Empowering local communities with strategies for monitoring and eliminating poverty in urban areas (Case study, District 9 of Tehran)

Abstract Usually, developing countries use the capacity of governments and municipalities to provide appropriate services to the population. This category will increase poverty in cities and urban areas. This article aims to empower local communities with strategies to monitor and eliminate poverty in the neighborhoods of District 9 of Tehran. In this paper, we used the four geographic models in ArcGIS 10.3 for the spatial analysis and assessment of poverty, which includes Getis-OrdGi* (G-i-star) statistics (hot and cold spots), Moran's spatial autocorrelation, Vikor model and SWOT. First, from the economic, sociocultural, and physical indicators, urban poverty was identified at the neighborhood level in District 9 of Tehran. A quantitative model (Vikor) was used to rank the indicators. Getis-OrdGi* (G-i-star) (hot and cold spots) was used to display and spatially analyze urban poverty at the locality level of District 9. In addition, using the QSPM model, internal and external strategies of urban areas were identified. Moran's spatial autocorrelation was used to correlate the indicators. After placing the target locations, the SWOT technique was used to present the strengths and weaknesses of the target locations. The results showed the highest correlation between sociocultural indicators and urban poverty is in the southern neighborhoods of District 9 of Tehran, which corresponds to South Mehrabad, Shamshiry, South Sarasiab, and part of the Imamzadeh Abdullah neighborhood. In addition, due to high population density, immigration, and low land prices, the impact of the socio-cultural index on urban poverty in the north of District 9 has increased with a confidence level of 99%. While the hot spots of the economic dimension of poverty are at the level of districts of District 9 of Tehran, the southern and southeastern districts due to unemployment, high rent, and low household income, economic poverty clusters have been formed in terms of spatial autocorrelation, Moran's Index is 0/026180 showed. However, in the spatial distribution of poverty in terms of the physical dimension at the level of neighborhoods in District 9 of Tehran, nearly 10% of the neighborhoods have the worst poverty level. Only the Shamshiry neighborhood has the lowest per capita commercial, sports, and health facilities among the neighborhoods. Regarding Moran's spatial autocorrelation pattern, Moran's index and Z-score in the Shamshiri neighborhood showed 0.007270 and 4/224861, respectively. In addition, people's and non-governmental organizations are essential and effective strategies for monitoring and eliminating poverty at the level of target neighborhoods.