Modelling Individual Consumer Behaviour
Lead Research Organisation:
University of Leeds
Department Name: Sch of Geography
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Alison Heppenstall (Principal Investigator) |
Publications
Yang Y
(2019)
Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility
in ISPRS International Journal of Geo-Information
Xiang L
(2018)
A geodemographic classification of sub-districts to identify education inequality in Central Beijing
in Computers, Environment and Urban Systems
Manson S
(2020)
Methodological Issues of Spatial Agent-Based Models
in Journal of Artificial Societies and Social Simulation
Malleson N.
(2010)
Evaluating an agent-based model of Burglary
in Working Paper of the University of Leeds, School of Geography
Malleson N
(2010)
Implementing comprehensive offender behaviour in a realistic agent-based model of burglary
in SIMULATION
Malleson N
(2010)
Crime reduction through simulation: An agent-based model of burglary
in Computers, Environment and Urban Systems
Ma J
(2014)
Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing
in Computers, Environment and Urban Systems
Ma J
(2014)
Daily travel behaviour in Beijing, China: An analysis of workers' trip chains, and the role of socio-demographics and urban form
in Habitat International
Ma J
(2015)
Exploring transport carbon futures using population microsimulation and travel diaries: Beijing to 2030
in Transportation Research Part D: Transport and Environment
Hood N
(2020)
Sociodemographic and spatial disaggregation of e-commerce channel use in the grocery market in Great Britain
in Journal of Retailing and Consumer Services
| Description | A new piece of software called the 'Flexible Modelling Framework' was created and has now been made available to interested researchers (http://mass.leeds.ac.uk/resources/software/). The central idea of attaching a behavioural framework to an agent-based model proved to be successful. Crime, retail and education examples were all developed (Malleson et al, 2010, 2012; Heppenstall et al, 2013; Olner et al, 2013; Birkin and Heppenstall 2011; Harland and Heppenstall, 2012, 2013). Summarising the main findings from each area: Retail: The methodology was applied to looking at the processes in the retail petrol market at a national scale (Scotland). The approach was found to be successful at replicating the main dynamic drivers in the system (Heppenstall et al, 2013). Rural/Urban divide in pricing was successfully replicated and predictions successfully made about which petrol stations were vulnerable to closure. Comparisons with previous work in this area (Heppenstall ESRC funded PhD), showed that incorporating individual behaviour to significantly improve the results; this research is currently being written up. Crime: The application to crime was entirely novel; the work from this area was the first example of a behavioural framework to be incorporated into an agent-based model for simulating the behaviour of burglars. This work was undertaken by the PI's PhD student (Malleson) who worked alongside the main project researcher. Results correlated well with actual burglaries and showed areas vulnerable to burglaries after target hardening. Education: The model was applied to looking at behaviour in school place allocation. By incorporating socio-demographic information, parental choice was explicitly accounted for. The model was run alongside that of the local LEA for allocation of school places (that matched the parental choice); the model was found to successfully allocate more pupils to schools of choice than the current system. The LEA have asked for this work to be extended. In total six journal papers, one book chapter and two grants have come from the work in the project. There are 3 further papers in development. The work has been presented at both national (RGS, GISRUK) and international conferences (AAG, Geocomputation). The work has already been extended by another funded ESRC project: Geo-spatial Restructuring of Industrial Trade (GRIT). In addition, further grants in health (MRC) and archaeology (Leverhulme) are currently under review. The PI plans on submitting a future grant looking at how to use this framework to simulate the effects of different climate change scenario's on individuals, for example changing water usage or uptake of green technologies. |
| Exploitation Route | The ability to realistically simulate an individual's behaviour has a wide-ranging impact across all of the social sciences. For example, work from this project has been used to model the behaviour of burglars in a city and there are plans to further extend this work more generally for testing out crime reduction policies. The methodology that this approach derived is currently being used in another ESRC funded project (GRIT) to understand the short and long-term impacts on the UK economy of rising fuel costs. The microsimulation framework that was developed is now released under an open source licence and being used by researchers in the area of health, demographics and archaeology. A particular area of research that this framework is being used for is the simulation of ageing populations to identify where spatially stresses will appear in the NHS as the population ages. In the medium to long-term, the basis of this work potentially has great significance with the rapid uptake of agent-based modelling by researchers in different disciplines who are interested in understanding individual behaviour, how to change behaviour and the impact of different policies on an individuals course of actions. |
| Sectors | Energy,Environment,Retail,Transport |
| Description | One of the central outcomes of the grant was the development of a framework to embed behaviour successfully into agent-based models. Part of this work involved early "big data" work through mining diverse data sets for important behavioural information that was not captured in more established data sets. This work has now served as the basis of several other projects. Notably, work with the local crime partnership (Safer Leeds). Here we developed models replicating behaviour of burglars that linked to existing crime patterns. These have been further developed through continuing studentships. Current work using these are aimed at understanding how changing the environment (e.g. increased street-lighting) could reduce crime through understanding how people will behave and use different spaces. This part of the work links to another important methodological development that the grant supported, that of creating realistic synthetic individual level populations. These populations were derived from the UK census, but enhanced from other data sets e.g. BHPS. The importance of this work is twofold; (i) creating customizable populations that can increase the accuracy of models and (ii) developing a method that can produce individual-level populations that can be potentially be a resource to fill the void if the UK Census does not continue in the future. Another area of continued development is using the framework to simulate school place allocation. Rising school role and the resulting shortage of places is a significant problem both in Leeds and nationally. The outputs from the grant have led to collaboration with the local education authority. We were invited to submit a tender for redesigning their school allocation system. The outcome of this is unknown at present. However, this is an area of ongoing collaboration. As a result of the work from this grant, I am now working with Leeds City council on several areas of mutual interest, notably the transitioning of the day to night time economy. This involves creating a profile of individuals within the city and examining footfall data to see when, why and how people are using the city. Advanced analysis relating to this is currently ongoing, specifically using Tour de France to assess the positive economical impacts of Leeds hosting such events is. Leeds is considering going for European city of culture in 2023 - this works ties in with auditing how prepared Leeds is to handle visitors on a mass scale and what the real economic benefits are. The methodology that this approach derived is currently being used in another ESRC funded project (GRIT) to understand the short and long-term impacts on the UK economy of rising fuel costs. The microsimulation framework that was developed is now released under an open source licence and being used by researchers in the area of health, demographics and archaeology. A particular area of research that this framework is being used for is the simulation of ageing populations to identify where spatially stresses will appear in the NHS as the population ages (I currently have a PhD student working on this). In the medium to long-term, the basis of this work potentially has great significance with the rapid uptake of agent-based modelling by researchers in different disciplines who are interested in understanding individual behaviour, how to change behaviour and the impact of different policies on an individual's course of actions. Finally, the data mining work from the grant has been used as the basis for the recent ESRC funded Big Data CDRC (jointly funded between Leeds and UCL). As outlined above, work from this project has been used to model the behaviour of burglars in a city by Safer Leeds and there are plans to further extend this work more generally for testing out crime reduction policies. The major challenges to gain impact for this project are (i) dissemination to non-academic parties and (ii) making practitioners aware of the software. We have contacted the local Council and links are being established here in the areas of Education and Economic development. Further work is being undertaken to develop ideas with environmental consultancies to understand behaviour of individuals involved in uptake of Green Technologies. Part of the challenge is to develop the software to a point that non-experts can use it. This is an issue that is ongoing. This work has been delayed due to my maternity leave Nov 13 - Nov 14. |
| First Year Of Impact | 2012 |
| Sector | Education,Energy,Healthcare,Retail |
| Impact Types | Societal,Economic |
| Title | FMF |
| Description | This model allows any types of data to be used to create a micro simulated population. It uses several algorithms and can be linked to other software, e.g. ABM. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2012 |
| Provided To Others? | Yes |
| Impact | This tool has been used by researchers at The London School for Tropical Medicine as well as by researchers at the National Centre for Computation in Maynooth, Queen Mary's (Uni of London) and the University of Liverpool. As the software is open source and freely available, tracking use and uptake is not straightforward. |
| URL | http://mass.leeds.ac.uk/html/coding/fmf/intro/index.html |