Identifying and Measuring Spatial Changes of Effective Factors in the Formation of Poverty Zones (Case Study: Gorgan)

Abstract

Poverty is one of the most important challenges of urbanization today. The fuzzy method was used to scale the indicators, the factor analysis method was used to measure poverty and the Moran spatial correlation method was used for spatial analysis. According to the results, the situation of some indicators such as livelihood burden, dependency burden, total unemployment, rented housing, aging rate, and divorce ratio has worsened compared to before. In the zoning of 1390, about 32.52% of the population and 26.12% of the area were in the poverty zone, while in 2016, this amount increased to 75.44% of the population and 48.22% of the area. Also, the growth of the number of blocks, area, population, and household in very poor and impoverished areas has been more than affluent and very affluent areas. A comparison of the percentage change of each of the four variables shows that the largest change is primarily related to the household variable and the population in the very poor area, which grew by 201 and 183%, respectively, and secondly by the household and population located in It is a poor area that grew by 163 and 135 percent, respectively. According to the results of the Moran model, urban poverty in both 1390 and 1395 in Gorgan has a pattern of cluster distribution and spatial correlation.

Keywords

• spatial pattern
• poverty zone
• percentage of change
• gorga