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Analysis of Urban Poverty in Hong Kong : = A Spatio-Social Perspective.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Analysis of Urban Poverty in Hong Kong :/
Reminder of title:	A Spatio-Social Perspective.
Author:	Wang, Jionghua.
Description:	1 online resource (170 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Contained By:	Dissertations Abstracts International81-04A.
Subject:	Social work. -
Online resource:	click for full text (PQDT)
ISBN:	9781392389461

Analysis of Urban Poverty in Hong Kong : = A Spatio-Social Perspective.
Wang, Jionghua.

Analysis of Urban Poverty in Hong Kong :A Spatio-Social Perspective. - 1 online resource (170 pages)

Source: Dissertations Abstracts International, Volume: 81-04, Section: A.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2019.

Includes bibliographical references

Urban poverty is a phenomenon where poor people living in urban areas are deprived of necessities due to their lack of possessions. With the rapid development of cities globally, urban poverty has received increasing attention. Poor people often have limited access to the opportunities of employment, education, and health services or may have to deal with poor housing conditions or living environments. These phenomena make urban poverty a complex, multifaceted problem, but those factors are more than a collection of characteristics since urban poverty, as a dilemma, has deep roots in the social capital in urban areas. For the poor, then, facing their problems is a dynamic process that occurs under the specific urban context where they live and to what groups they belong. In other words, the urban context functionalizes as an undivided part of urban poverty itself. Given this, an appropriate measurement and impact assessment of urban poverty both rely on the inclusiveness of the urban context, which may differ across regions and social groups. Moreover, today, morethan half of the global population is living in cities, and this number is only increasing every day. Such transformations pose a challenge to the measurement and impactassessment method of poverty. Cities are covering more extensive territories and attracting more diverse populations-both of which may have various necessities in terms of importance and type, and they may also be impacted by poverty with different modes. Within this context, a geographically or socially heterogeneous method has been developed to model the various relationships, respectively. Wherein, geographically weighted regression (GWR) is one of the most popular algorithms to solve for heterogeneity from a spatial perspective. However, the literature indicates that few methods are designed for examining social heterogeneity in the poverty problem, especially in urban areas. Further, spatio-social heterogeneity is rarely solved simultaneously.In this thesis, the heterogeneity in urban poverty problem is examined from two perspectives: the poverty measurement and the poverty impact. The poverty measurement refers to the method for assessing the individual poverty level and the overall poverty situation with given circumstances (e.g., income, deprivation level, subjective poverty status), whereas the poverty impact focuses on the influence of poverty as a social determinant. In the context of spatio-social heterogeneity, the relationships between factors (e.g., income), poverty measurement, and poverty impact are modeled and later compared with the corresponding results from the conventional homogeneous, purely geographical, or social heterogeneity context.In terms of the methodology, this thesis extends the conventional GWR to a social and spatio-social weighted regression (SWR and SSWR, respectively). These algorithms allow this study to individually model the subregions and subgroups among the study area. The proposed algorithms thus provide a paradigm for analyzing social and spatiosocialheterogeneity. Wherein, the goodness of fit in the heterogeneity problems (e.g., subjective poverty line) is improved. The achieved improvement also implies that these modeling techniques (SWR and SSWR) are appropriate for other problems containing social or spatio-social heterogeneity. Further, the model results, coefficient models, are compared to depict the unique characteristics of each subdivided region and group. Meanwhile, the thesis also includes a traditional social heterogeneity detecting method, latent profile analysis (LPA), and investigates the spatial heterogeneity among its result. This thesis incorporates the spatial heterogeneity detector (hot-spot analysis) into a social heterogeneous modeling tool (LPA). The evidence for the co-existence of spatial and social heterogeneity is yielded, which may encourage more focus on the spatiality among other social heterogeneity studies.Hong Kong, a world-famous city in China, was selected as the study area to validate the proposed approach (spatio-social heterogeneity in poverty problem). In the first stage, the primary objective was to build up the spatio-social heterogeneity model for the poverty problem. With locally weighted regression, the thesis investigates the correlation between the subjective poverty line and its impact factors. The result, based on the coefficient models, confirms the existence of an adaptive effect (poor people with lower income may propose a lower subjective poverty line). The result of the coefficient models also shows that the adaptive effect on middle-aged citizens is relatively weaker according to the heterogeneous coefficient models for different age groups. Meanwhile, factors associated with subjective poverty status were tested. The results indicate that low mental health, like income, is also correlated with subjective poverty. By applying spatial clustering analysis, the finding further identified some regions (Yuen Long and Kwun Tong) where, compared to females, males are more likely to feel poor in Hong Kong. In the second stage, the thesis investigates the social heterogeneity between the poor and the majority. By building up models for these two groups (middle- to low-income group and the majority), the association between living conditions (housing and community) and multiple health outcomes is investigated. Two conclusions are drawn for this part of the research. First, middle- to low-income people are sensitive to poorer living conditions but potentially face more severe health outcomes when they have one problem (report positive on one living condition issue). Second, the significant social determinants (housing conditions and community environments) vary among the different health measurement metrics even though they both target the individual's general health status and are conventionally used exclusively. In this sense, it is better to evaluate the impact of one living condition with multiple health indicators. In the third stage, the thesis investigates the effect of income on subjective well-being using a conventional social heterogeneity modeling tool (LPA). Further, the classified results are verified with a spatial analysis tool (hot spot analysis). The result indicates that the socially heterogeneous subgroups are simultaneously spatial clustering, implying a co-existence of spatial and social heterogeneity.Overall, the findings of this thesis suggest that spatio-social heterogeneity widely exists in the poverty problem (e.g., measurement and impact) in Hong Kong. In this sense, a heterogeneous diagram is thus more desirable than a homogeneous one. Especially, a heterogeneous diagram is capable of understanding the variations among regions and social groups instead of misinterpreting them with a roughly averaged result. By leveraging heterogeneous modeling techniques, this thesis also provides a new diagram for identifying the vulnerable groups and key factors for the poverty problem from the spatio-social perspective. Meanwhile, that perspective also identified new challenges in the poverty problem. Thus, for further research directions, multidimensional spatio-social heterogeneity, heterogeneity in categorical social variables, the spill-out effect, and multi-region comparative studies are recommended.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9781392389461Subjects--Topical Terms:

1008643
Social work.
Subjects--Index Terms:

Hong KongIndex Terms--Genre/Form:

554714
Electronic books.

Analysis of Urban Poverty in Hong Kong : = A Spatio-Social Perspective.
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Given this, an appropriate measurement and impact assessment of urban poverty both rely on the inclusiveness of the urban context, which may differ across regions and social groups. Moreover, today, morethan half of the global population is living in cities, and this number is only increasing every day. Such transformations pose a challenge to the measurement and impactassessment method of poverty. Cities are covering more extensive territories and attracting more diverse populations-both of which may have various necessities in terms of importance and type, and they may also be impacted by poverty with different modes. Within this context, a geographically or socially heterogeneous method has been developed to model the various relationships, respectively. Wherein, geographically weighted regression (GWR) is one of the most popular algorithms to solve for heterogeneity from a spatial perspective. However, the literature indicates that few methods are designed for examining social heterogeneity in the poverty problem, especially in urban areas. Further, spatio-social heterogeneity is rarely solved simultaneously.In this thesis, the heterogeneity in urban poverty problem is examined from two perspectives: the poverty measurement and the poverty impact. The poverty measurement refers to the method for assessing the individual poverty level and the overall poverty situation with given circumstances (e.g., income, deprivation level, subjective poverty status), whereas the poverty impact focuses on the influence of poverty as a social determinant. In the context of spatio-social heterogeneity, the relationships between factors (e.g., income), poverty measurement, and poverty impact are modeled and later compared with the corresponding results from the conventional homogeneous, purely geographical, or social heterogeneity context.In terms of the methodology, this thesis extends the conventional GWR to a social and spatio-social weighted regression (SWR and SSWR, respectively). These algorithms allow this study to individually model the subregions and subgroups among the study area. The proposed algorithms thus provide a paradigm for analyzing social and spatiosocialheterogeneity. Wherein, the goodness of fit in the heterogeneity problems (e.g., subjective poverty line) is improved. The achieved improvement also implies that these modeling techniques (SWR and SSWR) are appropriate for other problems containing social or spatio-social heterogeneity. Further, the model results, coefficient models, are compared to depict the unique characteristics of each subdivided region and group. Meanwhile, the thesis also includes a traditional social heterogeneity detecting method, latent profile analysis (LPA), and investigates the spatial heterogeneity among its result. This thesis incorporates the spatial heterogeneity detector (hot-spot analysis) into a social heterogeneous modeling tool (LPA). The evidence for the co-existence of spatial and social heterogeneity is yielded, which may encourage more focus on the spatiality among other social heterogeneity studies.Hong Kong, a world-famous city in China, was selected as the study area to validate the proposed approach (spatio-social heterogeneity in poverty problem). In the first stage, the primary objective was to build up the spatio-social heterogeneity model for the poverty problem. With locally weighted regression, the thesis investigates the correlation between the subjective poverty line and its impact factors. The result, based on the coefficient models, confirms the existence of an adaptive effect (poor people with lower income may propose a lower subjective poverty line). The result of the coefficient models also shows that the adaptive effect on middle-aged citizens is relatively weaker according to the heterogeneous coefficient models for different age groups. Meanwhile, factors associated with subjective poverty status were tested. The results indicate that low mental health, like income, is also correlated with subjective poverty. By applying spatial clustering analysis, the finding further identified some regions (Yuen Long and Kwun Tong) where, compared to females, males are more likely to feel poor in Hong Kong. In the second stage, the thesis investigates the social heterogeneity between the poor and the majority. By building up models for these two groups (middle- to low-income group and the majority), the association between living conditions (housing and community) and multiple health outcomes is investigated. Two conclusions are drawn for this part of the research. First, middle- to low-income people are sensitive to poorer living conditions but potentially face more severe health outcomes when they have one problem (report positive on one living condition issue). Second, the significant social determinants (housing conditions and community environments) vary among the different health measurement metrics even though they both target the individual's general health status and are conventionally used exclusively. In this sense, it is better to evaluate the impact of one living condition with multiple health indicators. In the third stage, the thesis investigates the effect of income on subjective well-being using a conventional social heterogeneity modeling tool (LPA). Further, the classified results are verified with a spatial analysis tool (hot spot analysis). The result indicates that the socially heterogeneous subgroups are simultaneously spatial clustering, implying a co-existence of spatial and social heterogeneity.Overall, the findings of this thesis suggest that spatio-social heterogeneity widely exists in the poverty problem (e.g., measurement and impact) in Hong Kong. In this sense, a heterogeneous diagram is thus more desirable than a homogeneous one. Especially, a heterogeneous diagram is capable of understanding the variations among regions and social groups instead of misinterpreting them with a roughly averaged result. By leveraging heterogeneous modeling techniques, this thesis also provides a new diagram for identifying the vulnerable groups and key factors for the poverty problem from the spatio-social perspective. Meanwhile, that perspective also identified new challenges in the poverty problem. Thus, for further research directions, multidimensional spatio-social heterogeneity, heterogeneity in categorical social variables, the spill-out effect, and multi-region comparative studies are recommended.
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