UMBC Geographic Information Systems (GIS)
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Item Location Patterns of Housing Choice Voucher Households Between 2010 and 2020(U.S. Department of Housing and Urban Development, 2024) Armstrong, Gretchen; Din, Alexander; Shcheglovitova, Mariya; Winegardner, RaeThe Housing Choice Voucher (HCV) program has sought to enable neighborhood choice for low-income assisted renter households in the United States, replacing previous policies focused on fixed-site housing. Since the launch of the program 50 years ago, researchers have shown interest in measuring the effects of the HCV program on the spatial deconcentration of poverty and voucher households’ access to new neighborhoods and higher opportunity areas, typically defined as neighborhoods with lower poverty rates. The authors find that during the study period (2010–20), demographics of households served by the HCV program changed from families with children representing the most common voucher household structure to a majority of households headed by elderly or disabled individuals. Nationally, the share of housing units below fair market rent guidelines declined this decade. The share of HCV households living in neighborhoods with a high density of voucher holders increased, and more than 40 percent of HCV households remained in high-poverty neighborhoods. Changes affecting the HCV program are expected to continue. This article highlights the need for further research to evaluate the effect of policy changes on HCV locational outcomes.Item Leveraging accessibility modeling to improve housing equity for low-income assisted renters: A case study in Bridgeport, Connecticut(Elsevier, 2024-01-18) Din, Alexander; Chen, XiangThe Housing Choice Voucher Program (HCVP) in the United States assists low-income households with a rent voucher to be redeemed in the private rental market. However, public housing authorities in certain states, such as Connecticut, may impose geographic restrictions that we coin as the “same-jurisdiction rule,” when implementing the HCVP. In practice, this constraint is translated to the requirement that assisted households must lease up within the same jurisdiction, which may eventually restrict access to high-quality housing or prevent relocation to higher-opportunity neighborhoods. To identify ways for policy improvement, we adopt the two-step floating catchment area method to evaluate the accessibility to HCVP-eligible housing in Bridgeport, Connecticut with and without the same-jurisdiction rule. The results indicate that when the geographic restrictions are lifted, there is a 9.23 % increase in median accessibility, and the increase is more significant in low-accessibility areas, which are often lower-opportunity neighborhoods. The results suggest that removing the same-jurisdiction rule can result in more housing options and thus improve housing equity for assisted renters. Since the HCVP and other rental assistance programs are spatial-driven, we call for establishing an operational workflow to corroborate the rigor of these programs to improve housing equity for low-income households.Item Population Change in Wildfire-Affected Areas in the United States: Evidence from U.S. Postal Service Residential Address Data(Springer Nature, 2024-07-29) DeWaard, Jack; Din, Alexander; McConnell, Kathryn; Fussell, ElizabethWe examine the utility of data on active and vacant residential addresses to inform local and timely monitoring and assessments of how areas impacted by wildfires and extreme weather events more broadly lose (or not) and subsequently recover (or not) their populations. Provided by the U.S. Postal Service to the U.S. Department of Housing and Urban Development and other users, these data are an underutilized and potentially valuable tool to study population change in disaster-affected areas for at least three reasons. First, as they are aggregated to the ZIP+4 level, they permit highly local portraits of residential and, indirectly, of population change. Second, they are tabulated on a quarterly basis starting in 2010 through the most recent quarter, thereby allowing for timely assessments than other data sources. Third, one mechanism of population change—namely, underlying changes in residential occupancies and vacancies—is explicit in the data. Our findings show that these data are sufficient for detecting signals of residential and, indirectly, of population change during and after particularly damaging wildfires; however, there is also noticeable variation across cases that requires further investigations into, for example, the guidance the U.S. Postal Services provides its postal offices and carriers to classify addresses as vacant.Item What Do Visualizations of Administrative Address Data Show About the Camp Fire in Paradise, California?(U.S. Department of Housing and Urban Development, 2022) Din, AlexanderThe Camp Fire destroyed most structures and displaced most of the population in Paradise, California. Since the wildfire, Paradise has returned to approximately one-fourth of its pre-wildfire population. This article visualizes administrative address data before and after the wildfire to measure population displacement and return. Administrative address data is likely underutilized for that purpose.Item Measuring Blight(U.S. Department of Housing and Urban Development, 2022) Din, AlexanderCommunities across the United States struggle with blighted urban environments. Negative associations with blight include crime (Branas, Rubin, and Guo, 2012), falling property values (Han, 2014), poor social determinants of health (Garvin et al., 2013), sprawl (Brueckner and Helsley, 2011), and dwindling tax bases but increased burdens (Tri-COG Collaborative, 2013). Despite substantial research into the negative effects of blight, no single definition of blight emerges (Morckel, 2014). The context of defining blight matters for identifying the proper measurement and data source for evaluating blight. Discussing the ever-evolving definition of blight, Gordon (2004) quotes a California state legislator who said, “defining blight became an art form” which also applies to the measurement of blight. Measuring blight continues to remain important because during the 2010s, approximately one-fifth of metropolitan areas and one-half of micropolitan areas lost population (Mackun, Comenetz, and Spell, 2021). As communities shrink, structures will be abandoned. Because the definition of blight is ambiguous, measuring this phenomenon is difficult. Measuring blight requires substantial work, which can be labor-intensive and can quickly become outdated (Pagano and Bowman, 2000). Windshield and parcel surveys have been sources of good-quality data but are expensive to produce and maintain. Administrative records are increasingly popular measurements of blight because the information already exists, although this data frequently uses other indicators as a proxy for blight. Efforts to measure blight using administrative records have included housing code violations (Hillier et al., 2003), tax delinquency (Whitaker and Fitzpatrick, 2013), 311 calls-forservice (Athens et al., 2020), and postal delivery status records (Molloy, 2016). This issue of Cityscape explores recent developments in the measurement of blight. Administrative data, particularly housing vacancy data, continue to be a leading proxy for blight. Novel techniques using image classification ameliorate early warnings of housing abandonment, which may enable blight intervention programs to become more proactive rather than reactive. This symposium also describes how the measurement of blight is also correlated to the measurement of other phenomena, such as sprawl.Item Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire(U.S. Department of Housing and Urban Development, 2023) Din, AlexanderLittle is known about U.S. Department of Housing and Urban Development (HUD)-assisted households following a natural disaster, including continued participation status in low-income rental assistance and post-disaster location outcomes. This article compares changes in participation in HUD assistance and neighborhood poverty status between HUD-assisted households in Paradise and Magalia, California, and the rest of Butte County following the 2018 Camp Fire. The wildfire destroyed most of the community, making it the deadliest and most destructive wildfire in California’s history. Approximately one-half of HUD-assisted households were not participating in HUD assistance in 2019. Of households that remained assisted, most had moved out of their neighborhood, often to higher poverty neighborhoods. This research suggests that further research is necessary to measure changes in participation in HUD assistance and locational trends for low-income subsidized households following a natural disaster.Item Neighborhood Incarceration Rate Hot Spots in Maryland(U.S. Department of Housing and Urban Development, 2023) Din, AlexanderMaryland’s 2010 No Representation Without Population Act requires that census data used for political redistricting be adjusted so that Marylanders incarcerated in state and federal prisons will be enumerated at their last known address rather than their place of incarceration. This report briefly describes why this population adjustment process is important and then uses spatial analysis to identify neighborhood incarceration rate clusters, also referred to as hot spots or cold spots, and outliers. The results are mapped to visualize Maryland’s areas of incarceration hot spot and cold spot clusters and outlier areas.Item Increased transit delays in fall of 2021 and the potential impact on high school commutes(D.C. Policy Center, 2022-11-14) Din, AlexanderIn the fall of 2021, students in DCPS and public charter schools returned in-person, after spending roughly a year and a half learning at home. Students returned to school at roughly the same time that most of Metro’s 7000-series trains were removed from service due to safety concerns. The reduction in service doubled wait times at Metro stations and put additional strain on the Metro’s bus network. This is concerning because transportation vulnerability, including increased commute times or unreliable service, has been linked to issues with school attendance—which may result in loss of academic achievement.Item Hispanic Housing Experience in the United States Part II—Hispanic Homeownership and Rental Access Quality, Gentrification, and the Resulting Impact on Neighborhood Context(U.S. Department of Housing and Urban Development, 2021) Din, Alexander; Hemphill, Portia R.The access of Hispanics—the largest ethnic-racial minority in the United States—to housing has been understudied. A Cityscape call for papers to fill that gap resulted in more publishable submissions than would fit in one symposium. Therefore, in the last issue, George Carter III presented “The Hispanic Housing Experience in the United States, Part I,” which focused on homelessness, segregation, anti-immigrant ordinances, and mobility. In this issue, our symposium (Part II) focuses on one old theme (segregation) but also several new ones: assisted housing, homeownership, and the transition of wealth and real property between generations.Item New Data Fields for HUD Aggregated USPS Administrative Data on Address Vacancies(U.S. Department of Housing and Urban Development, 2021) Din, AlexanderSince 2005, the United States Department of Housing and Urban Development (HUD) has worked in partnership with the United States Postal Service (USPS) to receive administrative data on address vacancies. HUD has made that data available to government entities and nonprofit researchers. Since 2012, HUD has received more than 3,100 requests for access to the data. In the most recent agreement between HUD and USPS, new fields have become available regarding (1) the USPS preferred name and preferred state for a ZIP Code, (2) the count of addresses added to the USPS Address Management System (AMS) during the quarter, and (3) drop counts for entities such as mobile home communities and gated communities where mail is delivered to a single recipient but no data are collected for the addresses using that node. The purpose of acquiring those extra data was to better understand address vacancy and neighborhood change. It is expected that these new data fields will continue to be available for future datasetsItem Does the Inclusion of Residential No-Stat Addresses Along Rural Postal Carrier Routes Improve Vacancy Rate Estimates?(U.S. Department of Housing and Urban Development, 2022) Din, Alexander; Han, PeterBlighted housing is a problem in communities throughout the United States. Many definitions of blight and data sources attempt to quantify and measure blight. One common measure of housing blight is housing vacancy, and one common data source for housing vacancy is the U.S. Department of Housing and Urban Development (HUD) Aggregated U.S. Postal Service (USPS) Administrative Data on Address Vacancies (USPS address data). This dataset provides granular and timely data into active and vacant housing. However, the USPS address data is not without its flaws. The label “not-a-statistic” (“no-stat”) to describe housing that is vacant, under construction, or otherwise not receiving mail is an ambiguous designation and has puzzled researchers. It is not possible to discern between no-stat for blight versus no-stat for development in the data. This error may lead researchers to false conclusions about housing vacancy or neighborhood characteristics of high housing vacancy areas if the housing vacancy rate is not accurately calculated. The label no-stat has even attracted Congressional attention to decipher no-stat for blight versus no-stat for development.Item Tree Equity Scores and Housing Choice Voucher Neighborhoods(U.S. Department of Housing and Urban Development, 2022) Din, Alexander; Krisko, PerrinUrban greenery has considerable advantages to populations, particularly mental and physical health benefits. Tree canopy in urban areas is linked to reductions in surface temperature, reductions in chronic illnesses, improvements in air quality, and more. A new dataset, the Tree Equity Score, is a metric that describes the intersection between urban tree canopy cover and socioeconomic factors. This analysis examines Tree Equity Scores in six cities chosen on the basis of their participation in the C40 Cities Climate Leadership Group, then evaluates if differences exist between neighborhoods where Housing Choice Voucher households are present and neighborhoods where they are absent. In five of six cities, Tree Equity Scores are higher in neighborhoods where Housing Choice Voucher households are absent.Item Leveling the Playing Field: School District Spending in Diverse Communities(U.S. Department of Housing and Urban Development, 2016) Din, AlexanderThe United States is the only industrialized nation that funds its public schools from local- and state-level taxes (Payne and Biddle, 1999). School resource disparities across districts reflect economic differences between the wealthy and poor. A school district’s spending per student in each district is based on the economic needs of the students or the school as a whole, which typically is based on median household income. School districts typically determine how much funding each school receives by calculating a cost per student that is the ratio of total school cost to the number of students. The cost-per-student ratio is then divided by the median household income in that district to derive a spending-to-income (SIC) ratio— SIC ratio = [cost per student/median household income]. Using Montgomery County, Maryland, as an example, these costs can be visualized in a spatial analysis to determine if spending is distributed according to income differences.Item Visualizing Residential Vacancy by Length of Vacancy(U.S. Department of Housing and Urban Development, 2017) Din, AlexanderThe United States Postal Service (USPS) collects counts of occupied and vacant residential and business addresses across the United States. The counts of vacant addresses are broken down by length of vacancy. This information is useful for researchers, planners, analysts, and others concerned with vacancy issues in making informed decisions to address them. For example, communities affected by recent vacancy may require different approaches and solutions than communities affected by long-term vacancy. I demonstrate how to use a modified box plot with swarm plot, paired with a micromap, to visualize ratios of different lengths of residential vacancy compared with total residential vacancy. I developed visualizations at the census tract level for the Pittsburgh, Pennsylvania Core Based Statistical Area (CBSA) on a quarterly basis from the first quarter of 2012 until the first quarter of 2017.Item Visualizing and Comparing Residential Permit Data Using Lollipop Plots(U.S. Department of Housing and Urban Development, 2019) Din, AlexanderResidential permits are a common indicator of housing market activity. Residential permits indicate the demand for new homes, and by categorizing homes into different construction types, it is possible to understand what types of homes are in-demand in the market and the types of homes that the market is producing. In this article, I use a cross between a scatter plot and a bar chart called a lollipop plot to visualize residential permits by year for single-family dwellings (SFDs) and townhomes in Montgomery County, Maryland. These data were obtained from dataMontgomery (2019), the open data portal for the county. These data are for construction permits that were finalized between 2000 and 2018 for SFDs and townhomes, as far back as data were available. Between 2000 and 2018, there were 14,831 and 6,322 permits for SFDs and townhomes, respectively.Item Applying Spaghetti and Meatballs to Proximity Analysis(U.S. Department of Housing and Urban Development, 2020) Din, AlexanderThe spaghetti and meatballs technique is a geoprocessing method used in a Geographic Information System (GIS) that counts the number of overlapping polygons that are of unequal size and shape. Often, this method is used to calculate densities of coverage areas including, but not limited to, the extent of an oil spill over a period of time or the extent of a burn during a wildfire, or to compare perceptions of a region. In this demonstration, I use the spaghetti and meatballs technique to measure the density of proximity to points of interest, or amenities, in Washington, DC. I calculate summary statistics to describe the densities of amenities by the District’s eight city council wards.Item HUD Crosswalk Files Facilitate Multi-State Census Tract COVID-19 Spatial Analysis(U.S. Department of Housing and Urban Development, 2021) Din, Alexander; Wilson, RonThe coronavirus COVID-19 has infected millions of Americans. Datasets like the national county-level aggregation of COVID-19 case counts that Johns Hopkins University & Medicine assembled have been widely used, but few analyses have been performed at the local level due to the low supply of data. Like many things American, the distribution of COVID-19 data varies due to differing state, county, and local government reporting policies. The result is a patchwork of COVID-19 data at the local level, mostly aggregated to ZIP Codes due to ease of data processing rather than census tracts which are a better geographical unit for analysis. Local level COVID-19 data are rare and often only available for small areas. In this article, we demonstrate how the U.S. Department of Housing and Urban Development (HUD) Crosswalk Files can be used to assemble a census tract-level dataset of COVID-19 case rates in the Washington, D.C. Metropolitan Statistical Area across multiple states.Item Measuring Neighborhood Opportunity with Opportunity Atlas and Child Opportunity Index 2.0 Data(U.S. Department of Housing and Urban Development, 2021) Mast, Brent D.; Din, AlexanderResearchers have recently introduced two datasets measuring neighborhood opportunity: the Harvard University Opportunity Atlas data (Chetty et al., 2018b) and the Brandeis University Child Opportunity Index (COI) 2.0 data (Noelke et al., 2020). The Opportunity Atlas data measure neighborhood opportunity longitudinally on the basis of children’s outcomes in adulthood for the years 1989 to 2015. The COI 2.0 data measure neighborhood opportunity contemporaneously for the years 2010 and 2015 on the basis of 29 child welfare indicators categorized into three domains: (1) education, (2) health and environment, and (3) social and economic. In this article we describe the two datasets and present a data analysis example estimating what the Part I crime distribution in Dallas would be if neighborhood opportunity distributions (based on both neighborhood opportunity data sources) in Dallas were more similar to those of Chicago. We adjust for neighborhood opportunity differences between the two cities using the nonparametric propensity score matching technique (Barskey et al., 2002). We conclude that neighborhood opportunity differences explain little of the crime differences between the two cities.Item The Geography of Hispanic HUD-Assisted Households(U.S. Department of Housing and Urban Development, 2021) Din, AlexanderIn 2019, Hispanic households constituted 18.4 percent of all HUD-assisted households. The share of Hispanic households varied from state to state and by program area. Most states’ share of Hispanic HUD-assisted households was smaller than its share of the Hispanic population in that state or Washington, DC. Hispanic HUD-assisted households were more likely than Hispanic non-HUD-assisted households to live in urban counties but at about the rates similar to non-Hispanic HUD-assisted households. Hispanic HUD-assisted households were less likely to live in low-poverty neighborhoods and more likely to live in high-poverty and extremely high-poverty neighborhoods compared with non-Hispanic HUD-assisted households.Item Measuring Distance to Resources(U.S. Department of Housing and Urban Development, 2017) Wilson, Ron; Din, AlexanderMapping counts or rates of residents by areal geographies is useful for visualizing distributions across regions. However, this approach limits the understanding of resource proximity to visual approximations. Taking advantage of exact location information in a geographic information system (GIS), direct proximity statistics can be created by geoprocessing residence locations to population centers. In this article, we demonstrate how to geoprocess location information to create a table of the distances between resident locations and the nearest population centers to gain a more precise understanding of how far people live, as groups, from their closest resource centers.