Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-23T08:53:37.391Z Has data issue: false hasContentIssue false

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

Published online by Cambridge University Press:  06 October 2021

Eva-Maria Griesbauer*
Affiliation:
University College London, London, UK.
Ed Manley
Affiliation:
University of Leeds, Leeds, UK.
Daniel McNamee
Affiliation:
University College London, London, UK.
Jeremy Morley
Affiliation:
Ordnance Survey, Southampton, UK
Hugo Spiers
Affiliation:
University College London, London, UK.
*
*Corresponding author. E-mail: [email protected]

Abstract

Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

Type
Research Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balaguer, J., Spiers, H., Hassabis, D. and Summerfield, C. (2016). Neural mechanisms of hierarchical planning in a virtual subway network. Neuron, 90(4), 893903. doi:10.1016/j.neuron.2016.03.037.CrossRefGoogle Scholar
Bomba, P. C. and Siqueland, E. R. (1983). The nature and structure of infant form categories. Journal of Experimental Child Psychology, 35(2), 294328.CrossRefGoogle Scholar
Brunec, I. K., Javadi, A. H., Zisch, F. E. and Spiers, H. J. (2017). Contracted time and expanded space: The impact of circumnavigation on judgements of space and time. Cognition, 166, 425432.CrossRefGoogle ScholarPubMed
Brunec, I. K., Ozubko, J. D., Ander, T., Guo, R., Moscovitch, M. and Barense, M. D. (2020). Turns during navigation act as boundaries that enhance spatial memory and expand time estimation. Neuropsychologia, 107437, doi:10.1016/j.neuropsychologia.2020.107437CrossRefGoogle ScholarPubMed
Büchner, S., Hölscher, C. and Strube, G. (2007). Path Choice Heuristics for Navigation Related to Mental Representations of a Building. In Proceedings of the European Cognitive Science Conference, 504509.Google Scholar
Byrne, R. W. (1979). Memory for urban geography. The Quarterly Journal of Experimental Psychology, 31(1), 147154. doi:10.1080/14640747908400714CrossRefGoogle Scholar
Campari, I. (1996). Uncertain boundaries in urban space. Geographic Objects with Indeterminate Boundaries, 2, 5769.Google Scholar
Chase, W. G. (1983). Spatial representations of taxi drivers. In: The Acquisition of Symbolic Skills. Boston, MA: Springer, 391405. doi:10.1007/978-1-4613-3724-9\_43CrossRefGoogle Scholar
Clements-Stephens, A. M., McKell-Jeffers, G. O., Maddux, J. M. and Shelton, A. L. (2011). Strategies for spatial organization in adults and children. Visual Cognition, 19(7), 886909. doi:10.1080/13506285.2011.595742CrossRefGoogle Scholar
Coluccia, E. and Louse, G. (2004). Gender differences in spatial orientation: A review. Journal of Environmental Psychology, 24, 329340. doi:10.1016/j.jenvp.2004.08.006CrossRefGoogle Scholar
Costa, M. and Bonetti, L. (2018). Geometrical distortions in geographical cognitive maps. Journal of Environmental Psychology, 55, 5369. doi:10.1016/j.jenvp.2017.12.004CrossRefGoogle Scholar
Coutrot, A., Silva, R., Manley, E., de Cothi, W., Sami, S., Bohbot, V. D., Wiener, J. M., Hölscher, C., Dalton, R. C., Hornberger, M. and Spiers, H. J. (2018). Global determinants of navigation ability. Current Biology, 28(17), 28612866.CrossRefGoogle ScholarPubMed
Epstein, D. G. (1973). Brasília, Plan and Reality: A Study of Planned and Spontaneous Urban Development. Berkley: University of California Press.Google Scholar
Filomena, G., Verstegen, J. A. and Manley, E. (2019). A computational approach to ‘The image of the City’. Cities, 89, 1425. doi:10.1016/j.cities.2019.01.006CrossRefGoogle Scholar
GOV.UK. (2020). Taxi and Private Hire Vehicle Statistics, England: 2020. Retrieved from: https://www.gov.uk/government/statistics/taxi-and-private-hire-vehicle-statistics-england-2020Google Scholar
Griesbauer, E. M., Manley, E., Wiener, J. M. and Spiers, H. J. (2021). Learning the Knowledge: How London taxi drivers build their cognitive map of London. bioRxiv.CrossRefGoogle Scholar
Hanson, B. A. and Seeger, C,J. (2016). Creating Geospatial Data with geojson.io. Extension and Outreach Publications. 128. Available at: https://lib.dr.iastate.edu/extension_pubs/128Google Scholar
Hillier, B. (2007). Space is the Machine: A Configurational Theory of Architecture. London: Space Syntax.Google Scholar
Hommel, B., Gehrke, J. and Knuf, L. (2000). Hierarchical coding in the perception and memory of spatial layouts. Psychological Research, 64(1), 110. doi:10.1007/s004260000032CrossRefGoogle ScholarPubMed
Horner, A. J., Bisby, J. A., Wang, A., Bogus, K. and Burgess, N. (2016). The role of spatial boundaries in shaping long-term event representations. Cognition, 154, 151164.CrossRefGoogle ScholarPubMed
Hurts, K. (2005). Common Region and Spatial Performance Using Map-like Displays. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 49(17). Los Angeles, CA: Sage Publications, 15931597. doi:10.1177/154193120504901720CrossRefGoogle Scholar
Javadi, A. H., Emo, B., Howard, L. R., Zisch, F. E., Yu, Y., Knight, R., Silva, P. S. and Spiers, H. J. (2017). Hippocampal and prefrontal processing of network topology to simulate the future. Nature Communications, 8(1), 111. doi:10.1038/ncomms14652CrossRefGoogle ScholarPubMed
Jiang, B. and Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B: Planning and Design, 31(1), 151162. doi:10.1068/b306CrossRefGoogle Scholar
Klippel, A., Knuf, L., Hommel, B. and Freksa, C. (2004). Perceptually induced distortions in cognitive maps. In: International Conference on Spatial Cognition, Berlin, Heidelberg: Springer, 204213. doi:10.1007/978-3-540-32255-9\_12Google Scholar
Layers of London. (2020). Retrieved from: https://www.layersoflondon.org/Google Scholar
TFL. (n.d.). Learn the Knowledge of London. Transport for London. Retrieved from: https://tfl.gov.uk/info-for/taxis-and-private-hire/licensing/learn-the-knowledge-of-londonGoogle Scholar
O'Brien, O. (2013). London's Roman roads. Mapping London, 25 February 2013. Retrieved from: https://mappinglondon.co.uk/2013/londons-roman-roads/.Google Scholar
Lordan, R. (2018). The Knowledge: Train Your Brain Like A Cabbie. London: Quercus.Google Scholar
Lynch, K. (1960). The Image of the City, Vol. 11. Cambridge: MIT Press.Google Scholar
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. and Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97(8), 43984403. doi:10.1073/pnas.070039597CrossRefGoogle ScholarPubMed
Maguire, E. A., Woollett, K. and Spiers, H. J. (2006a). London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis. Hippocampus, 16(12), 10911101.CrossRefGoogle Scholar
Maguire, E. A., Nannery, R. and Spiers, H. J. (2006b). Navigation around London by a taxi driver with bilateral hippocampal lesions. Brain, 129(11), 28942907. doi:10.1093/brain/awl286CrossRefGoogle Scholar
Malinowski, J. C. and Gillespie, W. T. (2001). Individual differences in performance on a large-scale, real-world wayfinding task. Journal of Environmental Psychology, 21(1), 7382. doi:10.1006/jevp.2000.0183CrossRefGoogle Scholar
Manley, E. (2014). Identifying functional urban regions within traffic flow. Regional studies. Regional Science, 1(1), 4042. doi:10.1080/21681376.2014.891649Google Scholar
Manley, E. J., Orr, S. W. and Cheng, T. (2015). A heuristic model of bounded route choice in urban areas. Transportation Research Part C: Emerging Technologies, 56, 195209. doi:10.1016/j.trc.2015.03.020CrossRefGoogle Scholar
Mapbox. (2020). Retrieved from: https://www.mapbox.com/Google Scholar
Masucci, A. P., Smith, D., Crooks, A. and Batty, M. (2009). Random planar graphs and the London street network. The European Physical Journal B, 71(2), 259271. doi:10.1140/epjb/e2009-00290-4CrossRefGoogle Scholar
Masucci, A. P., Arcaute, E., Hatna, E., Stanilov, K. and Batty, M. (2015). On the problem of boundaries and scaling for urban street networks. Journal of the Royal Society Interface, 12(111), 20150763. doi:10.1098/rsif.2015.0763CrossRefGoogle ScholarPubMed
McNamara, T. P. (1986). Mental representations of spatial relations. Cognitive Psychology, 18(1), 87121. doi:10.1016/0010-0285(86)90016-2CrossRefGoogle ScholarPubMed
McNamee, D. (2019). Hierarchical model-based policy optimization: From actions to action sequences and back. arXiv preprint arXiv:1912.01448.Google Scholar
McNamee, D., Wolpert, D. M. and Lengyel, M. (2016). Efficient state-space modularization for planning: Theory, behavioral and neural signatures. Advances in Neural Information Processing Systems, 29, 45114519.Google Scholar
Milgram, S. (1976). Psychological maps of Paris. In: Proshansky, H. M., Ittelson, W. H. and Rivlin, L. G. (eds.). Environmental Psychology: People and Their Physical Settings. 2nd ed., New York: Rinehart & Winston, 88113.Google Scholar
Okabayashi, H. and Glynn, S. M. (1984). Spatial cognition: Systematic distortions in cognitive maps. The Journal of General Psychology, 111(2), 271279. doi:10.1080/00221309.1984.9921116CrossRefGoogle ScholarPubMed
O'Keefe, J. and Nadel, L. (1978). The Hippocampus as a Cognitive Map. Oxford: Clarendon Press.Google Scholar
Open Geography Portal. (2020). Retrieved from: https://geoportal.statistics.gov.uk/Google Scholar
OS MasterMap Integrated Transport Network (ITN) Layer. (2018) Coverage: London, October 2018. Ordnance Survey, GB. Using: EDINA Digimap Ordnance Survey Service. Retrieved from: https://digimap.edina.ac.uk/Google Scholar
Pailhous, J. (1969). Représentation de l'espace urbain et cheminements. Le Travail Humain, 32, 87139.Google Scholar
Pailhous, J. (1984). The representation of urban space: Its development and its role in the organisation of journeys. Social Representations, 311–327.Google Scholar
Palominos, N. and Smith, D. A. Identifying and Characterising Active Travel Corridors for London in Response to Covid-19 Using Shortest Path and Streetspace Analysis. Working Papers Series. Centre for Advanced Spatial Analysis, University College London (2020). Retrieved from: https://www.ucl.ac.uk/bartlett/casa/sites/bartlett/files/casa_working_paper_222_2.pdf.Google Scholar
Robinson, A. T. (2020).The impact of spatial boundaries on wayfinding and landmark memory: a developmental perspective. Ph.D. thesis, University of Alabama, U.S.A.Google Scholar
Schick, W., Halfmann, M., Hardiess, G., Hamm, F. and Mallot, H. A. (2019). Language cues in the formation of hierarchical representations of space. Spatial Cognition & Computation, 19(3), 252281. doi:10.1080/13875868.2019.1576692CrossRefGoogle Scholar
Spiers, H. J. and Maguire, E. A. (2006). Thoughts, behaviour, and brain dynamics during navigation in the real world. Neuroimage, 31(4), 18261840.CrossRefGoogle ScholarPubMed
Spiers, H. J. and Maguire, E. A. (2007). A navigational guidance system in the human brain. Hippocampus, 17(8), 618626.CrossRefGoogle ScholarPubMed
Spiers, H. J. and Maguire, E. A. (2008). The dynamic nature of cognition during wayfinding. Journal of Environmental Psychology, 28(3), 232249.CrossRefGoogle ScholarPubMed
Stevens, A. and Coupe, P. (1978). Distortions in judged spatial relations. Cognitive Psychology, 10(4), 422437. doi:10.1016/0010-0285(78)90006-3CrossRefGoogle ScholarPubMed
MOLA. (2014). The London Evolution Animation. Museum of London Archaeology. Retrieved from: https://www.mola.org.uk/london-evolution-animationGoogle Scholar
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189.CrossRefGoogle ScholarPubMed
Tversky, B. (1981). Distortions in memory for maps. Cognitive Psychology, 13(3), 407e433. doi:10.1016/0010-0285(81)90016-5CrossRefGoogle Scholar
Tversky, B. (1992). Distortions in cognitive maps. Geoforum, 23(2), 131e138. doi:10.1016/0016-7185(92)90011-RCrossRefGoogle Scholar
van der Ham, I. J. and Claessen, M. H. (2020). How age relates to spatial navigation performance: Functional and methodological considerations. Ageing Research Reviews, 58, 101020. doi:10.1016/j.arr.2020.101020CrossRefGoogle ScholarPubMed
Wiener, J. M. and Mallot, H. A. (2009). ‘Fine-to-coarse’ route planning and navigation in regionalized environments. Spatial Cognition and Computation, 3(4), 331358. doi:10.1207/s15427633scc0304\_5CrossRefGoogle Scholar
Wiener, J. M., Schnee, A. and Mallot, H. A. (2004). Use and interaction of navigation strategies in regionalized environments. Journal of Environmental Psychology, 24(4), 475493. doi:10.1016/j.jenvp.2004.09.006CrossRefGoogle Scholar
Wiener, J. M., Ehbauer, N. N. and Mallot, H. A. (2009). Planning paths to multiple targets: Memory involvement and planning heuristics in spatial problem solving. Psychological Research PRPF, 73(5), 644658. doi:10.1007/s00426-008-0181-3CrossRefGoogle ScholarPubMed