We are currently looking for candidates to fill one funded PhD position.
Please reach out to Martin Fleischmann (martin.fleischmann@natur.cuni.cz) if you’re interested and considering applying.
Bridging urban morphology and community ecology to study structure, organisation and evolution of cities
Cities, composed of a plethora of layers, physical or not, made by humans or nature, are entities that, due to their complexity, tend to be abstracted and oversimplified when being analysed. However, they can be conceptualised as complex adaptive systems, with many layers being intertwined with the others. When focusing on a layer of urban form - the physical built-up aspects - reflecting the structure of cities and an environment within which all the other components take place, the field of urban morphology tends to simplify the variety into a set of well-known archetypes. This project will move beyond the archetypal conceptualisation of urban form and apply data-driven techniques to explore the ecology of urban form.
In many aspects, ecological communities are similar to cities as both are spatially constrained complex adaptive systems. Where urban morphology struggles to analyse the variety of urban form and opts for archetypes, community ecology offers a theory and a robust set of advanced methods for analysing such systems that can be brought to urban morphology. The core of this project will focus on understanding the ability of community ecology to describe the physical environment of cities and uncover underlying patterns forming the places we live in. The project’s scope will be primarily methodological research leading to the development of new techniques, potentially affecting both urban morphology and community ecology, and creating tools to use them.
This project will take the form of an open, data-driven, multidisciplinary research done in cooperation with the Department of Social Geography and Regional Science (Martin Fleischmann) and the Department of Ecology (David Hořák, EcoSpace group). It should bring together quantitative community ecology and spatial data science with links to scientific software development (in Python). Applicants should have a degree in geography, ecology, urban sciences (urban studies, planning, architecture) or other relevant fields. Inclination towards data science and geoinformatics is expected. Experience with programming languages for data science is a benefit but not a strict requirement.
Supervisors: Martin Fleischmann, David Hořák
