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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.


Discrete and inconsistent address assignment and distribution in a parcel dataset.
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ijerph-13-00930-f002: Discrete and inconsistent address assignment and distribution in a parcel dataset.

Mentions: Figure 2 displays some typical scenarios about the addresses assigned to the parcels that could be beyond our normal cognition and comprehension about the distribution of street addresses along the roads. It can be found that many addresses assigned to the parcels along the street of “COLLIER RD NW” are actually using the street name of “PEACHTREE RD NW”. In fact, the street name for US Route 19 is “PEACHTREE RD NE”. The suffix of “NW” is only assigned to a few parcels on the west of US Route 19 that are used by the Piedmont Hospital. Particularly, the parcel of “275 COLLIER RD” is embedded within two other parcels that have the same street address of “0 PEACHTREE RD NW”, while the street numbers are not assigned continuously or consistently because this section of COLLIER RD only has two other street numbers of 5 and 11 then the street number jumped to 275. For the street addresses between “0 PEACHTREE RD NW” and “2060 PEACHTREE RD NW”, only four other street numbers are assigned to a few parcels as 1900, 1938, 1968, and 2020. The same street address can be assigned to multiple parcels. When address points, such as the centroid of parcel polygons, are used for geocoding, more problems could be involved as even the boundary of the property could not be perceived. For example, in Figure 1, when a large amount of spaces are distributed, the centroid of a parcel polygon cannot help to determine which address is closer to which parcel property.


Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining
Discrete and inconsistent address assignment and distribution in a parcel dataset.
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-13-00930-f002: Discrete and inconsistent address assignment and distribution in a parcel dataset.
Mentions: Figure 2 displays some typical scenarios about the addresses assigned to the parcels that could be beyond our normal cognition and comprehension about the distribution of street addresses along the roads. It can be found that many addresses assigned to the parcels along the street of “COLLIER RD NW” are actually using the street name of “PEACHTREE RD NW”. In fact, the street name for US Route 19 is “PEACHTREE RD NE”. The suffix of “NW” is only assigned to a few parcels on the west of US Route 19 that are used by the Piedmont Hospital. Particularly, the parcel of “275 COLLIER RD” is embedded within two other parcels that have the same street address of “0 PEACHTREE RD NW”, while the street numbers are not assigned continuously or consistently because this section of COLLIER RD only has two other street numbers of 5 and 11 then the street number jumped to 275. For the street addresses between “0 PEACHTREE RD NW” and “2060 PEACHTREE RD NW”, only four other street numbers are assigned to a few parcels as 1900, 1938, 1968, and 2020. The same street address can be assigned to multiple parcels. When address points, such as the centroid of parcel polygons, are used for geocoding, more problems could be involved as even the boundary of the property could not be perceived. For example, in Figure 1, when a large amount of spaces are distributed, the centroid of a parcel polygon cannot help to determine which address is closer to which parcel property.

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.