Big data and big data services for modelling geographical accessibility to healthcare: A scoping review

Question; Research on modelling geographical accessibility to healthcare services has witnessed rapid methodological advancements. One of the contributing factors is the increasing availability of big data detailing the link between the population in need of care and the health facility such as infrastructure, travel modes and speeds. This has allowed more granular computation of geographic access metrics. However, there are no reviews providing a comprehensive overview of the availability and use of big data for assessing geographical accessibility to healthcare. To fill this gap, we conduct a scoping review of the existing literature on the application of big data in evaluating geographical accessibility to healthcare. Methods; The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extensions for Scoping Reviews guidelines. We searched scientific databases (PubMed, Scopus, Web of Science, EBSCOhost-CINAHL, Cochrane, Embase, and MEDLINE), and grey literature. All literature in English or French without any restrictions of publication type, geographical location or year of publication was included provided it met the inclusion criteria. A thematic narrative approach was used in characterization of big data. Results; Preliminary results show open-source and paid sources for big data services. Open-access options include OpenStreetMap, Uber Movement, WorldPop and Gaode Map Open Platform. Paid providers include Google Maps, HERE Technologies, MapQuest, SafeGraph and Mapbox, however, these providers also have a limited open access option. The open source and paid providers offer a range of datasets useful for computing spatial access. These big datasets include roads, pathways, public transport, and points of interest, population distribution, mobility, real-time traffic, transit, and global routing data, and travel speeds. Other types of big data include historical urban mobility data, congestion, mobile phone records and population movements. Conclusions; The use of big data in modelling geographical accessibility to services has large potential to improve estimates to reflect more on ground reality for the link between the population in need and the location of services. The findings of this review provides a knowledge base to guide researchers and policymakers in leveraging big data to improve healthcare accessibility, while advancing equitable and timely delivery of healthcare services. Key words; Big data, geographic accessibility, travel time, transport networks, healthcare services, geospatial modelling, navigation, routing