Bryan, Dominic

Davies, Gemma

Dixon, John

Hocking, Bree

Huck, Jonny

Jarman, Neil

Sturgeon, Brendan

Whyatt, Duncan

English

The University of Manchester - Institutional Repository

The University of Manchester ResearchAn Empirical Agent Based Modelling approach toEvaluating Intergroup Contact TheoryDocument VersionFinal published versionLink to publication record in Manchester Research ExplorerCitation for published version (APA):Huck, J., Whyatt, D., Dixon, J., Davies, G., Sturgeon, B., Hocking, B., Jarman, N., & Bryan, D. (2021). An EmpiricalAgent Based Modelling approach to Evaluating Intergroup Contact Theory. In N. Pinto, Y. Gamal, & H. Odell(Eds.), European Colloquium on Theoretical and Quantitative Geography 2021: Book of Extended Abstracts (pp.31)Published in:European Colloquium on Theoretical and Quantitative Geography 2021Citing this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:08. Jun. 2022Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   1 An Empirical Agent Based Modelling approach to Evaluating Intergroup Contact Theory  Huck J.J.1, Whyatt, J.D.2, Dixon, J.3, Davies, G.4, Sturgeon, B.5, Hocking B., Jarman, N.6, Bryan, D.7   1 MCGIS, Department of Geography, The University of Manchester Oxford Road, Manchester, M13 9PL, UK jonathan.huck@manchester.ac.uk  (correspondent author) 2,4 Lancaster Environment Centre, Lancaster University Library Avenue, Lancaster, LA1 4YQ, UK d.whyatt@lancaster.ac.uk, gemma.davies@lancaster.ac.uk  3 Department of Psychology, The Open University  Walton Hall, Milton Keynes, MK7 6AA, UK  john.dixon@open.ac.uk  5, 7 Department of Anthropology, Queens University University Rd, Belfast, BT7 1NN, UK  b.sturgeon@qub.ac.uk, d.bryan@qub.ac.uk   6 Peace Direct  Second Floor, 72-74 Mare St, London E8 4RT, UK  n.jarman@conflictresearch.org.uk  Keywords: Contact Hypothesis, Bayesian, Segregation, Activity Space, Belfast Introduction Intergroup contact theory (ICT) states that if contact occurs between members of diverse groups under reasonably favourable conditions then it will reduce intergroup prejudice (Allport, 1954). ICT remains one of the most strongly supported theories in social psychology, and a fundamental component in the study of segregation (Pettigrew, 2021). However, the implications of this effect at the population level have yet to be examined in detail. There is a long history of the study of segregation using ABM approaches, dating back to Schelling (1969; 1971), but these studies generally remain focused only on the production of residential segregation, in spite of an increasing recognition that segregation affects individuals across a range of socio-geographical spaces (Wong and Shaw, 2011; Farber et al., 2012; Huck et al., 2019). This paper briefly describes the use of an empirical ABM in order to evaluate the progression of activity space segregation and implications of ICT using a case study location in North Belfast, which is a community with a long history of segregation and sectarian violence. Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   2 Method  Figure 1: Input Datasets: (A) Census; (B) Survey; (C) PGIS; (D) GPS Traces; reproduced from Huck et al. (2021).  This study relies upon four key datasets:  1. Northern Ireland Small Area (SA) data of the 2011 census was obtained from NISRA1, including the percentage of Catholic, Protestant and ‘Other’ residents in each area (Figure 1A).   1 https://www.nisra.gov.uk/support/geography/northern-ireland-small-areas Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   3 2. A paper-based questionnaire was administered to 488 participants (242 Catholic, 246 Protestant) in a door-to-door survey, exploring various aspects of identity, perceptions and experiences of segregation in Belfast (Dixon et al., 2020a; 2020b) (Figure 1B).  3. A Participatory GIS (PGIS) dataset was collected using the freely available Map-Me platform2 (Huck et al., 2014; 2019). 33 participants (14 Catholic, 17 Protestant, 2 ‘Other’) used the system to define places that they consider to be Catholic, Protestant, and Mixed (Figure 1C).  4. GPS traces were collected using a bespoke Android mobile application3 from 197 participants (93 Catholic, 92 Protestant, and 12 ‘Other’) for a period of up to 14 days (Dixon et al., 2020a) and cleaned as per Davies et al. (2017) (Figure 1D). Modelling Community Belonging The experience of segregation is incompatible with the official administrative boundaries with which communities are frequently represented by authorities and researchers (Huck et al., 2019). In order to better reflect community understandings of territory, we build upon the ideas posited by Huck et al. (2018) by using Bayesian Inference to produce evidence-based surfaces of community belonging to define territorial patterns across the study area. Formally, these surfaces represent the probability that the next individual observed at a given location will from a given group; but here are used as a proxy for the extent to which a location might be considered to be part of Catholic or Protestant group territories. A separate model was used for each cell in a continuous raster surface with 20m resolution. ‘Prior’ belief values for each model taken from the census data, after which evidence is iteratively added from the Survey, PGIS and GPS data using an IDW2 approach. Each set of ‘evidence’ was treated as a multinomial distribution of observations, with the parameter vector drawn from a Dirichlet distribution. Finally, we used a Markov Chain Monte Carlo (No U-Turn Sampler) approach to estimate the posterior distribution from 1000 sample draws. We took the mean and credible interval (CI; 95% of the values around the mean) from the resulting distribution, giving both estimated values and uncertainties for each cell in the output surface. This analysis was implemented in Python4 using a High-Performance Computing (HPC) facility. The resulting surfaces are illustrated in Figure 2.  2 http://map-me.org 3 https://github.com/jonnyhuck/bmp-pathways-app 4  https://github.com/jonnyhuck/bmp-community-belonging  Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   4  Figure 2: Probabilistic community belonging surfaces describing the degree to which a point in space ‘belongs’ to Catholic / Protestant groups; reproduced from Huck et al. (2021).  Agent Based Model These community belonging surfaces (Figure 2) were used alongside the GPS data to create an empirical ABM of individual behaviour in the study area. Each individual agent’s behaviour is determined by a set of values drawn from probability distributions for a range of variables including group; gender; tolerances residence location, movement and destination location (based on the community belonging surface for the ‘other’ group); number, type, timing and mode of daily journeys; car ownership; walking speed etc. Distributions were created through analysis of the GPS, Survey and community belonging datasets. Routing for agent journeys is performed using modified implementation of the A* algorithm (Hart et al., 1968), which identifies the shortest route within the agents’ specified tolerances. The model runs in ‘steps’ of 1 minute (in ‘model time’), with segregation evaluated for each agent at each step using the interaction index proposed by Palmer (2013) as a generalised index for activity-space segregation (Equation 1).  𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 =  ∑𝑦𝑖𝑏𝑖𝑛𝑛𝑖=1 Equation 1. Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   5 Where: 𝑦𝑖 is the number of out-group person-hours that fall within individual 𝑖’s personal interaction space; 𝑏𝑖 is the number of person-hours of both groups that fall within individual 𝑖’s personal interaction space; and 𝑛 is the total in-group members. In order to maximise efficiency, the model runs on an HPC facility, with agent movements at each step undertaken in parallel, whilst contact encounters and interaction metrics are calculated in series after all movements are complete. Positive and negative contact encounters affect agent tolerances based on effect sizes reported in the literature, and therefore influence their future behaviour. The mean of all agents’ interaction scores is recorded each step as a measure of ‘overall’ segregation for the whole area. Prior to the introduction of any interventions, model performance will be validated through comparison of the interaction index profile of the agents against that generated using the ‘reference’ dataset of GPS traces. This will permit us to confirm that the simulation ‘baseline’ is reflective of the input datasets prior to the introduction of any interventions and examination of their impact on the temporal profile of segregation values.  Conclusion This research has the potential to reveal novel insights into the implications of ICT at the population level, thus advancing our understanding of how ICT might be deployed in order to influence patterns of sharing and segregation through policy intervention.  Acknowledgments This research was conducted as part of the Belfast Mobility Project (https://belfastmobilityproject.org), funded by the ESRC (ES/L016583/1). The authors thank the residents of North Belfast who contributed openly and generously to this study. References Allport GW (1954) The Nature of Prejudice. Boston: Addison-Wesley. Davies G, Huck J, Whyatt D, et al. (2017) Belfast mobility: Extracting route information from GPS tracks. Dixon J, Tredoux C, Davies G, et al. (2020a) Parallel lives: Intergroup contact, threat, and the segregation of everyday activity spaces. Journal of personality and social psychology 118(3): 457-480. Dixon J, Tredoux C, Sturgeon B, et al. (2020b) ‘When the walls come tumbling down’: The role of intergroup proximity, threat, and contact in shaping attitudes towards the removal of Northern Ireland’s peace walls. British Journal of Social Psychology 59(4): 922-944. Farber S, Páez A and Morency C (2012) Activity spaces and the measurement of clustering and exposure: A case study of linguistic groups in Montreal. Environment and Planning A 44(2): 315-332. Hart PE, Nilsson NJ and Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics 4(2): 100-107. Huck et al. An ABM approach to Intergroup Contact Theory Book of Abstracts of ECTQG2021, Manchester, United Kingdom, 3 to 5 November 2021   6 Huck J, Whyatt J and Coulton P (2014) Spraycan: A PPGIS for capturing imprecise notions of place. Applied Geography 55: 229-237. Huck J, Whyatt J, Dixon J, et al. (2021) Empirical Agent-Based Modelling for exploring Intergroup Contact in a Segregated Society. Proceedings of the 29th Geographical Information Science Research UK Conference. Huck JJ, Whyatt JD, Dixon J, et al. (2019) Exploring segregation and sharing in Belfast: A PGIS approach. Annals of the American Association of Geographers 109(1): 223-241. Huck JJ, Whyatt JD, Hocking B, et al. (2018) A Bayesian Approach to ‘Community Belonging’ and Segregation in a Divided City. In: The 26th Annual GIS Research UK Conference, Leicester, UK. Palmer JR (2013) Activity-space segregation: Understanding social divisions in space and time. Princeton University. Pettigrew TF (2021) Advancing intergroup contact theory: Comments on the issue's articles. Journal of Social Issues. Schelling TC (1969) Models of segregation. The American Economic Review 59(2): 488-493. Schelling TC (1971) Dynamic models of segregation. Journal of Mathematical Sociology 1: 143-186. Wong DW and Shaw S-L (2011) Measuring segregation: An activity space approach. Journal of Geographical Systems 13(2): 127-145.  

An Empirical Agent Based Modelling approach to Evaluating Intergroup Contact Theory

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An Empirical Agent Based Modelling approach to Evaluating Intergroup Contact Theory

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