PhD Candidate: GIS Analysis and Spatial Modelling of Urban Heat, Health and Ecosystem Disservices

Are you an aspiring researcher looking for a new opportunity in the field of urban planning, climate adaptation and GIS? Would you like to join a friendly and open environment where you can develop your skills and learn new things? Then you have a part to play as a PhD Candidate within the interdisciplinary BENIGN research project funded by the Climate Adaptation and Health programme of the Dutch Research Agenda .

The BENIGN (BluE and greeN Infrastructure desiGned to beat the urbaN heat) project aims to investigate how blue (lakes, canals) and green infrastructure (trees, other plants) can be employed in urban areas to create healthy living conditions.

As a successful PhD candidate, you will be expected to analyse and model the effect of built environment characteristics on the indoor and outdoor climate in relation to heat stress for vulnerable groups, water quality and plant pollen diversity using vulnerability mapping and spatial modelling. This will enable you to monitor and predict the positive (i.e. services) and negative (i.e. disservices) effects of blue and green interventions. You will then integrate the resulting models into a decision support system co-designed with end users and other experts.

In short, your core duties may include (1) the construction of GIS models of natural and built environments (2) (rudimentary) segmentation and classification of remote sensing images and Google Street View images of built environments where existing datasets are not sufficient, (3) qualitative validation of GIS models using questionnaires and/or interviewing techniques, and (4) using statistical analysis to explore whether specific elements of the environment correlate with aspects of public health. In general, we do not advocate a technology-driven research approach to spatial analysis. Instead, we promote a critical perspective on GIS, and look for new ways of complementing qualitative approaches with quantitative GIS and statistical analysis.