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An Analysis of Modes of Commuting in Urban and Rural Areas

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Abstract

This study compares global and local analyses of non-car commuting modes and the probability of increasing modes use in different urban and rural areas for a case study in Yorkshire, UK, with commuter residence areas used as the response variable. The analyses compared Generalized Linear Models of commuting by bus, cycling and walking to estimate the probability of increasing sustainable modes use in commuters in urban areas, relative to rural areas. The three variables were found to be significant predictors for the models and indicate differential odds of commuting from urban areas relative to rural ones. An analysis of the non-stationarity of was undertaken using a Geographically Weighted Regression analysis, which showed how the probability of residing in a particular type of urban and rural area, as described by commuting patterns, varied spatially within the study region. The local analyses provide a critical information able to support and guide local policy in its ambition to increase sustainable transport modes and to reduce car dependence in both rural and urban areas.

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References

  • Anable, J. (2005). ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transport Policy, 12(1), 65–78.

    Article  Google Scholar 

  • Baiocchi, G., Creutzig, F., Minx, J., & Pichler, P. P. (2015). A spatial typology of human settlements and their CO 2 emissions in England. Global Environmental Change, 34, 13–21.

    Article  Google Scholar 

  • Beck, M. J., & Hess, S. (2016). Willingness to accept longer commutes for better salaries: Understanding the differences within and between couples. Transportation Research Part A: Policy and Practice, 91, 1–16.

    Google Scholar 

  • Beirão, G., & Cabral, J. S. (2007). Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy, 14(6), 478–489.

    Article  Google Scholar 

  • Bibby, P., & Brindley, P. (2013). The 2011 rural-urban classification for small area geographies: A user guide and frequently asked questions (v1. 0). UK Government Statistical Service.

  • Bowman, A. W. (1984). An alternative method of cross-validation for the smoothing of density estimates. Biometrika, 353–360.

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298.

    Article  Google Scholar 

  • Buehler, R., & Pucher, J. (2012). Walking and cycling in Western Europe and the United States: Trends, policies, and lessons. TR News, (280).

  • Champion, T. (2009). Urban–rural differences in commuting in England: A challenge to the rural sustainability agenda? Planning Practice and Research, 24(2), 161–183.

    Article  Google Scholar 

  • Charlton, M., Fotheringham, S., & Brunsdon, C. (2009). Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland Maynooth.

  • Chasco, C., García, I., & Vicéns, J. (2007). Modeling spatial variations in household disposable income with geographically weighted regression.

  • Clark, B., Chatterjee, K., & Melia, S. (2016). Changes to commute mode: The role of life events, spatial context and environmental attitude. Transportation Research Part A: Policy and Practice, 89, 89–105.

    Google Scholar 

  • Comber, A. J., Brunsdon, C., & Radburn, R. (2011). A spatial analysis of variations in health access: Linking geography, socio-economic status and access perceptions. International Journal of Health Geographics, 10(1), 44.

    Article  Google Scholar 

  • Dujardin, S., Pirart, F., Brevers, F., Marique, A. F., & Teller, J. (2012). Home-to-work commuting, urban form and potential energy savings: A local scale approach to regional statistics. Transportation Research Part A: Policy and Practice, 46(7), 1054–1065.

    Google Scholar 

  • Fan, J. X., Wen, M., & Wan, N. (2017). Built environment and active commuting: Rural-urban differences in the US. SSM-Population Health, 3, 435–441.

    Article  Google Scholar 

  • Francisco, E., Whigham, P., Aranha, F., & Zambaldi, F. (2008). A consumer income predicting model based on survey data: An analysis using geographically weighted regression (Gwr). ACR Latin American Advances.

  • Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2013). GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413.

  • Goodman, A. (2013). Walking, cycling and driving to work in the English and welsh 2011 census: Trends, socio-economic patterning and relevance to travel behaviour in general. PLoS One, 8(8), e71790.

    Article  Google Scholar 

  • Greene, D. L., & Wegener, M. (1997). Sustainable transport. Journal of Transport Geography, 5(3), 177–190.

    Article  Google Scholar 

  • Greene, D. L., Jones, D. W., & Delucchi, M. A. (1997). The full costs and benefits of transportation: Contributions to theory, method and measurement; with 62 tables. Springer Science & Business Media.

  • Jakob, A., Craig, J. L., & Fisher, G. (2006). Transport cost analysis: A case study of the total costs of private and public transport in Auckland. Environmental Science & Policy, 9(1), 55–66.

    Article  Google Scholar 

  • Katz, P., Scully, V., & Bressi, T. W. (1994). The new urbanism: Toward an architecture of community (Vol. 10). New York: McGraw-Hill.

    Google Scholar 

  • Kim, C., Sang, S., Chun, Y., & Lee, W. (2012). Exploring urban commuting imbalance by jobs and gender. Applied Geography, 32(2), 532–545.

    Article  Google Scholar 

  • Kingham, S., Dickinson, J., & Copsey, S. (2001). Travelling to work: Will people move out of their cars. Transport Policy, 8(2), 151–160.

    Article  Google Scholar 

  • Kitamura, R. (1989). A causal analysis of car ownership and transit use. Transportation, 16(2), 155–173.

    Article  Google Scholar 

  • Li, T., Corcoran, J., Pullar, D., Robson, A., & Stimson, R. (2009). A geographically weighted regression method to spatially disaggregate regional employment forecasts for south East Queensland. Applied Spatial Analysis and Policy, 2(2), 147–175.

    Article  Google Scholar 

  • Limtanakool, N., Dijst, M., & Schwanen, T. (2006). The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium-and longer-distance trips. Journal of Transport Geography, 14(5), 327–341.

    Article  Google Scholar 

  • Litman, T. (2011). Introduction to multi-modal transportation planning. Victoria Transport Policy Institute, 15.

  • Lloyd, C. D. (2014). Exploring spatial scale in geography. John Wiley & Sons.

  • Lloyd, C. D. (2016). Spatial scale and small area population statistics for England and Wales. International Journal of Geographical Information Science, 30(6), 1187–1206.

    Article  Google Scholar 

  • Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: From individual to regional levels. Journal of Transport Geography, 34, 282–296.

    Article  Google Scholar 

  • Mann, E., & Abraham, C. (2006). The role of affect in UK commuters' travel mode choices: An interpretative phenomenological analysis. British Journal of Psychology, 97(2), 155–176.

    Article  Google Scholar 

  • Mennis, J. (2006). Mapping the results of geographically weighted regression. The Cartographic Journal, 43(2), 171–179.

    Article  Google Scholar 

  • ONS (2011). 2011 Census-England and Wales output geography: Policy and products. ONS.

  • Sapiro, P. (2016). Beyond the strictly orthodox/mainstream divide: Applying geodemographic analysis to a small nationwide sub-population. Computers, Environment and Urban Systems, 56, 36–47.

    Article  Google Scholar 

  • Schwanen, T., Dieleman, F. M., & Dijst, M. (2001). Travel behaviour in Dutch monocentric and policentric urban systems. Journal of Transport Geography, 9(3), 173–186.

    Article  Google Scholar 

  • Sharkey, J. R., & Horel, S. (2008). Neighborhood socioeconomic deprivation and minority composition are associated with better potential spatial access to the ground-truthed food environment in a large rural area. The Journal of Nutrition, 138(3), 620–627.

    Article  Google Scholar 

  • Shoff, C., Yang, T. C., & Matthews, S. A. (2012). What has geography got to do with it? Using GWR to explore place-specific associations with prenatal care utilization. GeoJournal, 77(3), 331–341.

    Article  Google Scholar 

  • Susilo, Y. O., & Kitamura, R. (2008). Structural changes in commuters’ daily travel: The case of auto and transit commuters in the Osaka metropolitan area of Japan, 1980–2000. Transportation Research Part A: Policy and Practice, 42(1), 95–115.

    Google Scholar 

  • Titheridge, H., & Hall, P. (2006). Changing travel to work patterns in south East England. Journal of Transport Geography, 14(1), 60–75.

    Article  Google Scholar 

  • Travisi, C. M., Camagni, R., & Nijkamp, P. (2010). Impacts of urban sprawl and commuting: A modelling study for Italy. Journal of Transport Geography, 18(3), 382–392.

    Article  Google Scholar 

  • Wilding, S., Martin, D., & Moon, G. (2016). The impact of limiting long term illness on internal migration in England and Wales: New evidence from census microdata. Social Science & Medicine, 167, 107–115.

    Article  Google Scholar 

  • Woodcock, J., Edwards, P., Tonne, C., Armstrong, B. G., Ashiru, O., Banister, D., et al. (2009). Public health benefits of strategies to reduce greenhouse-gas emissions: Urban land transport. The Lancet, 374(9705), 1930–1943.

    Article  Google Scholar 

  • Yang, L., Hipp, J. A., Adlakha, D., Marx, C. M., Tabak, R. G., & Brownson, R. C. (2015). Choice of commuting mode among employees: Do home neighborhood environment, worksite neighborhood environment, and worksite policy and supports matter? Journal of Transport & Health, 2(2), 212–218.

    Article  Google Scholar 

  • Yang, H., Lu, X., Cherry, C., Liu, X., & Li, Y. (2017). Spatial variations in active mode trip volume at intersections: A local analysis utilizing geographically weighted regression. Journal of Transport Geography, 64, 184–194.

    Article  Google Scholar 

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Funding

This study was funded by the China Scholarship Council (grant number 201606405025).

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Authors and Affiliations

  1. School of Land Science and Technology, China University of Geosciences, Beijing, 100083, China

    Xiangwu Tao & Zongtang Fu

  2. School of Geography, University of Leeds, Leeds, LS2 9JT, UK

    Alexis J. Comber

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  1. Xiangwu Tao
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  2. Zongtang Fu
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  3. Alexis J. Comber
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Correspondence to Xiangwu Tao.

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Tao, X., Fu, Z. & Comber, A.J. An Analysis of Modes of Commuting in Urban and Rural Areas. Appl. Spatial Analysis 12, 831–845 (2019). https://doi.org/10.1007/s12061-018-9271-9

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