Training spatio-temporal OSM-indicators based on the resilience core from Cutter (2016) and exploring the implications for urban planning in the light of revealed thematic tags in the region of Stuttgart.
“Nobody on this planet is going to be untouched by the impacts of climate change”
Rayendra Pachauri (2014)

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 also the IPCC. Hand in hand with this development the definitions and operationalizations are innumerable and starting to lay a smoke screen above it. Conjoined, there is a clear lack of validation of resilience measures, including spatio-temporal aspects but also of the single component of it (Bakkensen 2017). 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 (Burton 2014). Hence, this experimental study explores and develops new spatial indicators through machine learning methods derived from OpenStreetMap data to replicate conventional core indicators. In order to cover all spatial attributes indicators for points, lines and areas will be deduced and separately as well as in a combined analysis investigated by means of supervised and unsupervised algorithms. The outcome is expected to uncover hidden spatial relations and patterns of urban resilience. Moreover, Burton (2014) stresses the need for new data sources to better understand the multifaceted phenomena of urban resilience. Therefore this study is contributing in developing robust and reliable socio-economic indicators contributing to this challenge to clear up the smoke.

Developing learning crowed based spatio-temporal indicators to model the components of the WorldRiskIndex based on OSM tags and machine learning

Climate change is already reality in many parts of the world and even more threatening our future well-being. The SDG 1.5 explicitly aims to reduce by 2030 the vulnerability and exposure to climate related hazards. The World Risk Index (WRI) is one well-respected approach in profiling countries risk to natural hazard. To effectively monitor development and detect decision knots on the climate resilience pathway (IPCC 2014) data of high resolution in space and time about the worlds countries is of urgent importance. Hence, the core of this work is the development of learning indicators. Learning in the sense of a methodological approach combining PostGIS for data management, R for statistical learning and QGIS for spatial analysis on crowd based information assessing the OSM-database and addressing the need of societal learning in the face of severe climate change. The World Risk Index (Birkmann et al. 2015) will guide the supervised learning part resulting in an indicator set derived from OSM tags, establishing on one hand an open risk index and adding deep explanatory power to its components by a qualitative discussion of the OSM themes. The second part explores with unsupervised algorithms the inherent characteristic of country groups classified by the open risk index and deduces common patterns of socio-economic vulnerability but also societal resilience. Hence, the inherent challenge of this work is to substitute existing static indicators with new dynamic indicators, but not only substituting them but also painting a more detailed picture. Moreover, new data sources still questioned often by their reliability compared to World Bank or census data, and therefore its opportunities are neglected instead of critically exploring the potential. Therefore, this thorough statistical approach in quantifying uncertainty contributes to the acceptance and hence use of crowd based information adding necessary reliability for policy and planning. This unique combination is not yet done and bares huge potential moreover united with the open source geo community to contribute a little piece of the puzzle for achieving the SDG 1.5.