Microscopic simulations of complex metropolitan dynamics

In this paper we will introduce an individual-based model of a British city. The approach draws its inspiration from both microsimulation and agent-based modelling. Microsimulation is used to reconstruct the entire population of a city region at both the household and individual scale. We illustrate this for Leeds, a city with three-quarters of a million inhabitants grouped into more than 300 thousand households. The resulting population is profiled by demographic and social attributes which are richly specified. In order to incorporate dynamic individual behaviour, we argue that agent-based simulations are more appropriate, and rules will be presented which allow the identi�cation of �ve essential behaviours which we term domestic living, education, work, recreation and shopping. Through this modelling process we seek not just to understand residential patterns within the city, but the dynamic ebb and flow of the population in everyday metropolitan life. The novel feature of our research is that we will use up-to-date social network data to calibrate our agent behaviours. Although social network data are likely to be somewhat skewed and unreliable, they are abundant and continually refreshed and also provide temporally-accurate daily behavioural information that are often absent from traditional sources (such as censuses). We will make an attempt to evaluate the robustness and (potential) value of this approach

Agent-based modeling and the city: A gallery of applications

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities

Handling the MAUP: methods for identifying appropriate scales of aggregation based on measures on spatial and non-spatial variance

The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models

A dynamic microsimulation model for epidemics.

Funder: Aerospace Technology InstituteFunder: UK Research and InnovationFunder: The Alan Turing InstituteA large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations

Geovisualisation, spatial analysis and simulation

This talk was presented at the 5th ESRC Research Methods Festival in July 2012