School of Urban Planning, University of Tehran, Tehran, Iran


BACKGROUND AND OBJECTIVES: One of the issues that have been evident in previous researches on urban poverty is the existence of a methodological gap in identifying spatial representation of urban poverty. This paper suggests a methodology for identifying the spatial representation of urban poverty and applies it to Isfahan Metropolis in Iran.
METHODS:A hybrid model of exploratory factor analysis and analytical network process was used with urban poverty indicators. Using the model, the compiled database consisted of 27 indicators with 12196 specific data per indicator was analyzed to determine the domains of urban poverty and relational importance coefficient of each indicator. A composite index of urban poverty was then constructed to evaluate urban poverty in each urban block. Also, the autocorrelation test and cluster and outlier analysis were used to find the spatial distribution pattern and concentrations of urban poverty in the metropolis.
FINDING: Seven domains of urban poverty in Isfahan metropolis were extracted which cumulatively explain about 57.3 percent of the data variance including “general poverty (13.25%), crowdedness in the housing unit (10.09%), economic poverty (9.462%), intrinsic poverty (8.23%), infrastructure poverty (6.243%), migrant’s poverty (5.276%) and unhealthy living condition (4.173%). Classifying urban blocks based on the composite index has shown that 9.8% of the population and 15.7% of urban blocks had the highest poverty rate. The autocorrelation test (Moran’s index=0.459; p-value=0.000) has indicated that urban poverty was clustered. Using Cluster and outlier analysis, it was determined that 70% of urban poverty concentrations were located in suburbs and peripheral districts.
CONCLUSION: Urban policymakers can adopt relevant policies in relation to various types of urban poverty identified in metropolises and determine policy priorities based on the weight calculated for each indicator. They can also suggest policies at the macro-micro levels using the urban poverty distribution pattern and concentration map.


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