Understanding Urban Movements through Big Data and Social Simulation
Lead Research Organisation:
University of Leeds
Department Name: Sch of Geography
Abstract
This research will fundamentally alter our understanding of daily urban movement patterns through a combination of 'big data' analysis and cutting-edge computer simulation. It will develop new methods to produce data that will help us to address key issues in crime and health.
A big data "revolution" is underway that has the potential to transform our understanding of daily urban dynamics and could have big impacts on the ways that scientists conduct social science research. Vast quantities of new data are being gathered about people in cities. New services are capturing information about peoples' daily actions from their use of social media, public transport systems and mobile telephones, to name a few. Data from these sources, although noisy, messy and biased are unprecedented in their scope, scale and resolution. This research will first develop new geographical methods that can make sense of these data and derive information about peoples' daily movements in space and time. It then proposes to develop a computer simulation of city-wide daily urban movements that will be calibrated automatically from streams of crowd-sourced data.
This research is important because previous projects that have attempted to model detailed urban movements have been hampered by a lack of high-resolution data and by methods that have difficulty in modelling the complex individual-level interactions of people that ultimately characterise cities. Large-volume sources, such as censuses, capture attributes and characteristics of the population, rather than their attitudes and behaviours. On the other hand, detailed surveys that attempt to capture this behavioural information are naturally limited by their size and scope. In contrast, new 'big' public data streams are voluminous and contain information about a user's location as well as a textual or multimedia component that often describes their behaviour or actions. The new simulation model will make use of these data to create a much more accurate picture of urban dynamics than we have had before now.
This new picture will have the capacity to alter our understanding of key social phenomena that depend on where people are at different times of day, rather than simply where they live. It will use the simulation outputs to generate new estimates of where people are and apply these estimates to two empirical areas:
1. Crime. The research will re-analyse crime rates based on estimates of where groups of potential victims are, rather than simply where people live. This will then show us where crime is higher or lower than expected, given the number of people who are in the area at the time and might be victimised. This will have obvious impacts for crime reduction policies and the project will work with the police and crime-reduction experts to make the best use of the results.
2. Health. The second project will calculate peoples' exposure to air pollution based on where they actually spend their time, rather than where they live. Normally, peoples' home location is used to estimate how susceptible they are to air pollution, but this ignores the fact that many people will be exposed whilst away from home (e.g. going to work, travelling to the shops, etc.). By more accurately estimating peoples' exposure, this project could have substantial impacts on EU/UK air quality laws and lead to an overall improvement in national health.
In summary, this project will make use of new 'big' data and advanced computer simulation to better understand how people move around cities. It will then apply this new knowledge to try to better understand rates of crime and to assess the impacts of air pollution on peoples' health.
A big data "revolution" is underway that has the potential to transform our understanding of daily urban dynamics and could have big impacts on the ways that scientists conduct social science research. Vast quantities of new data are being gathered about people in cities. New services are capturing information about peoples' daily actions from their use of social media, public transport systems and mobile telephones, to name a few. Data from these sources, although noisy, messy and biased are unprecedented in their scope, scale and resolution. This research will first develop new geographical methods that can make sense of these data and derive information about peoples' daily movements in space and time. It then proposes to develop a computer simulation of city-wide daily urban movements that will be calibrated automatically from streams of crowd-sourced data.
This research is important because previous projects that have attempted to model detailed urban movements have been hampered by a lack of high-resolution data and by methods that have difficulty in modelling the complex individual-level interactions of people that ultimately characterise cities. Large-volume sources, such as censuses, capture attributes and characteristics of the population, rather than their attitudes and behaviours. On the other hand, detailed surveys that attempt to capture this behavioural information are naturally limited by their size and scope. In contrast, new 'big' public data streams are voluminous and contain information about a user's location as well as a textual or multimedia component that often describes their behaviour or actions. The new simulation model will make use of these data to create a much more accurate picture of urban dynamics than we have had before now.
This new picture will have the capacity to alter our understanding of key social phenomena that depend on where people are at different times of day, rather than simply where they live. It will use the simulation outputs to generate new estimates of where people are and apply these estimates to two empirical areas:
1. Crime. The research will re-analyse crime rates based on estimates of where groups of potential victims are, rather than simply where people live. This will then show us where crime is higher or lower than expected, given the number of people who are in the area at the time and might be victimised. This will have obvious impacts for crime reduction policies and the project will work with the police and crime-reduction experts to make the best use of the results.
2. Health. The second project will calculate peoples' exposure to air pollution based on where they actually spend their time, rather than where they live. Normally, peoples' home location is used to estimate how susceptible they are to air pollution, but this ignores the fact that many people will be exposed whilst away from home (e.g. going to work, travelling to the shops, etc.). By more accurately estimating peoples' exposure, this project could have substantial impacts on EU/UK air quality laws and lead to an overall improvement in national health.
In summary, this project will make use of new 'big' data and advanced computer simulation to better understand how people move around cities. It will then apply this new knowledge to try to better understand rates of crime and to assess the impacts of air pollution on peoples' health.
Planned Impact
The spatio-temporal dynamics of urban movement patterns are poorly understood at high resolutions. This strongly disadvantages the organisations responsible for managing public resources that rely on accurate estimates of daily population density. This research will utilise new forms of data, coupled with advanced simulation modelling, to create much a more accurate picture of urban dynamics. Both the ESRC and the Government have recognised the importance of engaging with this `big data' paradigm, making this research highly topical. Beneficiaries include:
West Yorkshire Police;
Safer Leeds;
Jill Dando Institute of Crime Science;
Metropolitan Police;
Leeds Data Thing;
Department of Health Committee on the Medical Effects of Air Pollutants (COMEAP);
World Health Organisation;
Environmental Protection UK (EPUK).
Initially, local organisations will receive the greatest impact through direct engagement (for details see Pathways to Impact). West Yorkshire Police will receive benefit as new crime rate estimates will have a impact on their crime reduction initiatives. For the first time measures of crime can be estimated much more accurately through knowledge about the underlying population of potential victims which will indicate the places (and times) where crime is significantly higher than would otherwise be expected and, importantly, where it is significantly lower. Also, there are a number of non-profit organisations interested in how big data research will shape society in the future. One group in particular, Leeds Data Thing, hold regular events to discuss new data and will undoubtedly benefit from the methodological outcomes of the research.
In the longer term, the project will have broader impacts to the Leeds Community Safety Partnership (Safer Leeds) and their partner organisations. Safer Leeds have a broad agenda for improving community safety through the coordination of numerous bodies. Following preliminary engagement meetings the organisation is excited about the possibilities of applying the research outputs to application areas such as the identification of bicycle accident hotspots, predicting graffiti and litter locations and optimising the standing locations of accident & emergency vehicles. Understanding urban dynamics will have significant impact on each of these areas and has the potential to substantially improve resources allocation. These are ideal areas for collaborative PhD studentships that the PI will attempt to attract funding for. There are also national crime research organisations who will receive indirect impact through this research. The Jill Dando Institute for Crime Science will benefit from the new approach, developed by this project, for measuring the population at risk of crime. New methods that can more accurately take account of the spatio-temporal behaviour of potential victims will undoubtedly influence the research and the crime science classes that they run.
The exploratory project that will apply the research outputs to the study of health and air quality will also have national and international impact in the longer term. New estimates of exposure to polluted air, based on mobile rather than residential population estimates, will be invaluable to COMEAP (Department of Health), the World Health Organisation Global Health Estimates and the charity EPUK . These organisations assess the impact of air quality on health and, in particular, estimate the number of yearly deaths as a result of exposure to polluted air. Estimates based on the residential (night time) population will substantially under-estimate personal exposure and downplay the affects of air pollution on health. Hence the results of this study could fundamentally influence the ways that these organisations assess exposure and quantify the impacts of air pollution. Ultimately, this has the potential to result in more stringent air quality targets at an EU level.
West Yorkshire Police;
Safer Leeds;
Jill Dando Institute of Crime Science;
Metropolitan Police;
Leeds Data Thing;
Department of Health Committee on the Medical Effects of Air Pollutants (COMEAP);
World Health Organisation;
Environmental Protection UK (EPUK).
Initially, local organisations will receive the greatest impact through direct engagement (for details see Pathways to Impact). West Yorkshire Police will receive benefit as new crime rate estimates will have a impact on their crime reduction initiatives. For the first time measures of crime can be estimated much more accurately through knowledge about the underlying population of potential victims which will indicate the places (and times) where crime is significantly higher than would otherwise be expected and, importantly, where it is significantly lower. Also, there are a number of non-profit organisations interested in how big data research will shape society in the future. One group in particular, Leeds Data Thing, hold regular events to discuss new data and will undoubtedly benefit from the methodological outcomes of the research.
In the longer term, the project will have broader impacts to the Leeds Community Safety Partnership (Safer Leeds) and their partner organisations. Safer Leeds have a broad agenda for improving community safety through the coordination of numerous bodies. Following preliminary engagement meetings the organisation is excited about the possibilities of applying the research outputs to application areas such as the identification of bicycle accident hotspots, predicting graffiti and litter locations and optimising the standing locations of accident & emergency vehicles. Understanding urban dynamics will have significant impact on each of these areas and has the potential to substantially improve resources allocation. These are ideal areas for collaborative PhD studentships that the PI will attempt to attract funding for. There are also national crime research organisations who will receive indirect impact through this research. The Jill Dando Institute for Crime Science will benefit from the new approach, developed by this project, for measuring the population at risk of crime. New methods that can more accurately take account of the spatio-temporal behaviour of potential victims will undoubtedly influence the research and the crime science classes that they run.
The exploratory project that will apply the research outputs to the study of health and air quality will also have national and international impact in the longer term. New estimates of exposure to polluted air, based on mobile rather than residential population estimates, will be invaluable to COMEAP (Department of Health), the World Health Organisation Global Health Estimates and the charity EPUK . These organisations assess the impact of air quality on health and, in particular, estimate the number of yearly deaths as a result of exposure to polluted air. Estimates based on the residential (night time) population will substantially under-estimate personal exposure and downplay the affects of air pollution on health. Hence the results of this study could fundamentally influence the ways that these organisations assess exposure and quantify the impacts of air pollution. Ultimately, this has the potential to result in more stringent air quality targets at an EU level.
Publications
Crooks Andrew
(2018)
Agent-Based Modelling and Geographical Information Systems: A Practical Primer
Crooks A
(2018)
Comprehensive Geographic Information Systems
Kieu L
(2019)
A stochastic schedule-following simulation model of bus routes
in Transportmetrica B: Transport Dynamics
Malleson N
(2018)
The characteristics of asymmetric pedestrian behavior: A preliminary study using passive smartphone location data
in Transactions in GIS
A. Heppenstall
(2015)
How big data and The Sims are helping us to build the cities of the future
in The Conversation
Heppenstall A
(2016)
"Space, the Final Frontier": How Good are Agent-Based Models at Simulating Individuals and Space in Cities?
in Systems
Mishra S
(2023)
'Cyclic syndrome' of arrears and efficiency of Indian judiciary.
in SN business & economics
Clay R
(2021)
Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter
in Simulation Modelling Practice and Theory
Kieu LM
(2020)
Dealing with uncertainty in agent-based models for short-term predictions.
in Royal Society open science
Ward J
(2016)
Dynamic calibration of agent-based models using data assimilation
in Royal Society Open Science
Heppenstall, A.
(2020)
Building cities from slime mould, agents and quantum field theory
in Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Malleson N
(2019)
Identifying the appropriate spatial resolution for the analysis of crime patterns.
in PloS one
Ternes P
(2021)
Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters.
in Open research Europe
Kieu M
(2022)
Towards real-time predictions using emulators of agent-based models
in Journal of Simulation
Andresen M
(2016)
Crime at Places and Spatial Concentrations: Exploring the Spatial Stability of Property Crime in Vancouver BC, 2003-2013
in Journal of Quantitative Criminology
Malleson N
(2016)
Exploring the impact of ambient population measures on London crime hotspots
in Journal of Criminal Justice
McCulloch J
(2022)
Calibrating Agent-Based Models Using Uncertainty Quantification Methods
in Journal of Artificial Societies and Social Simulation
Malleson N
(2020)
Simulating Crowds in Real Time with Agent-Based Modelling and a Particle Filter
in Journal of Artificial Societies and Social Simulation
Whipp A
(2021)
Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources
in ISPRS International Journal of Geo-Information
Crols T
(2019)
Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility.
in GeoInformatica
Heppenstall A
(2021)
Future Developments in Geographical Agent-Based Models: Challenges and Opportunities.
in Geographical analysis
Description | The most significant finding to date is that one of the sub-tasks (developing data assimilation methods for agent-based models) is much more complex than originally envisaged. I have recently been awarded €1.5M from the European Research Council (ERC) to continue this line of work in particular. This is becoming a new research area in itself! The agent-based model that we are developing for this project will still have utility, but it will be unlikely that the model will be optimised in real time. |
Exploitation Route | In the short term (within 6 months) we hope that the model that is being developed will be able to be used in practice in a small case study area (the town of Otley, West Yorkshire presently). This will be of benefit to the local Business Improvement District (BID) who are interested in better understanding how the town is used by residents. In the longer term, the new ERC-funded project called DUST (http://dust.leeds.ac.uk/) will continue to develope methods for data assimilation into agent-based models with the ultimate aim of creating accurate short-term forecasts of population dynamics in major cities. |
Sectors | Security and Diplomacy,Transport |
URL | http://surf.leeds.ac.uk/presentations.html |
Description | The SURF project aimed to create a simulation of an urban area and use real-time 'big' data to reduce the modelling errors (i.e. bring the model more in to line with reality). This was an ambitious goal, and one that required substantial methodological work to allow the chosen simulation method ('agent-based modelling') to adapt to streams of data (this is something that is not possible currently). Ultimately the methodological work was much more complicated than initially invisaged; so much so that the relevant work-package (which was originally scheduled for 6 months) was subsequently re-written as an entire project in its own right and has received €1.5M funding from the European Research Council over 5 years. Therefore although there is no social/economic impact at this point, the SURF project was the catalyst for the much larger ERC project, and hence any new impact that arrises between now and the end of the ERC project in 2022 can reliably be traced back to SURF. The methodological work is still ongoing through the ERC project and this section will be updated accordingly. |
First Year Of Impact | 2019 |
Description | Alan Turing Institute - Fellows and Turing sponsored projects |
Amount | £399,768 (GBP) |
Organisation | Alan Turing Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2019 |
End | 03/2021 |
Description | Knowledge Exchange Fellowship |
Amount | £10,877 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2018 |
Description | Responsive mode impact fund |
Amount | £12,853 (GBP) |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2018 |
Description | Starting Grant |
Amount | € 1,500,000 (EUR) |
Funding ID | 757455 |
Organisation | European Research Council (ERC) |
Sector | Public |
Country | Belgium |
Start | 01/2018 |
End | 12/2022 |
Description | Transforming Urban Simulation Methods through Real-Time Data Assimilation |
Amount | £105,351 (GBP) |
Organisation | University of Leeds |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2019 |
End | 10/2019 |
Title | Leeds City Council Footfall Camera Aggregated Data |
Description | The dataset contains footfall counts in Leeds City Centre supplied by Leeds City Council. There are 10 cameras around the city that monitor numbers of people walking past. These cameras calculate numbers on an hourly basis. The raw data is freely available on Data Mill North; however a cleaned and aggregated dataset has been produced for the purposes of analysis by Leeds Institute for Data Analytics in collaboration with the Consumer Data Research Centre. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | n/a |
URL | https://data.cdrc.ac.uk/dataset/leeds-city-council-footfall-camera-aggregated-data |
Title | Manually Collected Footfall Counts in Leeds |
Description | Manual footfall counts (counts of people who walk past a particular point) were collected at ten sites between the 5th to the 9th of July 2021 between 10:00 and 16:00 each day. At the time of data collection, footfall cameras were installed at three of the ten sites: Briggate, Headrow and Commercial Street. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | n/a |
URL | https://data.cdrc.ac.uk/dataset/manually-collected-footfall-counts-leeds |
Description | LCC Footfall |
Organisation | Leeds City Council |
Country | United Kingdom |
Sector | Public |
PI Contribution | I, with the support of an intern, am analysing footfall data provided by the Council to better understand changes in patterns of footfall over the last 5+ years, and the factors that encourage or discourage attendance in the city centre. |
Collaborator Contribution | The Council (Brereton) are providing specific, expert knowledge about Leeds footfall as well as data. |
Impact | None yet - ongoing |
Start Year | 2016 |
Description | MIT Senseable Cities Lab |
Organisation | Massachusetts Institute of Technology |
Department | Senseable City Lab |
Country | United States |
Sector | Academic/University |
PI Contribution | I visited the MIT Senesable Cities laboratory for two months over the summer in 2016 to collaborate on work focussed on simulating and understanding mobility. I am leading a paper that uses data owned by the lab to better understand urban dynamics. |
Collaborator Contribution | Senseable Cities hosted the visit, gave me the opportunity to collaborate with their researchers, and provided access to their data. |
Impact | Outputs still under development |
Start Year | 2016 |
Description | A little less conversation* and more [impact and citations] please |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Postgraduate students |
Results and Impact | Invited to present at the ARCC Network event for postgraduate students: "Showcasing research to deliver impact" |
Year(s) Of Engagement Activity | 2017 |
Description | Agent-Based Modelling, the Next 20 Years: Dynamic Data Assimilation (RGS 2016) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | I presented the ongoing surf work at the Royal Geographical Society Annual International Conference (30 August - 2 September) as part of a session entitled "The Future of Geocomputation" |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-08-rgs-geocomputation.html |
Description | Collaboration with Improbable |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Industry/Business |
Results and Impact | One week visit to Improbable to demonstrate the emerging agent-based model of urban movements and experiment with their SpatialOS platform. |
Year(s) Of Engagement Activity | 2015 |
URL | http://surf.leeds.ac.uk/announce/2015/12/10/ImprobableSim.html |
Description | Dynamic, data-driven, agent-based modelling for simulating cities: Understanding Crime and Pollution |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited presentation to the Office for National Statistics (ONS) Big Data Campus, Titchfield. 1 December 2016. |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-12-01-ONS_Data_Campus.html |
Description | Exploring the Impact of Ambient Population Measures on Crime Hotspots (AAG 2016) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Around 20 people attended a session at the Association of American Geographers International Conference that I helped to organise. |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-03-ambient_population.pdf |
Description | Forecasting Short-Term Urban Dynamics: Data Assimilation for Agent-Based Modelling. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentation to the Social Simulation Conference - the 13th Annual Conference of the European Social Simulation Association (ESSA). 25-29 September 2017, Dublin, Ireland. Slides available: http://surf.leeds.ac.uk/p/2017-09-26-essa-da.html |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-09-26-essa-da.html |
Description | Invited NSCR (Amsterdam) seminar: "Crime Analytics and the Role of Dynamic Simulation Models" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | Invited presentation to the Netherlands Institute for the Study of Crime and Law Enforcement (NSRC) - 21st November, 2017, Amsterdam. |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-11-20-NSCR-Crime_Analytics_ABM.html |
Description | ONS Big Data Campus visit and presentation |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Met with members of the Big Data and Crime teams of the ONS to discuss the implications for this research. Will hopefully lead to further collaborations around modelling/quantifying ambient populations. |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-12-01-ONS_Data_Campus.html |
Description | Organised AAG 2015 Session: "Geosimulation and Big Data: A Marriage made in Heaven or Hell?" |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Type Of Presentation | workshop facilitator |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | We organised three sessions at the conference which were all well attended. The last session lead to a discussion that lasted an hour or so. n/a |
Year(s) Of Engagement Activity | 2015 |
Description | Predictive data analytics for urban footfall - presentation to Leeds City Council |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Policymakers/politicians |
Results and Impact | I presented the outcomes of a 4-month project, conduced in collaboration with Leeds City Council, to better understand footfall in Leeds city centre using data sourced from the Council's footfall cameras. The work was conducted by a Leeds Institute for Data Analytics intern, Molly Asher. It was very well received by the Council and I hope that once the work is complete it will lead to policy impacts. In particular, it might change the ways that the Council assess the success of events that they organise (in terms of footfall attracted). |
Year(s) Of Engagement Activity | 2017 |
Description | Presentation to the Association of American Geographers (AAG) Annual Meeting 2015 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Type Of Presentation | paper presentation |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Talk sparked questions and discussion afterwards n/a |
Year(s) Of Engagement Activity | 2015 |
Description | Presentation to the Crime and Policing Analysis group at the Home Office, London. 15th November 2016. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | Around 15 practitioners from the Home Office attended a talk that I gave entitled "Crime Mapping, Crime Predictors, and Crime Simulation". The discussion afterwards centred on the different methods used to quantify the ambient population and the impacts for crime rates. |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-11-15-HomeOffice-CrimeMapping.html |
Description | Quantifying Personal Pollution Impacts to Inform Transport Scheme Innovation through New Generation Mobility Data |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentation to the European Colloquium on Theoretical and Quantitative Geography (ECTQG) 7-11 September, 2017, York, UK. |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-09-08-ectqg-surf.pdf |
Description | Quantifying the Ambient Population using Big Data and Agent-Based Modelling |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentation to the European Colloquium on Theoretical and Quantitative Geography (ECTQG) 7-11 September, 2017, York, UK |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-09-11-ectqg-surf.pdf |
Description | Simulating Urban Flows to Estimate the Disease Burden of Air Pollution (RGS 2016) |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | I presented at the Royal Geographical Society Annual International Conference in London (30 August - 2 September) as part of a session that I organised entitled "Urban Analytics". |
Year(s) Of Engagement Activity | 2016 |
URL | http://surf.leeds.ac.uk/p/2016-08-rgs-urban_analytics.html |
Description | Simulation as the Catalyst for Geographical Data Science and Urban Policy Making. Presentation to the Association of American Geographers (AAG), 4th - 8th April, Boston, as part of the Special Sessions on Geographic Data Science |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation at an academic conference: Simulation as the Catalyst for Geographical Data Science and Urban Policy Making. Presentation to the Association of American Geographers (AAG), 4th - 8th April, Boston, as part of the Special Sessions on Geographic Data Science |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-04-06-aag-sim_as_catalyst.html |
Description | Understanding Input Data Requirements and Quantifying Uncertainty for Successfully Modelling 'Smart' Cities. |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Presentation to the 3rd International Workshop on Agent-Based Modelling of Urban Systems (ABMUS), part of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018). 10-15 July, Stockholm |
Year(s) Of Engagement Activity | 2018 |
URL | https://urban-analytics.github.io/dust/p/2018-07-15-abmus-da.html |
Description | Urban Analytics: Example projects from LIDA |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited presentation to the Office for National Statistics, 27th July, Titchfield. |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-07-27-ONS-LIDA-UrbanAnlalytics.html |
Description | Virtuocity Future Transport System Design - Stakeholder Engagement Workshop |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | A workshop at the Transport Systems Catapault to present the Synergy PRIME project. That project aims to integrate the SURF model with others. The aim of the workshop was to discuss these models, and plans for the future of transport systems more generally. |
Year(s) Of Engagement Activity | 2019 |
Description | Workshop - AGILE PhD School |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | The AGILE PhD school brings together PhD students from different countries. The purpose of the school is for PhD students to exchange ideas. This will allow them to: • Gain insight about the nature of GI science is, to be exposed to the diversity of fields and common features of our domain • Exchange about the PhD process itself • Develop their own networks The school offers a platform for PhD students to present their research to date, their future activities and critically to discuss the GI-related methods and the context for their research. I ran an Expert Session entitled "Hands on practical on Agent Based Modelling in LIDA" |
Year(s) Of Engagement Activity | 2017 |
URL | http://surf.leeds.ac.uk/p/2017-10-ABM_AGILE_Summer_School.html |