Inevitably, inconsideration of the population evacuation of villages can have irreparable consequences for the human settlements. Analysis and identification of the qualities of this issue can greatly help planners and decisionmakers in the spatial planning domain to prevent full evacuation of population from rural settlements. The study at hand was an applied research project done using descriptive-analytical approach. The statistical population comprised of villages with less than 100 residents in Khorasan Razavi province from 1986 to 2016. Moran’s local spatial analysis was used to investigate the spatial dimensions, and arithmetic mean and skewed distribution were used to examine the direction and range of distribution. The results showed that in the 1986, 1996, 2006, and 2016 censuses, 92.2, 90.1, 94.8, and 79.9 percent of the villages evacuated in the previous decade have had lower than 100 residents. The results of Moran’s spatial autocorrelation analysis demonstrated that the distribution pattern of villages on the threshold of evacuation during these three decades is cluster-like. Moreover, the results of arithmetic mean and skewed distribution indicated that 68 percent of the villages that are on the threshold of population evacuation are within the oval domain, and except for the 1986-1996 period – when the distribution direction of villages on the threshold of evacuation has been northwest-southeast, the direct has been northeast-southwest from 1996 to 2016.

Water provision service is one of the most valuable ecosystem services that is important as a key service for the healthfulness and management of water resources. In this study, using the Water Yield model of InVEST software, the water supply and demand in Ilam watershed was modeled. The data related to average annual precipitation, annual evaporation and transpiration, root limiting depth, water available for plants,  land use map, water consumption and estimation of water provision quantity, basin boundary, and subbasins of the watershed were the inputs of this model. The results of model application showed that in Ilam watershed, 45 million cubic meters of water provided annually, with the highest and lowest water provision volumes being in Arghavan (7 million cubic meters) and Chalimar (802 thousand cubic meters) subbasins, respectively. According to results, there are great differences in the water supply and demand in the subbasins of this watershed. Therefore, planner and policymakers should pay attention to this important issue in the layout of land uses and foresee sustainable use of rich forests in the high yield subbasins such as Arghavan subbasin. The results provided in this study, along with showing the importance of modeling surface water demand and its used in macro-policies of water allocation, can function as a guideline and help the managers and planners of Ilam city to adopt reasonable decisions in managing ecosystem and correctly using land in this area.

Due to the vast changes it brings about in the land use and land cover, the rapid expansion of cities has had many negative effects on the environmental quality at global level. Examples include air quality, increased temperature, changes in perspective, and the alteration of agricultural lands into barren lands that leads to the loss of biodiversity. Since land use changes happen at extensively, remote sensing technology is a necessary, efficient, and valuable means to monitor changes. In this study, the effect of land use changes on the temporal-spatial patterns of land surface temperature in the urban lands of Ilam city in a 30-year period (1990-2020) was examined using Landsat satellite images and simulation of changes using fuzzy ARTMAP neural network model. Landsat satellite sensors (TM, OLI, ETM) were used to investigate the longitudinal and spatial changes (LST) in Ilam city.  To provide the land use map, the pixel-based classification for all periods (1990-1995-2000-2005-2010-2015-2020) was applied using ENVI and Ecognitio software packs and then estimations were made using NDVI and LST models. The results showed that Ilam city ecosystem has moderate levels of NDVI. The average temperature of residential land use level in June is 35.8 degrees centigrade, and the temperatures of three land uses of horticulture, agriculture, and other uses (uncovered lands) were 32.22, 37.25, and 38.46, respectively, with the main lands with the minimum temperature being green highlands. In city, existence of high rise buildings and building shading, use of materials with less heat absorption such as Isogam, urban green space and furniture, and air pollution lead to lesser absorption of energy. On the other hand, the spatial distribution of NDVI was aligned with LSP values. The results regarding land use areas using fuzzy-neural network show that residential land uses has risen from 19.18 percent in 1990 to 39.35 percent in 2020, which shows the city expansion and development. On the other hand, the horticultural land use has declined from 8.64 in 1990 to 3.49 in 2020, which can be attributed to the development of urban space.

Spatial justice of uses, or in other words, fair, wise, and balanced distribution of urban uses is one of the most important issues of urban planning. The present study is research in terms of purpose and analytical in nature. The library information of this research is collected from books and articles. For the analysis, the per capita index of service users was used, which was calculated and obtained in the GIS software. The statistical population of the research is the eight districts of Qom City. This research has tried to analyze the Spatial justice of service uses in the eight districts of Qom City with the ORESTE technique, based on indicators that are service uses per capita. For this purpose, the indicators were first weighted using Shannon's entropy method, the highest weight was assigned to tourism, and the lowest weight was assigned to the park. After weighing, the ORESTE technique was performed, and Region 7 had the highest and Region 6 had the lowest amount per capita. Then, with the dispersion coefficient, the two-by-two distribution per capita of the service uses of the regions was calculated, and the results showed that regions 2 and 8 have the highest and regions 5 and 7 have the lowest spatial distribution of service uses. Also, with the Spearman correlation coefficient, the correlation between the population of each region and the per capita service users was calculated, which showed a negative correlation, which means that as the population increases, the service users per capita decreases.

The most obvious example of human activities on the land due to the increase in population and the need for development is land use changes, which lead to changes in the provision of ecosystem services. To that end, the present study was done with the aim of quantitatively evaluating the carbon storage capacity and habitat quality and showing their spatial distribution to achieve social-ecological stability, sustainable use of land resources, and develop appropriate plans and policies in Chaharmahal and Bakhtiari province. The modeling of selected ecosystem services was calculated using InVEST software models and the capacity to provide them in land use classes using Zonal Statistics analysis in a GIS software environment. The results indicated that high-value carbon storage areas are focused in areas with forest cover, which have strong carbon storage capacity and are the largest carbon sinks in ecosystems. Likewise, the highest habitat quality was also observed in these areas, and in places where the vegetation is fragmented and the surrounding environment is occupied by human threat sources, the quality of the habitat has decreased. Spatially, carbon storage and habitat quality Indicated a low-high-medium spatial distribution pattern from north to south. The maps of ecosystem service created in this study can be useful in identifying potential areas of carbon storage supply and habitat quality, and provide a scientific basis for further discussion by policymakers about future land use planning, from the perspective of minimizing climate change and increasing biodiversity.

The purpose of this research is to select temporary accommodation centers after the earthquake crisis to meet the needs of the victims. Therefore, this research has tried to identify temporary accommodation sites in District 22 of Tehran with the help of effective criteria. The required data has been obtained from the Tehran Disaster Mitigation and Management Organization and the results of the general population and housing census of 2017. The novelty of this research is to present a new combination approach to determine the effective criteria for locating temporary shelter sites. In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) with a binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels was obtained at 0.04616 and 0.0097, respectively, which indicates the high compatibility of the tri-cube kernel with effective criteria. According to the obtained maps, Chitgar Park and Azadi Sports Complex are some of the widest and most suitable areas for the construction of temporary shelter sites after the earthquake crisis in the case study. By identifying temporary shelters, relevant organizations can provide the appropriate infrastructure for these selected centers so that there is no need to spend time to provide these services in the event of an earthquake.

The integrated endogenous regional model as favorable locative-spatial consequences and brings about balanced regional development in various dimensions. Due to the applied nature of this study, its objective as to examine the locative-spatial consequences derived from the integrated endogenous regional development model. Then, the strategic question revolved around the effects and consequences of the aforementioned model. To answer this question, the comparative strategy based on quantitative methodology was used. The statistical population of the study was comprised of the scientific and administrative elites and experts in Lorestan Province of Iran (Selseleh city), from among whom 30 participants were selected systematically and based on sampling logic. Data analysis was done through inferential statistics based on the measurement model fitness indicators and structural modeling in SmartPLS software. The results showed that measurement model indicators of “endogenous integrated model” and “spatial evolution” confirmed the measurement model of the study. In the structural model of the study, the accuracy of the relationship between structural variables / latent variables of the study based on T value was assessed and confirmed. Moreover, based on R2 confident of determination and F2 effect size measure, the strength and effect of the relationship between “endogenous integrated model” and “spatial evolution” was confirmed. In addition, the general goodness of fit (GOF) showed that the model had a strong fitness and the data confirm the experimental model of the study.

Today, the expansion of urbanism has led to many problems for various countries in the world, especially developing countries. Iran is one of the developing countries that in recent years has faced numerous damages due to population increase, especially in urban areas. In recent years, countries in the world have tried to overcome the problems deriving from unsustainable urbanism via using sustainable urban development. Accordingly, the authors carried out this study to determine the similarities and differences among sustainability evaluation systems existing in developing and developed counties and explain the needs of developing countries, like Iran, to improve management of urban areas. Therefore, in the study at hand, the global urban sustainability indicators such as BREEAM Community، CASBEE-UD، GBI Township، LEED-ND، IGBC Green Townships، GRIHA-LD، Green Star, and IUSAF were examined and were compared with the intended system in Iran (IUSI). In this process, the similarities and differences of selected evaluation systems were compared with each other in terms of type and importance level of indicators. The obtained result showed that the urbanism challenges in various countries in the world are different based on the development level and environmental, social, economic, and geopolitical conditions. In addition, the most important indicators of urban development measurement in developed countries were identified as energy, resources and social welfare, smartening, and environment, while the prioritized indicators in the developing countries were determined to be planning and developing the society, managing infrastructure resources, transportation, local security and economy, employment, and – mostly importantly – access to healthy water. Moreover, the findings of this study indicate that via localization of the systems and indicators related to sustainability evaluation for every country, region, or province, a more precise evaluation can be attained. This can offer great assistance to future planning to advance the sustainable development goals.

With a growth rate higher than global GDP, exports, etc., Marine business (especially container transport) has had great effects on the spatial structure of different regions and areas. Ports have an important role in this regard, because they are the main gates of goods import and export in a country. This study explores the structure and interconnection of Iran’s northern ports (on Caspian  Sea) with the ports of other countries neighboring Caspian  Sea due to the great importance of marine transportation and its effectiveness on Iran’s spatial structure interconnection. In order to examine the evolution of the spatial interconnection network structure of Iran’s northern ports at local and international level between the years 1989 to 2019, the model used in this study analyzed the passenger and goods transportation among Caspian  Sea ports of Iran and those of other countries around Caspian  Sea using UCINET software. The results showed that the interconnection of goods and passenger transportation in Iran’s northern provinces (in general) and Iran’s ports on Caspian  Sea (in particular) is not in good conditions. In fact, the connection between Iran and other countries neighboring Caspian  Sea is not so high except for scarce, insignificant cases (i.e., connection with Russia through Amirabad port), and the spatial interconnection of other borders of Iran, especially the southern coastline, is much higher than that of Caspian  Sea coastline.

In this study, the spatial-temporal distribution analysis of housing prices in District 5 of Tehran Municipality and the factors effective on it were investigated. To this end, the data related to housing buying and selling in this district in the years 2018, 2019, and 2020 were used to model the housing price. The results were obtained using GTWR method, which gave in a better measure compared to GWR and OLS methods. The adjusted coefficient of determination in OLS, GWR, and GTWR algorithms were found to be 0.759, 0.798, and 0.835, respectively. GTWR is a method that can model the spatial-temporal heterogeneities that exist in the housing price data. Based on the obtained results, the currency exchange rate (dollar to rial) has the highest effect on modeling housing prices. After that, the physical characteristics of housing – such as its footage and age – are important in modeling housing prices. Finally, the access rate to urban services – such as distance to hospitals, sports centers, educational centers, religious sites, green space, highways, and urban public transportation stations – can improve the modeling of housing prices. The findings of this study show that using dollar-to-rial exchange rate as the independent variable, we can model the housing price with a proper precision.