BSc Computer Science

MSc Multidisciplinary Informatics

PhD (Computational) Geography

Main Interests

Agent-based modelling

Crime analysis

Analysis of 'big' social data to learn about how cities work










Why visit SENSEable Cities?

World leader in understanding cities through novel data analysis

Find out how you interrogate cities through ‘big’ data

Collaborate on a particular case study area?

Malleson, N. and M. Andresen (2015b) 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) 112-121. [DOI: 10.1080/15230406.2014.905756]

Context: Crime Analysis

Good crime data, but poor understanding about population-at-risk

Need accurate, high-resolution footfall estimates

Applicable to other critical areas, e.g. pollution

... more on this shortly ...

Agent-Based Modelling

Autonomous, interacting 'agents'

Model phenomena from the 'bottom-up'


Modelling complexity, non-linearity, emergence

Natural description of a system

Bridge between verbal theories and mathematical models

History of the evolution of the system

Burglary ABM

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, Theory, Data

Natural description: Easy to incorporate social/behavioural theory

BUT: Need detailed, high-resolution, individual-level data

Advantages of model complexity and flexibility are tempered by the difficulties in finding suitable values for model parameters based on empirical evidence.

'Big' data can help (?)

Individual behaviour/actions from big 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 aims

surf ABM


Simulate daily urban dynamics

Shopping, commuting, education, etc.

Better understand daily urban mobility patterns

(Exploring the use of Improbable's SpatialOS)

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]

Work with SENSEable

Broad aim of research: Use dynamic data to calibrate a city-wide activity model

Simple case study: Breeze model

Activity patterns have broad regularities, but vary with (e.g.) seasonal trends

Build a model of traditional commuting behaviour (to MIT campus?)

Calibrate the model using Breeze traces.

That should help to account for long-term trends

Ultimately try to answer: where is someone when their app is off?

... or something else ??

CDR data

Broader patterns in human mobility


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: