Unit description and aims

This unit introduces students to the research fields of Urban Analytics and City Science. It enables them to utilise cutting-edge methods from these fields and pair them with new sources of data to answer urging research questions about cities. Moreover, the students will develop a critical understanding of these tools and data as well as their applicability and value for urban research, planning and urban policy making. Emphasis will be placed on the interactions between digital technologies and urban space and the smart city agenda.

Emphasis will be placed on the network understanding of our cities. Networks have long formed a distinctive element of urban research. Various sub-fields of geography, such as transport, economic and urban geography, are heavily based on networks both from a conceptual and an analytical point of view. Moreover, the digital revolution and developments related to social media and connectivity as well as heightened flows of information within and between urban regions, have greatly enhanced the relevance of a network approach to contemporary socio-economic and cultural trends. This module will approach the above issues both from a theoretical and practical perspective.

The first part of this unit will be dedicated to concepts and tools from social network analysis, including hands-on practicals. This part will provide the fundamental knowledge of this module. Networks analysis will then lead to the concept of scaling in urban science, which will be linked with essential urban economic elements such as agglomeration economies and urban hierarchies. The practical knowledge of network analysis will be used as a stepping stone to understand ideas and methods from the field of transport geography regarding modelling flows within and between cities. Common thread here is the network structure which is an essential element of both transport geography and social network analysis. The empirical element of this part will deal with basic concepts such as gravity models as well as connectivity and accessibility measures.

The unit aims:

Intended Learning Outcomes

  1. Be able to creatively utilise cutting edge methods from Urban Analytics and Urban Science in order to answer urban research questions.

  2. Be able to critically assess the value of and utilise new sources of big data for urban research.

  3. Identify key concepts and theories of digital transformations and smart cities.

  4. Present the results of statistical analysis in a clear, cogent manner, using effective visualizations, tables, and written argument.

Assessment details

One 3500-word report (100% of the unit mark) for an urban data analysis project. The report will be written in a reproducible manner and will include the necessary code for the data analysis and the outputs (e.g. an Rmarkddown document).


All the analysis will be done using R and RStudio.