Papers

This paper investigates the challenges and opportunities arising from incommensurate spatial partitions (ISPs) in regional science and spatial econometrics, focusing on how processes with overlapping yet distinct boundaries, interact and influence each other. ISPs are prevalent in various domains, including housing markets, employment centers, voting districts, and educational institutions, often complicating spatial econometric modeling and analysis. Using the intersection of school catchment areas and neighborhoods as a primary case study, the paper introduces a novel methodological framework utilizing bipartite graphs. This approach reframes the relationship between different spatial units, allowing for the analysis of multi-process spatial contexts without needing harmonization of spatial supports. The paper also develops new spatial weights derived from the bipartite graph, facilitating both exploratory spatial data analysis and confirmatory spatial econometric modeling. These methods are illustrated through a case study of San Diego, California analyzing 198 neighborhoods and 370 public elementary school catchments.

This research addresses the critical need for a comprehensive understanding of the spatial dynamics between public schools and residential neighborhoods, both of which significantly influence children’s life outcomes. The study introduces innovative methodologies for examining the spatial relationships between school catchment areas and neighborhoods through geodemographic clustering and spatial congruence measures. By applying these methods to the largest 110 metropolitan areas in the United States, encompassing 24,507 elementary school catchments and 17,608 neighborhoods, the research provides novel insights into how these spatial interactions affect ethnoracial integration and educational diversity. The findings reveal the potential of targeted policy interventions that leverage these spatial relationships to promote equity and reduce segregation in educational settings, thereby offering a valuable framework for policymakers to improve access to opportunities across diverse communities.

Understanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of “neighborhood” depends on the study or application, however, so neighborhood analysis and modeling requires both flexibility and adherence to a formal pipeline. Maintaining that balance is challenging for a variety of reasons. To address those challenges, geosnap, the Geospatial Neighborhood Analysis Package provides a suite of tools for exploring, modeling, and visualizing the social context and spatial extent of neighborhoods and regions over time. It brings together state-of-the-art techniques from geodemographics, regionalization, spatial data science, and segregation analysis to support social science research, public policy analysis, and urban planning. It provides a simple interface tailored to formal analysis of spatiotemporal urban data.

In this paper, I leverage a unique dataset containing unit-level observations on rental prices for units in Montgomery County, Maryland to build a series of hedonic models using a spatial econometric panel framework. Over the dataset’s time horizon (2015-2018), The Purple Line, a new light rail was announced and its plans revealed, introducing exogenous variation in land prices through a new potential amenity. I explore whether this amenity is capitalized ahead of its construction, as well as the role of spatial spillovers and, importantly, the implications of ignoring spatial effects in the context of applied policy analysis. Specifically, I show that classic methods for causal inference result in starkly different estimates for the “causal effect,” yielding dramatically different policy conclusions. Using a series of spatial econometric panel models, this analysis emphasizes the importance albeit difficulty of interpreting spatial effect parameters in a causal research design. Specifically, I show that the announcement of the Purple Line probably caused a decrease in rents for units nearest the station areas, and this effect is mistakenly estimated as an increase if spatial effects are ignored.

In this paper we examine the evolution of urban spatial structure in U.S. metropolitan areas over nearly two decades. Using annual block-level data from the Longitudinal Employment Household Dynamics database, we introduce a technique for identifying regional employment centers that both adheres to urban economic theory and pays homage to classic contributions in local spatial statistics. Centers are defined as local spatial statistical outliers on the network-based job accessibility surface. We proceed by identifying the location and employment makeup of centers for each metropolitan region in the USA from 2002 to 2019 and discuss emergent trends across time and space. Critically, we not only explore empirical patterns, but we discuss the relationship between polycentricity, the evolution of urbanization and localization economies, and regional specialization. We confirm again the pattern of polycentricity in U.S. metros and show that the structure of metropolitan employment is largely stable over time. We also document a continuing trend away from urbanization economies into more specialized subcenters

Racial residential segregation is a longstanding topic of focus across the disciplines of urban social science. Classically, segregation indices are calculated based on areal groupings (e.g. counties or census tracts), with more recent research exploring ways that spatial relationships can enter the equation. Spatial segregation measures embody the notion that proximity to one's neighbors is a better specification of residential segregation than simply who resides together inside the same arbitrarily-drawn polygon. Thus, they expand the notion of "who is nearby" to include those who are geographically close to each polygon rather than a binary inside/outside distinction. Yet spatial segregation indices often resort to crude measurements of proximity, such as the Euclidean distance between observations, given the complexity and data requirements of calculating more theoretically-appropriate measures, such as distance along the pedestrian travel network. In this paper, we examine the ramifications of such decisions. For each metropolitan region in the U.S., we compute both Euclidean and network-based spatial segregation indices. We use a novel inferential framework to examine the statistical significance of the difference between the two measures and following, we use features of the network topology (e.g. connectivity, circuity, throughput) to explain this difference using a series of regression models. We show that there is often a large difference between segregation indices when measured by these two strategies (which is frequently significant). Further, we explain which topology measures reduce the observed gap and discuss implications for urban planning and design paradigms.

Racial segregation in public education has been declared as unconstitutional for over 60 years in the United States. Yet many public school districts remain largely separate and unequal. A commonly used approach to reduce school segregation is redelineating school attendance zones to create more racially diverse classrooms. However, there is a need for a school districting approach that can minimize racial or socioeconomic segregation at the district level. In this paper, we develop a spatial optimization model that delineates school attendance zones with the aim of minimizing racial segregation of school district to enable the assessment of the impacts of school attendance zones on the racial segregation of school district. Applications of this model to Riverside Unified School District (RUSD) and San Diego Unified School District (SDUSD) in California, USA show that it is possible to reduce racial segregation by 64% at RUSD and 56% at SDUSD, demonstrating the potential of the proposed model

American Community Survey (ACS) data have become the workhorse for the empirical analysis of segregation in the U.S.A. during the past decade. The increased frequency the ACS offers over the 10-year Census, which is the main reason for its popularity, comes with an increased level of uncertainty in the published estimates due to the reduced sampling ratio of ACS (1:40 households) relative to the Census (1:6 households). This paper introduces a new approach to integrate ACS data uncertainty into the analysis of segregation. Our method relies on variance replicate estimates for the 5-year ACS and advances over existing approaches by explicitly taking into account the covariance between ACS estimates when developing sampling distributions for segregation indices. We illustrate our approach with a study of comparative segregation dynamics for 29 metropolitan statistical areas in California, using the 2010–2014 and 2015–2019. Our methods yield different results than the simulation technique described by Napierala and Denton (Demography 54(1):285–309, 2017). Taking the ACS estimate covariance into account yields larger error margins than those generated with the simulated approach when the number of census tracts is large and minority percentage is low, and the converse is true when the number of census tracts is small and minority percentage is high.

This chapter examines predominant and emerging approaches for conducting spatial analyses of gentrification; It begins with a brief review of relevant theoretical literature to describe why spatial analyses require formalizing gentrification studies in ways that unavoidably simplify a complex social, economic, political, and geographic process. It proceeds to make the case that, while there are many ways to define and quantify "gentrification, " spatial structure remains a fundamental presence in both the theoretical underpinnings and empirical findings throughout. Following, it provides an overview of contemporary strategies for modeling the phenomenon, drawn from across the social sciences, and describes the assumptions and intentions of each modeling framework, the questions each is designed to answer, and the tradeoffs each approach embodies. The chapter concludes with a discussion of the promising avenues offered by new data sources and urban computation, as well as the potential pitfalls they offer for ethics and inference alike.

This paper investigates the long-term impacts of the federal Home Owners’ Loan Corporation (HOLC) mortgage risk assessment maps on the spatial dynamics of recent income and racial distributions in California metropolitan areas over the 1990-2010 period. We combine historical HOLC boundaries with modern Census tract data and apply recently developed methods of spatial distribution dynamics to examine if legacy impacts are reflected in recent urban dynamics. Cities with HOLC assessments are found to have higher levels of isolation segregation than the non-HOLC group, but no difference in unevenness segregation between the two groups of cities are found. We find no difference in income or racial and ethnic distributional dynamics between the two groups of cities over the period. At the intra-urban scale, we find that the intersectionality of residing in a C or D graded tract that is also a low-income tract falls predominately upon the minority populations in these eight HOLC cities. Our findings indicate that neighborhoods with poor housing markets and high minority concentrations rarely experience a dramatic change in either their racial and ethnic or socioeconomic compositions—and that negative externalities (e.g. lower home prices and greater segregation levels) emanate from these neighborhoods, with inertia spilling over into nearby zones.

PySAL is a library for geocomputation and spatial/geographic data science. Written in Python, the library has a long history of supporting novel science and broadening methodological impacts far afield of academic work. Recently, many new techniques,methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature (Rey and Anselin 2007, e.g.). As such, we provide an introduction to the library as it stand snow, as well as the scientific and conceptual underpinnings of its core set of authors.Finally, we provide a prospective look at the library’s future evolution.

"A recent wave of scholarship examines the ways that daily activity spaces contribute to the experience of racial and ethnic segregation in large cities. In this paper, we take a different approach, leveraging administrative data on the residential and workplace locations of employees in large American metropolitan regions to examine daily and annual fluctuation in multiscalar segregation. In each MSA we measure racial and ethnic segregation in local residential and workplace "egohoods", defined as the set of census blocks accessible within a 25 minute walk along the pedestrian transportation network. We then construct multiscalar segregation profiles by increasing the travel bandwidth and re-computing our segregation index. Measuring the gap between residential and workplace segregation statistics at each scale reveals the extent that residential locations play in exacerbating urban segregation, and the role that daily commuting plays in overcoming these patterns to achieve temporary integration. Repeating this process for each year between 2010 and 2017, we quantify the variance in segregation levels over time for each location and at each spatial scale. Our results show that during work hours, the vast majority of cities are highly racially integrated at all spatial scales, thanks to the cosmopolitan nature of urban labor markets, but daily transport patterns and persistent residential segregation work to overcome this temporary state of togetherness, leaving most neighborhoods deeply segregated at night and on the weekends, particularly at smaller spatial scales. We interpret these findings in light of recent COVID-related trends that include increased teleworking and a return to suburbanization."

From the 1960s through the 1990s empirical literature established that the process of gentrification, the in-movement of higher-class individuals into disinvested urban areas, was occurring in cities across America. This phenomenon occurred despite population decline in major cities and the physical expansion of their suburbs over the same decades. Simultaneously, arguments for smart growth and redevelopment of decaying urban cores rose in tenor. What smart growth advocates did not consider, however, were the negative impacts of advocating for inner-area redevelopment. Limits to suburban growth can increase urban density but in tandem increase land and housing prices. Smart growth policies can create a loss of affordable housing and accelerate gentrification and displacement. In this chapter, we investigate the relationship between smart growth policies and gentrification. We then detail how a 21st century vision of smart growth can more equitably mitigate the consequences of gentrification.

In this paper we move away from a static view of neighbourhood inequality and investigate the dynamics of neighbourhood economic status, which ties together spatial income inequality at different moments in time. Using census data from three decades (1980–2010) in 294 metropolitan statistical areas, we use a statistical decomposition method to unpack the aggregate spatiotemporal income dynamic into its contributing components: stability, growth and polarisation, providing a new look at the economic fortunes of diverse neighbourhoods. We examine the relative strength of each component in driving the overall pattern, in addition to whether, how, and why these forces wax and wane across space and over time. Our results show that over the long run, growth is a dominant form of change across all metros, but there is a very clear decline in its prominence over time. Further, we find a growing positive relationship between the components of dispersion and growth, in a reversal of prior trends. Looking across metro areas, we find temporal heterogeneity has been driven by different socioeconomic factors over time (such as sectoral growth in certain decades), and that these relationships vary enormously with geography and time. Together these findings suggest a high level of temporal heterogeneity in neighbourhood income dynamics, a phenomenon which remains largely unexplored in the current literature. There is no universal law governing the changing economic status of neighbourhoods in the US over the last 40 years, and our work demonstrates the importance of considering shifting dynamics over multiple spatial and temporal scales.

"In this study, we examine how the choice of methods and data sources affect observed relationships between park accessibility and physical health, as measured through Body Mass Index (BMI). With a longitudinal study of the effects of neighborhoods on human development and cognitive aging as our backdrop, we examine how choices of spatial representation, sources of spatial data, and methods of spatial analysis yield a variety of different conclusions regarding the fundamental research questions about the associations between neighborhoods and their residents' life. Our results help clarify the amount of variance that subjective decisions like these introduce into quantitative studies. We use these results to provide guidance on how certain decisions should be made, and when researchers' omitted discussions about such choices should raise red flags; in so doing, we set the stage for a broader discussion about social science's replication crises in the special context of spatial data."

'Spatial income inequality between neighborhoods within and across cities has been attractingsubstantive attention. As a static view cannot provide a complete picture for understanding thedriving processes of urbanization and spatial polarization, this paper turns its lens to spatial incomemobility, which ties together spatial inequality at different moments in time and provides insights intothe underlying inequality dynamics. Specifically, this paper provides an empirical study of the urbanspatial income mobility in the United States with the decennial census and American CommunitySurvey (ACS) datasets for 294 metropolitan statistical areas (MSAs) over periods 1980, 1990, 2000,and 2010. We use decomposition methods to unpack the overall spatial mobility into contributingcomponents, which are Exchange, Growth, and Dispersion mobility, to get new insights into themultidimensional urban processes. One focal point is to investigate the dominant force, as wellas whether, how, and why it changed across space and over time. We find a very clear declinetrend in the dominant position of Growth mobility, along with a trend of Exchange mobility graduallydominating the overall process over 1980-2010, indicating a high level of temporal heterogeneity inthe spatial income inequality dynamics which remains underexplored in the current literature. Thetemporal heterogeneity is also reflected in how the spatial income mobility within each MSA evolved,and how this has been driven by different socioeconomic factors over time.'

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.

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.

Despite lively interest and much active research, there remains little consensus on the appropriate ways to measure gentrification and neighborhood change, and even less on the best ways to model the phenomenon. In this paper, we enter the debate on gentrification by considering a novel model of neighborhood change. Drawing from regional science, social theory, and unsupervised machine learning, we construct a model of gentrification that accounts simultaneously for multiple dimensions of change and incorporates both spatial and temporal effects. 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. We develop our model using annual, block-level LEHD data which include information about the location of both workers and employers in the USA. As a result, our model captures a wide variety of crucial information often overlooked in quantitative studies of neighborhood change. We model the nuanced process of residential turnover in concert with economic restructuring using data with high spatial and temporal resolution and we incorporate concepts of neighborhood spillovers into our model. We develop such models for the 15 largest metros in the U.S. and describe how the application of modern geographic data science can lend both insight and forewarning into the process of neighborhood change.

Dimension reduction is one of the oldest concerns in geographical analysis. Despite significant, longstanding attention in geographical problems, recent advances in non-linear techniques for dimension reduction, called manifold learning, have not been adopted in classic data-intensive geographical problems. More generally, machine learning methods for geographical problems often focus more on applying standard machine learning algorithms to geographic data, rather than applying true "spatially-correlated learning," in the words of Kohonen. As such, we suggest a general way to incentivize geographical learning in machine learning algorithms, and link it to many past methods that introduced geography into statistical techniques. We develop a specific instance of this by specifying two geographical variants of Isomap, a non-linear dimension reduction, or "manifold learning," technique. We also provide a method for assessing what is added by incorporating geography and estimate the manifold's intrinsic geographic scale. To illustrate the concepts and provide interpretable results, we conducting a dimension reduction on geographical and high-dimensional structure of social and economic data on Brooklyn, New York. Overall, this paper's main endeavor--defining and explaining a way to "geographize" many machine learning methods--yields interesting and novel results for manifold learning the estimation of intrinsic geographical scale in unsupervised learning.

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.

"Neighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. However, existing approaches for neighborhood delineation are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency."

Comparative segregation analysis holds the potential to provide rich in- sights into urban socio-spatial dynamics. However, comparisons of the lev- els of segregation between two, or more, cities at the same point in time complicated by different spatial contexts. The extent to which differences in segregation between two cities is due to differences in spatial structure or to differences in composition remains an open question. This paper de- velops a framework to disentangle the contributions of spatial structure and ethnic composition in carrying out comparative segregation analysis. The approach uses spatially explicit counterfactuals embedded in a Shapley de- composition. We illustrate this approach in a case study of the 50 largest metropolitan statistical areas in the U.S.

In urban geography and social sciences, segregation, usually consider five dimensions ina given society such as evenness, isolation, clustering, concentration and centralization. Allof these measure can either ignore spatial context or take it into consideration. Currently,several segregation measures are available in the literature, but they lack of wide spreaduse, in part, due to their complex calculations. In addition, there are only a few works thataddress the problem of inference in segregation measures for either single measure or forcomparison between multiple measures. This work tries to fill this gap by constructing anopen-source segregation module in the Python Spatial Analysis Library (PySAL). This newmodule tackles the problem of segregation point estimation for some well-known non-spatialsegregation indexes such as Dissimilarity (and its related), Gini, Entropy, Isolation, Concen-tration Profile, Correlation Ratio, and spatial indexes such as Spatial Proximity, RelativeClustering, Relative Concentration, Relative Centralization. Furthermore, it also presents anovel feature that performs inference for segregation and for comparative segregation, relyingon simulations under the null hypothesis. We illustrate the use of this new library using tractlevel census data in American counties of non-Hispanic black population.

Regional income convergence and divergence has been an active field of research for more than twenty years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data have been sparsely developed. This study introduces a visual analytics system for the space-time analysis of income dynamics. We use state level U.S. income data from 1929 to 2009 to demonstrate the visual analytics system and its utility for exploring similar data. The system consists of two modules, visualization, and analytics. The visualization module, a Web-based front-end called Rank Path Visualizer (RPV), draws inspiration from the cartographic technique of flow mapping, originally developed by Tobler and embodied in his canonical Flow Mapper application

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.

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotemporal patterns of evacuation travels by leveraging users’ location information from millions of tweets posted in the hours prior and concurrent to Hurricane Matthew. Our analysis yields several practical insights, including the following: (1) We identified trajectories of Twitter users moving out of evacuation zones once the evacuation was ordered and then returning home after the hurricane passed. (2) Evacuation zone residents produced an unusually large number of tweets outside evacuation zones during the evacuation order period. (3) It took several days for the evacuees in both South Carolina and Georgia to leave their residential areas after the mandatory evacuation was ordered, but Georgia residents typically took more time to return home. (4) Evacuees are more likely to choose larger cities farther away as their destinations for safety instead of nearby small cities. (5) Human movements during the evacuation follow a log-normal distribution.

The purpose of this update is to provide the most current information about both the Colorado Adoption Project (CAP) and the Longitudinal Twin Study (LTS) and to introduce the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife), a product of their merger and a unique study of lifespan behavioral development and cognitive aging. The primary objective of CATSLife is to assess the unique saliency of early childhood genetic and environmental factors to adult cognitive maintenance and change, as well as proximal influences and innovations that emerge across development. CATSLife is currently assessing up to 1600 individuals on the cusp of middle age, targeting those between 30 and 40 years of age. The ongoing CATSLife data collection is described as well as the longitudinal data available from the earlier CAP and LTS assessments. We illustrate CATSLife via current projects and publications, highlighting the measurement of genetic, biochemical, social, sociodemographic and environmental indices, including geospatial features, and their impact on cognitive maintenance in middle adulthood. CATSLife provides an unparalleled opportunity to assess prospectively the etiologies of cognitive change and test the saliency of early childhood versus proximal influences on the genesis of cognitive decline.

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

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.

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.

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.

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.

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

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

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.

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.