UMBC Geographic Information Systems (GIS)

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    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, Alexander
    The 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.
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    Measuring Blight
    (U.S. Department of Housing and Urban Development, 2022) Din, Alexander
    Communities 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 Measuring blight requires substantial work, which can be labor-intensive and can quickly become outdated (Pagano and Bowman, 2000).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.
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    Exits From HUD Assistance and Moves to Higher Poverty Neighborhoods Following the Camp Fire
    (U.S. Department of Housing and Urban Development, 2023) Din, Alexander
    Little 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.
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    Neighborhood Incarceration Rate Hot Spots in Maryland
    (U.S. Department of Housing and Urban Development, 2023) Din, Alexander
    Maryland’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.
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    Increased transit delays in fall of 2021 and the potential impact on high school commutes
    (D.C. Policy Center, 2022-11-14) Din, Alexander
    In 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.
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    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.
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    New Data Fields for HUD Aggregated USPS Administrative Data on Address Vacancies
    (U.S. Department of Housing and Urban Development, 2021) Din, Alexander
    Since 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 datasets
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    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, Peter
    Blighted 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.
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    Tree Equity Scores and Housing Choice Voucher Neighborhoods
    (U.S. Department of Housing and Urban Development, 2022) Din, Alexander; Krisko, Perrin
    Urban 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.
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    Leveling the Playing Field: School District Spending in Diverse Communities
    (U.S. Department of Housing and Urban Development, 2016) Din, Alexander
    The 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.
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    Visualizing Residential Vacancy by Length of Vacancy
    (U.S. Department of Housing and Urban Development, 2017) Din, Alexander
    The 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.
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    Visualizing and Comparing Residential Permit Data Using Lollipop Plots
    (U.S. Department of Housing and Urban Development, 2019) Din, Alexander
    Residential 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.
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    Applying Spaghetti and Meatballs to Proximity Analysis
    (U.S. Department of Housing and Urban Development, 2020) Din, Alexander
    The 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.
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    HUD Crosswalk Files Facilitate Multi-State Census Tract COVID-19 Spatial Analysis
    (U.S. Department of Housing and Urban Development, 2021) Din, Alexander; Wilson, Ron
    The 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.
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    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, Alexander
    Researchers 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.
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    The Geography of Hispanic HUD-Assisted Households
    (U.S. Department of Housing and Urban Development, 2021) Din, Alexander
    In 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.
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    Measuring Distance to Resources
    (U.S. Department of Housing and Urban Development, 2017) Wilson, Ron; Din, Alexander
    Mapping 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.
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    Intersecting Opportunity Zones with Vacant Business Addresses
    (U.S. Department of Housing and Urban Development, 2018) Din, Alexander
    On December 22, 2017, President Donald Trump signed into law the Tax Cuts and Jobs Act of 2017. One provision of this bill was to create Opportunity Zones, low-income census tracts that encourage economic development by providing tax incentives. The states, territories, and Washington, D.C. were in charge of nominating their own Opportunity Zones and then submitting an application to the U.S. Treasury Department for approval. Each jurisdiction was able to nominate up to 25 percent of its low-income census tracts as Opportunity Zones. Once approved, selected census tracts will remain Opportunity Zones for 10 years. Investors are able to use unrealized capital gains as part of an Internal Revenue Service- (IRS) approved Opportunity Fund for businesses within the Opportunity Zones. Tax incentives for investing in Opportunity Zones include a temporary deferral for capital gains invested into the Opportunity Zone, a step-in basis for up to 15 percent of the original capital gains investment to be excluded from taxation, and a permanent exclusion from taxation on gains made after the investment into the Opportunity Zone and if the investment is held for at least 10 years (EIG, 2018). There has been debate about who will benefit from investments into Opportunity Zones (Looney, 2018).
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    Graphic Detail: Using Heatmaps to Explore Capital Bikeshare Data
    (U.S. Department of Housing and Urban Development, 2019) Din, Alexander
    Capital Bikeshare is the major bikeshare system for the Washington, D.C. area. The network has more than 4,300 bicycles that serve commuters, tourists, and others who are interested in using a bicycle to travel. In 2017, more than 3.7 million trips were made using the Capital Bikeshare service. With so many observations (trips), the best visualizations are necessary to explore and make sense of the data. In this article, I demonstrate how to use heatmaps to get an overview of the data. A heatmap is a shaded matrix that displays values via a graduated color scheme. The greater the number of observations binned into each category in the matrix, the greater the display color. By binning the data, some precision is lost but clarity may be made of a large dataset. The heatmap is a visualization that may show clusters or dispersion in the data. Results from exploratory analysis and visualization can answer basic questions about the data or provide insight how to further examine the data.
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    Using HUD Crosswalk Files to Improve COVID-19 Analysis at the ZIP Code and Local Level
    (U.S. Department of Housing and Urban Development, 2020) Din, Alexander; Wilson, Ron
    As the novel coronavirus disease (COVID-19) continues to infect, harm, and kill thousands of Americans, many jurisdictions and institutions are publishing data at the ZIP Code-level, including counts of tests performed, people infected, hospitalizations, and deaths. These data are leading to quickly produced publications with strong conjectures about the forming of geographic patterns. We present an alternative to ZIP Codes when working with local COVID-19 data.