This project aims at creating a fast setup for simulating city development in the context of urban water management. A main objective of the work will be to identify ways to consider not only new developments in suburbs, but also re-development in the city core in in simplified model, and to test the importance of such representations with regards to hydrological parameters.
We are currently running urban development simulations in a vector-based framework. In this setup, city development both in the suburbs and the city core can be represented by identifying areas that are not built-up or where buildings may be demolished, and then implementing new buildings in these areas. The downside of this framework is that it is slow and not suitable for, for example, sensitivity analysis.
In a raster-based framework, simulations can be performed much faster, because we don't need to perform computations on the actual geometries of buildings. However, in this setup, it is not immediately obvious how we can represent the amount of undeveloped space within a raster cell, and how the amount of undeveloped space changes when new buildings (of different types) are developed.
Data and approach
You starting point will be a larger number of vector-based urban development simulation results for a city in Denmark. You can use these data to assess how available space changes when new urban developments are implemented, and subsequently you can link this information to the parameters that were used for the urban development simulation. This should allow us to create a raster-based representation of the urban development process, which can be compared against the original data.
Requirements and work setup
This project considers rather large amounts of GIS data which you will access using Python and the GDAL libraries. Numpy is a likely candidate for implementing a raster-based representation of urban development in Python.
This implies that you must be keen on working with data and in a scripting environment. You can expect an introduction into handling GIS data and modelling urban development. There will be continuous sparring and this project is a good opportunity to become proficient in handling large amounts of data in a programmatic way. However, you must have a general curiosity for data and models and, to some degree, previous programming experience.
Interest in working with data and scripting, general curiosity for models