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Millions of individuals and families have been permanently resettled in host countries after having fled their homes in order to seek safety from war or persecution. These resettled refugees may experience extreme hardship prior to and during flight, and may continue to experience difficulties upon resettlement, all of which can negatively influence health status. However, many also exhibit strength and resilience. Understanding the myriad social and environmental factors that affect (and are affected by) health status for any population can be difficult in general, and there are many additional contextual factors to consider when working with refugees in particular. This dissertation contributes to knowledge of refugee health status in two ways. The first is to provide an integrated model for the social determinants of health a tool that can be used to organize what is already either theorized or known, and what is lacked, about the health of a population of interest. This framework is applied to resettled refugees as both an example and as a way of structuring the extant knowledge for this population. The second is to produce evidence about the health status of resettled refugees via three empirical studies. One uses a large existing dataset from the UK, employing latent growth curve modeling to investigate changes in health status over time after resettlement. The other two utilize local data from Nashville, TN collected in partnership with a refugee-serving non-profit organization. One series of research questions focuses on relationships between adjustment to life in the US and health status, drawing on focus groups with Somali residents (n=12). Another series investigates the relationship between life stressors (including traumatic events and chronic stressors) and health status, drawing from survey data collected from Somali residents (n=145). Overall, it is clear that some refugees do quite well upon resettlement, but that there is also great need among others, especially for emotional problems. Approaches to estimating and alleviating the health burdens require attention to cultural issues.

Due to lack of rigorous evaluations, there is limited evidence that homelessness prevention programs effectively reduce rates of homelessness and efficiently direct services where they can make the most difference. Effectiveness is the ability to reduce rates of homelessness among people who would otherwise experience it. Efficiency is the ability to direct services to those who would benefit most. Evidence of effectiveness requires a counterfactual typically a comparison between a treatment group and a similar group that does not receive treatment. Evidence of efficiency necessitates development of a risk model and investigation of the levels of risk where services make the most difference. Investigations sometimes confound effectiveness and efficiency: evaluators might believe that services are effective when those services are imprecisely targeted. The current study examines effectiveness and efficiency for prevention programs in two sites. It develops risk models for homelessness using Cox proportional hazard models for 2,761 applicants for Homelessness Prevention and Rapid Re-Housing in Alameda County and for 10,220 individual applicants for HomeBase prevention services in New York City. Further, it uses a regression discontinuity design for the sample in Alameda County to examine the effectiveness of services. The findings provide limited evidence that prevention programs can reduce entries into homelessness and stronger evidence that programs can work better by focusing on individuals and families at highest risk. Triage models that exclude some applicants as too risky to serve are not supported by the data. The studies also contribute to the understanding of the causes of homelessness, via the examination of risk factors in the two sites. The results suggest that structural issues are the driving forces of homelessness in two housing markets with limited access to affordable housing. Future research is necessary to obtain more precise estimates of prevention effects and to examine similarities and differences in findings across housing markets. Prevention programs might do better not only to provide immediate prevention services for individuals and families but also to combat the structural forces that lead to high rates of homelessness.

Attracting affluent households and skilled labor has long been a component of local development across U.S. cities. Place attractiveness has, however, taken on a new significance over the past forty years as the diffusion of neoliberal social policy, and the development of the post-productivist economy have effectively tied the prospects of local development to the financial, social, and human capital of this select population. As prevalent as the idea may be that local development is contingent on a potentially-mobile class, there is little research on income-differentiated household migration and its local outcomes. This study considers the impact of income-differentiated household migration on locales across the U.S., and communities situated therein. Of particular interest is the relationship over time between income dispersion in place, as manifest in local income inequality and segregation, and income dispersion between places, as affected by inequality within the population of migrating households. Findings indicate that high- and low-income migration patterns are categorically distinct, affecting sociospatial fragmentation at both the local and regional level, and that class-biased approaches to local development contribute to increasing income inequality and segregation.

The automatic recognition of human actions in video is of great interest in many applications such as automated surveillance, content-based video summarization, video search, and indexing. The problem is challenging due to a wide range of variations among the motion pattern of a given action such as walking across different subjects and the low variations among similar motions such as running and jogging. This thesis has three contributions in a discriminative bottom-up framework to improve the multi-resolution analysis of the motion patterns in video for better recognition of human actions. The first contribution of this thesis is the introduction of a novel approach for a robust local motion feature detection in video. To this end, four different multi-resolution temporally causal and asymmetric filters of log Gaussian, scale-derivative Gaussian, Poisson, and asymmetric sinc are introduced. The performance of these filters is compared with the widely used multi-resolution Gabor filter in a common framework for detection of local salient motions. The features obtained from the asymmetric filtering are more precise and more robust under geometric deformations such as view change or affine transformations. Moreover, they provide higher classification accuracy when they are used with a standard bag-of-words representation of actions and a single discriminative classifier. The experimental results show that the asymmetric sinc performs the best. The Poisson and the scale-derivative Gaussian perform better than log Gaussian and that better than the symmetric temporal Gabor filter. The second contribution of this thesis is the introduction of an efficient action representation. The observation is that the salient features at different spatial and temporal scales characterize different motion information. A multi-resolution analysis of the motion characteristic should be representative of different actions. A multi-resolution action signature provides a more discriminative video representation. The third contribution of this thesis is on the classification of different human actions. To this end, an ensemble of classifiers in a multiple classifier systems (MCS) framework with a parallel topology is utilized. This framework can fully benefit from the multi-resolution characteristics of the motion patterns in the human actions. The classification combination concept of the MCS has been then extended to address two problems in the configuration setting of a recognition framework, namely the choice of distance metric for comparing the action representations and the size of the codebook by which an action is represented. This implication of MCS at multiple stages of the recognition pipeline provides a multi-stage MCS framework which outperforms the existing methods which use a single classifier. Based on the experimental results of the local feature detection and the action classification, the multi-stage MCS framework, which uses the multi-scale features obtained from the temporal asymmetric sinc filtering, is recommended for the task of human action recognition in video.

While an uncontested symbol of the American dream is homeownership, only members of the higher echelons of society can achieve this dream with ease. Lower-income populations take the greatest risk and suffer the most severe consequences when homeownership fails. This study explores a creative solution for low-income homeowners, called community land trusts (CLT), and the associated challenges for broader acceptance of the CLT model due to its relationship to the low-income population it serves. My research questions attempt to understand two issues affecting the acceptance of the CLT model the physical conditions of CLT properties and public perceptions of the properties. To answer these questions, an online survey instrument was developed using photographs of CLT properties in one neighborhood in Tucson, Arizona. These photos, their corresponding question responses, and the corresponding data elicited interesting information on how both norms associated with low-income housing and respondent demographics affect perceptions of CLT houses. Together these inquiries provide important information about how CLTs may contribute to developing more positive perceptions of affordable housing.

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This paper explores the intersection of religious and sexual identity formation amongst Muslim-American youth, employing psychological theories of identity, sociological theories of performance, and theological perspectives of gender and sexuality in Islam. The mixed-method design utilizes both focus groups and a survey instrument to understand the religious and sexual identity development of Muslim American students (ages 10-14) in a medium-sized city in the southern United States. The project contrasts three particular school settings public schools that have a large Muslim population, public schools that have few Muslim students, and Islamic schools. The study seeks to understand Muslim youths beliefs regarding the Islamic teachings on gender and sexuality, Muslim youths beliefs regarding their peers expectations on gender and sexuality, the impact of social contexts on religious and sexual identity formation, and ways in which sexuality is moderated by the religious beliefs of Muslim youth. Findings suggest that gender and sexuality are often part of a null curriculum for Muslim youth issues that are considered taboo and thus un-Islamic. However, Muslim youth who do utilize religious scripts are found to be able to make agentic choices about their sexual identity. In addition, improving parental scripts on gender and sexuality can have a positive impact on the sexual identity formation of Muslim American youth.

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