Identification of the Components Influencing Urban Poverty (Case Study: Akhmafhayeh Neighborhood of Tabriz)

Introduction Urban poverty, generally appeared with unemployed or expelled workers and poor rural immigrants, transforms social structure of spatial space in urban areas. Urban poor groups are the rural community (from immigrants to the city), as well as production poverty and reproduction in urban society (by intra-urban displacement) and it is known for its characteristics, such as high commercialization rates, higher health and environmental hazards, social rupture, crime and conflicts with the government. Urban poverty and its spatial extent are among the most challenging issues in most of cities of Iran. Given that urban poverty is increasing day by day in Tabriz metropolitan area, therefore, it is necessary to consider the poverty through scientific methods to determine the effective factors. There are many poor and very poor urban quarters in the past  in the urban spaces of Tabriz, the most important of these spaces are in the southwest part of the city (Aqmaqiya sector). Aqmaqiya is one of the old sectors in the  district 7 of the city, next to the Tabriz to Khosroshahr highway. This sector was one of villages in the vicinity of Tabriz city in the past and after the expansion of this city it was known as one of the Tabriz sectors. Presence of these spaces in the metropolitan city of Tabriz gives them the need to identify the factors affecting the scope of urban poverty. In this study, we have attempted to identify the factors affecting urban poverty by providing an appropriate framework for policymaking of urban managers in order to reduce the extent of urban poverty. Methodology Given that this investigation seeks to develop applied knowledge in identifying the components influencing the extent of urban poverty, this study is an applied research in terms of purpose and descriptive-analytical in terms of method. We have examined the households in the neighborhood of Aqmaqiya in Tabriz city, in district 7 of the municipality. This neighborhood in the past was known for the villages around the city of Tabriz. After the expansion of this city, the villages are considered as the neighborhoods of Tabriz City. To determine the sample size, we have followed the special rules of the partial least squares method as the model used in this research, so that the sample size required in the modeling of the partial least squares method is considerably smaller than the structural equation method. In this method, we have one of the newest rules for sample size selection. It suggests that the sample size should be equal to or greater than the number of structural indices with the highest number of combinations of reagents; ten times the largest number of structural paths ending up in a particular structure in the internal route model. The sample size was 362 using the Cochran formula, which was reduced to 342 using the modified Cochran formula. Given the rule of partial least square model and some of the questionnaires, we have gatheredheterogeneous and unreliable data. Results and discussion Based on the output of PLS algorithm, load factor and observable variables and the Cronbach's alpha are acceptable for all obvious variables. According to the available data in the research, the average of variance criteria for hidden variables is more than 0.5 and their Cronbach's alpha is higher than 0.7. Redundancy and average subscriptions, as a measure of the quality of the model, are indicative of quality of the variables. As indicated, the values of 0.67, 0.33 and 0.19 for endogenous variables are described as significant, moderate and weak in the structural path model, respectively. The results of this study indicate that the obvious variables have the highest reliability and validity of the measurement model. The determination coefficient index shows percentage of the variation of the endogenous variable. The values of 0.19, 0.33 and 0.67 are weak, moderate and significant, respectively. The coefficient of magnitude of the complementary path is significant for model. At the values of 90%, 95% and 99% are compared with minimum statistics of 1.64, 1.96 and 2.58, respectively. In this study, model GOF index was found to be 0.688 that indicates the model overall utility. Conclusion The results show that all of the affected components in extent of urban poverty have a positive and significant role and there is an external relationship between the components oflack of social capital, lack of psychological capital, and the physical disability. The external indicators of consumer behavior and utility are effective on the demographic index, which means that the two indicators have internal relationship with their defined variables and external relationshipwith the extent of urban poverty of the target community. The internal and external relationships between consumer behavior and utility are considered in the demographic range with their variables and they are together in the next category. The third priority is also explained by a strong correlation between the indicators of social and political exclusion and disability with the extent of physical deprivation. The existence of a strong positive relationship between the components affecting urban poverty in the study area brings to mind that the components including absence of social capital, absence of psychological capital, and consumer behaviorare a key indicator to determine the extent of urban poverty in the study area. This finding is partly consistent with the results of the study by Das et al. (2014), which considers social indicators as the greatest impact on poverty. The results of this research are consistent with the radical theory and the view of poverty, which stemmed from the state of the political economy that crystallizes in social, political, and economic relations. The adversaries of the study view are cleared by investigating the structures that create poverty  to address root causes of poverty.