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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.


Physicians’ location vs. parcels that have the same address.
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ijerph-13-00930-f003: Physicians’ location vs. parcels that have the same address.

Mentions: Although the result of geocoding would have inaccuracy problems, geocoded physician addresses could be helpful for researchers to perceive the spatial location and distribution of physicians from a macroscopic perspective. However, geocoding itself cannot help to differentiate whether the address provided by a physician is the home address or practice address. Matching the physicians’ addresses to the addresses in the parcel data through spatial overlay analytics could be problematic since the parcels nearest to the geocoded physician’s location may not be the correct parcels that have the same street address as the physicians. In the case of Fulton County that provides parcel data as polygon features, for example, the geocoded location of a physician may be far away from the corresponding parcel property. As described in Figure 3, several physicians are working at the same location “5445 MERIDIAN MARKS RD”. The parcels nearest to this geocoded point location have the address as “0 MERIDIAN MARKS RD”, while the corresponding parcel “5445 MERIDIAN MARKS RD” is 130 meters away displayed at the bottom of the Figure 3. Given the other physicians’ location at “5455 MERIDIAN MARKS RD”, the street address of the closest parcel is “1001 JOHNSON FERRY RD”, while the parcel that has the same address is about 45 meters away from the physicians’ geocoded location. When the parcel centroid data is used in DeKalb County, because the geocoded location is along the street centerlines, even if the physician has the same address as that in a parcel, there are two different point locations representing the centroid of a polygon and the approximate location along a street centerline. In this case, the nearest points from two different sources may not have the same street addresses.


Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining
Physicians’ location vs. parcels that have the same address.
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00930-f003: Physicians’ location vs. parcels that have the same address.
Mentions: Although the result of geocoding would have inaccuracy problems, geocoded physician addresses could be helpful for researchers to perceive the spatial location and distribution of physicians from a macroscopic perspective. However, geocoding itself cannot help to differentiate whether the address provided by a physician is the home address or practice address. Matching the physicians’ addresses to the addresses in the parcel data through spatial overlay analytics could be problematic since the parcels nearest to the geocoded physician’s location may not be the correct parcels that have the same street address as the physicians. In the case of Fulton County that provides parcel data as polygon features, for example, the geocoded location of a physician may be far away from the corresponding parcel property. As described in Figure 3, several physicians are working at the same location “5445 MERIDIAN MARKS RD”. The parcels nearest to this geocoded point location have the address as “0 MERIDIAN MARKS RD”, while the corresponding parcel “5445 MERIDIAN MARKS RD” is 130 meters away displayed at the bottom of the Figure 3. Given the other physicians’ location at “5455 MERIDIAN MARKS RD”, the street address of the closest parcel is “1001 JOHNSON FERRY RD”, while the parcel that has the same address is about 45 meters away from the physicians’ geocoded location. When the parcel centroid data is used in DeKalb County, because the geocoded location is along the street centerlines, even if the physician has the same address as that in a parcel, there are two different point locations representing the centroid of a polygon and the approximate location along a street centerline. In this case, the nearest points from two different sources may not have the same street addresses.

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.