Zoning and Spatial Analysis of Urban Poverty (Case Study: Hamedan)

Currently, urbanization and poverty reduction are two important aspects related to global sustainable development. Eradicating urban poverty is one of the main priorities of the new United Nations Urban Plan. Urban poverty is a complex phenomenon that has undermined the sustainable development of some cities, regions and countries around the world. Systemic change and sustainable development policies require renewal and addressing the many problems that the nature of urban poverty has for housing, environment, health, education, social security, livelihood, and special needs create vulnerable groups. In the meantime, it is necessary to identify the different dimensions of poverty in order to try to solve the existing problems of Hamedan city. In general, it can be said that the socio-political and economic structures of the country are responsible for urban poverty and the emergence of marginalized, poor people and informal housing in Hamedan city and the suburbs, which leaves poor people with the least facilities in these areas. Therefore, today, in the effort to achieve human development, measuring and determining urban poverty is an issue that is pursued in human development policy and management in many countries facing it, and this requires the adoption of scientific methods, especially in geographical determining of the urban poverty zones through the use of statistical methods and the definition of appropriate indicators to recognize the different dimensions of urban poverty. In this regard, in the present study, an attempt has been made to answer the following question:
-What is the spatial distribution of urban poverty indicators in Hamadan city?

Methodology
The present research is applied and descriptive-analytical in terms of purpose, nature and research method, respectively. The statistical population of this study is Hamedan city in 2016. The data related to the theoretical foundations of the research have been prepared in a library and documentary manner and the raw data of the research have been extracted from the statistical blocks of Hamadan in 2016. Due to the large number of indicators used to measure the amount, in order to reduce the number of indicators to several factors, factor analysis was exerted in the SPSS software. Factor analysis is a multivariate method used to summarize or reduce data. Thus, this method converts many variables that explain a subject into a smaller number of hidden dimensions that are called factors. In the first part, confirmatory factor analysis was performed. Then, in order to achieve better results, exploratory factor analysis was performed. Using factor analysis method, 18 indicators were classified into 4 factors. By combining four factors, the final factor of poverty was obtained. Hot spot analysis was performed first for all four factors and then the final factor (combination); by converting the hot spot map of each factor to a raster layer, a poverty zoning map was drawn. Finally, to measure spatial autocorrelation, the local Moran model in the GeoDa software was used. This method reveals the structure of spatial autocorrelation within regions by identifying local clusters with high or low values and regions with a greater share in overall spatial autocorrelation. In fact, this method also identifies specific areas or a group of adjacent areas that have deviated from the general pattern of spatial autocorrelation.


Results and discussion
According to the zoning of the final cause of poverty in Hamedan, the concentration of poverty in the northeastern and eastern parts, namely the third and fourth districts, including Silo, Azadegan, Haj Enayat, Janat, Rezvan alley and Phase 2 civil town in District 3 of the municipality. Also, Sadeghieh, Motahhari and Farhangian neighborhoods include phases one to three in District 4 of the municipality, and the prosperous area in the central and southwestern parts, including Etemadiyeh, Black Alley, Imran, Beheshti and Mazdagineh in District 2 of the municipality. In total, the poverty zone includes 2647 blocks and 27.36 percent of the area and 48.84 percent of the population and 49.22 percent of the city household and the welfare zone includes 1622 blocks and with 20.47 percent of the area and 7.85 percent of the population and 8 percent of the household.

Conclusion
In this study, an attempt was made to investigate the distribution pattern of urban poverty in the blocks of Hamadan. For this purpose, 18 indicators extracted from statistical blocks have been used. The results of combining the indicators showed that the concentration of poverty is observed in the northeastern and eastern and western parts of Hamadan and the welfare zone in the central and southwestern parts. Other results showed that in total, the poverty zone accounted for 38.09% of urban blocks, 27.36% of area and 48.84% of the population and the welfare zone covers 23.34% of the urban blocks, 20.47% of the urban area and 7.85% of the city population. Also, based on the ranking of neighborhoods in terms of poverty, it can be said that the highest number with 32 neighborhoods belongs to the middle range with a class range (200.50-73.23), the second class with 20 neighborhoods belongs to the desirable spectrum with class range (0.00-73.23), and the third class with 12 neighborhoods is related to the undesirable spectrum with class range (200.50-499.81). Finally, using Moran statistics, the spatial autocorrelation of poverty in the city was investigated and the value of 0.677 indicates a cluster pattern with a high concentration of poverty in Hamadan city. The spatial distribution of poverty in Hamedan shows that inequality is increasing day by day and the city is moving towards polarization, which reflects the fact that Hamedan city is weak in terms of infrastructure investment and economic and social development. In fact, it can be said that the increase in poverty in this city is a reflection of poor administrative and political performance at various levels of urban, regional and territorial, which has led to intensified spatial injustice and increased dissatisfaction of the poor.