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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Sample parcel polygon data for Fulton County vs. parcel centroid data for DeKalb County.
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ijerph-13-00930-f001: Sample parcel polygon data for Fulton County vs. parcel centroid data for DeKalb County.

Mentions: As a pilot research project, two counties, Fulton and DeKalb, in the metropolitan area of Atlanta, Georgia were selected as the study area since both counties have parcel data available for this research. In the year 2014, Fulton County had 353,723 parcel polygons, while DeKalb County released 235,681 parcels as point features. Figure 1 displays the two different types of parcel data sets covering a part of the two counties, since the full scale of the parcel datasets cannot be clearly represented and differentiated in such a small figure. Both datasets contain the information about the unique identifier, address, and land use type of the parcel along with other information. The zoning code for the land use class of the parcel could reveal whether the parcels are used as agricultural, business, commercial, industrial, residential, exempted areas, or for other purposes. The class code in the parcel datasets helps to differentiate whether a certain address is a home address or not by checking whether the corresponding parcel is classified as a residential area or not.


Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining
Sample parcel polygon data for Fulton County vs. parcel centroid data for DeKalb County.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036762&req=5

ijerph-13-00930-f001: Sample parcel polygon data for Fulton County vs. parcel centroid data for DeKalb County.
Mentions: As a pilot research project, two counties, Fulton and DeKalb, in the metropolitan area of Atlanta, Georgia were selected as the study area since both counties have parcel data available for this research. In the year 2014, Fulton County had 353,723 parcel polygons, while DeKalb County released 235,681 parcels as point features. Figure 1 displays the two different types of parcel data sets covering a part of the two counties, since the full scale of the parcel datasets cannot be clearly represented and differentiated in such a small figure. Both datasets contain the information about the unique identifier, address, and land use type of the parcel along with other information. The zoning code for the land use class of the parcel could reveal whether the parcels are used as agricultural, business, commercial, industrial, residential, exempted areas, or for other purposes. The class code in the parcel datasets helps to differentiate whether a certain address is a home address or not by checking whether the corresponding parcel is classified as a residential area or not.

View Article: PubMed Central - PubMed

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.

No MeSH data available.