Accessibility analyses have proven to be a successful concept of spatial analysis, offering valuable insights into various domains. One widely used application is the assessment of accessibility to health infrastructure with isochrone methods on local [1], regional [2] and global [3] scale as well as contexts of disaster [1, 4]. Moreover, these analyses have evolved beyond simple metrics, incorporating potential demand and capacity to generate more comprehensive insights [5].

Such analyses, often based on open-source routing engines like openrouteservice [6] with OpenStreetMap as a data source, have enabled researchers to evaluate access to essential services. However, many of these analyses suffer from limitations that hinder their real-world impact. They rely on global assumptions about road networks and routing priorities, lacking consideration for the local context, and frequently ignore traffic conditions [6].

In this talk, we will present an improved approach to isochrone accessibility analysis with openrouteservice by incorporating traffic speed data from Uber in the context of Nairobi, Kenya. By addressing the limitations of existing methods, which tend to underestimate travel time by neglecting traffic conditions, we aim to provide more accurate and realistic insights into accessibility for necessary services within the city.

The global nature of OpenStreetMap and the tools based on it allow us to easily evaluate accessibility phenomena on a global scale. However, to achieve meaningful impact, it is crucial to bridge the gap between these analyses and the real world. By integrating traffic speed data into accessibility analysis, we can overcome part of the challenge.

The integration of traffic speed data into accessibility analysis holds substantial real-world implications. Accurately representing travel time empowers urban planners, policymakers, and transportation authorities to make informed decisions regarding infrastructure development, public transportation routes, and traffic management strategies.

In addition, we will discuss the transferability of our results to other regions, given the scarcity of open traffic data sets from Uber and the commercial nature of other providers. This talk aims to shed light on opportunities for opening or crowdsourcing such data, facilitating broader applicability and knowledge sharing.

References:

[1] Klipper, I. G., Zipf, A., and Lautenbach, S.: Flood Impact Assessment on Road Network and Healthcare Access at the example of Jakarta, Indonesia, AGILE GIScience Ser., 2, 4, , 2021. https://doi.org/10.5194/agile-giss-2-4-2021

[2] 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. https://doi.org/10.1016/S2666-7568(20)30010-6))

[3] Reinmuth, M.: Open Healthcare Access Map. Isochrone based accessibility and population estimates globally. HeiGIT 2022. https://apps.heigit.org/healthcare_access/#/

[4] Petricola, S., Reinmuth, M., Lautenbach, S. et al. Assessing road criticality and loss of healthcare accessibility during floods: the case of Cyclone Idai, Mozambique 2019. Int J Health Geogr 21, 14 (2022). https://doi.org/10.1186/s12942-022-00315-2

[5]Reinmuth, M., Kitzinger M. and Zipf, A.:Access to safe abortion in Germany: An overview of current accessibility by car. How will the situation evolve after the removal of §219a? State of the Map 2022, FIrenze, Italy. https://files.osmfoundation.org/s/MfweMaQyzaZiQ4p

[7] Zia M, Fürle J, Ludwig C, Lautenbach S, Gumbrich S, Zipf A. SocialMedia2Traffic: Derivation of Traffic Information from Social Media Data. ISPRS International Journal of Geo-Information. 2022; 11(9):482. https://doi.org/10.3390/ijgi11090482