An up-to-date list of publications can be found on my google scholar page, but I try to keep this page updated with relevant work
Neighborhood change, often associated with its maligned counterparts, gentrification and displacement, has become an increasingly prominent area of study in the urban studies. Further, gentrification has been spreading across additional US metropolitan areas, and accelerating within certain metropolitan areas (Hwang & Lin, 2016). As a result, both urban scholars and practicing planners are anxious for greater insights into the causes and consequences of neighborhood change, as well as quantitative models that anticipate such change so that housing and community development policies can be crafted to promote more equitable cities and regions. Despite lively interest and much active research, however, there remains little consensus on the appropriate ways to meaure gentrification and even less on the best ways to model the phenomenon (Freeman, 2005; Hwang & Lin, 2016). In this paper, we enter the debate on gentrification by considering a novel model of neighborhood change. Drawing from both regional science and newly developed machine learning techniques, we construct a model of gentrification that accounts simultaneously for multiple dimensions of change and incorporates both spatial and temporal effects. Following, we present a series of empirical examples, which demonstrate that our approach is more readily applicable to sociological theories of urban ecology and neighborhood succession. The crux of our approach is the consideration of a neighborhood as a bundle of demographic attributes which together describe a discrete ‘neighborhood state’ rather than a single or series of continuous variable(s). To measure gentrification, we thus develop a spatial Markov Chain to examine the ways in which neighborhoods transition between states as a function of their previous state and the states of the surrounding neighborhoods. Conceptually, this approach is similar to Royall (2016), although we use a different classification algorithm, identify different neighborhood states, and include a spatially explicit Markov Chain.
Our approach is as follows. Following Spielman & Singleton (2015), we perform a geodemographic typology that classifies neighborhoods into one of six discrete types based on four measures of neighborhood composition analyzed commonly in the gentrification literature: income, race, educational attainment, and home values. We develop our typology using an unsupervised machine learning algorithm known as affinity progagation, which we then apply to longitudinal census data from 1980 through 2015 in five different metropolitan regions in the U.S. (Washington D.C., Los Angeles, New York City, Minneapolis-St. Paul, and Seattle). This analysis yields a dataset in which each tract in each of the five study regions is discretized into classes that vary by kind rather than degree; we conceive of these classes as ‘demographic states’. To understand the process of dynamic neighborhood change, we then construct a spatial Markov chain which models the probability of transition from one state to another (Rey, 2014). Our results yield new insight into the dynamics of neighborhood change that is both nuanced and context-sensitive. We are able to identify several types of neighborhood change that include both ascent and decline, and we are also able to identify particular sequences of gentrification, which have been difficult to identify in previous quantitative studies. Harkening to the early Chicago School, we find patterns of neighborhood succession in cities like Washington D.C. where low-income minority neighborhoods transitioned to moderate-income minority neighborhoods before transitioning to moderate-income white neighborhoods. We explore several other emergent sequences and discuss the policy implications for neighborhoods on the verge of tipping.
This study uses a series of discrete choice models to study residential sorting patterns of housing choice voucher (HCV) holders in the Baltimore Metropolitan Region. Using data from 50,000 voucher holders and 800,000 residential parcels, I examine which spatial characteristics influence the likelihood that a voucher recipient will occupy a particular housing unit. The results indicate pervasive racial inequality in the neighborhood experience of voucher holders; racial segregation is the dominant mode by which voucher holders sort into neighborhoods, with white voucher holders also enjoying benefits such as better schools and higher quality housing units. In addition to white and black HCV recipients, I also model the residential sorting of participants in the Baltimore Housing Mobility Program, a special voucher program that provides housing counseling, transportation assistance, and credit counseling in addition to vouchers. These households show important differences from both their black and white counterparts in the HCV program, and are more likely to live in high quality units in integrated communities with good schools. Together, these results suggest that systemic inequality in housing assistance remains an important issue, but that changes to housing voucher programs and metropolitan policy, more broadly, could help combat these patterns.
In the following paper, I examine the residential trajectories of housing choice voucher holders in the Baltimore metropolitan region. For decades, the Baltimore region has been a laboratory for housing mobility policies, having participated in the Moving to Opportunity experiment during the 1990s and 2000s, and hosting its own unique mobility program from 2003 to present. In addition to its voucher programs, Baltimore is notorious for its legacy of segregation and racial inequality. It was the first city in the United States to enact a racial zoning ordinance, the legacy of which can still be seen today. More recently, the 2015 death of Freddie Gray in police custody and the resulting public unrest brought issues of the region’s racial and spatial inequality to the front pages of the media, and a 2016 investigation by the U.S. Justice Department found widespread civil rights violations on behalf of the Baltimore police department. With this history as a backdrop, this paper uses a longitudinal multilevel model to study how different household characteristics and different types of vouchers influence whether a household moves into a high-opportunity neighborhood. The results suggest that voucher holders in general, and black households in particular are likely to move into low-opportunity neighborhoods. Specialized voucher programs, like the Baltimore Housing Mobility Program (BHMP), however, can reverse this trend, significantly. If voucher programs are intended to be used as a vehicle for Affirmatively Furthering Fair Housing, these results suggest that they could incorporate design changes to facilitate improved locational outcomes.
As evidence on the contextual e ects of place upon individual outcomes has become increasingly solid over time, so too have urban policies and programs designed to connect underserved people with access to spatial opportunity. To this end, many attempts have been made to quantify the geography of opportunity and quite literally plot it on a map by combining evidence from studies on neighborhood effects with rapidly expanding spatial data resources and GIS technology. Recently, these opportunity maps have not only become increasingly common but their preparation has been encouraged and facilitated by the US Department of Housing and Urban Development (HUD). On one hand, the increasing prominence of opportunity mapping is a useful and important step forward for equity planning. Maps are powerful means of displaying the concept of opportunity and its variation across space. On the other hand, the institutionalization of opportunity mapping portends a need to examine critically the foundations that underlie the construction of opportunity metrics, the display of maps, and the application of these techniques in public policy. A closer look at the conceptual foundations and analytical methods that underlie these exercises offers important lessons not just for the practice of opportunity mapping but also for the practice of equity planning in general.
In the following essay, I examine the practice of opportunity mapping from both theoretical and methodological perspectives, highlighting several weaknesses of the common methods. Following, I outline an improved theoretical framework based on Galster’s (2012) categorization of the mechanisms of neighborhood effects. Using data from the Baltimore metropolitan region, I then use confirmatory factor analysis to specify a measurement model that verifies the construct validity of the proposed theoretical framework. The model provides estimates of four latent variables that may be conceived as the essential dimensions of spatial opportunity: Social-Interactive, Environmental, Geographic, and Institutional. Finally, I develop a neighborhood typology by applying an unsupervised machine learning algorithm to the four dimensions of opportunity. The results suggest that the practice of opportunity mapping can be improved substantially through (1) a better connection to the empirical literature on neighborhood effects, (2) a multivariate statistical framework, and (3) more direct relevance to public policy interventions.
Interest continues to grow in spatial opportunity structures and their role in shaping housing policy, community development, and regional planning. To this end, many have tried to quantify the geography of opportunity and quite literally plot it on a map. In addition, the Department of Housing and Urban Development has proposed new rules requiring its grant recipients to prepare assessments of fair housing based in part on opportunity maps. In this paper we provide a brief history of the events, policies, and research that led to opportunity analyses and maps. We then describe our opportunity mapping efforts in Baltimore. Finally, we offer recommendations for improving the practice of opportunity mapping based on insights derived from our experience in Baltimore and the literature on neighborhood effects.
In the 1990s and early 2000s, the Department of Housing and Urban Development sponsored two major experiments to test whether housing choice vouchers propelled low-income households into greater economic security, the Moving to Opportunity for Fair Housing program (MTO) and the Welfare to Work Voucher program (WTW). Using data from these programs, this study examines differences in residential location and employment outcomes between voucher recipients with access to automobiles and those without. Overall, the findings underscore the positive role of automobiles in outcomes for housing voucher participants.
With encouragement and assistance from the US Department of Housing and Urban Development many local governments and metropolitan coalitions are mapping opportunity to inform the development of regional sustainable communities plans. The notion that the neighborhood in which a person lives shapes their social and economic opportunities is not new, but how opportunity is to be measured, displayed, and used to guide policy decision making remains under examined. In this paper we conduct such an examination using data from the Baltimore metropolitan area. Specifically, we examine the conceptual foundations and standard analysis framework of opportunity mapping developed by the Kirwan Institute. We then present results from an opportunity mapping analysis conducted in the Baltimore region as part of a regional Sustainability Planning effort. We make numerous improvements on the Kirwan approach, such as including indicators drawn from substantial social network literature, incorporating advanced spatial analytical techniques, and going through the planning process to gain local input. We conclude with recommendations for how opportunity maps can be used to inform metropolitan sustainability plans.
The "Sustainable neighborhoods" concepthas become widely proposed objective of urban planners, scholars, and local government agencies. However, after decades of discussion, there is still no consensus on the definition of sustainable neighborhoods (Sawicki and Flynn, 1996; Dluhy and Swartz 2006; Song and Knaap,2007; Galster 2010). To gain new information on this issue, this paper develops a quantitative method for classifying neighborhood types. It starts by measuring a set of more than 100 neighborhood sustainable indicators. The initial set of indicators includes education, housing, neighborhood quality and social capital, neighborhood environment and health, employment and transportation. Data are gathered from various sources, including the National Center for Smart Growth (NCSG) data inventory, U.S. Census, Bureau of Economic Analysis (BEA), Environmental Protection Agency (EPA), many government agencies and private vendors. GIS mapping is used to visualize and identify variations in neighborhood attributes at the most detailed level (e.g census tracts). Factor analysis is then used to reduce the number of indicators to a small set of dimensions that capture essential differences in neighborhood types in terms of social, economic, and environmental dimensions. These factors loadings are used as inputs to a cluster analysis to identify unique neighborhood types. Finally, different types of neighborhoods are visualized using a GIS tool for further evaluation. The proposed quantitative analysis will help illustrate variations in neighborhood types and their spatial patterns in the Baltimore metropolitan region. This framework offers new insights on what is a sustainable neighborhood.
Transit oriented development (TOD) is a widely accepted policy objective of many jurisdictions in the United States. Much of the focus of both policy and research on TOD, however, has been on property values, rents, and residential development. There is both anecdotal and empirical evidence to suggest that the vitality of TODs and the transit boardings from any TOD depends significantly on the extent of retail development in the transit station area. We focus here, therefore, on the relationship between transit and retail location. Specifically, we focus in this paper, on the determinants of retail location in two counties, Montgomery County and Prince George’s County, Maryland, with a particular focus on the influence of proximity to rail transit stations. We proceed as follows. First we review the literature review and offer a brief history of retailing, its concentration in central cities, and its suburbanization in the post-war period drawing on two classic retail location theories. We then examine the determinants of retail location using building-level data and a rich set of locational data for Prince George’s and Montgomery County, Maryland. We find that retail location is strongly influenced by access to transportation facilities, especially bus and light rail transit stops. We find also that retail location is strongly influenced by street network connectivity and proximity other retail establishment. These findings offer strong support for regional development strategies that focus on pedestrian activity centers connected by bus and rail transit.
Agencies at the federal, state and local level are aiming to enhance the public transportation system (PTS) as one alternative to alleviate congestion and to cater to the needs of captive riders. To effectively act as a viable alternative transportation mode, the system must be highly efficient. One way to measure efficiency of the PTS is connectivity. In a multimodal transportation system, transit is a key component. Transit connectivity is relatively complex to calculate, as one has to consider fares, schedule, capacity, frequency and other features of the system at large. Thus, assessing transit connectivity requires a systematic approach using many diverse parameters involved in real-world service provision. In this paper, we use a graph theoretic approach to evaluate transit connectivity at various levels of service and for various components of transit, such as nodes, lines, and transfer centers in a multimodal transportation system. Further, we provide a platform for computing connectivity over large-scale applications, using visualization to communicate results in the context of their geography and to facilitate public transit decision-making. The proposed framework is then applied to a comprehensive transit network in the Washington-Baltimore region. Underpinning the visualization, we introduce a novel spatial data architecture and Web-based interface designed with free and open source libraries and crowd-sourced contextual data, accessible on various platforms such as mobile phones, tablets and personal computers. The proposed methodology is a useful tool for both riders and decisionmakers in assessing transit connectivity in a multimodal transit network in a number of ways such as the identification of under-served transit areas, prioritization and allocation of funds to locations for improving transit service.
We present in this paper an analysis of economic centers and their role in shaping employment development patterns and travel behavior in the state of Maryland. We begin by identifying 23 economic centers in the Baltimore-Washington region. We then examine these centers first in their role as centers of economic activity then in their role as nodes in the state’s transportation system. Finally, we identify the commute sheds of each center, for multiple modes of travel and travel times, and examine jobs-housing balance within these various commute sheds. We find that Maryland’s economic centers not only promote agglomerative economies and thus facilitate economic growth; they also generate a disproportionate number of trips and promote transit ridership. These results provide empirical support for policies that promote polycentric urban development, and especially policies that promote polycentric employment development. Further, they suggest that polycentrism as a sustainable development strategy requires careful coordination of regional transportation systems designed to balance jobs and housing within a center’s transit commute shed. Based on these findings we recommend that the Maryland state development plan and regional sustainable communities’ plans across the nation should encourage the concentration of employment within economic centers and encourage housing development within the transit commute sheds of those centers