Sucessfull HeiKA autumn school urban data science

The HeiKA autumn school urban data science that took place last week was a good success with participating students varying between five and fifteen during the week. Topics covered included visualization, spatial statistics, analysis of movement data, intrinsic data quality assessment based on the ohsome API and the use of the openrouteservice API. Hands-on exercises were based on QGIS, GeoDa, R and Python. The lab work was augmented by inspiring talks by guest lecturers:

  • Alexander Zipf (GIScience HD, HeiGIT)
  • Peter Vortisch (KIT-IFV)
  • Sina Keller (KIT-IPF)
  • Anita Graser (AIT)
  • Karin Hitscherich (PTV Group)
  • Julian Bruns & Akira Sriamulu (Disy)
  • Till Nagel (HS Mannheim)

Examples from the labwork:

Local Moran’s I for residuals of a regression analysis on factors associated with the distrubiton of charging stations for e-mobility

Checking completeness of OSM features for Lima with help of the ohsome API.

Fitting a double logistic curve to the cumulative OSM contributions for highway=* in Lima.

Population in different driving distances around hospitals in Lima.

If you want to know more about our ohsome framework, don’t hesitate to reach out to us via info(at) or contact any member of our team directly. Stay ohsome and happy cycling!

Information on the ohsome OpenStreetMap History Data Analytics Platform and more examples of how to use the ohsome API can be found here:

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