Description

The urban spatial structure was widely introduced in the urban study and urban planning to describe the city's layout. Traditionally, physical infrastructure distribution and conventional administrative division were used to detect the urban spatial structure. However, the dynamic aspect of the urban spatial structure represented by human activity distribution was left out. With the abundance of data, it becomes possible to detect the dynamics of urban spatial structure.

Human activities showed more spatial-temporal variances than the relatively static physical infrastructure distribution and conventional administrative division. In this research, you are expected to use transport ridership as the proxy of human activities to detect urban spatial structure dynamics at different timeframe. A spatial network of transport ridership will need to be constructed before using the network clustering method (community detection). The results obtained could be studied to reveal similarities and differences. Various socio-economic indicators and features, such as the POI, population, land use depending on the case study, could be brought in to study and possible driven force behind such difference. The spatial interaction model (or LUTI model) and various machine learning methods at the student's choice are expected to be applied in this process.

Objectives and Methodology

In this MSc research, you will need to use network science to detect urban spatial structure dynamics with multiple timeframes using public transport data. Next, use the spatial interaction model (or LUTI) to explore the driven force behind the different dynamics revealed.

Further reading

Zhong, C., Arisona, S. M., Huang, X., Batty, M., & Schmitt, G. (2014). Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28(11), 2178–2199. https://doi.org/10.1080/13658816.2014.914521
Siła-Nowicka, K., & Fotheringham, A. S. (2019). Calibrating spatial interaction models from GPS tracking data: An example of retail behaviour. Computers, Environment and Urban Systems, 74, 136–150. https://doi.org/10.1016/j.compenvurbsys.2018.10.005