Analysis of Housing Partnerships using the Balanced Scorecard framework

Housing Partnerships (HPs) are collaborative arrangements that assist communities in the delivery of affordable housing by combining the strengths of the public and private sectors. They emerged in several states, counties, and cities in the eighties as innovative solutions to the challenges in affordable housing resulting from changing dynamics of delivery and production. ^ My study examines HPs with particular emphasis upon the identification of those factors associated with the successful performance of their mission of affordable housing. I will use the Balanced Scorecard (BSC) framework in this study. The identification of performance factors facilitates a better understanding of how HPs can be successful in achieving their mission. The identification of performance factors is significant in the context of the current economic environment because HPs can be viewed as innovative institutional mechanisms in the provision of affordable housing. ^ The present study uses a mixed methods research approach, drawing on data from the IRS Form 990 tax returns, a survey of the chief executives of HPs, and other secondary sources. The data analysis is framed according to the four perspectives of BSC: the financial, customer, internal business, and learning and growth. Financially, revenue diversification affects the financial health of HPs and overall performance. Although HPs depend on private and government funding, they also depend on service fees to carry out their mission. From a customer perspective, the HPs mainly serve low and moderate income households, although some serve specific groups such as seniors, homeless, veterans, and victims of domestic violence. From an internal business perspective, HPs’ programs are oriented toward affordable housing needs, undertaking not only traditional activities such as construction, loan provision, etc., but also advocacy and educational programs. From an employee and learning growth perspective, the HPs are small in staff size, but undertake a range of activities with the help of volunteers. Every part of the HP is developed to maximize resources, knowledge, and skills in order to assist communities in the delivery of affordable housing and related needs. Overall, housing partnerships have played a key role in affordable housing despite the housing market downturn since 2006. Their expenses on affordable housing activities increased despite the decrease in their revenues.^

The influence of HOPE VI public housing on public schools

In the United States, public school enrollment is typically organized by neighborhood boundaries. This dissertation examines whether the federally funded HOPE VI program influenced performance in neighborhood public schools. In effect since 1992, HOPE VI has sought to revitalize distressed public housing using the New Urbanism model of mixed income communities. There are 165 such HOPE VI projects nationwide. Despite nearly two decades of the program's implementation, the literature on its connection to public school performance is thin. My dissertation aims to narrow this research gap. There are three principal research questions: (1) Following HOPE VI, was there a change in socioeconomic status (SES) in the neighborhood public school? The hypothesis is that low SES (measured as the proportion of students qualifying for the Free and Reduced Lunch Program) would reduce. (2) Following HOPE VI, did the performance of neighborhood public schools change? The hypothesis is that the school performance, measured by the proportion of 5th grade students proficient in state wide math and reading tests, would increase. (3) What factors relate to the performance of public schools in HOPE VI communities? The focus is on non-school, neighborhood factors that influence the public school performance. For answering the first two questions, I used t-tests and regression models to test the hypotheses. The analysis shows that there is no statistically significant change in SES following HOPE VI. However, there are statistically significant increases in performance for reading and math proficiency. The results are interesting in indicating that HOPE VI neighborhood improvement may have some relationship with improving school performance. To answer the third question, I conducted a case study analysis of two HOPE VI neighborhood public schools, one which improved significantly (in Philadelphia) and one which declined the most (in Washington DC). The analysis revealed three insights into neighborhood factors for improved school performance: (i) a strong local community organization; (ii) local community's commitment (including the middle income families) to send children to the public school; and (iii) ties between housing and education officials to implement the federal housing program. In essence, the study reveals how housing policy is de facto education policy.

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

The Influence of HOPE VI Public Housing on Public Schools

In the United States, public school enrollment is typically organized by neighborhood boundaries. This dissertation examines whether the federally funded HOPE VI program influenced performance in neighborhood public schools. In effect since 1992, HOPE VI has sought to revitalize distressed public housing using the New Urbanism model of mixed income communities. There are 165 such HOPE VI projects nationwide. Despite nearly two decades of the program’s implementation, the literature on its connection to public school performance is thin. My dissertation aims to narrow this research gap. There are three principal research questions: (1) Following HOPE VI, was there a change in socioeconomic status (SES) in the neighborhood public school? The hypothesis is that low SES (measured as the proportion of students qualifying for the Free and Reduced Lunch Program) would reduce. (2) Following HOPE VI, did the performance of neighborhood public schools change? The hypothesis is that the school performance, measured by the proportion of 5th grade students proficient in state wide math and reading tests, would increase. (3) What factors relate to the performance of public schools in HOPE VI communities? The focus is on non-school, neighborhood factors that influence the public school performance. For answering the first two questions, I used t-tests and regression models to test the hypotheses. The analysis shows that there is no statistically significant change in SES following HOPE VI. However, there are statistically significant increases in performance for reading and math proficiency. The results are interesting in indicating that HOPE VI neighborhood improvement may have some relationship with improving school performance. To answer the third question, I conducted a case study analysis of two HOPE VI neighborhood public schools, one which improved significantly (in Philadelphia) and one which declined the most (in Washington DC). The analysis revealed three insights into neighborhood factors for improved school performance: (i) a strong local community organization; (ii) local community’s commitment (including the middle income families) to send children to the public school; and (iii) ties between housing and education officials to implement the federal housing program. In essence, the study reveals how housing policy is de facto education policy.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.