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Int. J. Environ. Res. Public Health 2016, 13(9), 930; doi:10.3390/ijerph13090930

Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining

1
Department of Geoscience, University of Arkansas, Fayetteville, AR 72701, USA
2
Association of American Medical Colleges, Washington, DC 20001, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 27 May 2016 / Revised: 2 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
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Abstract

In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services. View Full-Text
Keywords: spatial uncertainty; physician distribution; spatial analytics; text mining; visual examination spatial uncertainty; physician distribution; spatial analytics; text mining; visual examination
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MDPI and ACS Style

Shi, X.; Xue, B.; Xierali, I.M. Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. Int. J. Environ. Res. Public Health 2016, 13, 930.

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