Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative study

Geographical accessibility is important for promoting health equity, and calculating it requires the locations of all existing healthcare facilities in a region. Authoritative location data collected by governments is accurate but mostly not publicly available, while point-of-interest (POI) data from online sources, such as Baidu Maps and AutoNavi Maps are easily accessible. However, the accuracy of the latter has not been thoroughly analyzed. Taking Baotou, a medium-sized city in China, as aneample, we assessed the suitability of using POI data for measuring geographic accessibility to healthcare facilities.We computedthe difference of geographic accessibility calculated based on POI data and that on authoritative data.Logistic regression and a multiple linear regression model was applied to identify factors related to the consistency between the two data sources. Compared to authoritative data, POI data exhibited discrepancies, with completeness of 54.9% and accuracy of 63.7%. Geographic accessibility calculated based on both data showed similar patterns, with good consistency for hospitals and in urban areas. However, large differences (>30 minutes) were shown in rural areas for primary healthcare facilities. The differences were small regarding to population- weighted average accessibility (with slight underestimation of 3.07 minutes) and population coverage across various levels of accessibility (with differences less than 1% of the population) for the entire area. In conclusion, POI data can be considered foruse in both urban areas and at the level of entire city; however, awareness should be raised in rural areas.

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Publication Details

Journal
Geospatial health
Published
2024-12-20
DOI
https://doi.org/10.4081/gh.2024.1322
Primary Topic
Urban Transport and Accessibility
Type
article
Field-Weighted Citation Impact
0.00

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article

Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative study

Xiao-Fang Gan, Ben-Song Xian, Ying‐Si Lai, Nan Zhang et al.
Geospatial health
Urban Transport and Accessibility
article

Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative study

Xiao-Fang Gan, Ben-Song Xian, Ying‐Si Lai, Nan Zhang, Huijuan Liang, Heng-Qian Huang-fu, Li Wang
article en

Abstract

Geographical accessibility is important for promoting health equity, and calculating it requires the locations of all existing healthcare facilities in a region. Authoritative location data collected by governments is accurate but mostly not publicly available, while point-of-interest (POI) data from online sources, such as Baidu Maps and AutoNavi Maps are easily accessible. However, the accuracy of the latter has not been thoroughly analyzed. Taking Baotou, a medium-sized city in China, as aneample, we assessed the suitability of using POI data for measuring geographic accessibility to healthcare facilities.We computedthe difference of geographic accessibility calculated based on POI data and that on authoritative data.Logistic regression and a multiple linear regression model was applied to identify factors related to the consistency between the two data sources. Compared to authoritative data, POI data exhibited discrepancies, with completeness of 54.9% and accuracy of 63.7%. Geographic accessibility calculated based on both data showed similar patterns, with good consistency for hospitals and in urban areas. However, large differences (>30 minutes) were shown in rural areas for primary healthcare facilities. The differences were small regarding to population- weighted average accessibility (with slight underestimation of 3.07 minutes) and population coverage across various levels of accessibility (with differences less than 1% of the population) for the entire area. In conclusion, POI data can be considered foruse in both urban areas and at the level of entire city; however, awareness should be raised in rural areas.

Geospatial healthVol. 19(2)
Sun Yat-sen University (CN), Guangzhou Center for Disease Control and Prevention (CN), Early Warning (United States) (US), Inner Mongolia Medical University (CN)
Agency for Healthcare Research and Quality
Sustainable cities and communities
Openalex Percentile: Top 79%
Urban Transport and Accessibility
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