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Nearby outdoor recreation modelling: An agent-based approach
Abstract
Abstract Modelling and simulating the movement of humans during outdoor nearby recreational activities can deliver important insight into the landscape services available to people in urbanized areas. Recreational activities such as walking, jogging and cycling are known to have a positive effect on people’s mental and physical health, however in urban areas access to nearby recreation areas is sometimes lacking, under-developed or impeded by man-made infrastructures. In this context, understanding the spatial behaviour of humans during outdoor recreation is a crucial step towards implementing improvement measures. In this paper, we investigated the recreation strategies of multiple individuals and the flow in the movements of these individuals using a spatially-explicit agent-based model. The model combined information on the available infrastructures (roads, housing areas), the recreational potential of the landscape and rule-based movement strategies mimicking recreationists decision-making. At the individual level, we demonstrated that the shortest-path and weighted-path strategies were used the most during nearby recreation activities. At the population level, we successfully predicted recreationist flow patterns, highlighting gaps in path infrastructure and the most preferred path sections in a small town environment. We further illustrated the versatility of the model and its potential to support planning decisions to increase the accessibility of nearby recreation areas.
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“A spatiotemporal Agent-Based Model (ABM) that simulates visitor movement patterns influenced by various landscape characteristics is a useful tool to address this gap (Morelle et al 2019).”
From Agent-based modelling of beach visitation patterns: insights for sustainable coastal design and management (2025) · cites this paper as Morelle et al 2019
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Cites
A standard protocol for describing individual-based and agent-based models
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S. K., Huse, G., et al. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.04.023 · Cited by 2,981
A Phenomenology of Tourist Experiences
Cohen, E. (1979) · Sociology · doi:10.1177/003803857901300203 · Cited by 2,170
Lakens, D. (2017) · Social Psychological and Personality Science · doi:10.1177/1948550617697177 · Cited by 2,161 · cited 1×
“This equivalence test was performed using the TOSTER package in R (Lakens 2017).”
MethodAgent-Based Models of Geographical Systems
(2012) · Springer eBooks · doi:10.1007/978-90-481-8927-4 · Cited by 649
People needs in the urban landscape: Analysis of Landscape And Urban Planning contributions
Matsuoka, R. H., & Kaplan, R. (2008) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2007.09.009 · Cited by 563
Testing Statistical Hypotheses of Equivalence and Noninferiority
Wellek, S. (2010) · doi:10.1201/ebk1439808184 · Cited by 548 · cited 2×
“We performed a two one-sided t-test (TOST) (Wellek 2010).”
MethodDescribing human decisions in agent-based models – ODD + D, an extension of the ODD protocol
Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., Schlüter, M., Schulze, J., Weise, H., & Schwarz, N. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2013.06.003 · Cited by 542
Brown, G. (2004) · Society & Natural Resources · doi:10.1080/08941920590881853 · Cited by 394
Individual-based models in ecology after four decades
DeAngelis, D. L., & Grimm, V. (2014) · F1000Prime Reports · doi:10.12703/p6-39 · Cited by 360
How Far, by Which Route and Why? A Spatial Analysis of Pedestrian Preference
Weinstein Agrawal, A., Schlossberg, M., & Irvin, K. (2008) · Journal of Urban Design · doi:10.1080/13574800701804074 · Cited by 332
Navigating coastal values: Participatory mapping of ecosystem services for spatial planning
Klain, S. C., & Chan, K. M. A. (2012) · Ecological Economics · doi:10.1016/j.ecolecon.2012.07.008 · Cited by 318
Path selection and route preference in human navigation: A progress report
Golledge, R. G. (1995) · Lecture Notes in Computer Science · doi:10.1007/3-540-60392-1_14 · Cited by 265
Pedestrian environment and route choice: evidence from New York City and Hong Kong
Guo, Z., & Loo, B. P. Y. (2013) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2012.11.013 · Cited by 256
How Far, by Which Route, and Why? Spatial Analysis of Pedestrian Preference
Weinstein, A., Bekkouche, V. L., Irvin, K., & Schlossberg, M. (2007) · Cited by 229
Influence of environmental street characteristics on walking route choice of elderly people
Borst, H. C., de Vries, S. I., Graham, J. M. A., van Dongen, J. E. F., Bakker, I., & Miedema, H. M. E. (2009) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2009.08.002 · Cited by 228
Accessibility and use of peri-urban green space for inner-city dwellers: a comparative study.
Žlender, V., & Ward Thompson, C. (2017) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2016.06.011 · Cited by 200
A Review of Sampling Effects and Response Bias in Internet Participatory Mapping (PPGIS/PGIS/VGI)
Brown, G. (2016) · Transactions in GIS · doi:10.1111/tgis.12207 · Cited by 188
Agent‐based Modeling of Animal Movement: A Review
Tang, W., & Bennett, D. A. (2010) · Geography Compass · doi:10.1111/j.1749-8198.2010.00337.x · Cited by 186
Analysis of factors affecting the choice of route of pedestrians
Seneviratne, P. N., & Morrall, J. (1985) · Transportation Planning and Technology · doi:10.1080/03081068508717309 · Cited by 185
Roovers, P., Hermy, M., & Gulinck, H. (2002) · Landscape and Urban Planning · doi:10.1016/s0169-2046(02)00011-7 · Cited by 174
GIS-assisted mapping of landscape suitability for nearby recreation
Kienast, F., Degenhardt, B., Weilenmann, B., Wäger, Y., & Buchecker, M. (2012) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2012.01.015 · Cited by 174
Self-citationSmartphone GPS tracking—Inexpensive and efficient data collection on recreational movement
Korpilo, S., Virtanen, T. A., & Lehvävirta, S. (2016) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2016.08.005 · Cited by 167
On agent-based modeling and computational social science
Conte, R., & Paolucci, M. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00668 · Cited by 160
A regression-based equivalence test for model validation: shifting the burden of proof
Robinson, A. P., Duursma, R. A., & Marshall, J. D. (2005) · Tree Physiology · doi:10.1093/treephys/25.7.903 · Cited by 158
Demonstrating the importance of intangible ecosystem services from peri-urban landscapes
Vejre, H., Jensen, F. S., & Thorsen, B. J. (2009) · Ecological Complexity · doi:10.1016/j.ecocom.2009.09.005 · Cited by 155
A conceptual framework for carrying capacity determination
Shelby, B., & Heberlein, T. A. (1984) · Leisure Sciences · doi:10.1080/01490408409513047 · Cited by 145
A Spatial Agent-Based Model for the Simulation of Adults' Daily Walking Within a City
Yang, Y., Roux, A. V. D., Auchincloss, A. H., Rodríguez, D. A., & Brown, D. G. (2011) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.11.017 · Cited by 136
Pocewicz, A., Nielsen‐Pincus, M., Brown, G., & Schnitzer, R. (2012) · Transactions in GIS · doi:10.1111/j.1467-9671.2011.01287.x · Cited by 127
A short introduction to the art of programming
Dijkstra, E. E. (1971) · TU/e Research Portal · doi:10.26153/tsw/52882 · Cited by 122
Conedera, M., Del Biaggio, A., Seeland, K., Moretti, M., & Home, R. (2015) · Urban Forestry & Urban Greening · doi:10.1016/j.ufug.2015.01.003 · Cited by 112
Add or Multiply? A Tutorial on Ranking and Choosing with Multiple Criteria
Tofallis, C. (2014) · INFORMS Transactions on Education · doi:10.1287/ited.2013.0124 · Cited by 112
Rodrı́guez, D. A., Brisson, E. M., & Estupiñán, N. (2009) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2009.06.001 · Cited by 109
Wayfinding Through Orientation
Schwering, A., Krukar, J., Li, R. S., Anacta, V. J. A., & Fuest, S. (2017) · Spatial Cognition and Computation · doi:10.1080/13875868.2017.1322597 · Cited by 98
Komossa, F., van der Zanden, E. H., Schulp, C. J. E., & Verburg, P. H. (2017) · Ecological Indicators · doi:10.1016/j.ecolind.2017.10.015 · Cited by 88
Spatial Information Theory A Theoretical Basis for GIS
(1995) · Lecture notes in computer science · doi:10.1007/3-540-60392-1 · Cited by 84
Koppen, G., Sang, Å. O., & Tveit, M. S. (2014) · Urban forestry & urban greening · doi:10.1016/j.ufug.2013.11.005 · Cited by 81
Buchecker, M., & Degenhardt, B. (2015) · Journal of Outdoor Recreation and Tourism · doi:10.1016/j.jort.2015.06.007 · Cited by 79
Self-citationManley, E. J., Addison, J. D., & Cheng, T. (2015) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2015.01.006 · Cited by 76
Taczanowska, K., González, L.-M., Garcia-Massó, X., Muhar, A., Brandenburg, C., & Toca-Herrera, J.-L. (2014) · Applied Geography · doi:10.1016/j.apgeog.2014.09.011 · Cited by 71
Kennedy, D. P., & Adolphs, R. (2011) · Nature · doi:10.1038/474452a · Cited by 62
Modelling recreational visits to forests and nature areas
de Vries, S., & Goossen, M. (2002) · Urban forestry & urban greening · doi:10.1078/1618-8667-00002 · Cited by 53
Boll, T., von Haaren, C., & von Ruschkowski, E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108638 · Cited by 53
Management of Recreational Areas: GIS, Autonomous Agents, and Virtual Reality
Bishop, I. D., & Gimblett, H. R. (2000) · Environment and Planning B Planning and Design · doi:10.1068/b2637 · Cited by 46
Meyer, S. M., Melver, J., & Carmines, E. G. (1982) · Journal of Policy Analysis and Management · doi:10.2307/3324822 · Cited by 46
RBSim: Geographic Simulation of Wilderness Recreation Behavior
Gimblett, H. R., Richards, M. T., & Itami, R. M. (2001) · Journal of Forestry · doi:10.1093/jof/99.4.36 · Cited by 36
Degenhardt, B., Frick, J., Buchecker, M., & Gutscher, H. (2011) · Leisure Sciences · doi:10.1080/01490400.2011.606780 · Cited by 35
Self-citationExploring the Nature of Management
Siegrist, D., Clivaz, C., Hunziker, M., & Iten, S. (2006) · Cited by 34
Spatial simulation: A spatial perspective on individual-based ecology—a review
Wallentin, G. (2017) · Ecological Modelling · doi:10.1016/j.ecolmodel.2017.01.017 · Cited by 32
Brief introductory guide to agent-based modeling and an illustration from urban health research
Auchincloss, A. H., & García, L. (2015) · Cadernos de Saúde Pública · doi:10.1590/0102-311x00051615 · Cited by 31
Agent-based Bicycle Traffic Model for Salzburg City
Wallentin, G., & Loidl, M. (2015) · GI_Forum · doi:10.1553/giscience2015s558 · Cited by 27
Modelling pedestrian behaviour in downtown shopping areas
Borgers, A. A., & Timmermans, H. H. (2005) · TU/e Research Portal · Cited by 24
Freuler, B., & Hunziker, M. (2007) · Cited by 23
Degenhardt, B., & Buchecker, M. (2012) · Leisure Sciences · doi:10.1080/01490400.2012.714706 · Cited by 19
Self-citationAn Individual-Based Modeling Approach to Simulating Recreation Use in Wilderness Settings
Gimblett, R. H., Daniel, T. C., & Meitner, M. J. (2000) · Cited by 14
Jochem, R., van Marwijk, R. B. M., Pouwels, R., & Pitt, D. G. (2008) · Socio-Environmental Systems Modeling · Cited by 12
Forest recreation and nature tourism
Bell, S. (2010) · Urban forestry & urban greening · doi:10.1016/j.ufug.2010.03.001 · Cited by 11
Mapping Bicycling Patterns with an Agent-Based Model, Census and Crowdsourced Data
Leao, S. Z., & Pettit, C. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-51957-9_7 · Cited by 8
Taczanowska, K., Arnberger, A., & Muhar, A. (2007) · Cited by 6
Research works printed but not linked: 2
- Bell, 2010, 9, 69
- Bögli, 2016, Welchen Stellenwert haben Gewässer bei der Naherholung?, Wasser Energie Luft, 118, 303
Cited by
Bakhshianlamouki, E., Augustijn, E.-W., Brugnach, M., Voinov, A., & Wijnberg, K. (2025) · Environmental Research Communications · doi:10.1088/2515-7620/adc353 · Cited by 1
Metadata
- Authors
- Morelle, K., Buchecker, M., Kienast, F., & Tobias, S.
- Venue
- Urban Forestry & Urban Greening
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 40
- Pages
- 286-298
- ISSN
- 1618-8667
- Cited by
- 29
- References
- 58
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Swiss Model Project on Sustainable Spatial Development: “Attractive Access Network to Nearby Recreation Areas”
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