Abstract

An understanding of 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 on a population it is important to understand when and where the activities of individuals intersect with pollution hotspots. However, modelling dynamic populations is extremely difficult as most well-established data sources contain sparse information about non-residential activities. Fortunately, emerging data sources such as those arising through the use of 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 research proposes an original approach: to use agent-based modelling as the tool to bring together disparate data sources and create an accurate, high-resolution model of individual-level daily urban flows. The model will draw on advances from disciplines such as meteorology to dynamically calibrate the agent-based model as new data streams become available. Ultimately the results of the simulation will be married to estimates of pollutant concentrations to estimate the disease burden of some pollutants. Preliminary results illustrate that the model holds promise as a reliable tool for modelling the ambient urban population.

Outline

Context: The Ambient Population

Crime victimisation

Disease burden of pollution

Quantifying the ambient population

A new approach to merging urban flow data: Agent-Based Modelling?

Conclusions and outlook

The 'Age of Big Data'

Explosion in data volume.

'Datafication' of hitherto private thoughts/actions.

Proliferation of GPS-enabled devices

Opportunity to better understand urban flows

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

Difficult to quantify hotspot severity without good population at risk estimates

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

Urban Flow Data

Social Media

Urban Flow Data

Mobile Communications

Large population coverage

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

Urban Flow Data

Geo-Apps

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

Great potential for understanding (some) urban dynamics

Urban Flow Data

Agent-Based Modelling (ABM)

Autonomous, interacting 'agents'

Model phenomena from the 'bottom-up'

Advantages:

Modelling complexity, non-linearity, emergence

Natural description of a system

Bridge between verbal theories and mathematical models

History of the evolution of the system

Dynamic Data Assimilation

Ensemble Kalman Filter (EnKF)

Outline

References

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