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Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer., (© 2019 American Cancer Society.)

Background: Although recommended lung cancer screening with low‐dose computed tomography scanning (LDCT) reduces mortality among high‐risk adults, annual screening rates remain low. This study complements a previous nationwide assessment of access to lung cancer screening within 40 miles by evaluating differences in accessibility across rural and urban settings for the population aged 50 to 80 years and a subset eligible population based on the 2021 US Preventive Services Task Force LDCT lung screening recommendations. Methods: Distances from population centers to screening facilities (American College of Radiology Lung Cancer Screening Registry) were calculated, and the number of individuals who had access within graduating distances, including 10, 20, 40, 50, and 100 miles, were estimated. Census tract results were aggregated to counties, and both geographies were classified with rural‐urban schemas. Results: Approximately 5% of the eligible population did not have access to lung cancer screening facilities within 40 miles; however, different patterns of accessibility were observed at different distances, between regions, and across rural‐urban environments. Across all distances and geographies, there was a larger percentage of the population in rural geographies with no access. Although the rural population represented approximately 8% of the eligible population, the larger percentage of the rural population with no access was noteworthy and translated into a larger number of individuals with no access at longer distance thresholds (≥40 miles). Conclusions: Disparities in access should be examined as both percentages of the population and numbers of individuals with no access in order to tailor interventions to communities and increase access. Geospatial analysis at the census tract level is recommended to help to identify optimal focus areas and reach the most people. Lay Summary: As annual lung cancer screening rates remain low, this study examines access to lung cancer screening nationwide and across rural and urban settings.A geographic information system network analysis of census tract–level populations is used to estimate access at different distances, including 10, 20, 40, 50, and 100 miles, and the results are aggregated to counties.Approximately 5% of the eligible population does not have access to screening facilities within 40 miles; however, different patterns of accessibility are observed at different distances, between regions, and across rural‐urban environments.Across all distances and geographies, there is a larger percentage of the population in rural geographies with no access. This study uses geospatial analysis to examine access to lung cancer screening at graduating distances nationwide and across rural‐urban settings. Approximately 5% of the eligible population does not have access to lung cancer screening facilities within 40 miles; however, different patterns of accessibility are observed at different distances, between regions, and across rural‐urban environments. [ABSTRACT FROM AUTHOR]

The paper presents results and analysis of a methodology for automatic building extraction that integrates remote sensing sources and readily available geographic information system data (tax assessor parcel data). The methodology consists of a series of image processing and spatial analysis techniques and incorporates an initial simplification, parcel-based image partitioning procedure. The results showed great promise for such image partitioning in reducing the complexity of the building extraction procedure by localizing its search while maintaining the integrity of the structures (85 percent of buildings reside completely within parcels and the majority of the rest consists of very small portions outside the parcel boundaries). Herein, we discuss the results as they apply to three distinct building types (commercial, residential, and high-rise). The paper presents successful building extraction scenarios as well as challenges, difficulties and drawbacks of the process and identifies the underlying factors that impact the success of footprint extraction for each type of building. The results show that 83 percent of commercial buildings are successfully extracted with average footprint area discrepancy less than 10 percent, while challenges in high-rise and residential scenes are examined and further investigated. [ABSTRACT FROM AUTHOR]

Initial building footprint extraction as well as inventory update are still achieved via manual, labor-consuming digitizing from imagery. An extraction methodology is presented that integrates automatic and manual processes. This integration provides the advantage of a repetitive action accomplished by a computer and superior human decision making. The suggested approach incorporates readily available GIS parcels layers to provide an easily replicable simplification process that can precede any extraction procedure. The results emphasize a great savings in time (and subsequently in cost) by using parcel-based partitioning during the image preprocessing and integrating with current common extraction workflows, including manual digitizing. We present a quantitative comparison between the suggested methodology and both an automatic and a fully manual extraction procedure. The approach shows consistent savings of more than 30 percent in time, extracting buildings for both commercial and residential parcels. [ABSTRACT FROM AUTHOR]

Earthquakes cause massive loss of property and lives, and mitigating their potential effects requires accurate modeling and simulation of their impacts. Earthquake building damage modeling and risk assessment applications require accurate accounts of inventories at risk and their attributes such as structure type, usage, size, number of stories, shape, year built, value, etc. This paper describes the development of algorithms for automatically extracting and recognizing 2-D building shape information using integrated aerial imagery processing and Geographic Information Systems data. We use vector parcel geometries and their attributes to simplify the building extraction task by limiting the processing geography. Extraction is significantly improved by innovatively weighting the histograms. Extracted buildings are cleaned, simplified, and run through 2-D shape recognition routines that classify the footprint. We discuss reasons for successes and failures in both extraction and recognition. [ABSTRACT FROM AUTHOR]