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Social capital is described as the concept of social network or social interaction among residents in a neighborhood. In times past, physical environment factors enhancing the level of social capital were main issues to researchers: land-use type and neighborhood design. However, based on various benefits gained from social capital theory, it is needed to study about the influence of social capital. Thus, the impact of social capital on the physical urban environment is investigated in this dissertation research in order to make more livable, healthier, and more active community. Most researches dealing with social capital and housing condition have not been empirically tested comprehensively because there is a lack of consensus about the measurement of social capital. And only structural housing parts were dealt with in their research. In this dissertation, however, the level of social capital is measured through public data sources not household surveys, such as U.S. Census, the City of Lincoln, Lincoln Police Department, etc. Non-structural housing parts related to dwelling environment are also discussed to measure overall housing condition.
Main focus in this dissertation research is to investigate whether the condition of dwelling structure and environment of neighborhoods with a high level of social capital will be better than the condition of dwelling structure and environment of neighborhoods with a low level of social capital. Also, social capital indices closely associated with housing condition are identified.
According to the results of statistical analysis, there is some impact of social capital on the condition of dwelling structure and environment while controlling other neighborhood characteristics. Especially, structural housing condition and housing exterior condition are affected by the level of social capital significantly. And some social capital indices such as social mobility, marriage rate, own children under 18 years old, homeownership rate, voter turnout, and crime incidence are significant to explain variations of dependent variables’ values.
Adviser: J. Clark Archer