Master's Thesis Jana Rudnick


Modelling of a generator for urban districts in pyCity

EFH (Single Family House), MFH ( Multi Family House), GHD (Building for business, trade and services) and NWG (non- residential building) Copyright: EBC Identification of the building type with the generator for urban districts. This picture shows the assignment of the building types EFH (Single Family House), MFH ( Multi Family House), GHD (Building for business, trade and services) and NWG (non- reside

Due to the exit from nuclear and fossil-fuel energy the optimization of city districts carries a great potential in the future. The inventory-taking of the actual city districts involves uncertainties concerning the data quality and symbolizes a great effort. Virtual city districts could be used here as a reference for the following analysis and optimization processes.

Within this master thesis, a city district generator has to be invented and implemented in pyCity, which is based on OpenStreetMap data. The aim of the generator is to capture the structure of a city quickly without having background information. Nevertheless, data about city districts have to be manually integrated into the city generator Python since there is no acurate generator, which is capable of embedding data on its own, available. The aim of the thesis is to simplify and expedite the process.

For a general overview, literature regarding city districts and their topology is being analyzed in order to merge the infomation into the generator. The implementation is made in the Python package „pyCity“ as well as in the add-on „pyCity_Calc“.

Buildings can be classified into three different building types („Single Family Houses“, „Multi Family Houses“ and „Non residential buildings“) by considering the ground floor area and the number of neighbours. Based on the specific characteristics of each building type, data enrichment of every single building is done within the generator.

The parameters, which are implemented into the generation, are from various professional studies, articles and papers and identify the average values of the build year and type. Those data enriches the parametrization of a city district in a logical and statistical way, but not with the values of every building.

Further information of the city district can be assessed by incorporating the data of the distributed building types. Those ensure the classification of the city districts into urban, suburban or industrial areas. The evaluation shows a representative distribution in urban and suburban districts, though industrial districts can often not be identified correctly due to the lack of information in the OSM data.

Thus a representative distribution of builing typologies is evaluated by the city district generator. An optimization of the data enrichment can additionally be established through an even higher input of information. The generator can be used as a base for further analysis at the Institute for Energy Efficient Buildings and Indoor Climate (EBC).