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Special Issue "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 10173
Special Issue Editors
Interests: GIS; spatial statistics; urban public health
Special Issues, Collections and Topics in MDPI journals
Interests: quantitative methods (especially, spatial statistical approaches) for geographical research; geographic data uncertainty; population migration; interregional commodity flows; urban crime; environmental justice
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
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 semimonthly 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.
- 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