Analyzing spatial linking of environmental factors and the Ssocial status of Tehran’s residents viewed from environmental justice

ABSTRACT
This research aimed to investigate the relationship between the spatial distribution of socio-economic classes and environmental benefits and burdens in Tehran. The components of the independent variable include green space coverage, the number of clean days, and the environment's health. With equal Euclidean distance, 62866 random points are the basis for preparing data and creating information layers for each GIS index. Spatial analysis (Interpolation-IDW), Spatial Join, and Fuzzy Overlay functions in ArcGIS are employed to analyze the data. Findings show that the correlation between the distribution of social classes and the distribution of the number of days with clean air is direct, and it has an inverse relationship with green space coverage and environmental health. In general, there is an inverse relationship between the spatial distribution of economic classes and environmental factors in Tehran. Spatially, in the west of the city, the areas with the highest correlation are located next to the areas with the lowest correlation. Over time, the spatial relation between economic classes distribution and environmental benefits and burdens has been moderated.
Extended Abstract
Introduction
Cities are usually characterized by various socio-economic groups based on income, social status, gender, job, race, ethnicity, age, and ability. The poorer and lower-income classes, as well as ethnic and racial minorities, are usually pushed towards the more dangerous, vulnerable, and crowded areas of cities, and this condition increases their vulnerability to environmental threats. It also provides them with fewer opportunities to enjoy facilities and services. The unequal distribution of environmental benefits and burdens has raised the concept of environmental justice. Environmental justice can be defined as equal access to a clean environment and protection from environmental damage regardless of race, income, class, or any other distinguishing socio-economic characteristic. This paper aimed to determine the extent of environmental justice in Tehran and to determine to what extent the spatial distribution of social classes is compatible with the environmental benefits and burdens.
 
 Methodology
This is a descriptive-analytical research. The data on housing price (HS), as one of the dependent variables (DVs), is extracted from the Ministry of Roads and Urban Development website (the price of one square meter of residential building in February 2024). Another variable is the residential area per capita (RAP). The data is obtained by calculating the GIS file of the 2016 Iranian Public Census of Population and Housing statistical blocks; household economic data, especially income and expenditure data, is not available, considering that housing and its price is a suitable indicator of the households’ economic groups, the multiplication of the two variables of HS and RAP are selected as dependent variables. It is named housing credit (HC). The components of the independent variable (IDV) and their sources of the data are as follows: green space coverage (GSC) (GIS file that is received from Tehran Municipality (TM), number of clean days (measurements from 35 air quality measurement stations of TM), environmental health (information about the number of sweeping workers, waste collection centers, carrier vehicles, recyclable waste buyer centers (received from the Urban Services Organization of TM).
The Z-Score formula is used to normalize the figures. Data collection is based on 352 points representing the geometric center of each neighborhood.  Interpolation-IDW is used to drive zoning maps. The vector layers are converted into Raster layers to make point layers with coordinates corresponding to 352 points. ArcGIS software and Spatial Analyst, Spatial Join, and Zonal Statistic functions are used to generate the required data in digital layers. Analytical models include Geography Weighted Regression and Ordinary Least Square. Band collection statistics tools are also used to calculate the correlation. Tehran, with an area of 615 km2 and a population of about 7.8 million people, is the area studied in this research.
 
Results and discussion
The zoning maps of HS show that it increases from south to north. The highest HS belong to the municipality districts (MDs) of 1, 2, and 3, respectively, and districts 20, 14, and 18, respectively, have the lowest residential housing prices among the total of 22 MDs. There is a 13.8 times difference in the average price between District 1, the northernmost district, and District 20, the southernmost district of the municipality. The housing area per capita (HAP) increases from west to east and south to north. The average HAP in Tehran is 23.41 m2. The three districts of TM with the highest figures of HC are District 3, District 2, and District 1, respectively. Also, the lowest RAP belongs to Districts 12, 19, and 9, respectively. There is a difference of about 21.2 times between districts 3 and 12.
In the fringe parts of the west, south-west and south of the city, the HC is lower than other parts. In general, if we divide the city into two halves of south-west and north-east, the HC is much lower in the south-west half. Among the MDs, the three districts with the highest figures are Districts 1, 2, and 3, respectively.
Overall, in the areas where higher income classes live, ecological indicators are in a weaker situation. Here, the green space coverage shows the highest inverse correlation figure. Also, the lowest correlation figures belong to the indicators of health and cleanliness of the environment, which show an inverse correlation. Higher-income households live in parts of the city where the number of days with clean air per year is higher. In the western corner of the city,  the areas with the lowest correlation between DP and IDP are next to the areas with the highest correlation.
 
Conclusion
Tehran has long been known for adapting to the geographical and socio-economic north. However, since the revolution of 1978, changes happened that have disrupted the situation to a great extent. Since these years, urban management's control over urban planning has been weakened, and some rules and regulations have been revoked. The chaotic conditions during the imposed war led to the arrival and settlement of various economic households of low-income immigrants who could create informal settlements anywhere, including in the city's northern half. The construction of residential complexes anywhere in the northern areas of the city by worker and employee housing cooperatives and especially military organizations' coopratives, during the war and after it, caused a large number of employees and soldiers as middle and lower economic groups to be settled in the northern half of Tehran.
 
Funding
There is no funding support.
 
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
 We are grateful to all the scientific consultants of this paper.