Volume XLII-4/W14
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 213–220, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-213-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 213–220, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-213-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Aug 2019

23 Aug 2019

EXPLORATORY STUDY OF URBAN RESILIENCE IN THE REGION OF STUTTGART BASED ON OPENSTREETMAP AND LITERATURE RESILIENCE INDICATORS

H. Sauter, D. Feldmeyer, and J. Birkmann H. Sauter et al.
  • IREUS, Institute of Spatial and Regional Planning, University of Stuttgart, Germany

Keywords: Urban resilience, Indicators, Machine learning, R, RStudio, PostGIS

Abstract. The overarching nature of building resilience across disciplines and its inherent positive mutual understanding due to the association with the immune system also amongst the non-scientific community, makes it an attractive and increasing popular concept which everybody seems able to grasp its necessity. Hence, there is an exponential increase, even limited down to the key words “urban resilience”, in scientific literature over the last decade. Moreover the concept is also taken up by the New Urban Agenda Habitat III, the SDG goals and the IPCC. Hand in hand with this development the definitions and attempts of operationalization are innumerable. Conjoined, there is a lack of validation of resilience measures, including spatio-temporal aspects but also of the single component of it. Moreover, traditional data sources like census or governmental data miss out on certain important facets making empirical validation impossible and lack the spatio-temporal resolution necessary to cover the characteristics of resilience. Hence, this experimental study explores and develops new spatial indicators through machine learning methods applied to OpenStreetMap data to replicate conventional core indicators. In order to cover all spatial attributes indicators for points, lines and areas are deduced and investigated with supervised and unsupervised algorithms.