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Spatial Data Uncertainty in Public Health Research
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Spatial Data Uncertainty in Public Health Research

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 16835

Special Issue Editors

Prof. Dr. Daniel A. Griffith

E-Mail Website
Guest Editor
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: spatial statistics; GIS; spatial epidemiology; quantitative urban geography
Special Issues, Collections and Topics in MDPI journals
Dr. Yongwan Chun

E-Mail Website
Guest Editor
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, Texas 75080, USA
Interests: GIS; spatial statistics; spatial interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Spatial data is comprised of two components, namely, attribute and location information. Attribute information describes the non-locational characteristics of features, whereas locational information indicates relative and/or absolute positioning of these features. Like aspatial data, attribute data have parameters that can be estimated with sample data. Thus, one major source of uncertainty is sampling error (i.e., deviations of sample statistics from their corresponding population parameter values). Most often, the scoring of attributes also contains measurement error (i.e., differences between pairs of true and measured values). This additional major source of uncertainty involves the proximity of an instrument reading to its corresponding true value, and includes rounding of numbers and sometimes recording mistakes. Because all models are simplified descriptions of reality, these descriptions contain specification error (i.e., differences between reality and a model’s description of it); one goal of science is to minimize this error so that it is not too serious. Only approximate, rather than absolute, positions of features can be tagged to a coordinate system, resulting in location data also having uncertainty (i.e., deviations between true and approximate positions), introducing a third major source of error to georeferenced data. All four of these sources of uncertainty interact, impacting upon the quality of spatial data and spatial analyses, frequently embracing stochastic noise that further corrupts signals from and map patterns in georeferenced data. 

This Special Issue seeks papers that contribute to filling this gap in research practice by exploring various important geographic uncertainty situations. Studies that propose an innovative methodological approach and present novel applications addressing a wide range of uncertainty or accuracy issues in spatial environmental or health data are welcome. Preferred themes include, but are not limited to, measurement error patterns in space and/or space-time data, individual health exposure measurement uncertainty, sampling design to improve data accuracy, error propagation in spatial analysis, and incorporation of measurement errors in spatial data modeling. 

Prof. Dr. Daniel A. Griffith
Dr. Yongwan Chun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data uncertainty
  • Error propagation
  • Environmental exposures
  • Measurement error
  • Model misspecification
  • Sampling error
  • Spatial accuracy
  • Spatial autocorrelation
  • Spatial health data accuracy
  • Spatial sampling
  • Spatial statistics
  • Specification error
  • Uncertain geographic context problem
  • Uncertainty modeling

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Published Papers (6 papers)

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Research

21 pages, 3919 KiB  
Article
Assessing Trauma Center Accessibility for Healthcare Equity Using an Anti-Covering Approach
by Heewon Chea, Hyun Kim, Shih-Lung Shaw and Yongwan Chun
Int. J. Environ. Res. Public Health 2022, 19(3), 1459; https://doi.org/10.3390/ijerph19031459 - 27 Jan 2022
Cited by 2 | Viewed by 2438
Abstract
Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient’s chances of survival and recovery. However, not all [...] Read more.
Motor vehicle accidents are one of the most prevalent causes of traumatic injury in patients needing transport to a trauma center. Arrival at a trauma center within an hour of the accident increases a patient’s chances of survival and recovery. However, not all vehicle accidents in Tennessee are accessible to a trauma center within an hour by ground transportation. This study uses the anti-covering location problem (ACLP) to assess the current placement of trauma centers and explore optimal placements based on the population distribution and spatial pattern of motor vehicle accidents in 2015 through 2019 in Tennessee. The ACLP models seek to offer a method of exploring feasible scenarios for locating trauma centers that intend to provide accessibility to patients in underserved areas who suffer trauma as a result of vehicle accidents. The proposed ACLP approach also seeks to adjust the locations of trauma centers to reduce areas with excessive service coverage while improving coverage for less accessible areas of demand. In this study, three models are prescribed for finding optimal locations for trauma centers: (a) TraCt: ACLP model with a geometric approach and weighted models of population, fatalities, and spatial fatality clusters of vehicle accidents; (b) TraCt-ESC: an extended ACLP model mitigating excessive service supply among trauma center candidates, while expanding services to less served areas for more beneficiaries using fewer facilities; and (c) TraCt-ESCr: another extended ACLP model exploring the optimal location of additional trauma centers. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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20 pages, 3771 KiB  
Article
Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods
by David W. S. Wong
Int. J. Environ. Res. Public Health 2021, 18(18), 9848; https://doi.org/10.3390/ijerph18189848 - 18 Sep 2021
Cited by 5 | Viewed by 2144
Abstract
Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if [...] Read more.
Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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27 pages, 13768 KiB  
Article
Modeling Community Health with Areal Data: Bayesian Inference with Survey Standard Errors and Spatial Structure
by Connor Donegan, Yongwan Chun and Daniel A. Griffith
Int. J. Environ. Res. Public Health 2021, 18(13), 6856; https://doi.org/10.3390/ijerph18136856 - 26 Jun 2021
Cited by 6 | Viewed by 3607
Abstract
Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or [...] Read more.
Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes’ theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.’s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE–mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55–64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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28 pages, 7530 KiB  
Article
Soil Sample Assay Uncertainty and the Geographic Distribution of Contaminants: Error Impacts on Syracuse Trace Metal Soil Loading Analysis Results
by Daniel A. Griffith and Yongwan Chun
Int. J. Environ. Res. Public Health 2021, 18(10), 5164; https://doi.org/10.3390/ijerph18105164 - 13 May 2021
Cited by 1 | Viewed by 2030
Abstract
A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, [...] Read more.
A research team collected 3609 useful soil samples across the city of Syracuse, NY; this data collection fieldwork occurred during the two consecutive summers (mid-May to mid-August) of 2003 and 2004. Each soil sample had fifteen heavy metals (As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr), measured during its assaying; errors for these measurements are analyzed in this paper, with an objective of contributing to the geography of error literature. Geochemistry measurements are in milligrams of heavy metal per kilogram of soil, or ppm, together with accompanying analytical measurement errors. The purpose of this paper is to summarize and portray the geographic distribution of these selected heavy metals measurement errors across the city of Syracuse. Doing so both illustrates the value of the SAAR software’s uncertainty mapping module and uncovers heavy metal characteristics in the geographic distribution of Syracuse’s soil. In addition to uncertainty visualization portraying and indicating reliability information about heavy metal levels and their geographic patterns, SAAR also provides optimized map classifications of heavy metal levels based upon their uncertainty (utilizing the Sun-Wong separability criterion) as well as an optimality criterion that simultaneously accounts for heavy metal levels and their affiliated uncertainty. One major outcome is a summary and portrayal of the geographic distribution of As, Cr, Cu, Co, Fe, Hg, Mo, Mn, Ni, Pb, Rb, Se, Sr, Zn, and Zr measurement error across the city of Syracuse. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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16 pages, 724 KiB  
Article
The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5
by Eun-hye Yoo, Qiang Pu, Youngseob Eum and Xiangyu Jiang
Int. J. Environ. Res. Public Health 2021, 18(4), 2194; https://doi.org/10.3390/ijerph18042194 - 23 Feb 2021
Cited by 16 | Viewed by 2911
Abstract
The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related [...] Read more.
The impact of individuals’ mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors—individuals’ routine travel patterns and the local variations of air pollution fields. We investigated whether individuals’ routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time–activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual’s mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals’ routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data (‘a multi-sourced exposure model’). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data (‘a single-sourced exposure model’). This study showed that there was a significant association between individuals’ mobility and the long-term exposure measurement error. However, the effect could be modified by individuals’ routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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13 pages, 762 KiB  
Article
Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study
by Jared A. Fisher, Maya Spaur, Ian D. Buller, Abigail R. Flory, Laura E. Beane Freeman, Jonathan N. Hofmann, Michael Giangrande, Rena R. Jones and Mary H. Ward
Int. J. Environ. Res. Public Health 2021, 18(4), 1637; https://doi.org/10.3390/ijerph18041637 - 9 Feb 2021
Cited by 4 | Viewed by 2506
Abstract
Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and [...] Read more.
Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies. Full article
(This article belongs to the Special Issue Spatial Data Uncertainty in Public Health Research)
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