„March for Sozialgeographie“? Rechtspopulismus als Zumutung und die regressive Moderne als Herausforderung der Humangeographie

The paper outlines an approach to right wing populism in recent years not only in critizing the use of so called alternative facts but using the concept of a regressive modernisation as a debate which includes populist movements into a broader social theory and diagnosis of western societies. The evolution of regressive modernisation reveals both, the success of a neoliberal globalization and a rise of marginality and perceived dangers of a downward mobility. These findings are used in order to explain certain transformations of academic fields in general and social geography in particular. It is argued that the success of postmodern geographies may be seen as an overcoming of hegemonic discourses of positivism as well as of marxism, but was and is unable to counteract geographies of recent right wing populism. Even the positions of the march for science which has been a major initiative to fight for academic integrity are seen as not being sufficient to rebuilt an antipopulist social geography. This situation leads to some suggestions and recommendation for further work in this field.</p

Classification and modelling of urban micro-climates using multisensoral and multitemporal remote sensing data

Remote sensing has widely been used in urban climatology since it has the advantage of a simultaneous synoptic view of the full urban surface. Methods include the analysis of surface temperature patterns, spatial (biophysical) indicators for urban heat island modelling, and flux measurements. Another approach is the automated classification of urban morphologies or structural types. In this study it was tested, whether Local Climate Zones (a new typology of thermally 'rather' homogenous urban morphologies) can be automatically classified from multisensor and multitemporal earth observation data. Therefore, a large number of parameters were derived from different datasets, including multitemporal Landsat data and morphological profiles as well as windowed multiband signatures from an airborne IFSAR-DHM. The results for Hamburg, Germany, show that different datasets have high potential for the differentiation of urban morphologies. Multitemporal thermal data performed very well with up to 96.3 % overall classification accuracy with a neuronal network classifier. The multispectral data reached 95.1 % and the morphological profiles 83.2 %.The multisensor feature sets reached up to 97.4 % with 100 selected features, but also small multisensoral feature sets reached good results. This shows that microclimatic meaningful urban structures can be classified from different remote sensing datasets. Further, the potential of the parameters for spatiotemporal modelling of the mean urban heat island was tested. Therefore, a comprehensive mobile measurement campaign with GPS loggers and temperature sensors on public buses was conducted in order to gain in situ data in high spatial and temporal resolution

Empirical Evidences for Urban Influences on Public Health in Hamburg

Abstract: From the current perspectives of urban health and environmental justice research, health is the result of a combination of individual, social and environmental factors. Yet, there are only few attempts to determine their joint influence on health and well-being. Grounded in debates surrounding conceptual models and based on a data set compiled for the city of Hamburg, this paper aims to provide insights into the most important variables influencing urban health. Theoretically, we are primarily referring to the conceptual model of health-related urban well-being (UrbWellth), which systemizes urban influences in four sectors. The systematization of the conceptual model is empirically confirmed by a principal component analysis: the factors derived from the data correspond well with the deductively derived model. Additionally, a multiple linear regression analysis was used to identify the most important variables influencing the participant’s self-rated health (SRH): rating of one’s social network, rating of neighborhood air quality, rating of neighborhood health infrastructure, heat stress (day/outdoors), cold stress (night/indoors). When controlling for age, income and smoking behavior, these variables explain 12% of the variance of SRH. Thus, these results support the concept of UrbWellth empirically. Finally, the study design helped to identify hotspots with negative impact on SRH within the research areas