Assessment of the spatial difference at the level of neighborhoods based on accessibility variables in Yazd city, Iran

Neighborhood accessibility improvement has been recently one of the main challenges of Yazd municipality, Iran, due to population growth. In response to this problem, the municipality established a special commission, called Transportation and Commuting Commission in 2022 and decided to expand public transportation across the city. Since neighborhood accessibility assessment can help to identify low-accessible neighborhoods to allocate financial resources more efficiently, this article investigated spatial dissimilarity between neighborhoods according to three accessibility variables of population, land use, and street network in Yazd city. Since the scale of the variables was different, they were normalized to be used in the K-mean cluster method in SPSS to classify neighborhoods into five clusters regarding the global level of accessibility. Before using the K-mean cluster method, the number of clusters was determined by the hierarchical cluster analysis in SPSS. In the next step, the local Moran index was carried out in ArcGIS to examine whether there was a statistically significant difference between neighborhoods according to their level of accessibility variables. Besides, the Pearson correlation coefficient was applied in SPSS to explore the relationship between population, land use, and street network. According to the local Moran index, there was a spatio-statistically significant difference between some neighborhoods considering the global accessibility level. However, the Pearson correlation coefficient showed only a significant but negative correlation between population and land use, implying that population and land use were more influential than the street network in determining the total accessibility of neighborhoods.