Urban poverty patterns in Italy: the case of Milan

Urban poverty is one of multidimensional issue in Nepal. Increasing immigration from the outer parts of Kathmandu due to rural poverty, unemployment and weak security of the lives and the properties are core causes pushing people into urban areas. In this context how squatter urban area people sustain their livelihoods is major concern. The objectives of the study are to find out livelihood assets and capacities squatters coping with their livelihood vulnerability in adverse situation. Both qualitative and quantitative methods are applied for data collection. It is found that squatters social security is weak, victimized by severe health problems earning is not regular with lack of physical facilities and overall livelihood is critical. This study helps to understand what the changes that have occurred in livelihood patterns and how poor people survive in urban area.

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Dynamique De La Pauvrett Urbaine AU BBnin: Une Analyse En Termes DDEntrres Et Sorties (Dynamics of Urban Poverty in Benin: An Analysis of Inputs and Outputs)

SSRN Electronic Journal ◽

10.2139/ssrn.3171567 ◽

2008 ◽

Author(s):

Mededji Damien

Keyword(s):

Urban Poverty ◽

Inputs And Outputs

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Urban Poverty & Health in Developing Countries: Household & Neighborhood Effects - A Summary

SSRN Electronic Journal ◽

10.2139/ssrn.910184 ◽

2006 ◽

Author(s):

Asis Kumar Pain ◽

Rituparna Bhattacharya

Keyword(s):

Developing Countries ◽

Neighborhood Effects ◽

Urban Poverty

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ENHANCING THE QUALITY OF URBAN LIFE IN URBAN POVERTY AREAS THROUGH A STRATEGY OF INTEGRAL MULTI APPROACHES

10.21625/resourceedings.v1i1.175.g76 ◽

2018 ◽

Author(s):

Ahmed Elewa

Keyword(s):

Urban Poverty ◽

Urban Life ◽

Poverty Areas ◽

Quality Of Urban Life

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Urban Poverty in a Cross-cultural Context.Edwin Eames , Judith Granich Goode

American Journal of Sociology ◽

10.1086/225840 ◽

1974 ◽

Vol 80 (2) ◽

pp. 589-590

Author(s):

Remi Clignet

Keyword(s):

Urban Poverty ◽

Cross Cultural

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Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽

10.3390/land10060648 ◽

2021 ◽

Vol 10 (6) ◽

pp. 648

Author(s):

Guie Li ◽

Zhongliang Cai ◽

Yun Qian ◽

Fei Chen

Keyword(s):

Machine Learning ◽

High Resolution ◽

Satellite Imagery ◽

Low Income ◽

Urban Poverty ◽

Image Features ◽

Housing Affordability ◽

Learning Approaches ◽

Housing Inequality ◽

High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

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