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.

The topic of climate change and the dangers that lie ahead is part of the debate in land management. The dynamics of global change and the sovereign approach of global governments have opened new perspectives on land management issues. One of the hidden challenges in this regard is the increasing risk of the occurrence of floods. The purpose of this study was to undertake metric or measurement model as a spatial basis unit to predict flood occurrence. In the present article, in line with using MikeUrban 2019 software, a wide range of tools and quantitative processing steps were used in accordance with the research objectives. In order to predict floods, the past incident factors were studied, namely a review of the principles and operational indicators related to each parcel using the OLI sensor images of Landsat 8 satellite in the year 2020 through the integrated interpretation method and an examination of the basic map of Ilam city in wet seasons (autumn, winter, and spring). Then, the city coverage map was prepared in two uses, i.e., False Color (Urban) and Land / Water. Next, Google Earth images were used to determine the accuracy and precision of the coverage maps. By combining four selected measures with the highest spatial correlation in 50 random points of the city, hexagonal measures with optimal areas were selected and spatial patterns were analyzed. According to the results, in the actual event (1), eight spatial measures with an area of ​​68 hectares and coverage of 1.5% of the entire city are at risk of flood. In events 0.8 and 0.9, 19 measures with an area of ​​170 hectares, 3.6% of the land use coverage of the area, are at potential risk. While there is a high correlation between flood event and type of measure, amount and direction of slope, as well as density and width of road network on one side and drainage network on the other side, in high events (1-8), 28 measures in the residential uses in the detailed design scale with an area of ​​76.5 hectares, barren and enclosed uses with an area of 70.55 hectares, roads network with an area of 29.75 hectares, and parks and green spaces with 17 hectares were identified as the uses targeted by the flood danger. Among all risky user groups, 29.5% were identified in the newly built group, 44.5% in the maintainable group, and 18% in the decayed urban environment group.

Spatial imbalances and spatial inequalities exist at different levels, and the realization of spatial and social justice at these levels depends on eliminating these imbalances through spatial planning. Service organizations to clients and, most importantly, the Welfare Organization, may have a lot of data and figures from their clients, but in many cases, existing data does not have a spatial dimension, and they are purely descriptive. The main objective of this research is to analyses spatially clients covered by welfare in the scale of Mashhad, in both regional and neighborhood level. The statistics required for this study are collected from the Department of Welfare of Khorasan Razavi and Mashhad. The methods used for spatial analysis are the standard deviation elliptic index, regional and neighborhood demographic density, kernel density, basic graphic statistical methods including the nearest neighbor's index, spatial autocorrelation measurement models such as general Moran I and LISA. The results of this research, presented in various maps and diagrams, show that the welfare beneficiaries in the city of Mashhad have not been distributed equally and different regions and neighborhoods have a significant difference, with the highest number of clients in regions 2, 3 and 4, the city of Mashhad where tjeay are located in the north and northeastern parts of the city. Also, at the neighborhood level, Shahid Ghorbani, Northern Tabarsi, Derevey and Bahman neighborhoods include the most welfare-clients population. In a general analysis based on the kernel density analysis, it can be stated that four condensed nuclei and a subunit formed in the city of Mashhad. Also, the results of Moran indicator and others indicate that the spatial distribution pattern of welfare clients in Mashhad is quite clustered and in particular parts of Mashhad, which mainly coincide with the informal settlements of the city and the eastern and northern parts of the city are distributed in cluster form.

The discussion of environmental change is currently one of the most important challenges which the international community faces. This issue comprises one of the most extensive scientific, economic, social, and even political debates at different global levels. Severe and sustained droughts have threatened many parts of the globe at different times and have led to rapid and profound changes at the economic and social development levels. The Middle East, especially Iran, has been no exception in this regard. The drying up of water resources, including lakes, is a prominent feature of these changes that has caused problems for the surrounding habitats. The purpose of this study is to evaluate the status of development indicators in 22 cities in Urmia Lake catchment area. In recent decades, water level has decreased significantly and drought level has increased in this lake. This study is an applied research project in terms of purpose and a descriptive-analytical one in terms of methodology. Data and information were collected from sources and documents of the Census Bureau for the years 2006 and 2016. Also, Vikor method and Moran's spatial autocorrelation index in Arc Gis software were used for the data analysis. The results of the statistical calculations showed that the development levels in the catchment area have transformed dramatically over the last 10 years, from the cluster distribution of 2006 to the dispersed distribution pattern of 2016. Moreover, the employment rate has declined and the unemployment rate has increased.Comparing the various statistics, one can clearly see the negative effects of the Lake water level decline on catchment area counties over the last 10 years. The gradual decline in the Lake water level (followed by a decrease in the employment rate and an increase in the unemployment rate) is the main reason for the imbalance in the development of the region.

Understanding how to distribute economic, social, cultural and other opportunities as first step in spatial development planning can improve service delivery and increase equilibrium between regions. This research aimed at study and analysis of 31 provinces of Iran based on social indicatros enjoyment. Therefore, the present study is applied in terms of purpose and descriptive-analytical in terms of nature and method of research. Required data collected through the statistical yearbook of 1395 (2016) in the form of 52 important social indicators including social, cultural, welfare, educational and health components. Shannon entropy, coefficient of variation (C.V), WASPAS used to Wheightining of criterias, analysis of indicators dispersion and determining the level of development of provinces respectively in the context of MATLAB. The result shows that there is a lack of social development balance between Iran provinces. Tehran, Isfahan, Khorasan Razavi and Fars are four provinces that placed at higher level of social development and Alborz, Zanjan, Chaharmahal Bakhtiari, Semnan, South Khorasan, North Khorasan, Ilam and Kohgiluyeh and Boyer Ahmad are placed at the most deprived areas of the country in terms of social indicators. Overall, the results show that social development status in Iran's provinces is not synonymous with social and spatial justice and requires proper and effective attention and management.