نشریه تحلیل فضایی مخاطرات محیطی
سال سوم شماره 3 (پاییز 1395)

Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements. Due to ease of calculation and use of available precipitation data, SPI index usually was calculated for any desired time scale and it’s known as one of the most appropriate indices for drought analysis, especially analysis of location. In connection time changes, most studies were largely based on an analysis of trends and changes in environment but today special attention is to the variability and spatial autocorrelation. In this study we tried to analyze drought zones in the North West of Iran, using the approach spatial analysis functions of spatial statistics and detecting spatial autocorrelation relationship, due to repeated droughts in North West of Iran and the involvement of this area in the natural disaster.
In this study, the study area is North West of Iran which includes the provinces of Ardebil, West Azerbaijan and East Azerbaijan. In this study, the 20-year average total monthly precipitation data (1995-2014) was used for 23 stations in the North West of Iran. In this study, to study SPI drought index, the annual precipitation data of considered stations were used. According to the statistical gaps in some studied meteorological stations, first considered statistics were completed. The correlation between the stations and linear regression model were used to reconstruct the statistical errors. Stations annual precipitation data for each month, were entered into Excel file for the under consideration separately and then these files were entered into Minitab software environment and the correlation between them was obtained to rebuild the statistical gaps. Using SPI values drought and wet period’s region were identified and zoning drought was done using ordinary kriging interpolation method with a variogram Gaussian model with the lowest RMS error. Using appropriate variogram, cells with dimensions of 5×5km were extended to perform spatial analysis on the study area. With the establishment of spatial data in ARC GIS10.3 environment, Geostatistic Analyze redundant was used to Interpolation analysis Space and Global Moran's autocorrelation in GIS software and GeoDa was used to reveal the spatial relationships of variables.
The results showed that most studied stations are relatively well wet and this shows the accuracy of the results of the SPI index. Validation results of the various models revealed that Ordinary Kriging interpolation method with a variogram Gaussian model best explains the spatial distribution of drought in North West of Iran. So, using the above method the stations data interpolation related to SPI index in North West of Iran was done. The results showed that Moran index values for the analysis of results of standardized precipitation index (SPI) in all studied years, is more than 0.95. Since Moran’s obtained values are positive close to 1, it can be concluded that drought, in the North West of Iran during the statistical period has high spatial autocorrelation cluster pattern of 90, 95 and 99 percent. Results also showed that in all the years of study, Moran's global index is more than 0.95 percent. This type of distributed data suggests that spatial distribution patterns of drought in North West of Iran changes in multiple scales and distances from one distance to another and from scale to another and this result shows special space differences in different distances and scales in this region of the country. Results also showed that drought in North West of Iran in 2008 is composed of two parts: Moderate drought in parts of West and North West region (stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad and Piranshahr) and severe drought in the southeastern part of the study area (stations: Sarab, Khalkhal, Takab, Tabriz and Mianeh). So the pattern of cluster drought in the North West of Iran in 2008 is on the first and fourth quarter. The results of this index showed that drought and rain periods are similar in the studied stations. The results of the application of Moran's index about identifying spatial distribution of drought patterns showed that The values of the different years during the period, have a positive a positive coefficient close to 1 (Moran's I> 0.959344) and this shows that the spatial distribution of drought is clustered. The results of the standard score Z values and the P-Value proved the clustering of spatial distribution of drought.
The results of the analysis of G public value, In order to ensure the existence of areas with clusters of high and low values showed that The stations of Maku, Khoy, Salmas, Urmia, naghadeh, Mahabad, Piranshahr and Parsabad follow the moderate drought pattern in the region and are significant at the 0.99 level. Jolfa station also has a mild drought of 0.95 percent confidence level and for Sardasht station is significant in 0.90 percent. High drought pattern in Sarab, Khalkhal, Takab, Tabriz and Mianeh stations was significant in 0.99 percent level and also for Ardabil, Sahand and Maragheh stations very high drought pattern was significant in 0.95 percent level and for Meshkinshahr and Ahar high drought pattern is significant in 0.90 percent. By detection of clusters of drought and rain in the North West of Iran using Moran’s spatial analysis technique and G general statistics a full recognition of the drought affected areas in this region can be obtained and take the necessary measures in its management.

Erosion is one of the most destructive and continuous phenomena that cannot be prevented and only could be controlled by studying the chemical and physical properties of soil. Marls are one of the most important sedimentary units in Iran which have high rate in sediment production and erodibility because of their Physico-chemical characteristics. These properties caused large environmental and civil damages and so, the study of erosion and erodibility of the marl units is essential. One of the most important points about marls is grain size nature and elements in them and their effects on amount of erosion. The physical and chemical proprieties of soil are very important in the development of badlands. This study deals with Physico-chemical properties of Marl and its impact on various land forms of erosion in Lotshur-Pakdasht region. Badlands are a typical landform of greatly dissected fine-grained materials in arid or semi-arid environments like Lotshour, although they are also found in different climatic conditions. Climate and geology are several factors determining the tendency to badland formation. Runoff, rain splash, marl and loose formations together with massive wasting processes such as creep, sliding and flow, become the dominant factors determining landform genesis, resulting in the formation of badlands in Clayey-silt slopes.
In this research, in addition to sampling the soil and sediments, rain simulated (using rain simulators) in two marl, two conglomerates and two alluvium units, in area with different forms of erosion and runoff and produced sediment amounts in each point were measured in laboratory. Also, at the same time, soil samples were taken from adjacent plot and the amount of runoff and sediment produced in the laboratory, separated and measured in the lab for all points. parameters such as Ph, electrical conductivity, content of sodium, potassium, calcium, magnesium, gypsum, chlore, carbonate, solfate, nitrate, organic carbon, CEC was measured. In analyzing the data, analysis of correlations and Pearson and Spearman comparison of means method were used in SPSS software. Also, grain size and Aterberg limits for all samples were determined in lab.
Mineralogical, geochemical and grain-size composition of soil and pore-water chemistry parameters was characterized on both eroded (south-facing) and non-eroded (north-facing). Only a few grain-size parameters and mineralogy discriminate eroded from non-eroded slope substrates. Erosion occurs where the fine fraction is abundant. This may be due to reduced permeability in the eroded soil, whereas the non-eroded one is more stable with respect to weathering, as it is more permeable. The abundance of clay minerals is affected by pedogenetic processes in the non-eroded slope, which increases in mixed layers and indirectly reduces the amounts of other minerals, making clay mineralogy a discriminating parameter in the two different types. Chemical data enable discrimination between eroded and non-eroded slopes. pH, SAR (sodium adsorption ratio), TDS (total dissolved salts), mineralogy and PS (percentage of sodium) are distinctive parameters for both eroded and non-eroded slopes. TDS increases in depth in the non-eroded slope, whereas the maximum TDS is just below the crust in the eroded one. On average, eroded substrates are higher in pH, SAR and PS than non-eroded ones. The ESP (exchangeable sodium percentage) of the eroded slope has a higher value than the non-eroded one. Crusts are less dispersive than eroded substrates, and non-eroded substrates behave as crusts. This suggests that the portion of the slope most severely exposed to weathering tends to stabilize, due to strong decreases in SAR, PS and ESP. Several diagrams reported in the literature show similarly anomalous crust samples on eroded slopes, compared with other samples coming from greater depths on eroded slopes. In the present case study, the exchangeable form of Na characterizes crusts more than the soluble form. The meaning of maximum SAR and TDS (and covariant parameters) is interpreted as the effect of decreased permeability, as suggested by a local increase in the fine-grained fraction, which coincides with maximum TDS. Variations in SAR values are of primary importance for soil erosion, because many authors have used solution chemistry (i.e., SAR, PS, TDS, EC) as a descriptor of dispersity.
Based on results of analysis of variance in various forms of erosion are significantly different in the sodium ion, sodium absorption ratio and the percentage of clay. The average amount of sodium ion and sodium absorption ratio in marl samples of region, increase from sheet to gully erosion forms while average clay percentage decreases in this trend. Finally, three variables of sodium ions, sodium absorption ratio and clay percentage of marl samples can be significant factors in erosion and related forms in this region. This study describes the erosional mechanism, which involves morphological and geographic exposure and climatic elements, as well as grain size, mineralogy, chemistry and exchangeable processes of soils.
In analyzing the data, correlation analysis and comparison of averages by the SPSS software has been used. As well as a brief comparison between north and south facing slopes that are different in terms of erosion, was also performed. Based on statistical analysis of in various land forms of erosion are significantly different in the sodium ion, sodium absorption ratio and the percentage of silt and clay. The average of sodium ion value and sodium absorption ratio increase from surface to gully erosion form and average silt percent reduced from surface to Gully erosion in marls outcrops in this area. Also, three variables of sodium ions, sodium absorption ratio and clay percent factors can be seen in the erosion of marl and create various land forms of erosion in the region.

Most of the large cities in developing countries have faced with the problem of informal settlements. The formation and growth of these settlements for reasons such as rapid and outside the customs building construction are the threatening issue for their communities. Informal settlements are areas that often shaped and expanded in major and middle cities of the Iran’s cities including the city of Parsabad. During the last decades, the rapid growth of urbanization and the lack of appropriate planning for low-income families housing leads to the formation of the urban informal settlements in most cities of the Iran. In most cases, these settlements have a structural and demographic dense texture. The structural texture of these settlements is often fine aggregate, impermeable, and unstable. In times of crisis, the possibilities of human and material losses to them are high.
Environmental hazards such as earthquakes are a serious threat to these settlements. However, these hazards in most developing countries, due to the unavailability and lack of preventive actions, end to the crisis. We cannot prevent earthquakes. But we can reduce the losses and damages caused by the earthquakes. Remove of the disaster is impossible, but it is possible to reduce the damage caused by the disaster. One of the most important ways to reduce the risk of earthquakes is preparation to deal with earthquakes. Preparation means having previous programs and plans.
Iran is one of the countries where earthquakes always happen. Because Iran located in the world's earthquake belt, each year on average about 1,000 earthquakes happening in Iran. Ardebil and Pars-Abad city, located in an area that the possibility of earthquakes shakings in these areas, is more. The Zire Nahre Torab Neighborhood is one of the Parsabad city’s informal settlements that located in the northwest of the city. Regarding the possibility of an earthquake in the city of Pars Abad, identification and assessment the vulnerability of the neighborhood during an earthquake, is essential. Therefore, identifying and assessing the vulnerability, especially in the poor neighborhoods to offer strategies for dealing with the injuries, is essential. The aim of this study is assessing vulnerability of the informal settlements during an earthquake by using spatial data and GIS. This study, have been prepared in fifth main parts including: introduction and background, methodology and presentation of case study, theoretical framework, analysis and conclusions.
This research in terms of the nature is practical and is descriptive and in terms of the method is analytical. Three methods including library, documentary and survey have been used for data collection. In the first phase, data and base maps were extracted from documents and reports of projects such as city comprehensive and detailed plans. Also, in this phase of the study data were updated. In the second phase, the problem, questions and research objectives were defined. In the third phase, the 3 criteria and 12 sub-criteria based on research literature and according to available data were selected. In the fourth phase, after preparation of databases related to each of the criteria in GIS, input layers were prepared for each of them. In the fifth step, the method of network analysis process (ANP) was used to determine the significance of criteria. In the sixth phase, the weighted overlay index (WOI) was used for combining output layers.
The results of this study show that more than 80% of neighborhood buildings are vulnerable against the risk of a possible earthquake. Also, research findings suggest that physical characteristics such as building structure, quality and age of the buildings will have the greatest role in determining the neighborhood buildings vulnerability level. Doing activities such as resisting buildings, improving roads, locating facilities in appropriate places, training and informing citizens to prevent a crisis caused by the possible earthquakes, is essential. Other recommendations are listed in below:1.Identifying vulnerable buildings
2.The use of GIS in the management of settlements
3.Preparations cities, to deal with urban hazards
4.Empowering citizens to deal with environmental hazards
5.Action to reduce earthquake risk
6.Civil engineering Renovation of buildings
7.New practices in the urban construction
8.Equip cities with facilities and relief supplies.
9.The use of specialists in urban planning.
10.Conducting workshops on urban resilience.

Understanding the changes in extreme precipitation over a region is very important for adaptation strategies to climate change. One of the most important topics in this field is detection and attribution of climate change. Over the past two decades, there has been an increasing interest for scientists, engineers and policy makers to study about the effects of external forcing to the climatic variables and associated natural resources and human systems and whether such effects have surpassed the influence of the climate’s natural internal variability. The definitions used in the 5th assessment report were taken from the IPCC guidance paper on detection and attribution, and were stated as follows: “Detection of change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense without providing a reason for that change. An identified change is detected in observations if its likelihood of occurrence by chance due to internal variability alone is determined to be small. Attribution is defined as the process of evaluating the relative contributions of multiple causal factors to a change or event with an assignment of statistical confidence”. Detection and attribution of human-induced climate change provide a formal tool to decipher the complex causes of climate change. In this study the optimal fingerprinting detection and attribution have been attempted to investigate the changes in the annual maximum of daily precipitation and the annual maximum of 5-day consecutive precipitation amount over the southwest of Iran.
This is achieved through the use of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources Project(APHRODITE) dataset as observation, a climate model runs and the standard optimal fingerprint method. To evaluate the response of climate to external forcing and to estimate the internal variability of the climate system from pre-industrial runs, the Norwegian Climate Center’s Earth System Model- NorESM1-M was used. We used up scaling to remap both grid data of observations and simulations to a large pixel. This remapped pixel coverages the area of the southwest of Iran. The optimal finger printing method needs standardized values like probability index(PI) or anomalies as input data, since the magnitude of precipitation varied highly from one region to another. The General Extreme Value distribution (GEV) is used to convert time series of the Rx1day and Rx5day into corresponding time series of PI. Then we calculated non-overlapping 5-year mean PI time series over the area study. In this research, we applied optimal fingerprinting method by using empirical orthogonal functions. The implementation of optimal fingerprinting often involves projecting onto k leading EOFs in order to decrease the dimension of the data and improve the estimate of internal climate variability. A residual consistency test used to check if the estimated residuals in regression algorithm are consistent with the assumed internal climate variability. Indeed, as the covariance matrix of internal variability is assumed to be known in these statistical models, it is important to check whether the inferred residuals are consistent with it; such that they are a typical realization of such variability. If this test is passed, the overall statistical model can be considered suitable.
Results obtained for response to anthropogenic and natural forcing combined forcing (ALL) for Rx1day and Rx5day show that scaling factors are significantly greater than zero and consistent with unit. These results indicate that the simulated ALL response is consistent with Rx1day observed changes. Also, it is found that the changes in observed extreme precipitation during 1951-2005 lie outside the range that is expected from natural internal variability of climate alone and greenhouse gasses alone, based on NorESM1-M climate model. Such changes are consistent with those expected from anthropogenic forcing alone. The detection results are sensitive to EOFs. We estimate the anthropogenic and natural forcing combined attributable change in PI over 1951–2005 to be 1.64% [0.18%, 3.1%, >90% confidence interval] for RX1day and 2.5% [1%,4%] for RX5day.

Urmia Lake is one of the largest hyper saline lakes in the world and largest inland lake in Iran which located in the north west of Iran, between the provinces of East Azerbaijan and West Azerbaijan. The lake basin is one of the most influential and valuable aquatic ecosystems in the country and registered as UNESCO Biosphere Reserve. In addition, it is very important in terms of water resources, environmental and economic. Unfortunately, lake water level has dramatically decreased in recent years, due to various reasons. This issue has created some problems for Local people, especially people living in rural area in east of the Lake. The results of this research are of great importance for regional authorities and decision-makers in strategic planning for people of inhabits in east coast village.
The present paper is an attempt to integrate a semi-automated Object-Based Image Analysis (OBIA) classification framework and a CA-Markov model to show impacts of Urmia Lake Retrogression On eastern coastal villages. OBIA present novel methods for image processing by means of integration remote sensing and GIS. Process and outcome of this methodology can be divided in three step including: Segmentation, Classification and Accuracy assessment.in the process of segmentation aims to create of homogeneous objects by considering shape, texture and spectral information. A necessary prerequisite for object oriented image processing is successful image segmentation. In our research the segmentation step was performed by applying multi-resolution segmentation and considering 0.2 for shape and 0.4 for the compactness. The scale of segmentation is also an important option which leads to determine the relative size of each object. Having great values for scale leads to create large objects while smaller value would result small objects respectively. In this study the scale parameter of 100 has been selected based on the size of objects in Scale of study area as well as spatial resolution of the satellite images were used for segmentation. In doing so, we employed spectral and visual parameters contains: texture, shape, color tone and etc. for developing object based rule-sets. To determine the characteristics of the spectral data and geometric features classes the fuzzy based classification was performed by employing fuzzy operators including: or (max) operator with the maximum value of the return of the fuzzy, the arithmetic mean value of fuzzy and the geometric mean value of fuzzy, and (min). After this step, the validation process was performed by using overall accuracy and Kappa coefficient. Then, using the CA-Markov Model The trend of changes was predicted in the future (For 2020). Another way to predict changes in land use and cover, used the CA-Markov model. Markov chain analysis is a useful tool for modeling land use changes. Markov chain model consists of three step: First step Calculating the probability conversion using Markov chain analysis, second step, Calculating the Cover and land use maps competently on the basis of multi-criteria evaluation, third step, assign locations cover and land use simulation based on the CA position operator.
Results of Satellite image processing indicate that the area of garden, Farmland, Zones of muddy-salty (Saline soils), moist salt and newly formed salt have increased while area of Urmia lake has rapidly dropped between 1984 and 2015. The area of Urmia lake declined from 4904.51 square kilometers in 1984 to 676.79 square kilometers in 2015. The farmland area increased from 177.72 square kilometers in 1984 to 542.37 square kilometers in 2015. The garden area increased from 83.71 square kilometers in 1984 to 227.28 square kilometers in 2015. The moist salt area increased from 111.89 square kilometers in 1984 to 945 square kilometers in 2015. Zones of muddy-salty (Saline soils) area increased from 859.01 square kilometers in 1984 to 2986.5 square kilometers in 2015. The newly formed salt increased from 171.27 square kilometers in 1984 to 921.99 square kilometers in 2015. Markov chain model results indicate in 2020 the garden area will be 638 square kilometers, the moist salt area will be 717 square kilometers, Zones of muddy-salty (Saline soils) area will be 4127 square kilometers, the farmland area will be 644 square kilometers, the newly formed salt area will be 363 square kilometers and the Urmia lake area will be 118 square kilometers.

Drought is one of the most important hazards that occur in all the earth especially in arid and semi-arid climates. Every year, about half of the earth’s surface experienced droughts and while drought is not a constant feature of any climate but occur more frequently in arid and semi-arid regions of the world. Although the occurrence of droughts cannot be prevented but by studying the nature and characteristics of droughts and also identify factors that affecting their occurrence useful information can be gained about drought and their destructive effects. The researches in recent years designed and proposed a lot of indices to study and analyze the droughts and today various characteristics such as intensity, duration, area and so on with these indices are studied. Many indices used by researches to analysis and identify properties of climatic droughts and dry periods. In these indices often the variables of precipitations, combination of precipitations and temperature, humidity or evaporation, crops yields and teleconnection climatic indices are used.
In this study using the CPEI index and 30 years (1980-2009) daily rainfall data in 40 synoptic stations overall Iran, to analysis and assess of Iran droughts suitable variables detected. Four seasons and annual period is considered in this study. To determine the appropriate variables in the design of suitable models and modeling of drought to assess and predict droughts Otun in 2005 proposed CPEI index as Conjunctive Precipitation Effectiveness Index. He selected 10 conjunctive precipitation variables as ORS(Onset of Rainy Season), CRS(Cessation of Rainy Season), LRS(Length of Rainy Season), TWD(The Total no of Wet Days), TDS(Total no of Dry Spell), TDW(Total no of Dry Days within a Wet Season), TDY(Total no of Dry Days within a Year), LDS(Length of the Dry Season), MDL(Maximum Dry Spell Length within a Wet Season), MAR(Mean Annual / Seasonal Rainfall Depth) and determined the relationships between variables in each synoptic stations and climatic regions. Since the units of measurement the rainfall variables are diverse, it is essential that the units be converted to a standard unit, in other words variables be standardized. The relationship between variables was determined by Pearson correlation coefficient. Finally, the right combination of precipitation variables for each station through the proposed formula Otun(2005) were determined. In the end, for each of the seasons and the annually period regionalization maps were prepared.
All 40 synoptic stations were evaluated by Otun’s method (Aton, 2005). The results showed that 95 percent of stations in spring, 75 percent in fall, 57 percent in winter and 75 percent in annual period are compatible with used method. Thus, spring, fall and winter seasons and also annual period are compatible with above mentioned index. Among the used variables MAR, MDL, TDY and TDS which with respectively are as follows: total amount of precipitation in any period, the maximum duration of dry periods in a wet period, the total number of dry days in a wet period and the total number of dry period during wet period among the stations are more abundant. In annually period, in addition to the above mentioned variables, precipitation variable of LPS (length of dry period) also seen among some stations. Also, results showed that CPEI index can be used on most stations and climatic regions of Iran. It was also found that the spring compared the other seasons and annual period is more comparable on the base of CPEI index.
Otun in 2010 used the CPEI index in semi-arid region of Nigeria and has achieved good results. The results of our study show good agreement with Otun’s work. The use of this index in the study of meteorology, climatology, agriculture and many environmental projects can be beneficial because in many of these fields of study, precipitation and its characteristics have an important role. In general we can say that in regions where CPEI index does not show a high proportion or set of variables are not enough it is better to use other indices such as SPI and RAI. The results obtained in similar climate zones such as Nigeria has shown that CPEI index has very good ability to identify and explain the precipitation effectiveness variables which can be used in modeling of droughts and dry periods. There are many similarities between combination of precipitation variables that identified by CPEI index for Iran and other regions of the world. Similarities, especially with respect to MAR, MDL, TDY and TDS are abundant.

Today slum refers to those areas of the city which are not necessarily situated at the corners of the city, but to those which are in margins from economic, social, cultural, and other urban life aspects, that has formed a settlement in which the least living-supplies of healthy water, electricity and gas, transportation system and a clean environment suffice their lives. This type of settlement is due to the asymmetry and commonality of features and conditions of living in the main parts of the city. And generally indicate the low level of living conditions in comparison with the average standards in the main city specifically, and also in living conditions in cities as a whole. On the other hand, informal settlement refers to the discordance of settlement with the approved regulations of governmental organizations and particularly of municipalities. Those areas which are situated outside the servicing scope of the general and governmental organizations such as electricity, gas, and telecommunications offices, along with municipalities accompany various phenomena such as urban poverty, poor housing, immigration from countryside to cities, environment pollution, unhealthy environments and etc.
In Iran, slum began in the 30s (solar calendar) with the immigration of village dwellers to the cities, and after a decade, it was prospered due to land reforms and economic-social policies of the day, a growing increase which has never stopped since. Slum or informal settlement in the outer parts of the cities is not just a physical notion but is an outcome of the macro structural factors in economic, social, cultural, and political aspects in a national or regional scope. The reasons for this phenomena vary which can differ from one place to another. Nevertheless immigration is one of the main reasons for slum settlements. It can simultaneously play two roles; it can be a solution to demographic crises. It leads the surplus population out of the region and accordingly the human power is directed where is needed most. It balances the structural asymmetries of population and by reducing the development imbalances in different regions result in the betterment of the status quo. And on the other hand, it might be possible that by immigration of the human power, the economic equilibrium between the source and destination community would be disturbed, and by having a community without any human power, it generates complex social and cultural situations; which all in all leads to a congested crowd overpopulating specific big cities and regions. In this way, it brings about problems in servicing and efficient regulation of issues and thus be regarded as a disturbing element of development and mutual understanding. The investigated region has been exposed to the crises of immigration and slum settlements recently, so much so that based on the population and housing census of 2006, population growth rate of Shahriar rose by a far distance from other cities to 8.7 percent. Thus, this research was conducted to investigate the elements of immigration and slum dwelling in Shahriar city. And it aims to answer these questions:1.How social, economic, and demographic factors influence the slum settlements of those who have migrated to this city?
2.How is the local dispersion in Shahriar?
On this basis, with the researches and investigations conducted at the outset of the study, district 2 was selected as a fit choice out of the three districts of 1, 2, and 3 which settled slums. Since all the locals were not slums in this specific districts, with proper investigation the slum areas were identified which had a high rate of immigration; with whom interviews were ran and questionnaires distributed. To this end, by following Cochran formula, 200 people were selected as samples through cluster random sampling out of the statistical community. To analyze data, descriptive statistical methods such as central index, dispersion and inferential statistics like Chi-square, Wilcoxon and Friedman tests were utilized.
The results of the study indicates that the slum in Shahriar are situated in the old and cheap sections of the three districts of 1, 2, and 3. Also, after a detailed examination it was proved that Shamloo local in district 2 is more suitable than the other ones. On the other hand, by investigating the economic factors (such as job opportunities and income) it was indicated that immigration is very important from the aspect of providing job opportunities. Secondly, social factors are more important in slum settlements issues. For instance, one can refer to urban and welfare facilities, educational facilities, health and recreation facilities are all social factors. On the other hand, those people who have migrated due to pursuing education, higher level of welfare, better facilities etc. are all below 30 years old. Based on the findings of this research, families were not significantly changed after immigration in comparison with the period before it, but it is a vital element in three membered families in times of immigration. All has been done to meet the financial needs of the family. Therefore, one can claim that most immigrations to slum areas have been due to economic and social deficiencies of the source society.