• Home
  • About

GIScience News Blog

News of Heidelberg University’s GIScience Research Group.

Feed on
Posts
Comments
« New mFund project: start of SocialMedia2Traffic - derivation of traffic information from social media data
Humanitarian OSM Stats: How to monitor humanitarian mapping in the HOT Tasking Manager? - Part 5: the American Red Cross »

Insights into OpenStreetMap healthcare attributes in India over time

Mar 16th, 2021 by Marcel Reinmuth

Last November, we covered the recent increase of healthcare related objects in OpenStreetMap (OSM) in India. In less than a year, the amount of facilities has increased from 6.956 to 48.101. This is mainly due to an import run by RMSI - an Indian GIS consulting company. In this blog we will take a closer look at attributes that provide further information on the type of healthcare facility and services offered.

Increase of healthcare related OSM objects over time

Fig. 1: Amount of healthcare related keys over the year 2020.

First of all, we note that the second half of 2020 saw a further strong increase in the amount of facilities. It has more than doubled. From 48.101 at the beginning of July to 123.073 in January 2021. All four tags that identify healthcare facilities show a growth of more than 50%.  Healthcare (78%) and doctors (91%) even clearly exceed this level. The keys clinic and doctors have plateaued over the past four months. However, looking at the growth of the keys healthcare and hospital, especially over the last few months, it seems that the import process may not yet complete. How the share of the other keys will develop in comparison to the hospital key remains open. Currently, the hospital key accounts for almost half of all facilities, a highly questionable ratio.

Frequency of tags


Fig. 2: Relative frequency for selected attributes in healthcare related OSM Objects (n=123.073).

This time, we do not stop at analyzing the changes in the amount of facilities over time. Rather, we look at the occurrence of attributes of the facilities. Attributes that allow for assessing the type of facility and available capacity are of particular interest. We start by calculating the frequency of every available attribute for our 123.073 facilities in India. The most common attribute is name (98%, see figure 1). The second most frequent attribute is addr:full. The name attribute can provide useful information in order to identify a facility beyond the amenity and healthcare tags. The addr:full attribute however, is mostly interesting for geocoding applications.  The next most frequent attributes are already amenity and healthcare. Both attributes can occur for the same object. This is the case for 8.45% of all facilities. With these two tags we also already undercut the 50% frequency, meaning that less than half of all healthcare related facilities do bear more than three tags. Important tags are rarely tagged. For example, the bed (0.08) attribute, which provides information on the inpatient care capacity of facilities. The emergency (1.03%) tag is another example that provides information about the presence of emergency care. Also rarely tagged is the key healthcare:speciality. The second part of the blog is dedicated to this key.

Frequency of healthcare:speciality values

Fig. 3: Absolute frequency of the 10 most common values for the key healthcare:speciality

The key healthcare:speciality was introduced with the healthcare tag back in 2010.  It was established to capture information on available medical specializations. From these specializations, eventually existing diagnostic/therapeutic capacities can be derived. The case that a facility has several specializations is represented by the multiple values scheme. Different values for the same key are separated by “;”. We extracted every value and ordered them by frequency (see fig. 3). Due to different spellings and typos, we also manually grouped some values. We found that “general”,  “gynaecology”, “paediatric” and “dental care” were the four most frequent speciality values mapped in India. Where “general” is the most common value, with more than three times as many occurrences than the second most common value.

Distribution of healthcare:speciality values

Fig. 4: Facet map of the distribution of the four most common values for the key healthcare:speciality

The four most frequent speciality values are further investigated with regard to their spatial distribution in India (see fig. 4). Four facet maps are shown, each for one of the values. The values are spread all over India, but with varying densities. These spatial variations values are almost identical for each of the values. Large clusters are located in the South-West (Kerala state), West (Maharashtra state) and North-West (Haryana, Delhi, Himachal Pradesh states). The occurrence of specialized hospital facilities seems to correspond to urban centers within the named states (Delhi, Chandigarh, Mumbai, Hyderabad, Kochi). The most dominant clusters are spread over the almost entire state of Kerala as well as along the axis of Shimla-Chandigarh-Delhi-Karnal. States like Assam, Kashmir and Odisha are rather sparsely covered with specialized facilities.  It remains open whether the clusters are due to an underlying process of locating specialized facilities in central places or whether they reflect the urban-rural divide within OSM.

Conclusion

Our descriptive analysis shows the import in India continues. Plenty of healthcare infrastructure data has been added. Though, for the vast majority of facilities critical information beyond the simple classification by the amenity tag is missing. The distribution of the few facilities with this information indicate a strong correlation with highly urban spaces. It cannot be determined what influence real world processes and bias in OSM have on this fact.

In a forthcoming analysis, we will broaden our scope of investigation. We will look at the distribution of healthcare facilities in OSM globally. How are they distributed and where are critical tags mapped and where not.

Related Literature and earlier work:

  • Geldsetzer, P.; Reinmuth, M.; Ouma, P. O., Lautenbach, S.; Okiro E. A.; Bärnighausen, T.; Zipf, A. Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis. The Lancet Healthy Longevity. 2020;1(1):e32-e42.
  • Herfort, B., Lautenbach, S., Porto de Albuquerque, J., Anderson, J., Zipf, A.The evolution of humanitarian mapping within the OpenStreetMap community. Scientific Reports 11, 3037 (2021). DOI: 10.1038/s41598-021-82404-z  https://www.nature.com/articles/s41598-021-82404-z
  • Schott, M.; Grinberger, A.Y.; Lautenbach, S.; Zipf, A. The Impact of Community Happenings in OpenStreetMap—Establishing a Framework for Online Community Member Activity Analyses. ISPRS Int. J. Geo-Inf. 2021, 10, 164. https://doi.org/10.3390/ijgi10030164
  • Auer, M.; Eckle, M.; Fendrich, S.; Griesbaum, L.; Kowatsch, F.; Marx, S.; Raifer, M.; Schott, M.; Troilo, R.; Zipf, A. (2018): Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring. ISCRAM 2018. Rochester. NY. US.
  • Scholz, S., Knight, P., Eckle, M., Marx, S., Zipf, A. (2018): Volunteered Geographic Information for Disaster Risk Reduction: The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement. Remote Sens., 10(8), 1239, doi:10.3390/rs10081239.Recent changes to OpenStreetMap healthcare infrastructure in India
  • Raifer, Martin; Troilo, Rafael; Kowatsch, Fabian; Auer, Michael; Loos, Lukas; Marx, Sabrina; Przybill, Katharina; Fendrich, Sascha; Mocnik, Franz-Benjamin; Zipf, Alexander (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data, Software and Standards.
  • Analysing OSM Completeness of health facilities in Sub-Sahara Africa in ohsomeHeX
  • Exploring OSM for healthcare access analysis in Sub-Saharan Africa
  • Accessibility to pharmacies in Germany with 15km Covid-19 restriction
  • Accessibility of covid-19 vaccination centers in Germany
  • Updated OSM Healthcare in Senegal (2020)
  • Exploring OSM history: the example of health related amenities
  • Mapping physical access to health care for older adults in sub-Saharan Africa and implications for the COVID-19 response: a cross-sectional analysis

Tags: Health, ohsome, OSM, OSM History Analytics, Public Health

Posted in OSM, Public Health

Comments are closed.

  • About

    GIScience News Blog
    News of Heidelberg University’s GIScience Research Group.
    There are 1,675 Posts and 0 Comments so far.

  • Meta

    • Log in
    • Entries RSS
    • Comments RSS
    • WordPress.org
  • Recent Posts

    • Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019
    • New paper on the automatic characterization of surface activities from 4D point clouds
    • OSHDB Version 1.0 Has Arrived
    • Job Opening for Postdoc / Senior Researcher on OpenStreetMap Road Quality Analysis
    • Geography Awareness Week 14.-19.11.2022
  • Tags

    3D 3DGEO Big Spatial Data CAP4Access Citizen Science Conference crisis mapping Crowdsourcing data quality deep learning disaster DisasterMapping GeoNet.MRN GIScience heigit HOT humanitarian humanitarian mapping Humanitarian OpenStreetMap team intrinsic quality analysis landuse laser scanning Lidar machine-learning Mapathon MapSwipe MissingMaps Missing Maps ohsome ohsome example Open data openrouteservice OpenStreetMap OSM OSM History Analytics Public Health Quality quality analysis remote sensing routing social media spatial analysis Teaching VGI Workshop
  • Archives

    • January 2023
    • December 2022
    • November 2022
    • October 2022
    • September 2022
    • August 2022
    • July 2022
    • June 2022
    • May 2022
    • April 2022
    • March 2022
    • February 2022
    • January 2022
    • December 2021
    • November 2021
    • October 2021
    • September 2021
    • August 2021
    • July 2021
    • June 2021
    • May 2021
    • April 2021
    • March 2021
    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
    • October 2014
    • September 2014
    • August 2014
    • July 2014
    • June 2014
    • May 2014
    • April 2014
    • March 2014
    • February 2014
    • January 2014
    • December 2013
    • November 2013
    • October 2013
    • September 2013
    • August 2013
    • July 2013
    • June 2013
    • May 2013
    • April 2013
  •  

    March 2021
    M T W T F S S
    « Feb   Apr »
    1234567
    891011121314
    15161718192021
    22232425262728
    293031  
  • Recent Comments

    GIScience News Blog CC by-nc-sa Some Rights Reserved.

    Free WordPress Themes | Fresh WordPress Themes