Beyond geometries of activity spaces: A holistic study of daily travel patterns, individual characteristics, and perceived wellbeing in Helsinki metropolitan area
Kamyar Hasanzadeh
Aalto University
http://orcid.org/0000-0002-0705-7662
Michal Czepkiewicz
University of Iceland
Jukka Heinonen
University of Iceland
Marketta Kyttä
Aalto University
Sanna Ala-Mantila
Aalto University
Juudit Ottelin
Aalto University
DOI: https://doi.org/10.5198/jtlu.2019.1148
Keywords: activity spaces, mobility, typology, PPGIS, wellbeing
Abstract
Activity space (AS) is a measure of spatial behavior used to summarize the mobility behavior of individuals. Current studies often highlight the fact that AS is highly complex and multidimensional in character. Therefore, the need for more holistic approaches providing more comprehensive descriptions of mobility patterns is evident. This article assesses the activity spaces of young adults aged 25–40 living in the Helsinki metropolitan area using a dataset collected with an online map survey. Using a wide range of measurements covering different aspects of AS, we identified seven components that define activity spaces, namely size, intensity of activities, volume of trips, exteriority, polycentricity, elongation, and destination specialization. We then used the components together with travel mode use to identify a typology of daily mobility patterns. The results show that individuals with different types of AS differ significantly in their socio-demographic characteristics, such as age, gender, employment, household characteristics, and residential neighborhood. Furthermore, the study reveals interesting associations between AS characteristics and different aspects of wellbeing. Overall, the results highlight the importance of multidimensional and comprehensive approaches to understanding daily mobility of urban residents.Author Biographies
Kamyar Hasanzadeh, Aalto University
PhD candidate, Department of built enviornment, School of engineeringJukka Heinonen, University of Iceland
Professor, Sustainable Built Environment Faculty of Civil and Environmental EngineeringMarketta Kyttä, Aalto University
Professor, Land Use Planning, Department of Built EnvironmentSanna Ala-Mantila, Aalto University
PhD candidate, Department of built enviornment, School of engineeringJuudit Ottelin, Aalto University
PhD candidate, Department of built enviornment, School of engineeringReferences
Axhausen, K. W. (2003). Social networks and travel: Some hypotheses. Transport and Society, 197, 90–108. doi.org/10.3929/ethz-a-004663201
Banister, D. (2008). The sustainable mobility paradigm. Transport Policy, 15(2), 73–80. doi.org/10.1016/j.tranpol.2007.10.005
Bertram, C., & Rehdanz, K. (2015). The role of urban green space for human well-being. Ecological Economics, 120, 139–152. doi.org/10.1016/j.ecolecon.2015.10.013
Brown, G. (2012). An empirical evaluation of the spatial accuracy of public participation GIS (PPGIS) data. Applied Geography, 34, 289–294. doi.org/10.1016/J.APGEOG.2011.12.004
Brown, G. (2016). A review of sampling effects and response bias in internet participatory mapping (PPGIS/PGIS/VGI). Transactions in GIS. doi.org/10.1111/tgis.12207
Brown, G., & Kyttä, M. (2014). Key issues and research priorities for public participation GIS (PPGIS): A synthesis based on empirical research. Applied Geography, 46, 126–136. doi.org/10.1016/j.apgeog.2013.11.004
Buliung, R. N., & Kanaroglou, P. S. (2006). Urban form and household activity-travel behavior. Growth and Change, 37(2), 172–199. doi.org/10.1111/j.1468-2257.2006.00314.x
Buliung, R. N., Roorda, M. J., & Remmel, T. K. (2008). Exploring spatial variety in patterns of activity-travel behavior: Initial results from the Toronto Travel-Activity Panel Survey (TTAPS). Transportation, 35(6), 697–722. doi.org/10.1007/s11116-008-9178-4
Burnett, P., & Hanson, S. (1982). The analysis of travel as an example of complex human behavior in spatially-constrained situations: Definition and measurement issues. Transportation Research Part A: General, 16(2), 87–102. doi.org/10.1016/0191-2607(82)90001-2
Creatore, M. I., Glazier, R. H., Moineddin, R., Fazli, G. S., Johns, A., Gozdyra, P.,…Booth, G. L. (2016). Association of neighborhood walkability with change in overweight, obesity, and diabetes. Journal of the American Medical Association, 315(20), 2211–2220. doi.org/10.1001/jama.2016.5898
Czepkiewicz, M., Jankowski, P., & Młodkowski, M. (2016). Geo-questionnaires in urban planning: Recruitment methods, participant engagement, and data quality. Cartography and Geographic Information Science, 44(6), 551–567. doi.org/10.1080/15230406.2016.1230520
de Hartog, J. J., Boogaard, H., Nijland, H., & Hoek, G. (2010). Do the health benefits of cycling outweigh the risks? Environmental Health Perspectives. doi.org/10.1289/ehp.0901747
Dijst, M. (1999). Two-earner families and their action spaces: A case study of two Dutch communities. GeoJournal, 48(3), 195–206. doi.org/10.1023/A:1007031809319
Dodge, R., Daly, A., Huyton, J., & Sanders, L. (2012). The challenge of defining wellbeing. International Journal of Wellbeing, 2(3), 222–235. doi.org/10.5502/ijw.v2i3.4
Ewing, R., & Cervero, R. (2010). Travel and the built environment. Journal of the American Planning Association, 76(3), 265–294. doi.org/10.1080/01944361003766766
Fan, Y., & Khattak, A. (2008). Urban form, individual spatial footprints, and travel: Examination of space-use behavior. Transportation Research Record: Journal of the Transportation Research Board, 2082, 98–106.
Farber, S., & Páez, A. (2009). My car, my friends, and me: A preliminary analysis of automobility and social activity participation. Journal of Transport Geography, 17(3), 216–225. doi.org/10.1016/j.jtrangeo.2008.07.008
Flamm, M. F., & Kaufmann, V. (2006). The concept of personal network of usual places as a tool for analyzing human activity spaces: A quantitative exploration. Conference Paper STRC 2006. doi.org/10.1108/02580540410567256
Friman, M., Gärling, T., Ettema, D., & Olsson, L. E. (2017). How does travel affect emotional well-being and life satisfaction? Transportation Research Part A: Policy and Practice, 106, 170–180. doi.org/10.1016/J.TRA.2017.09.024
Frumkin, H. (2002). Urban sprawl and public health. Public Health Reports. doi.org/10.1016/S0033-3549(04)50155-3
Gatersleben, B., & Uzzell, D. (2007). Affective appraisals of the daily commute: Comparing perceptions of drivers, cyclists, walkers, and users of public transport. Environment and Behavior, 39(3), 416–431. doi.org/10.1177/0013916506294032
Glazier, R. H., Creatore, M. I., Weyman, J. T., Fazli, G., Matheson, F. I., Gozdyra, P., … Booth, G. L. (2014). Density, destinations or both? A comparison of measures of walkability in relation to transportation behaviors, obesity and diabetes in Toronto, Canada. PLoS ONE, 9(1). doi.org/10.1371/journal.pone.0085295
Hägerstraand, T. (1970). What about people in regional science? Papers in Regional Science, 24(1), 7–24.
Handy, S. L., Boarnet, M. G., Ewing, R., & Killingsworth, R. E. (2002). How the built environment affects physical activity. American Journal of Preventive Medicine, 23(2), 64–73. doi.org/10.1016/S0749-3797(02)00475-0
Hanson, S., & Hanson, P. (1981). The travel-activity patterns of urban residents: Dimensions and relationships to sociodemographic characteristics. Economic Geography, 57(4), 332–347. doi.org/10.2307/144213
Harding, C., Patterson, Z., Miranda-Moreno, L., & Zahabi, S. (2012). Modeling the effect of land use on activity spaces. Transportation Research Record: Journal of the Transportation Research Board, 2323, 67–74. doi.org/10.3141/2323-08
Hasanzadeh, K. (2018). IASM: Individualized activity space modeler. SoftwareX, 7, 138–142. Retrieved from https://www.sciencedirect.com/science/article/pii/S2352711018300542
Hasanzadeh, K. (2019). Exploring centricity of activity spaces: From measurement to the identification of personal and environmental factors. Travel Behavior and Society, 14, 57–65. doi.org/10.1016/J.TBS.2018.10.001
Hasanzadeh, K., Broberg, A., & Kyttä, M. (2017). Where is my neighborhood? A dynamic individual-based definition of home zones. Applied Geography, 84(C), 1–10. doi.org/10.1016/j.apgeog.2017.04.006
Hasanzadeh, K., Laatikainen, T., & Kyttä, M. (2018). A place-based model of local activity spaces: Individual place exposure and characteristics. Journal of Geographical Systems, 20(3), 227. doi.org/10.1007/s10109-017-0264-z
Haustein, S., & Hunecke, M. (2013). Identifying target groups for environmentally sustainable transport: Assessment of different segmentation approaches. Current Opinion in Environmental Sustainability. doi.org/10.1016/j.cosust.2013.04.009
Haybatollahi, M., Czepkiewicz, M., Laatikainen, T., & Kyttä, M. (2015). Neighborhood preferences, active travel behavior, and built environment: An exploratory study. Transportation Research Part F: Traffic Psychology and Behavior, 29, 57–69. doi.org/10.1016/j.trf.2015.01.001
Hoehner, C. M., Handy, S. L., Yan, Y., Blair, S. N., & Berrigan, D. (2011). Association between neighborhood walkability, cardiorespiratory fitness and body-mass index. Social Science and Medicine, 73(12), 1707–1716. doi.org/10.1016/j.socscimed.2011.09.032
Horton, F. E. H., & Reynolds, D. R. (1971). Effects of urban spatial structure on individual behavior. Economic Geography, 47(1), 36–48. doi.org/10.1126/science.11.277.620
Hwang, H., Montréal, H., Dillon, W. R., & Takane, Y. (2006). An extension of multiple correspondence analysis for identifying heterogeneous subgroups of respondents. Psychometrika, 71(1), 161–171. doi.org/10.1007/s11336-004-1173-x
International Wellbeing Group (2013). Personal wellbeing index: 5th Edition. Melbourne: Australian Centre on Quality of Life, Deakin University. Retrieved from http://www.deakin.edu.au/research/acqol/instruments/wellbeing-index/index.php
Jarv, O., Muurisepp, K., Ahas, R., Derudder, B., & Witlox, F. (2015). Ethnic differences in activity spaces as a characteristic of segregation: A study based on mobile phone usage in Tallinn, Estonia. Urban Studies, 52(14), 2680–2698. doi.org/10.1177/0042098014550459
Kahila, M., & Kyttä, M. (2009). SoftGIS as a bridge-builder in collaborative urban planning. In Geojournal Library, Vol 95 (pp. 389–411). Basingstoke, UK: Springer Nature. doi.org/10.1007/978-1-4020-8952-7_19
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306(5702), 1776–1780. doi.org/10.1126/science.1103572
Kamruzzaman, M., & Hine, J. (2012). Analysis of rural activity spaces and transport disadvantage using a multi-method approach. Transport Policy, 19(1), 105–120. doi.org/10.1016/J.TRANPOL.2011.09.007
Kenyon, S., Lyons, G., & Rafferty, J. (2002). Transport and social exclusion: Investigating the possibility of promoting inclusion through virtual mobility. Journal of Transport Geography, 10(3), 207–219.
Krizek, K., & Waddell, P. (2002). Analysis of lifestyle choices: Neighborhood type, travel patterns, and activity participation. Transportation Research Record: Journal of the Transportation Research Board, 1807, 119–128. doi.org/10.3141/1807-15
Kwan, M.-P. (2012a). Geographies of health. Annals of the Association of American Geographers, 102(5), 891–892. doi.org/10.1080/00045608.2012.687348
Kwan, M.-P. (2012b). The uncertain geographic context problem. Annals of the Association of American Geographers, 102, 958–968. doi.org/10.1080/00045608.2012.687349
Kwan, M.-P., & Schwanen, T. (2016). Geographies of mobility. Annals of the American Association of Geographers, 106(2), 243–256. doi.org/10.1080/24694452.2015.1123067
Laatikainen, T. E., Hasanzadeh, K., & Kyttä, M. (2018). Capturing exposure in environmental health research: Challenges and opportunities of different activity space models. International Journal of Health Geographics, 17(1), 29.
Lebart, L., Morineau, A., & Piron, M. (2000). Statistique exploratoire multidimensionnelle. Paris: Dunod. Retrieved from https://books.google.fr/books/about/Statistique_exploratoire_multidimensionn.html?hl=fr&id=CS5KHQAACAAJ
Lee, N. C., Voss, C., Frazer, A. D., Hirsch, J. A., McKay, H. A., Winters, M., … Winters, M. (2015). Does activity space size influence physical activity levels of adolescents? A GPS study of an urban environment. Preventive Medicine Reports, 3, 75–78. doi.org/10.1016/j.pmedr.2015.12.002
Lord, S., Joerin, F., & Thériault, M. (2009). La mobilité quotidienne de banlieusards vieillissants et âgés: Déplacements, aspirations et significations de la mobilité. Le Géographe Canadien, 53(3), 357–375. doi.org/10.1111/j.1541-0064.2009.00269.x
Lu, X., & Pas, E. I. (1999). Socio-demographics, activity participation and travel behavior. Transportation Research Part A: Policy and Practice, 33(1), 1–18. doi.org/10.1016/S0965-8564(98)00020-2
Ludwig, J., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., Kling, J. R., & Sanbonmatsu, L. (2012). Neighborhood effects on the long-term well-being of low-income adults. Science, 337(6101), 1505–1510. doi.org/10.1126/science.1224648
Manaugh, K., & El-Geneidy, A. (2012). What makes travel “local”: Defining and understanding local travel behavior. Journal of Transport and Land Use, 5(3), 15–27. doi.org/10.5198/jtlu.v5i3.300
Meng, X. U. (2014). The impact of labor migration on regional disparities in economic growth. Journal of Zhanjiang Normal University, 3, 29.
Millard‐Ball, A., & Schipper, L. (2011). Are we reaching peak travel? Trends in passenger transport in eight industrialized countries. Transport Reviews, 31(3), 357–378. doi.org/10.1080/01441647.2010.518291
Miranda-Moreno, L. F., Eluru, N., Lee-Gosselin, M., & Kreider, T. (2012). Impact of ICT access on personal activity space and greenhouse gas production: Evidence from Quebec City, Canada. Transportation, 39(5), 895–918. doi.org/10.1007/s11116-012-9428-3
Morris, E. A., & Guerra, E. (2014). Mood and mode: Does how we travel affect how we feel? Transportation, 42(1), 25–43. doi.org/10.1007/s11116-014-9521-x
Naess, P. (2012). Urban form and travel behavior: Experience from a Nordic context. Journal of Transport and Land Use, 5, 21–45. doi.org/10.5198/jtlu.v5i2.314
Næss, P. (2013). Residential location, transport rationales and daily-life travel behavior: The case of Hangzhou metropolitan area, China. Progress in Planning, 79(1), 1–50. doi.org/10.1016/j.progress.2012.05.001
Newman, P., Kosonen, L., & Kenworthy, J. (2016). Theory of urban fabrics: Planning the walking, transit/public transport and automobile/motor car cities for reduced car dependency. Town Planning Review, 87(4), 429–458. doi.org/10.3828/tpr.2016.28
Newsome, T. H., Walcott, W. A., & Smith, P. D. (1998). Urban activity spaces: Illustrations and application of a conceptual model for integrating the time and space dimensions. Transportation, 25(4), 357–377. doi.org/10.1023/A:1005082827030
Northridge, M. E., & Freeman, L. (2011). Urban planning and health equity. Journal of Urban Health, 88(3), 582–597. doi.org/10.1007/s11524-011-9558-5
OECD. (2013). OECD guidelines on measuring subjective well-being. Paris: OECD. Retrieved from http://dx.doi.org/10.1787/9789264191655-en
Ohnmacht, T., Götz, K., & Schad, H. (2009). Leisure mobility styles in Swiss conurbations: Construction and empirical analysis. Transportation, 36(2), 243–265. doi.org/10.1007/s11116-009-9198-8
Patterson, Z., & Farber, S. (2015). Potential path areas and activity spaces in application: A review. Transport Reviews, 35(6), 679–700. doi.org/10.1080/01441647.2015.1042944
Perchoux, C., Chaix, B., Cummins, S., & Kestens, Y. (2013). Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility. Health & Place, 21, 86–93. doi.org/10.1016/j.healthplace.2013.01.005
Perchoux, C., Kestens, Y., Thomas, F., van Hulst, A., Thierry, B., & Chaix, B. (2014). Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study). Social Science and Medicine, 119, 64–73. doi.org/10.1016/j.socscimed.2014.07.026
Perez, L. G., Conway, T. L., Bauman, A., Kerr, J., Elder, J. P., Arredondo, E. M., & Sallis, J. F. (2018). Sociodemographic moderators of environment–physical activity associations: Results from the international prevalence study. Journal of Physical Activity and Health, 15(1), 22–29. doi.org/10.1123/jpah.2017-0163
Prillwitz, J., & Barr, S. (2011). Moving towards sustainability? Mobility styles, attitudes and individual travel behavior. Journal of Transport Geography, 19(6), 1590–1600. doi.org/10.1016/j.jtrangeo.2011.06.011
Pucher, J., Buehler, R., Bassett, D. R., & Dannenberg, A. L. (2010). Walking and cycling to health: A comparative analysis of city, state, and international data. American Journal of Public Health, 100(10), 1986–1992. doi.org/10.2105/AJPH.2009.189324
Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon and Schuster.
Rai, P., & Daumé III, H. (n.d.). The Infinite hierarchical factor regression model. Retrieved from http://people.ee.duke.edu/~lcarin/FA_BuffetProcess_NIPS08.pdf
Rai, R., Balmer, M., Rieser, M., Vaze, V., Schönfelder, S., & Axhausen, K. (2007). Capturing human activity spaces: New geometries. Transportation Research Record: Journal of the Transportation Research Board, 2021, 70–80. doi.org/10.3141/2021-09
Rainham, D., McDowell, I., Krewski, D., & Sawada, M. (2010). Conceptualizing the healthscape: Contributions of time geography, location technologies and spatial ecology to place and health research. Social Science and Medicine, 70(5), 668–676. doi.org/10.1016/j.socscimed.2009.10.035
Richardson, E. A., Pearce, J., Mitchell, R., & Kingham, S. (2013). Role of physical activity in the relationship between urban green space and health. Public Health, 127(4), 318–324. doi.org/10.1016/j.puhe.2013.01.004
Salonen, M., Broberg, A., Kyttä, M., & Toivonen, T. (2014). Do suburban residents prefer the fastest or low-carbon travel modes? Combining public participation GIS and multimodal travel time analysis for daily mobility research. Applied Geography, 53, 438–448. doi.org/10.1016/j.apgeog.2014.06.028
Sarjala, S.-M., Broberg, A., & Hynynen, A. (2015). Children and youth transport in different urban morphological types. Journal of Transport and Land Use, 9(2), 87–103. doi.org/10.5198/jtlu.2015.803
Schönfelder, S., & Axhausen, K. (2004). Structure and innovation of human activity spaces. Arbeitsberichte Verkehrs-Und Raumplanung, 258(October), 1–40.
Schönfelder, S., & Axhausen, K. W. (2002a). Measuring the size and structure of human activity spaces. Transportation Research, (February), 49. doi.org/10.3929/ethz-a-004444846
Schönfelder, S., & Axhausen, K. W. (2002b). On the variability of human activity spaces. Arbeitsbericht Verkehrs-Und Raumplanung, 149, 237–262.
Schönfelder, S., & Axhausen, K. W. (2003). Activity spaces: Measures of social exclusion? Transport Policy, 10(4), 273–286. doi.org/10.1016/j.tranpol.2003.07.002
Shephard, R. J. (2008). Is active commuting the answer to population health? Sports Medicine, 38(9), 751–758. doi.org/10.2165/00007256-200838090-00004
Sherman, J. E., Spencer, J., Preisser, J. S., Gesler, W. M., & Arcury, T. A. (2005). A suite of methods for representing activity space in a healthcare accessibility study. International Journal of Health Geographics, 4(1), 24. doi.org/10.1186/1476-072X-4-24
Shin, D. C., & Johnson, D. M. (1978). Avowed happiness as an overall assessment of the quality of life. Social Indicators Research, 5(1–4), 475–492. doi.org/10.1007/BF00352944
Söderström, P., Schulman, H., & Ristimäki, M. (2015). Urban form in the Helsinki and Stockholm city regions–Development of pedestrian, public transport and car zones. Report of the Finnish Environment Institute No. 16/2015. Retrieved from https://helda.helsinki.fi/handle/10138/155224
Stutzer, A., & Frey, B. S. (2008). Stress that doesn’t pay: The commuting paradox. Scandinavian Journal of Economics, 110(2), 33 9–366. doi.org/10.1111/j.1467-9442.2008.00542.x
Tana, Kwan, M.-P., Chai, Y. (2016). Urban form, car ownership and activity space in inner suburbs: A comparison between Beijing (China) and Chicago (United States). Urban Studies, 53(9), 1784–1802.
Vallée, J., Cadot, E., Roustit, C., Parizot, I., & Chauvin, P. (2011). The role of daily mobility in mental health inequalities: The interactive influence of activity space and neighborhood of residence on depression. Social Science & Medicine, 73(8), 1133–1144. doi.org/10.1016/j.socscimed.2011.08.009
van Acker, V., van Wee, B., & Witlox, F. (2010). When transport geography meets social psychology: Toward a conceptual model of travel behavior. Transport Reviews, 30(2), 219–240. doi.org/10.1080/01441640902943453
WHOQOL. (1993). Study protocol for the World Health Organization project to develop a quality of life assessment instrument (WHOQOL). Quality of Life Research, 2(2), 153–159. doi.org/doi:10.1007/bf00435734
Xu, Y., Shaw, S.-L., Zhao, Z., Yin, L., Lu, F., Chen, J., … Li, Q. (2016). Another tale of two cities: Understanding human activity space using actively tracked cellphone location data. Annals of the American Association of Geographers, 106(2), 489–502. doi.org/10.1080/00045608.2015.1120147
