Dynamic, data-driven, agent-based modelling for simulating cities:

Understanding Crime and Pollution


Nick Malleson

School of Geography, University of Leeds

nickmalleson.co.uk

These slides: http://surf.leeds.ac.uk/presentations.html

Abstract

An understanding about how people move around cities is vital for building up reliable estimates of the population at risk for phenomena that vary in space and time. For example, to reliably assess the disease burden of air pollution or the risk of crime victimisation, it is important to understand when and where the activities of individuals intersect with areas of potential harm. However, modelling dynamic populations is extremely difficult as most well-established data sources contain very little information about non-residential activities. Fortunately, emerging ‘big' data sources such as those arising through the use of mobile telephones, social media, or loyalty cards hold the promise of providing more reliable information about non-residential daily activities. The challenge, therefore, is to create a high-resolution model of urban flows that is able to take advantage of good quality residential and non-residential data sources.

This presentation will discuss an ongoing research project called Simulating Urban Flows (surf) that is using agent-based modelling as the tool to bring together varied data sources and create an accurate, high-resolution model of individual-level daily urban flows. Its vision is for a comprehensive model that is able to simulate the movements of individual synthetic people and subsequently build up accurate, dynamic footfall estimates. These will be compared to air pollution hotspots and to crime patterns to better understand how mobile populations are affected by these problems.

Abstract

Ambient populations - why they are important

Implications for crime and pollution

How to measure ambient populations

A better way (?) to model the ambient population: agent-based modelling

Data assimilation for ABM

Conclusions

computer room

First: About me

Education

BSc: Computer Science

MSc: GeoInformatics

PhD: (Computational) Human Geography

Now: Associate Professor in Geographical Information Science

Research Interests:

Agent-Based Modelling

Spatial analysis and simulation

Crime patterns / environmental criminology

Big data (particularly for social simulation)

Simulating Urban Flows

3-year research fellowship, funded by the ESRC (UK)

Build an agent-based simulation of daily urban dynamics

Calibrated using a combination of traditional sources (e.g. census) with dynamic, crowd-sourced data

Context - Big Data & Smart Cities

Image of big data globe (from Science Daily).

Explosion in data volume.

'Datafication' of hitherto private thoughts/actions.

Transformational impact on social sciences.

Smart cities & a new generation of models to understand cities

A greater role for (e.g.) ABM

What to do with all that data?

 

 

 

 

 

 

What to do with all that data?

Modelling the Ambient population

Surprisingly poor data to quantify mobile populations

Difficulties in designing policy

E.g. urban renewal / regeneration

Intellectual interest

E.g. Equality, accessibility, mobility

The Ambient Population

Crime Victims

What is the most appropriate denominator for crime rate calculations?

Residential population is the most common

But not always appropriate

Daily flows of people significantly impact crime

Difficult to quantify hotspot severity without good population at risk estimates

Malleson, N. and M. Andresen (2015) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science 42(2)
Hotspot disappearing from the city centre
Malleson, N. and M. Andresen (2015) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science 42(2)
Hotspot disappearing from the city centre
Malleson, N. and M. A. Andresen (2015a) Spatio-temporal crime hotspots and the ambient population. Crime Science 4(10).
Hotspot shifting from the city centre in the evening
Malleson, N., and M. A. Andresen (2016) Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice 46: 52-63
Different data sources used to measure the 'ambient' population - mobile phones, twitter, the census, pop247.

The Ambient Population

Impacts of Pollution

Largest cause of preventable deaths (WHO)

Improved models of pollution generation and dispersal

Including individual polluters (Nyhan et al., 2016)

But still relatively weak estimates of the population-at-risk

Individual Exposure

Related Work - Modelling dynamic /individual exposure

Travel diaries (& GPS tracking)

Calculate personal exposure (Yoo et al., 2015)

Travel surveys

Coupled activity model and dispersion model (Beckx et al., 2009, Dhondt et al, 2012, Setton et al., 2011)

Bulk mobile phone activity

. . .

Individual Exposure

Related Work - Modelling dynamic /individual exposure

Improved pollution models

Nyhan et al. (2016): large taxi data set to estimate street segment pollution (highly detailed: factored in acceleration!)

Consistent finding: Residential models underestimate exposure

Opportunity to use simulation to:

Combine data sources

Scale-up smaller surveys

Urban Flow Data

The ambient population is important

But how to quantify it?

And how to better understand urban flows?

Urban Flow Data

Social Media

Example activity spaces derived from twitter messages
Malleson, N and M. Birkin. (2014) New Insights into Individual Activity Spaces using Crowd-Sourced Big Data. Paper presented at the 2014 BigData conference, Stanford, CA, USA, 27-31 May. [paper (pdf)][slides (html)].
Evaluating aggregate mobile phone population accuracy
Comparing census daytime populations to aggregate mobile phone estimates

Urban Flow Data

How to quantify the ambient population and urban flows?

Mobile Communications

Large population coverage

Comparing census daytime populations to aggregate mobile phone estimates

Private, unknown methodology, privacy concerns, coarse resolution (?)

Traces of movement from a smart-phone app

Urban Flow Data

How to quantify the ambient population and urban flows?

Geo-Apps

Smart-phone apps that capture movement / location are becoming ubiquitous

Great potential for understanding (some) urban dynamics

Urban Flow Data

Difficulties

Skewness

Prolific users distort patterns

Representation

Online & public ≠ offline & private

Spatial accuracy

Bias

Participation inequality and the digital divide

Complicated!!

Messy, and "too big for Excel"

Benefits

Volume

Potential for large sub-samples

Velocity

Streaming / regularly updated

Potential for dynamic models

Need to consolidate diverse sources

Solution: Agent-Based Modelling ?

Agent-Based Modelling (ABM)

The Sims

Model the individual components that drive system behaviour directly

Autonomous, interacting 'agents'

Can model emergence, non-linearity, and other features of complex systems

Basic execution process

t=0 (initialisation) : create a population of agents and their environment

Each agent has variables that represent their state and rules to control their behaviour

t+1 : Each agent executes its behavioural rules and updates its state

This can involve moving, interacting with other agents, performing an action etc.

ABM Explanatory Example

Birks (2012) model environment

Birks et al. (2012)

Randomly generated abstract environments

Theoretical 'switches'

Rational choice perspective

Routine activity theory

Geometric theory of crime

Example crime theories

Validation against stylized facts:

Spatial crime concentration

Repeat victimisation

Journey to crime curve

ABM Predictive Example

Malleson, N., A. Heppenstall, L. See, A. Evans (2013) Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environment and Planning B: Planning and Design 40 405-426. [DOI: 10.1068/b38057]

 

 

 

 

 

 

 

 

 

Agent-Based Modelling

Appeal:

Modelling complexity, non-linearity, emergence

Natural description of a system

Bridge between verbal theories and mathematical models

Produces a history of the evolution of the system

Agent-Based Modelling

Will I play with the truck, or the duck?

Both!

Difficulties:

Stochasticity

Computationally expensive (not amenable to optimisation)

Complicated agent decisions

Lots of decisions!

Multiple model runs (robustness)

Modelling "soft" human factors

Need detailed, high-resolution, individual-level data

Simulating Urban Flows

3-year research fellowship, funded by ESRC (UK)

Build an agent-based simulation of daily urban dynamics

Calibrated using a combination of traditional sources (e.g. census) with dynamic, crowd-sourced data

New insights into urban mobility patterns and footfall estimates.

surf.leeds.ac.uk

surf aims

surf ABM

Simulate daily urban dynamics

Shopping, commuting, education, etc.

Better understand daily urban mobility patterns

(Exploring the use of Improbable's SpatialOS)

 

 

 

http://surf.leeds.ac.uk/announce/2015/12/10/ImprobableSim.html

Dynamic Data Assimilation

Incorporate data into models dynamically (c.f. meteorology models)

Preliminary example using an Ensemble Kalman Filter (EnKF)

Novel for ABM

Ward, J., A. Evans, N. Malleson (2016) Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science. 3:150703. (open access). [DOI: 10.1098/rsos.150703]

Last slide - LIDA and the CDRC

Leeds Institute for Data Analytics (LIDA)

Consumer Data Research Centre (CDRC)

Multi-million £ investments from Leeds and UK research councils

Collaborative space for big data analytics

Attract expertise from medicine/health, computer science, geography, mathematics, business ...

Thank you!


For more information:
My website: nickmalleson.co.uk/
(or www.geog.leeds.ac.uk/people/n.malleson/)
Simulating Urban Flows: surf.leeds.ac.uk/
These slides: surf.leeds.ac.uk/presentations.html