computer room

First: About me


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

Visiting SENSEable Cities Lab in MIT

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. 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.
Polluted city

Source: dreamingyakker (CC BY-NC-ND 2.0).

The Ambient Population

Impacts of Pollution

Largest cause of preventable deaths (WHO)

Improved models of pollution generation and dispersal

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

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?


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

Great potential for understanding (some) urban dynamics

Urban Flow Data



Prolific users distort patterns


Online & public ≠ offline & private

Spatial accuracy


Participation inequality and the digital divide


Messy, and "too big for Excel"



Potential for large sub-samples


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


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?




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


Big Data, ABM, Smart Cities, Ambient Populations

A city-wide dynamic ABM, constantly re-calibrated to streaming data

New insights into urban mobility patterns and footfall estimates.

Source: Improbable Spatial OS

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:
Simulating Urban Flows:
These slides: