Recent years have witnessed an explosion in the urbanisation of our society, with vast metropolitan areas growing across the world. The surge in the availability of large data sets documenting several aspects of our cities, as well as methodological and computational advances in the areas of statistical modelling, machine learning and complex networks, have opened up the opportunity of quantitatively studying urban environments. This module will provide you with a broad overview of the history of city science and the core principles of urban analytics, including what data sources are available and a practical understanding of how to visualise urban data. Key challenges in the study of urban environments will also be considered and discussed from the perspective of the data needed to address them.
The aim of this module is to provide you with the core knowledge and skills essential for studying urban systems. It will cover technical aspects of how cities can be modelled, as well as the key topics of research in this area. It will also provide an introduction to some of statistical and computational methods used to study urban systems, and crucially the data sets which researchers and practitioners use in this area. Throughout the module, a range of real-world case studies will be discussed to explore existing results and success stories of how city science has been applied to real-world problems.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
Module Specific Skills and Knowledge:
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Demonstrate competence in core techniques in the analysis of cities
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Use a variety of computational methods relevant to the study of cities
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Demonstrate competence in visualising spatial data
Discipline Specific Skills and Knowledge:
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Use computational methods to analyse complex data sets
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Use appropriate visualisation techniques to explore and communicate complex data sets
Personal and Key Transferable/ Employment Skills and Knowledge:
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Use Python, or other programming languages, for statistical and computational analysis of data
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Critically read and report on research papers
SYLLABUS PLAN - summary of the structure and academic content of the module
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:
This module will include topics in the areas listed below. Topics will be covered and discussed both in the lectures or the workshops, as most appropriate for each.
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Urban systems: an introduction to the science of cities
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Data driven smart sustainable urbanism
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The science of cities: models and methods
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Working and visualising urban and spatial data
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The spatial organization of cities
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Infrastructure
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Socioeconomic aspects
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Systems of cities
