Computation and Analysis
This work addresses key challenges in big data analytics for urban research, developing tools for demographic mapping, transport modelling and spatial analysis using real-world and open data sources.
There are major research challenges faced by academics and industry professionals in 'Big Data' analytics, especially concerning economic and financial analysis. In order to conduct world-class research, scientists currently require extensive computing expertise in low-level data management and programming to do 'deep' data mining and build computer simulation models. The other major research challenge is the access to and handling of sensitive and expensive 'real-world' data.
These computational, analytics and data challenges are significant research 'blockers' for scientists, policy and industry professionals, which our team are exploring as part of our programme of work.
Our teams are working with and developing different sets of analytical tools as part of our work.
Demographic mapping tools
Land-use transport tools
Urban morphology, design and accessibility
Existing toolkits
Space Syntax
Space Syntax Laboratory has taken its Space Syntax 'DepthMapX' software open source with EPSRC Platform funding (Penn, EP/G02619X/1), and with further adaptations and extensions the software will be utilised to handle the full range of infrastructure types and to introduce the concept of 'stocks' alongside the syntactic representation of 'flows'.
CASA
QUANT release 7 simulates the impact of changes in population, employment, and travel costs associated with movements on the transport network in UK Cities. QUANT uses a simple model of how workers choose the places where they live with respect to attractive those places are and the travel costs from their workplaces.
QUANT visualises employment, working population, and journeys to work from the 2011 Population Census and then compares these with predictions from the model. The process of running the model and making comparisons with what we observe is called calibration and this fine tunes the model to simulate the data as closely as possible
Geography
The geo-temporal demographics toolkit will enable regional policy analysts to create, maintain and evaluate the geo-temporal profiles of standard and bespoke functional regions. This will draw upon a full set of Open and Big Data sources, derived from administrative, business and social media sources, using the facilities of the UCL elements of the ESRC Consumer Data Research Centre and the ESRC Administrative Data Research Centre.
The specific output of this work will include a web-based mapping system, which can visualise regional profiles based on customised functional areas, building upon collaborative work with the Office for National Statistics, and drawing on data and facilities provided by the Consumer Data Research Centre and Administrative Data Research Centre.