The Dynamics of Urban Neighborhoods: A Survey of Approaches for Modeling Socio-Spatial Structure
Elijah Knaap, Levi Wolf, Sergio Rey, Wei Kang & Su Han
For close to a century, researchers from across the disciplines of Urban Studies have developed empirical models for understanding the spatial extent and social composition of urban neighborhoods–and how these dimensions change over time. Unfortunately, however, these techniques have often been developed within disciplinary silos and without broad exposure to other potentially interested constituencies. In this paper, we traverse the literatures of social science, computer science, and statistics to examine a variety of modeling techniques for understanding neighborhood dynamics. We begin our review by examining early concepts of spatial structure first outlined in the Chicago School and discuss how the notions of social ecology and quantitative neighborhood analysis permeated the urban studies for several decades to come. Our survey continues by reviewing contemporary statistical approaches for identifying urban neighborhoods, culminating with the state of the art in subfields known as ‘geodemographics’ and ‘regionalization’. Following this review, we offer insight into the field’s persistent conceptual issues, identify areas ripe for additional research, and highlight newly-developed computational methods that can inform more just and socially equitable public policy, community development, and accountable governance.
Neighborhood change in the United States - a comparison of sequence analysis methods
Wei Kang, Sergio Rey, Levi Wolf, Elijah Knaap & Su Han
There is a recent surge in research focused on urban trans- formations in the United States via empirical analysis of neighborhood sequences. The alignment-based sequence analysis methods have become the dominant techniques for the neighborhood sequence analysis. However, it is un- clear to what extent these methods are robust in terms of producing consistent and converging sequence typolo- gies. This article sheds light on this issue by applying five sequence analysis methods to the same data set - 50 largest Metropolitan Statistical Areas (MSAs) of the United States from 1970 to 2010.
Geosilhouettes: geographical measures of cluster fit
Levi Wolf, Elijah Knaap & Sergio Rey
Regionalization, under various guises and descriptions, is a longstanding and pervasive interest of urban studies. With an increasingly large number of studies on urban place detection in language, behavior, pricing, and demography, recent critiques of longstanding regional science perspectives on place detection have focused on the arbitrariness and non-geographical nature of measures of best fit. In this paper, we develop new explicitly-geographical measures of cluster fit. These hybrid spatial-social measures, called geosilhouettes, are demonstrated to capture the "core" of geographical clusters in racial data on census blocks in Brooklyn neighborhoods. These new geosilhouettes are also useful in a variety of boundary analysis and outlier detection uses. These new measures are defined, demonstrated, and new directions are suggested.
Metropolitan planning in a vacuum: Lessons on regional equity planning from Baltimore’s Sustainable Communities Initiative
Nicholas Finio, Willow Lung-Amam, Gerrit-Jan Knaap, Casey Dawkins & Elijah Knaap
The main policy initiative of the Obama administration’s first-term urban policy agenda was the Partnership for Sustainable Communities, which issued Sustainable Communities Regional Planning Grants (SCRPGs) to regions across the country. As participant activist scholars in the SCRPG planning process in Baltimore, Maryland, we present our analysis of 4 aspects of regional equity planning: community engagement, regional collaboration, regional housing policy, and the use of opportunity and equity-related data. We find that the process enabled adoption of a unique outreach strategy, engaged regional stakeholders in equity-focused conversations, and enabled comprehensive analysis of equity data in a plan focused on improving regional equity. However, despite some progress on regional housing issues, plan implementation has largely not occurred due to a lack of commitment to and coordination around implementation. The Baltimore experience suggests that in the absence of such commitments at the regional level or further federal requirements and funding for implementation, large federal grants have only limited success in pushing regional equity planning forward.
Machines Learn The Chicago School
Elijah Knaap, Nicholas Finio, Sergio Rey, Levi Wolf, & Wei Kang
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.
Spatio-temporal analysis of socioeconomic neighborhoods: The Open Source Longitudinal Neighborhood Analysis Package (OSLNAP)
Sergio Rey, Elijah Knaap, Su Han, Levi Wolf, Wei Kang
The neighborhood effects literature represents a wide span of the social sciences broadly concerned with the influence of spatial context on social processes. From the study of segregation dynamics, the relationships between the built environment and health outcomes, to the impact of concentrated poverty on social efficacy, neighborhoods are a central construct in empirical work. From a dynamic lens, neighborhoods experience changes not only in their socioeconomic composition, but also in spatial extent; however, the literature has ignored the latter source of change. In this paper, we discuss the development of a novel, spatially explicit tool: the Open Source Longitudinal Neighborhood Analysis Package (OSLNAP) using the scientific Python ecosystem.
Opportunity for Whom? The Diverse Definitions of Neighborhood Opportunity in Baltimore
Willow S. Lung‐Amam, Elijah Knaap, Casey Dawkins & Gerrit‐Jan Knaap
Across the United States, communities are increasingly interested in the spatial structure of opportunity. Recently, several federal programs have promulgated opportunity mapping as a tool to help increase disadvantaged communities' access to neighborhood opportunity. The increasing institutionalization of opportunity mapping raises questions about how opportunity is defined and by whom. This paper analyzes data from community engagement events held for a regional planning process throughout the Baltimore metropolitan area. During these events, over 100 residents were asked what it means to live in neighborhoods that provide opportunity. The results showed similarities as well as remarkable differences in residents’ definitions of opportunity across race, income, and geography. Racial and ethnic minorities, low‐income groups, and those living in distressed neighborhoods were more likely to identify job accessibility, employment, and job training as key components of and pathways to opportunity, whereas White, higher income groups, and wealthier neighborhoods placed a stronger emphasis on a sense of community, freedom of choice, education, and retirement savings. These differences challenge urban policymakers and planners to consider how greater flexibility in mapping tools, qualitative data, and community‐engaged processes might better reflect the diversity in the ways that residents view and experience opportunity in their everyday lives.
The Cartography of Opportunity: Spatial Data Science for Equitable Urban Policy
As evidence on the contextual effects 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.
Who Moves to Opportunity? Spatial Returns to Housing Assistance and the Effect of Specialized Mobility Programs
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.
Housing Assistance in Black and White: A Discrete Choice Model of Residential Sorting in Housing Voucher Programs
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
Polycentrism as a Sustainable Development Strategy: An Empirical Analysis from the State of Maryland
Elijah Knaap, Chengri Ding, Yi Niu & Sabyasachee Mishra
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
Driving to Opportunity: Understanding the Links among Transportation Access, Residential Outcomes, and Economic Opportunity for Housing Voucher Recipients
Rolf Pendall, Christopher R. Hayes, Taz George, Zachary J. McDade, Casey Dawkins, Jae Sik Jeon, Elijah Knaap, Evelyn Blumenberg, Gregory Pierce & Michael Smart
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.
A tool for measuring and visualizing connectivity of transit stop, route and transfer center in a multimodal transportation network
Sabyasachee Mishra, Timothy F. Welch, Paul M. Torrens, Cheng Fu, Haojie Zhu, & Elijah Knaap
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.
Driving to Opportunities: Voucher Users, Cars, and Movement to Sustainable Neighborhoods
Pendall, Rolf and Hayes, Christopher and George, Arthur and Dawkins, Casey and Jeon, Jae Sik and Knaap, Elijah and Blumenberg, Evelyn and Pierce, Gregory and Smart, Michael
Tenant-based rental vouchers have expanded housing choice for millions of low-income households, yet assisted households still face hurdles when trying to secure housing in high-opportunity neighborhoods with desirable economic, social, and environmental characteristics. Although inadequate transportation is arguably one of the most impor- tant hurdles to securing housing in high-opportunity neighborhoods, existing studies of voucher users' location choices have n ot yet explored the connections between trans- portation access and residential location outcomes. This article discusses the results from a recent study that attempts to close that gap. Our study draws on data from the Moving to Opportunity for Fair Housing demonstration program and the Welfare-to- Work Voucher Program, two residential mobility initiatives that randomly assigned rental vouchers to low-income households seeking housing assistance. Using a variety of approaches—including cluster analysis, bivariate comparisons, and multivariate analysis—we find evidence of important connections between automobile access and improved neighborhood conditions. We also find that neighborhoods with similar levels of poverty exhibit a wide array of other characteristics that matter differently for dif- ferent kinds of households. Our findings suggest a need for more integrated and holistic planning and program development to account for the importance of both cars and transit to low-income households' well-being.