This paper presents an enhancement of the two-step floating catchment area (2SFCA) method for measuring spatial accessibility, addressing the problem of uniform access within the catchment by ...applying weights to different travel time zones to account for distance decay. The enhancement is proved to be another special case of the gravity model. When applying this enhanced 2SFCA (E2SFCA) to measure the spatial access to primary care physicians in a study area in northern Illinois, we find that it reveals spatial accessibility pattern that is more consistent with intuition and delineates more spatially explicit health professional shortage areas. It is easy to implement in GIS and straightforward to interpret.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Objective
The objective of this study is to identify disparities in geographic access to medical oncologists at the time of diagnosis.
Data Sources/Study Setting
2014–2016 Pennsylvania Cancer ...Registry (PCR), 2019 CMS Base Provider Enrollment File (BPEF), 2018 CMS Physician Compare, 2010 Rural‐Urban Commuting Area Codes (RUCA), and 2015 Area Deprivation Index (ADI).
Study Design
Spatial regressions were used to estimate associations between geographic access to medical oncologists, measured with an enhanced two‐step floating catchment area measure, and demographic characteristics.
Data Collection/Extraction Methods
Medical oncologists were identified in the 2019 CMS BPEF and merged with the 2018 CMS Physician Compare. Provider addresses were converted to longitude‐latitude using OpenCage Geocoder. Newly diagnosed cancer patients in each census tract were identified in the 2014–2016 PCR. Census tracts were classified based on rurality and socioeconomic status using the 2010 RUCA Codes and the 2015 ADI.
Principal Findings
Large towns and rural areas were associated with spatial access ratios (SPARs) that were 6.29 lower (95% CI −16.14 to 3.57) and 14.76 lower (95% CI −25.14 to −4.37) respectively relative to urban areas. Being in the fourth ADI quartile (highest disadvantage) was associated with a 12.41 lower SPAR (95% CI −19.50 to −5.33) relative to the first quartile. The observed difference in a census tract's non‐White population from the 25th (1.3%) to the 75th percentile (13.7%) was associated with a 13.64 higher SPAR (Coefficient = 1.10, 95% CI 11.89 to 15.29; p < 0.01), roughly equivalent to the disadvantage associated with living in the fourth ADI quartile, where non‐White populations are concentrated.
Conclusions
Rurality and low socioeconomic status were associated with lower geographic access to oncologists. The negative association between area deprivation and geographic access is of similar magnitude to the positive association between larger non‐White populations and access. Policies aimed at increasing geographic access to care should be cognizant of both rurality and socioeconomic status.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Uneven distributions of population and service providers lead to geographic disparity in access for residents and varying workload for staff in facilities. The former can be captured by spatial ...accessibility in the traditional two-step floating catchment area (2SFCA) method; and the latter can be measured by potential crowdedness in the newly developed inverted 2SFCA (or i2SFCA) method. Residents-based accessibility and facility crowdedness are two sides of the same coin in examining the geographic variability of resource allocation. This short research note derives the formulations of both methods to solidify their theoretical foundation, and uses a case study to validate both. By doing so, the 2SFCA and i2SFCA are fully integrated into one conceptual framework, derived with extensions to the Huff model, and validated by empirical data.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological ...forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.
The strategies using transit-oriented development (TOD) to optimize transportation sustainability have been implemented in many metropolitan areas and extended beyond the role of exclusively offering ...transit services. Research findings from existing literature have largely shown that metro station catchment areas can attract a substantial number of urban functions and human activities that lead metro stations to be vital and vibrant places of urban daily life. In this work, we propose a data-driven semantic framework to characterize metro stations through points of interest (POIs) in Hong Kong. The analytical results reveal four thematic topics of urban functions that are closely related to commercial, residential, tourism, and industrial activities. Given the implementation of a hierarchical clustering approach on these thematic topics, the similarities among different stations are investigated. In particular, metro stations in the same thematic group tend to be spatially concentrated, suggesting an evident geographical proximity relating to similar urban functions. Plus, results from the Multinomial Logit Model (MNLM) confirm that the surrounding built environment of metro stations has close relationships with the heterogeneity of urban functions. Ultimately, this study introduces alternative insights into the urban functional heterogeneity exhibited by metro station areas, and the practical implications for more targeted TOD strategies are discussed.
•Latent Dirichlet allocation (LDA) is used to identify the thematic topics of urban functions for each station catchment area based on their POI types.•Four thematic topics are uncovered that mainly relate to commercial, residential, tourism, and industrial functions and activities.•Stations containing similar urban functions and relevant activities tend to be spatially concentrated.•Urban functional patterns within station catchment areas indicate strong connections with surrounding built environments.•The analytical findings can be used for formulating more targeted TOD strategies
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Hurricanes can have a significant impact on the functioning and capacity of healthcare systems. However, little work has been done to understand the extent to which hurricanes influence local ...residents’ spatial access to healthcare. Our study evaluates the change in spatial access to primary care physicians (PCPs) between 2016 and 2018 (i.e., before and after Hurricane Harvey) in Harris County, Texas. We used an enhanced 2‐step floating catchment area (E2SFCA) method to measure spatial access to PCPs at the census tract level. The results show that, despite an increased supply of PCPs across the county, most census tracts, especially those in the northern and eastern fringe areas, experienced decreased access during this period as measured by the spatial access ratio (SPAR). We explain this decline in SPAR by the shift in the spatial distribution of PCPs to the central areas of Harris County from the fringe areas after Harvey. We also examined the socio‐demographic impact in the SPAR change and found little variation in change among different socio‐demographic groups. Therefore, public health professionals and disaster managers may use our spatial access measure to highlight the geographic disparities in healthcare systems. In addition, we recommend considering other social and institutional dimensions of access, such as users’ needs, preferences, resource capacity, mobility options, and quality of healthcare services, in building a resilient and inclusive post‐hurricane healthcare system.
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BFBNIB, FSPLJ, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Few measures of healthcare accessibility have considered multiple transportation modes when people seek healthcare. Based on the framework of the 2 Step Floating Catchment Area Method (2SFCAM), we ...proposed an innovative method to incorporate transportation modes into the accessibility estimation. Taking Florida, USA, as a study area, we illustrated the implementation of the multi-mode 2SFCAM, and compared the accessibility estimates with those from the traditional single-mode 2SFCAM. The results suggest that the multi-modal method, by accounting for heterogeneity in populations, provides more realistic accessibility estimations, and thus offers a better guidance for policy makers to mitigate health inequity issues.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
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