other provinces. Results show the potential of the UGGCs to cover the dynamic characteristics of QoL and to complement the official data. Also, the functional capabilities of the CBD method in integrating heterogeneous criteria, handling uncertainty, and suitably providing the final results of QoL assessment in different scenarios allow authorities to get a more comprehensive, realistic view on QoL issues. The proposed method of this research is a step toward an integrated assessment of QoL using the data shared by people in the location-based social networks which could be regarded as a novel and useful tool for administrators in the field of QoL. [ABSTRACT FROM AUTHOR]

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models. [ABSTRACT FROM AUTHOR]

In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios. [ABSTRACT FROM AUTHOR]

Today, urban lifestyles lead to people suffering from a variety of illnesses, most of which are caused by a lack of physical activity. Walking is one of the best ways to prevent such illnesses from occurring. In this study, an integration method is proposed to help people choose the most appropriate and healthiest walking paths in the city of Shiraz in Iran. This method was developed based on data captured from remote sensing images and GIS-based analysis, the aim being to apply parameters related to people's health. The study directs its attention away from merely improving the path-finding method based on the shortest path, and instead analyzes parameters such as the existence of vegetation, reasonable temperatures and the presence of slopes, as well as the diversity of land uses found along road sections to find the optimum path. Paths with these characteristics should motivate people to walk further and for longer, and as a result improve their health. In this research Spatial Multi-criteria Evaluation (SMCE) process was used to determine the healthiest paths. Travel costs for each road section were calculated, to help develop a walkability index, and then by using path-finding analysis within GIS, the healthiest pathway was determined and compared with the shortest one. The proposed method was implemented in a case study. The results show that the simultaneous use of SMCE, remote sensing and GIS techniques along road sections, and the use of appropriate built environment indices, can help provide both urban planners and the public with the data and tools needed to take account of both the length and healthiness of a walking route, plus the services to be found along that route, when choosing to travel within a city. [ABSTRACT FROM AUTHOR]

By utilizing today's web-based technologies, people can act as sensors and share their perceptions, emotions and observations in a variety of data forms, such as images, videos, texts, Global Positioning System (GPS) trajectories and maps. These forms are collectively called user-generated content (UGC). These data are in different domains and have a multi-modality nature. Although recent efforts have probed the acquisition of local knowledge by using single-domain UGC data in specific applications, such efforts have not thus far presented a model considering multi-domain UGC specifically to enrich road network data. This article aims at presenting such a model wherein, with the help of each data domain of UGC, one aspect of people knowledge about the road segment is obtained. These different aspects of knowledge are integrated using a Skyline operator to support multi-criteria route finding. We name this model ERSBU (enriching road segments based on UGC). In ERSBU, road segments are basic spatial units, and their subjective properties have been extracted by using available UGC. The scenic score for each road segment was computed by using geo-tagged Panoramio photos. The accessibility level of each road segment to different facilities was calculated based on data captured from Wikimapia and OpenStreetMap. Moreover, for measuring the movement popularity of each road segment, Wikiloc and Everytrail GPS trajectories were utilized. For the implementation of the ERSBU model, Tehran region 6 was considered the case study area. The Evaluation of the results proved that road segments that achieved a high score based on knowledge extracted from UGC also mostly gained top scores by analyzing traditional maps. ERSBU allows users to accomplish more-qualitative path finding by considering the multi-view characteristics of road segments. [ABSTRACT FROM AUTHOR]

This research presents an intelligent planning support system based on multi-agent systems for spatial urban land use planning. The proposed system consists of two main phases: a pre-negotiation phase and an automated negotiation phase. The pre-negotiation phase involves interaction between human actors and intelligent software agents in order to elicit the actors’ social preferences. The agents employ social value orientation theory, which is rooted in social psychology, in order to model actors’ social preferences. The automated negotiation phase involves negotiation among autonomous software agents, the aim being to achieve consensus about the spatial problem on behalf of the relevant actors and using the information obtained. This study employs a computationally effective Bayesian learning technique, along with social value orientation theory, to design socially rational intelligent agents who work on behalf of real actors. The proposed system is applied to a real world urban land use planning case study. Human actors participate in a pre-negotiation phase, and their social preferences are elicited by intelligent software agents through a number of interactions. Then, software agents come together to engage in an automated negotiation phase and eventually reach an agreement on the spatial configuration of urban land uses on behalf of the actors. The results of the study show that the proposed system is effective at performing an automated negotiation, plus that the final plan – which is the output of the automated negotiation – produces higher social utility and better spatial land use configurations for the agents. [ABSTRACT FROM PUBLISHER]

Most spatial problems are multi-actor, multi-issue and multi-phase in nature. In addition to their intrinsic complexity, spatial problems usually involve groups of actors from different organizational and cognitive backgrounds, all of whom participate in a social structure to resolve or reduce the complexity of a given problem. Hence, it is important to study and evaluate what different aspects influence the spatial problem resolution process. Recently, multi-agent systems consisting of groups of separate agent entities all interacting with each other have been put forward as appropriate tools to use to study and resolve such problems. In this study, then in order to generate a better level of understanding regarding the spatial problem group decision-making process, a conceptual multi-agent-based framework is used that represents and specifies all the necessary concepts and entities needed to aid group decision making, based on a simulation of the group decision-making process as well as the relationships that exist among the different concepts involved. The study uses five main influencing entities as concepts in the simulation process: spatial influence, individual-level influence, group-level influence, negotiation influence and group performance measures. Further, it explains the relationship among different concepts in a descriptive rather than explanatory manner. To illustrate the proposed framework, the approval process for an urban land use master plan in Zanjan-a provincial capital in Iran-is simulated using MAS, the results highlighting the effectiveness of applying an MAS-based framework when wishing to study the group decision-making process used to resolve spatial problems. [ABSTRACT FROM AUTHOR]

Change detection of ground surface objects can provide essential and precious information for experts in the fields of Geomatics, emergency management, urban management, agriculture, and forestry. Space-borne remote-sensing images are one of the main sources for change detection. Various change detection methods have been proposed on remote-sensing applications. However, often, no single efficient method can be selected for a case study because the existing methods sometimes have good performance and sometimes perform poorly. Therefore, it is necessary to propose an integrated change detection method according to some change detection methods. Multi-criteria decision analysis is a powerful framework that can integrate several criteria that may be in contrast to each other. In this study, a multi-criteria decision analysis framework was used to integrate the spectral, textural, and transformed features for detecting building changes with the help of high spatial resolution satellite images. First, the spectral, textural, and transformed features were extracted from the pre- and post-event satellite images. Second, the spectral, textural, and transformed factor maps were produced by entering the related features to three separate Adaptive Network-Based Fuzzy Inference Systems (ANFIS). Third, the ANFIS model was used again to integrate the mentioned factor maps for producing the preliminary building change map. And finally, a comprehensive sensitivity analysis was carried out to determine the proper parameters of the ANFIS models leading to accurate change detection results. The proposed method was tested on the earthquake data set of Bam City in Iran. The achieved results indicated an overall accuracy of 89.62% for identifying the changed and unchanged building regions. Moreover, the obtained results proved the efficiency and accuracy of the proposed method with respect to other implemented methods regarding the Bam earthquake. Furthermore, the aggregation of the spectral, transformed, and textural features resulted in improving the change detection accuracy by about 5–15%, compared with the accuracy of every one of them for the mentioned purpose. [ABSTRACT FROM PUBLISHER]

Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential location and travel mode, integrated modelling of these choices has been proposed by some researchers. In this paper, an agent-based microsimulation model has been developed to evaluate impacts of different transport development plans on choices of residential location and commuting mode of tenant households in Tehran, the capital of Iran. In the proposed model, households are considered as agents who select their desired residential location using a constrained NSGA-II algorithm and in a competition with other households. In addition, they choose their commuting mode by applying a multicriteria decision-making method. Afterwards, effects of development of a new highway, subway and bus rapid transit (BRT) line on their residential location and commuting mode choices are evaluated. Results show that despite the residential self-selection effects, these plans result in considerable changes in the commuting mode of different socio-economic categories of households. Development of the new subway line shows promising results by reducing the private car use among the all socio-economic categories of households. But the new highway development unsatisfactorily results in increase in the private car use. In addition, development of the new BRT line does not show significant effects on the commuting mode change, particularly on decrease in the private car use. [ABSTRACT FROM PUBLISHER]

Facility location is an important problem in all kinds of businesses, including service and manufacturing efforts. The suitable selection of business location is a crucial component in the eventual success or failure of the firm. Proper facility location can be a strategic tool that can significantly improve market share growth and increase customer profitability. Facility location involves much more than just selecting a location with good visibility and access. Effective research on competition and market demand are also required. Each of the above mentioned business types have different location requirements that need to be considered when settling upon a location for starting or relocating a business. The most of decision making issues in the real-world are affected by various parameters and objectives. Due to the increasing urban population, providing every citizen with adequate municipal services is highly important. this study deals with optimal locating of municipal services and particularly shopping center and also I considered several criteria, such as the citizens demand, access to shopping centers, opponent shopping centers, the applicant's access to welfare facilities like public car parks parking and main roads and etc. Optimization is the process of searching for feasible solutions in a problem until no other superior solution can be found. Much of the current focus is on single objective optimization, even though most real-world problems require the simultaneous optimization of more than one objective function. In general, such problems consist of two or more conflicting objective functions with a set of constraints taken into consideration. At this point, it is not possible to obtain a single solution that optimizes simultaneously all the objective functions. Therefore, we need to find out a set of solutions that tradeoff the different objectives called Pareto front thus the concept of optimality in single objective optimization problems is replaced by the concept of Pareto front in multi-objective optimization problems. This set helps the decision maker to identify the best compromise solutions by elimination of inferior ones. The choice of one solution over the other entails additional knowledge of the problem such as the relative importance of different objectives. As mentioned, the definition of the effective objectives for facility location, requiring the use of multi objective methods for problem solving. There are two general approaches to solving multi objective problems; classic and metaheuristic approach. Classical methods do not provide all the Pareto optimal solutions so to achieve an objective they must be weighted. In addition to the weighting objectives, and considering experts opinions, the methods do not show good performance for multi objective problems. Therefore, in recent research the metaheuristic methods are preferred. Among the metaheuristic methods, we use an improved method of Non-dominated Sorting Genetic Algorithm (NSGA-II), called Fast Pareto genetic algorithm (FPGA), for implementation and solving shopping center location problem. This method is able to find optimal solutions for multi objective problems in objective space. FPGA uses a new ranking strategy for the simultaneous optimization of multiple objectives. New genetic operators are employed to enhance the algorithm's performance in terms of convergence behavior and computational effort. Computational results indicate that FPGA is a promising approach and it outperforms the improved NSGA-II. Study area, part of the city of Karaj, is including the North of Freeway Karaj - Qazvin. Data of this area for demand capture objective, obtained from Demographic information of the Statistical Center of Iran from the 1385 census. For other information layers, data of Bavand Consulting Engineers database used. The base map of database is of 1:10000 scales. These maps were used as the main source of information. This data layers used to achieving criteria of demand capture and accessibility objectives. For implementation and solving shopping center location problem, after identifying potential sites in the city of Karaj, with considering competitive conditions and defining two objectives; accessibility and demand capture then with using FPGA, multiple combinations of shopping centers were identified. The number of shopping centers is proposed as input and determined according to user needs and its purpose is to find suitable location as objectives of problem. Outputs of the algorithm are Pareto optimum solutions that show the trade-off between the objectives. By the Given input, we can see that the solutions of the algorithm, proposed locations away from existing shopping centers, and it cover a greater demand and have suitable accessibility. In order to validate the results of the FPGA-based approach, Results are compared with outputs of index overlay method. This comparison shows that 90 percent of optimum results in genetic algorithm method stand in two categories with high utility rates. This means that the results of FPGA are in areas with high utility rates. [ABSTRACT FROM AUTHOR]

We propose a combined method based on the strengths, weaknesses, opportunities and threats (SWOT) and analytic hierarchy process (AHP) to investigate the challenges and prospects of adopting geographic information systems (GIS) in developing countries. In this context, we identify, group, and analyse SWOT indicators in relation to the main GIS components: data, people, and technology. The relative significance of each SWOT indicator and its related SWOT groups in each GIS component is quantified. The method is then applied in a situation assessment of GIS adoption in the governmental organisations and strategic planning. The SWOT–AHP approach proves to be very useful in identifying and quantifying the relative significance of the major factors affecting GIS implementation, and effectively facilitates GIS strategic planning. [ABSTRACT FROM AUTHOR]

The world's cities are facing various challenges such as rapid urbanization, poverty, climate change, pollution, sustainable and inclusive development. Building futuristic smart and sustainable cities is proving to be a response to these challenges. Recent publications show that urban planning decision support is increasingly using artificial intelligence through machine learning methods to address these challenges. 172 papers were published in 2020 compared to 8 before 2010 with a forecast of at least 200 papers in 2021. Despite the explosive number of scientific publications on applications artificial intelligence for urban planning decision support, few studies have made a systematic assessment of the state of the art to inform future research. This would help focus on the approaches used, the planning problems most commonly addressed, the data used and even the study areas. We found that the top 5 most addressed urban planning issues include: land use/cover, urban growth, urban buildings, urban mobility and urban environment. Furthermore, a large amount of data was used from sensors and simple or ensemble machine learning methods were more used in this case. Deep learning methods are more used for land use/cover, buildings and climate issues which are mostly based on satellite image data. On the other hand, China and the United States are the most studied territories while Africa is almost not. A high intensity of collaboration between researchers affiliated with Chinese, American and English institutions was observed. Thus, urban planning researchers should benefit from this synthesis work by understanding the general idea of the application of machine learning methods in urban planning, its trends, issues, current challenges and future research directions. [ABSTRACT FROM AUTHOR]

The ecologic capability evaluation determines which human activities can be performed on which area of land and conversely, which activities are impossible, economically unviable, or detrimental to environmental sustainability. This study set out to evaluate the land ecologic capability in Golestan province of Iran to develop agriculture (cultivating wheat) through land use planning and to investigate the factors effective on the agriculture ecologic capability. The study was exploratory in terms of method and applied library research method for data collection purposes. The identified factors include climate, precipitation, temperature, evaporation, topography (altitude), slope, slope direction, land use, water resources, flood zones, land types, erosion, and soil. Then, the optimal location of land for wheat cultivation was done in four stages using ARC GIS software and its tests in order to zone the lands appropriate for what cultivation. To this end, the raw data of the intended layers was extracted and examined, and the areas appropriate for cultivation were determined and classified. The appropriate values were assigned to the aforementioned layers so as to generate the final layer map to zone the lands appropriate for wheat cultivation. Finally, via overlaying the wheat-cultivated lands on the intended layers, the ecologic capability map of the area was made. These were then classified into three categories, namely lands with appropriate and high capability (about 13%), lands with medium capability (over 80%), and lands with low or no capability (about 5%). The intermediate territory of the province has the most fertile lands with semi-humid, temperate Mediterranean climate (Alborz highland plains). As a result, the best cultivation areas of this province are located in its intermediary and southern parts that have deep, high quality agricultural soil and adequate rainfall. [ABSTRACT FROM AUTHOR]