A dasymetric map depicts a statistical surface, most commonly population density, as a set of simply connected regions, such that variation within each region is minimized and the region boundaries ...approximate the steepest escarpments of the surface. Dasymetric mapping has its roots in early thematic mapping of population, but has recently been taken up by researchers focusing on areal interpolation and population estimation using remote sensing. The process of dasymetric mapping typically involves the disaggregation of population data encoded in choropleth map form using an ancillary spatial data set, most commonly either an area‐class map or satellite image. The functional relationship between the ancillary data and the statistical surface being mapped may be specified a priori by the researcher or estimated using a variety of statistical techniques. Challenges facing dasymetric mapping research include handling spatio‐temporal data and the development of standardized and accessible methods.
Abstract This study investigates how racial and ethnic disparities in treatment episode completion vary across different problem substances in an urban sample of 416,224 outpatient treatment ...discharges drawn from the 2011 U.S. Treatment Episode Dataset-Discharge (TEDS-D) data set. Fixed effects logistic regression is employed to test for the association of race and ethnicity with treatment episode completion for different substances of use while controlling for confounding demographic, socioeconomic, and geographic clustering factors. Results show that African Americans and Hispanics are less likely to complete a treatment episode than Whites, and that these disparities vary among users of different substances. For African Americans, this disparity is observed over all substances, but is particularly acute among users of alcohol and methamphetamine, substances for which African Americans generally have lower rates of use disorder as compared to Whites. For Hispanics, this disparity is driven primarily by users of heroin, for which Hispanics are only 75% as likely as Whites to complete a treatment episode. For users of cocaine and methamphetamine, there is no significant difference between Hispanics and Whites in the likelihood of treatment episode completion. These results contribute to emerging research on the mechanisms of substance use disorder treatment outcomes and highlight the need for culturally appropriate treatment programs to enhance treatment program retention and associated positive post-treatment outcomes.
Incorporating geographic information science and technology (GIS&T) into COVID-19 pandemic surveillance, modeling, and response enhances understanding and control of the disease. Applications of ...GIS&T include 1) developing spatial data infrastructures for surveillance and data sharing, 2) incorporating mobility data in infectious disease forecasting, 3) using geospatial technologies for digital contact tracing, 4) integrating geographic data in COVID-19 modeling, 5) investigating geographic social vulnerabilities and health disparities, and 6) communicating the status of the disease or status of facilities for return-to-normal operations. Locations and availability of personal protective equipment, ventilators, hospital beds, and other items can be optimized with the use of GIS&T. Challenges include protection of individual privacy and civil liberties and closer collaboration among the fields of geography, medicine, public health, and public policy.
Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an ...equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density-median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Vacant land is a serious problem in many cities, and cities have recently begun to explore greening as a management strategy to reduce the negative influence of vacancy. The city of Philadelphia, ...Pennsylvania pioneered the use of a simple greening treatment—removal of debris coupled with planting grass and trees—as a means of improving blighted communities. Though they are becoming more popular, the actual economic impact of these programs is not well understood. This paper details the use of a spatial difference-in-differences approach for measuring the impact of Philadelphia's innovative vacant land greening program on nearby residential property values. This approach compares observed changes in property values surrounding treated vacant lots with observed changes around control lots—lots which might have been treated but were not. While property values throughout the city increased during the study period, properties surrounding greened vacant lots had a greater increase in value than properties surrounding nongreened vacant lots. By developing both global and local versions of the model, we also explore spatial variation in the impacts of the program—offering insight into which kinds of neighborhoods might derive the greatest economic benefit from vacant land greening programs.
Voluminous geographic data have been, and continue to be, collected with modern data acquisition techniques such as global positioning systems (GPS), high-resolution remote sensing, location-aware ...services and surveys, and internet-based volunteered geographic information. There is an urgent need for effective and efficient methods to extract unknown and unexpected information from spatial data sets of unprecedentedly large size, high dimensionality, and complexity. To address these challenges, spatial data mining and geographic knowledge discovery has emerged as an active research field, focusing on the development of theory, methodology, and practice for the extraction of useful information and knowledge from massive and complex spatial databases.
This paper highlights recent theoretical and applied research in spatial data mining and knowledge discovery. We first briefly review the literature on several common spatial data-mining tasks, including spatial classification and prediction; spatial association rule mining; spatial cluster analysis; and geovisualization. The articles included in this special issue contribute to spatial data mining research by developing new techniques for point pattern analysis, prediction in space–time data, and analysis of moving object data, as well as by demonstrating applications of genetic algorithms for optimization in the context of image classification and spatial interpolation. The papers concludes with some thoughts on the contribution of spatial data mining and geographic knowledge discovery to geographic information sciences.
County-level daily mobility change was correlated with the daily county growth rate of COVID-19 cases (12) 11 days later (to account for the average incubation period 13) plus the time delay between ...testing and state reporting (14), beginning on the day the first confirmed COVID-19 case was reported in each county. SEE PDF Action Although our findings should not be interpreted as a predictive model, these results provide evidence that reductions in population mobility may act to constrain the growth rate in COVID-19 cases, particularly in urban settings, though it is unclear whether the urban–rural differences we observed during the initial rise in COVID-19 cases in the United States will continue in the future, given the changing geography of the pandemic and differences in mitigation approaches used across the country. Lasry A, Kidder D, Hast M, Poovey J, Sunshine G, Winglee K, et al. ; CDC Public Health Law Program; New York City Department of Health and Mental Hygiene; Louisiana Department of Health; Public Health – Seattle & King County; San Francisco COVID-19 Response Team; Alameda County Public Health Department; San Mateo County Health Department; Marin County Division of Public Health.
The geospatial dataset presented here represents historical middle 19th century built environment features for the Chesapeake Bay Eastern Shore region of Maryland, USA, including present-day Cecil, ...Caroline, Dorchester, Kent, Queen Anne's, Somerset, Talbot, Wicomico, and Worcester counties. Individual geospatial data layers include roads, landings, ferries, churches, shops, mills, schools, hotels, towns with post offices, and towns with court houses. These data were digitized using Simon J. Martenet's (1866) Map of Maryland: Atlas Edition and contemporary geospatial road network data from the Maryland Department of Transportation.
The effects of the health system response to the COVID-19 pandemic on chronic disease management: a narrative review. Jansen T, Lee CM, Xu S, Silverstein N, Dugan B. The town-level prevalence of ...chronic lung conditions and death from COVID-19 among older adults in Connecticut and Rhode Island. Email: jmennis@temple.edu. 1Temple University, Philadelphia, Pennsylvania. 2Office of the Associate Director for Policy and Strategy, Centers for Disease Control and Prevention, Atlanta, Georgia. 3Muskie School of Public Service, University of Southern Maine, Portland, Maine. 4Maine Center for Disease Control and Prevention, Augusta, Maine.
Conventional multivariate regression can hide important local variations in the relationships among independent and dependent variables in models of environmental equity. Geographically weighted ...regression (GWR), in combination with choropleth mapping, can reveal this spatial nonstationarity and shed light on its form. We use GWR, in combination with conventional univariate and multivariate statistics, to model the density of air toxic releases in New Jersey, as listed in the U.S. Environmental Protection Agency's Toxic Release Inventory (TRI). The GWR analysis shows that the relationships among race, class, employment, urban concentration, and land use with air toxic release density in New Jersey vary significantly over space. Generally, there is a positively significant relationship of minorities with air toxic releases over a large swath of urban and suburban New Jersey, although this pattern is not evident for all urban areas. Northeast New Jersey, the most densely populated part of the state, contains areas of both significantly positive and negative relationships between concentrations of minorities and air toxic releases. The association of minorities with concentrations of air toxic releases, where observed, is often mediated by other factors, though the role of these mediating factors also varies from place to place. In some of these areas the minority-air-toxic-release association is mediated by high poverty rates, in other areas, by the presence of industrial, commercial, and transportation land uses.
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