More people are now living within urban areas than at any previous point in human history. As urban areas have expanded and adapted to accommodate this growth, so has the complexity of city structure and the variable conditions in which people live. Within urban geography, planning and sociology there is a lengthy history of work that has attempted to empirically study this geography of residential differentiation, mapping the composite socio-spatial structure of both the built and population characteristics between small areas. Much of my research has developed a broad critique of the ways in which such geodemographic methods can be refined through modern scientific approaches to data mining, geographic information science and quantitative human geography.
* Optimisation of the algorithms used to create geodemographic classifications
* Making the specification, estimation and testing of geodemographic classifications explicitly spatial
* Devising bespoke geodemographics that are appropriate to particular applications
* Enabling public participation in area classifications
* Development of methods to assess the performance and intersection between different types of area classification
Geocomputation is the intersection between advanced computational methods and geographical analysis and modelling. Geocomputation is applied and often interdisciplinary, with methodological developments typically embedded in applications seeking to address real world problems. Geocomputation excels as a framework for researching many contemporary social science problems associated with large volumes of dynamic and spatio-temporal ‘big data’, such as those generated in ‘smart city’ contexts or from crowdsourcing. Research within this theme concerns the intersecting challenges and opportunities brought about by new data and computational infrastructure for a range of social science problems.
* Optimisation of data mining and inferential statistical models for grid or multi-core environments
* Algorithms for street network routing of "Big Data"
* Geographically weighted CO2 emissions estimation associated with travel
* Visualisation and web based mapping platforms
* Big consumer data and retail systems
The Geography of Access to Higher Education
While in recent years the burgeoning Higher Education (HE) sector has been set an agenda of widening participation, few HE institutions have strategies in place for reaching the full range of potential students who would most benefit from (and successfully complete) their current subject and course offerings. Universities and colleges are often unsystematic in the ways in which they identify schools and colleges for outreach and widening participation initiatives, and sometimes uncoordinated in how they present the full institutional profile of subjects of study in these activities. This research has developed a systematic framework for widening participation and extending access in an era of variable fees, exploring both supply geography and their integration into the wider higher educational system.
* School catchment and choice models
* School-university transitions
* The health of geography as a discipline
* Higher education geodemographics
* Higher education flow modelling
Research Group Membership
- Business and Local Government Data Research Centres (Big Data)
- Evaluating the Potential of Secondary Data to Monitor Spatio-temporal Uncertainty and Inform Updates of the 2011 Census Output
- Supporting undergraduate teaching in quantitative geography: making connections between schools, universities and the workplace
- e-Resilience and the Highstreet
- Leveraging the Google Cloud to Estimate Individual Level CO2 Emissions Linked to the School Commute
- Estimating Emissions Linked to the School Commute in England
- Population change and geographic inequalities in the UK, 1971-2011