Analysis of Social-Spatial Separation Space in Inefficient Urban Contexts (Case Study: Qaitarieh Neighborhood of Tehran Metropolis)

Introduction :

Hasty management decisions, non-specialist urban management, and the lack of detailed studies in development plans in cities and especially metropolises have led to the creation of neighborhoods with inefficient textures. One of the consequences of these decisions is the creation of a problem called segregation in urban neighborhoods, which has resulted in the separation of different population groups, which can reduce the socio-economic interactions of the residents of the neighborhoods. Around it, there is a decrease in the entry of the investment sector for the development of these contexts, a change in the attitude of most citizens towards choosing their residential space, the migration of the main residents, and the entry of non-native population into the context and creating many problems for the residents of the said context. As a result of this separation, the contacts between the members of this poor class are high and inter-class communication decreases, and with the decrease of the middle class living in the inefficient context, the social anomalies in the neighborhoods become severe. The dilapidated fabric located in Qaitarieh neighborhood with a population of more than 3 thousand people is located in the old fabric of this neighborhood. The reason for choosing Qaytarieh neighborhood is the existence of fabric deterioration in a part of the neighborhood and its central core and the existence of the same socio-economic characteristics in two parts of the neighborhood (inefficient fabric area and the rest of the neighborhood). Of course, paying attention to the importance of the neighborhood as the basic element of the urban planning system in the basics and issues of urban planning and the level of satisfaction of citizens with their residence and their participation in the improvement process of neighborhoods and the lack of sufficient attention in this field, multiplies the importance and necessity of doing this research. In this regard, despite the existence of many problems in these contexts, investigating the relationship between these types of contexts and the issue of social-spatial segregation can be considered one of the most important issues of urban neighborhoods. Therefore, in this research, physical and social separation is investigated in the worn-out and inefficient context of Qaitarieh neighborhood of Tehran.

Materials and Methods :

The research method in the present study is applied in terms of purpose and descriptive-analytical in terms of method. The information available in the statistical blocks of Tehran city in 2015, detailed plan data, and the questionnaire were used to collect the research data. Different models have been defined to investigate the distribution of social and physical-spatial phenomena in the geographic information system environment. The nearest neighborhood average model, Moran’s local spatial autocorrelation model, and hot and cold spot analysis model are the models that are used in this research. The spatial autocorrelation analysis tool of Moran’s statistic also evaluates the spatial distribution pattern of complications and phenomena by simultaneously considering the spatial location and internal characteristics of these complications. The nearest neighborhood average model is one of the clustering tests used to determine phenomena’ distribution patterns. Based on this method, an index called Rn (proximity rate) is obtained, the range of which varies between zero and 2.15. This index of dispersion expresses the manner and pattern of the spatial distribution of phenomena and elements in the study area.

In this part of the research, 30 indicators have been used in three economic dimensions with six indicators, a social-demographic dimension with 15 indicators, and a physical dimension with nine indicators. The indicators used to measure the separation of the economic dimension from the statistical blocks of the population and housing census of 2015 are employment rate, sponsorship burden, activity rate, rental rate, ownership rate, and employment sex ratio. In order to measure the classes of the economic dimension, the TOPSIS model has been used, the output of the model was added to the statistical blocks in ArcGIS Pro 3.0, and by using the quantitative classification method, the classification of the population groups was done, which included 34.6 blocks in the lower class, 32.7% in the middle class, and 32.7% in the upper class. According to Moran’s model, in general, the distribution pattern of the economic dimension is clustered, and the index value is equal to 0.142. 15 indicators have been used to separate the social dimension, of which four indicators of the sense of belonging, peace, and tranquility, social relations, and trust were calculated through a questionnaire and 11 other indicators (literacy rate, household dimension, sex ratio, population density, immigrant ratio, Divorce ratio, sex ratio of literacy, sex ratio of the student population, percentage of the elderly, sex ratio of the elderly, the proportion of female heads of households) were calculated by calculating the indicators on the statistical blocks of the population and housing census. In order to measure the socio-demographic classes, the TOPSIS model has been used, and the output of the TOPSIS model is connected to the statistical blocks in ArcGIS Pro 3.0, and by using the quantitative classification method, the demographic groups have been classified, which is 34.6 blocks per class. Below, 32.7% are in the middle class and 32.7% are in the upper class. The global Moran index is equal to 0.007 and the distribution pattern is also random. In order to measure physical-spatial dimension classes, the TOPSIS model has been used, and the output of the TOPSIS model is linked to the statistical blocks in ArcGIS Pro 3.0, and population groups have been classified using the quantitative classification method; 34.6 blocks are located in the lower floor, 32.7% in the middle floor and 32.7% in the upper floor. According to the global Moran’s model, the distribution pattern of the physical-spatial dimension is clustered and the Moran’s index is 0.008. 

In the economic aspect, among the six indicators studied, the weight of the rental rate equal to 0.354 has the highest weight and the activity rate with the weight of 0.052 has the lowest weight. According to the economic indicators, 34.6 blocks are in the lower class, 32.7% are in the middle class, and 32.7% are in the upper class. Also, according to Moran’s model, the distribution pattern of the economic dimension is generally clustered and the amount of the index is also equal to 0.142. Based on the weighting output of the AHP model, among the investigated indicators in the socio-demographic dimension, the proportion of immigrants with a weight of 0.171 has the highest weight and the index of the household dimension with a weight of 0.019 has the lowest score. In the physical dimension, 11 indicators were investigated, the wear index of the parts has the highest score and its effect is equal to 0.224, and the household in the residential unit with a value of 0.028 has the lowest value in selection. From the total blocks of the neighborhood, 34.6 blocks are located on the lower floor, 32.7% on the middle floor, and 32.7% on the upper floor, and according to the global Moran model, the physical-spatial dimension distribution pattern is a cluster, and the Moran index is equal to 0.008. The selective segregation of the socio-demographic dimension of single-group indicators in the uniformity dimension is equal to its average at the neighborhood level of 0.6, in fact, it is located on the border of high segregation. In terms of the level of separation, the middle class is in first place with a score of 0.6964, the upper class is in second place with a score of 0.6776, and the lower class is in third place with a score of 0.658. In general, the average of this index for all three classes is equal to 0.67. According to Iceland et al.’s classification, it is located on the upper floor. However, since it is slightly away from 0.6, it is slightly beyond the threshold. Also, according to the entropy index, whose average is equal to 0.18 and because it is less than 0.3, separation has occurred at a low level. The Gini index is also equal to 0.683 and because it is higher than 0.6, separation has occurred at a high level. The highest level of separation is in the middle class with a score of 0.697, in the second place in the upper class with a score of 0.68, and in the lower class with a lower degree of separation its value is equal to 0.66 and in all levels of the Atkinson index, high-level separation has occurred and the average index is equal to It is 0.678.