Neighborhoods of Opportunity: Developing an Operational Definition for Planning and Policy Implementation

This article synthesizes existing literature to examine the emerging concept of neighborhoods of opportunity and places it in the context of past efforts to define neighborhood opportunity. Place-based and people-based approaches to urban revitalization and community development are linked to this concept. The place-based approach focuses on promoting inner-city revitalization in order to create neighborhoods of opportunity and the people-based approach focuses on connecting people to opportunities that already exist in the regions where they live. These approaches are examined in relation to how they influence emerging models for siting affordable housing in both distressed inner-cities and more opportunity rich suburbs that surround them. The article concludes with recommendations for a new tiered approach to place-based and people-based strategies for affordable housing siting in core city and regional contexts

Neighborhood characteristics and the location of HUD-subsidized housing in shrinking cities: an analysis to inform anchor-based urban revitalization strategies

This article focuses on the manner in which affordable housing fits into anchor-based strategies for urban revitalization. It involves quantitative analysis of the location of existing HUD-subsidized housing in relation to neighborhood characteristics. The goal of the article is twofold. First, we examine the degree to which neighborhood characteristics associated with neighborhoods of opportunity correlate with the location of HUD-subsidized housing in shrinking cities. Second, we make recommendations for more equitable approaches to anchor-based urban revitalization. Our analysis uses a unique database developed to measure neighborhood characteristics in shrinking US cities. Our findings suggest that the location of affordable housing is not correlated with proximity to institutional and neighborhood amenities, where anchor-based revitalization is targeted. As a result, we make recommendations to link future affordable housing siting to anchor-based strategies for inner-city revitalization. Keywords: urban planning; urban development; community organizing; grass-roots development; public policy The case for equitable urban revitalization in shrinking cities In the wake of decades of deindustrialization and disinvestment, the anchor-based model for urban revitalization has emerged in shrinking cities. 1 As large manufacturers and other private sector investors have retreated from older industrial cities in the USA, place-based nonprofits like hospitals and universities have emerged as core anchor institutions that drive urban revitalization. Urban scholars, policy-makers, and economic development practitioners have taken note of this shift and defined strategies to catalyze revitalization through investments by these types of anchor institutions as following the so-called eds and meds model for community development Although some researchers argue that local development and investment by large anchor institutions has had a stabilizing influence, others raise concerns about the degree to which anchor-based development promotes equitable outcomes This article was written to expand the debate about anchor-based revitalization, linking it to calls for the development of affordable housing and other equity measures. We explore two dimensions of this debate. First, we examine the manner in which the location of existing affordable housing correlates with institutional characteristics of neighborhoods in shrinking cities. In particular, we measure the degree to which existing affordable housing is located near anchor institutions and other amenities associated with anchor-based revitalization strategies. Second, we draw from that exploratory analysis to make recommendations for more equitable anchor-based revitalization strategies in shrinking cities. In the next section, we elaborate on the emerging anchor-based model for urban revitalization in shrinking cities. After highlighting key characteristics of the anchorbased strategy, we turn to a discussion of other place-based strategies to promote greater social equity through the urban revitalization process. We frame this discussion by drawing from the concept of neighborhoods of opportunity. We embed our focus on siting affordable housing in this broader framework in order to highlight that the provision of affordable housing is one component of a comprehensive strategy needed to promote equitable community development outcomes. The emerging anchor-based urban revitalization model in shrinking cities The anchor-based strategy The anchor-based model for urban revitalization coalesced during the early 2000s. The development of this model was spearheaded by university-based policy centers and nonprofit research institutes. The Penn Institute for Urban Research (Penn IUR) at the University of Pennsylvania was instrumental in the development of the anchor-based strategy for urban revitalization and it continues to serve as a lead organization for the national Anchor Institutions Task Force (http://www.margainc.com/initiatives/aitf/). The Penn IUR and the Anchor Institutions Task Force have organized national conferences and published white papers and other reports advocating for anchor institutions to take a lead role in inner-city revitalization efforts The anchor-based model is complemented by other place-based urban revitalization strategies that target investments near large institutions and infrastructure hubs, such as strategies based on transit-oriented development, the conversion of public housing to mixed-income development, school rebuilding, and other mixed-use development strategies (Center for Transit Oriented Development, 2007; The anchor-based model has been critiqued for its relative lack of attention to equity issues and the negative externalities of new development experienced by inner-city residents. Our past research points out that many applied studies and reports dealing with anchor institutions pay little attention to issues like residential displacement A small number of empirical studies have examined some of the impacts of anchorbased urban revitalization In order to promote more equitable outcomes, scholars have advocated for the inclusion of community benefits and other linkages in the anchor-based model. Largely, this literature focuses on the use of planning tools like community benefit agreements (CBAs) to promote equity in anchor-based urban revitalization Neighborhoods of opportunity Like the anchor-based strategy for urban revitalization, there is limited empirical analysis of the neighborhoods of opportunity approach. For the most part, the literature on this approach has been confined to policy briefs, case studies, and best practices. A 2011 White House report coined the term neighborhoods of opportunity in policy lexicon (The White House, 2011). The term was used to highlight a new comprehensive strategy for community development that channeled resources into high-poverty urban neighborhoods. This strategy entailed a neighborhood transformation approach that wedded investments in urban revitalization and physical redevelopment with enhanced social services and public assistance. It involved a variety of components such as infrastructure improvements, downtown revitalization, housing development, school reconstruction, tax incentive strategies, housing assistance, school reform, wrap-around social services, and other improvements to the built environment. An underlying theme of the neighborhoods of opportunity approach is that inner-city revitalization should be geographically targeted and built on public-private nonprofit partnerships. The approach argues for federal community development funding to be "braided" with other sources of funding (The White House, 2011, p. 11). The concept of braiding is based on the acknowledgment that public funding for urban revitalization is limited. Consequently, it should be applied to targeted revitalization efforts that draw from diverse resources. The neighborhoods of opportunity strategy fits into a broader approach to urban revitalization that seeks to leverage the resources of anchor 4 R.M. Silverman et al. Downloaded by ["University at Buffalo Libraries"] at 10:41 30 September 2015 institutions (particularly universities and hospitals) to promote inner-city revitalization Conceptualizing and measuring equity outcomes By wedding anchor-based strategies to the neighborhoods of opportunity approach, we bring social equity back to the forefront of the dialog concerning inner-city revitalization. The adoption of this framework allows us to take an advocacy planning stance and argue that public subsidies and support for anchor-based revitalization should include linkages to community benefits, particularly in relation to affordable housing. Thus, we argue that it is the role of planners, public administrators, elected officials, and others in the public sector to advocate for linkages that promote an equitable distribution of benefits from urban revitalization. The rationale for such an advocacy stance is well established in the disciplines of urban planning, social work, and public administration Implicit in our argument is the assumption that to some degree, existing anchorbased strategies fall short of promoting equitable outcomes. Recognizing that there is a need for multilevel analysis of the benefits that anchor institutions bring to minority and low-income communities, this exploratory study provides a starting point. In this study, we examine the degree to which affordable housing is located in proximity to anchor institutions and neighborhood amenities that have been associated with place-based urban revitalization strategies. Our focus on affordable housing and neighborhood amenities is an extension of recent work done by Powell In order to test our hypothesis, we operationalized measures that capture key institutional characteristics and neighborhood amenities associated with anchor-based revitalization. These building blocks for inner-city revitalization were examined in relation to socioeconomic and housing characteristics in shrinking cities. Together, these data were used to identify correlates with the location of subsidized housing in the 10 fastest shrinking cities in the USA between 2000 and 2010. Our findings suggest that the location of affordable housing is not correlated with proximity to institutional and neighborhood amenities, where anchor-based revitalization is targeted. As a result, we make recommendations to link future affordable housing siting to anchor-based strategies for inner-city revitalization. The recommendations that grow out of our analysis have particular applications to urban planning in shrinking cities, where other forms of urban revitalization are less prevalent. However, we believe this work can be elaborated upon and adapted to other urban geographies. Data and methods Our analysis used a unique database developed to measure neighborhood characteristics in shrinking US cities. In terms of population characteristics in core cities: the black population, poverty levels, and public assistance use were noticeably higher; and educational attainment, employment levels, and incomes were noticeably lower. In terms of core city housing conditions: the housing stock was older and less likely to be composed of single-family homes, housing values and owner occupancy rates were lower, monthly renter costs as a percent of household income were higher, and vacancy rates were higher. There were also a few noteworthy institutional contrasts between core cities and suburbs: there was a noticeably higher percent of census tracts with hospitals in core cities, the level of access and use of public transit was higher in core cities, and school performance was lower in core cities. These characteristics suggest that conditions are ripe for the adoption of anchor-based revitalization in core cities, particularly when they are pursued in conjunction with medical campus expansion and transit-oriented development. Likewise, the data suggest that a need for linkages to community benefits and other equity measures is present. We examined a correlation matrix for the variables displayed in 2 Four components were extracted from the factor analysis. The components and loadings are summarized in The first component explained 39.6% of the variance in the variables modeled. This component, SOCIOECONOMIC DISTRESS, functioned as a measure of the combined effects of poverty, public assistance and SSI use, unemployment, low educational attainment, minority status, lower median income, lower median housing values, and property vacancy. The second component explained 12.9% of the variance in the variables modeled. This component, SINGLE-FAMILY SETTING, functioned as a measure of the combined effects of single-family homes, larger households, and owner-occupied housing. The third component explained 8.0% of the variance in the variables modeled. This component, SOCIAL SECURITY COHORT, functioned as a measure of the effects of households with social security income. The fourth component explained 5.8% of the variance in the variables modeled. This component, INCOME INEQUALITY, functioned as a measure of the effects of an elevated GINI index of income inequality. The components derived from the factor analysis were used as independent variables in multivariate linear regression models. The models were used to identify correlations with a dependent variable measuring the percent of total housing units that were subsidized by HUD in a census tract. 3 Twelve other independent variables, described below, were examined in the regression analysis. A binary "dummy" variable, measuring whether a census tract was located in a core city, was used as a control variable in the fully specified model of the regression analysis. Two other independent variables measured the percent of HUD-subsidized units in a census tract that were HCV and public housing units, respectively. These variables were used as controls for the type of subsidized unit. Our assumption was that in census tracts where subsidized units were predominantly HCVs, there would be a lower percent of total housing units subsidized. Likewise, we assumed that in census tracts where subsidized units were predominantly public housing, there would be a higher percentage of total housing units subsidized. Another independent variable measured the ratio of jobs to the total population in a census tract. This served as a measure of employment density at the neighborhood level. Two other independent variables were used in the analysis that measured neighborhood infrastructure. One was a dummy variable that indicated if a transit line ran through a census tract. The other was a dummy variable that indicated if a park was located in a census tract. Four dummy variables were used in the analysis that measured institutional characteristics of census tracts. Each indicated if a hospital, college or university, public library, or K-12 school was located in a census tract. In addition, a control variable was used in the analysis that indicated if at least one school in a census tract met its academic year progress (AYP) goals in 2012. Finally, a control variable was used in the analysis that indicated if at least one school in a census tract did not meet its AYP goals in 2012. Multivariate regression results The variables described above were entered into multivariate linear regression models to determine if any meaningful and significant relationships existed between them and the percent of total housing units that were subsidized by HUD in a census tract. In addition to identifying significant effects, identifying variables with the greatest influence on the concentration of HUD-subsidized units in a census tract was a central interest to our hypothesis. Three models were examined. The first analyzed all of the census tracts in the MSAs, where the 10 fastest shrinking cities in the USA were located between 2000 Community Development 9 Downloaded by ["University at Buffalo Libraries"] at 10:41 30 September 2015 and 2010. The second model analyzed the subset of core city census tracts. The third model analyzed the subset of census tracts for suburban census tracts. Combined, these models allowed us to examine the overall relationships between the independent and dependent variables, and we were able to distinguish between relationships in core cities and suburbs. The adjusted-R 2 and the unstandardized (b) and standardized (β) multivariate regression coefficients for the effects of the independent variables on the dependent variable in each of the models are reported in The fully specified model Model 1 represents the fully specified regression analysis for all census tracts. The most noticeable feature of this model is that 6 of the 16 independent variables were significantly related to the percent of total housing units that were HUD subsidized. Three variables were correlated with higher concentrations of HUD-subsidized housing units: the factor measuring socioeconomic distress (p < .001), the percent of HUD-subsidized units that were public housing (p < .001), and the presence of a park in a census tract (p < .05). In contrast, three variables were correlated with lower concentrations of HUD-subsidized housing units: the factor measuring characteristics of single-family settings (p < .001), the factor measuring characteristics of a social security cohort (p < .01), and the percent of HUD-subsidized units that were HCVs (p < .001). The adjusted-R 2 indicated that 49.3% of the variance in the percent of total housing units that were HUD subsidized was attributed to the variables used in Model 1. These results corresponded with past research which found that subsidized housing, particularly traditional public housing, was concentrated in relatively isolated, distressed areas Still, some results were not easily interpreted when examining the fully specified model in Downloaded by ["University at Buffalo Libraries"] at 10:41 30 September 2015 Variable name .542*** *p < .05; **p < .01; ***p < .001. Community Development 11 Downloaded by ["University at Buffalo Libraries"] at 10:41 30 September 2015 boundaries. This finding highlights the need for future siting considerations to include an equity component since past siting of affordable housing has tended to be concentrated in socioeconomically distressed areas, regardless of their urban or suburban context. Insights into the results from the fully specified model are discussed further in relation to the partial models, isolating core city tracts from suburban tracts, particularly with respect to the results for the relationship between the factor associated with the social security cohort (β = −.031 in the fully specified model) and the location of HUDsubsidized housing. An examination of those models teases out the nuances of variables associated with the location of HUD-subsidized housing in different spatial and jurisdictional contexts. It is noteworthy that institutional characteristics central to the anchor-based strategy for urban revitalization that were included in the full model were not significantly related to the percent of total housing units that were HUD subsidized. Specifically, the presence of hospitals, colleges/universities, libraries, and high-performing public schools was not correlated with the concentration of subsidized housing in a census tract. This suggests that at the metropolitan-level affordable housing did not cluster in locations that benefit from proximity to sites where eds and meds revitalization are targeted. Instead, the fully specified model supports the hypothesis that a disconnect exists between where HUD-subsidized affordable housing was located and where amenities associated with anchor-based revitalization strategies clustered. Moreover, the magnitude of the βs for the significant variables in the fully specified model suggests that the location of affordable housing was mainly correlated with socioeconomic isolation and neighborhood distress. The core city model Model 2 represents the regression analysis for census tracts located in core cities. In this model, 6 of the 15 independent variables were significantly related to the percent of total housing units that were HUD subsidized. Two variables were correlated with higher concentrations of HUD-subsidized housing units: the factor measuring socioeconomic distress (p < .001) and the percent of HUD-subsidized units that were public housing (p < .001). In contrast, four variables were correlated with lower concentrations of HUD-subsidized housing units: the factor measuring characteristics of single-family settings (p < .001), the factor measuring characteristics of the social security cohort (p < .01), the factor measuring characteristics of income inequality (p < .05), and the percent of HUD-subsidized units that were HCVs (p < .001). The adjusted-R 2 indicated that 49.4% of the variance in the percent of total housing units that were HUD subsidized was attributed to the variables used in Model 2. These results provide a more refined understanding of the relationship between the independent variables and the clustering of HUD-subsidized housing in the core cities that were shrinking between 2000 and 2010. Like the MSAs they are located in, the variable with the largest β was the factor measuring socioeconomic distress. This factor had the strongest influence (β = .368) on where subsidized housing clustered in core cities. Likewise, the factor measuring characteristics of single-family settings had the second-strongest influence (β = −.297) in core cities. Similarly, the percent of HUDsubsidized units that were HCVs (β = −.296) and the percent of HUD-subsidized units that were public housing (β = .245) had relatively large standardized coefficients. As was the case at the MSA level, tracts with high percentages of HCVs had less concentrated HUD-subsidized housing, while tracts with high percentages of public housing had more concentrated HUD-subsidized housing. Another significant relationship related to income inequality (β = −.054). Tracts with greater income inequality had less clustering of HUD-subsidized housing. In an inner-city context, this reflected the degree to which tracts segregated by income, particularly tracts with concentrated poverty, had higher concentrations of HUD-subsidized housing. Finally, the standardized coefficient for the factor associated with the social security cohort (β = −.075) indicated that tracts where this cohort was more concentrated had less HUD-subsidized housing.

Synthesizing Data to Explore the Dynamic Spatial Patterns of Hotel Development

The spatio-temporal relationship between tourism product similarity and spatial proximity has not been adequately studied empirically because of data and methodological limitations. New forms of data available at high temporal frequencies and low levels of spatial aggregation, together with large commercial data and expanding computational ability allow a variety of theories, old and new to be explored and evaluated more meticulously and systemically than has been possible hitherto. This study uses spatial visualization and data harvesting to synthesize a variety of data for exploring the evolution of hotel clusters and co-location synergies in US cities. The findings question the reliability of the current data to be used for identifying and analyzing the formation of tourist destination clusters and their dynamics. We conclude that synthesizing social media and large commercial data can generate a more robust database for research on tourism development and planning and improving opportunities for the examining spatial patterns of tourism activities. We also devise a protocol to combine ‘social media’ sources with big commercial sources for tourism development and planning, and eventually other sectors

Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets

Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets

Where Did All the Remittances Go? Understanding the Impact of Remittances on Consumption Patterns in Rural China

We focus on the impact of migrants’ remittances on consumption patterns in rural China, allowing for endogeneity of remittances and county fixed-effects. We find that the marginal propensity to consume out of remittances is close to unity, which is far greater than that out of non-migrant earnings or farm income. These findings imply that rural households take remittances as permanent income and are consistent with the prevalence of circular and repeat migration which is largely caused by the combination of the restrictive hukou (household registration) system and the rigid land tenure system in China.Rural-Urban Migration; Remittances; Consumption Patterns; Fixed-Effect Instrumental-Variables Estimation

A rice chloroplast-localized ABC transporter ARG1 modulates cobalt and nickel homeostasis and contributes to photosynthetic capacity

Transport and homeostasis of transition metals in chloroplasts, which are accurately regulated to ensure supply and to prevent toxicity induced by these metals, are thus crucial for chloroplast function and photosynthetic performance. However, the mechanisms that maintain the balance of transition metals in chloroplasts remain largely unknown. We have characterized analbino-revertible green 1(arg1) rice mutant.ARG1encodes an evolutionarily conserved protein belonging to the ATP-binding cassette (ABC) transporter family. Protoplast transfection and immunogold-labelling assays showed that ARG1 is localized in the envelopes and thylakoid membranes of chloroplasts. Measurements of metal contents, metal transport, physiological and transcriptome changes revealed that ARG1 modulates cobalt (Co) and nickel (Ni) transport and homeostasis in chloroplasts to prevent excessive Co and Ni from competing with essential metal cofactors in chlorophyll and metal-binding proteins acting in photosynthesis. Natural allelic variation inARG1betweenindicaand temperatejaponicasubspecies of rice is coupled with their different capabilities for Co transport and Co content within chloroplasts. This variation underpins the different photosynthetic capabilities in these subspecies. Our findings link the function of the ARG1 transporter to photosynthesis, and potentially facilitate breeding of rice cultivars with improved Co homeostasis and consequently improved photosynthetic performance