Photos, tweets, and trails: Are social media proxies for urban trail use?
Xinyi Wu
University of Minnesota
Spencer A. Wood
The Natural Capital Project, Woods Institute for the Environment, Stanford University School of Environmental and Forest Sciences, University of Washington
David Fisher
The Natural Capital Project, Woods Institute for the Environment, Stanford University School of Environmental and Forest Sciences, University of Washington
Greg Lindsey
Humphrey School of Public Affairs, University of Minnesota
DOI: https://doi.org/10.5198/jtlu.2017.1130
Keywords: trail use, social media, Flickr, Twitter, geo-tagging, recreation, transportation
Abstract
Decision makers need information on the use of, and demand for, public recreation and transportation facilities. Innovations in monitoring technologies and diffusion of social media enable new approaches to estimation of demand. We assess the feasibility of using geo-tagged photographs uploaded to the image-sharing website Flickr and tweets from Twitter as proxy measures for urban trail use. We summarize geo-tagged Flickr uploads and tweets along 80 one-mile segments of the multiuse trail network in Minneapolis, Minnesota, and correlate results with previously published estimates of annual average daily trail traffic derived from infrared trail monitors. Although heat maps of Flickr images and tweets show some similarities with maps of variation in trail traffic, the correlation between photographs and trail traffic is moderately weak (0.43), and there is no meaningful statistical correlation between tweets and trail traffic. Use of a simple log-log bivariate regression to estimate trail traffic from photographs results in relatively high error. The predictor variables included in published demand models for the same trails explain roughly the same amount of variation in photo-derived use, but some of the neighborhood socio-demographic and built-environment independent variables have different effects. Taken together, these findings show that both Flickr images and tweets have limitations as proxies for demand for urban trails, and that neither can be used to develop valid, reliable estimates of trail use. These results differ from previously published results that indicate social media may be useful in assessing relative demand for recreational destinations. This difference may be because urban trails are used for multiple purposes, including routine commuting and shopping, and that trail users are less inclined to use social media on trips for these purposes.References
Backstrom, L., Sun, E., & Marlow, C. (2010). Find me if you can: Improving geographical prediction with social and spatial proximity. Proceedings of the 19th International Conference on World Wide Web (pp. 61–70). doi: 10.1145/1772690.1772698
Cranshaw, J., Schwartz, R., Hong, J. I., & Sadeh, N. (2012). The livehoods project: Utilizing social media to understand the dynamics of a city. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (pp. 58–65).
El Esawey, M., Lim, C., Sayed, T., & Mosa, A. I. (2013). Development of daily adjustment factors for bicycle traffic. Journal of Transportation Engineering, 139(8), 859–871.
Federal Highway Administration (FHWA). (2013). Traffic monitoring guide: Chapter 4 traffic monitoring for non-motorized traffic V1. Washington DC: U.S. Department of Transportation, Federal Highway Administration.
Griffin, G., Nordback, K., Gotschi, T., Stolz, E., & Kothuri S. (2014). Monitoring bicycle and pedestrian travel behavior: Current research and practice. Transportation Research Circular E-C183. Washington, DC: Transportation Research Board of the National Academies.
Griffin, G. P., & Jiao, J. (2015). Where does bicycling for health happen? Analyzing volunteered geographic information through place and plexus. Journal of Transport and Health, 2(2), 238–247.
Hankey, S., Lindsey, G., & Marshall, J. (2014). Day-of-year scaling factors and design considerations for non-motorized traffic monitoring programs. Transportation Research Record, 2468, 64–73.
Jackson, K. Stolz, E., & Cunningham, C. (2015). Non-motorized site selection methods for continuous and short-duration volume counting. Transportation Research Record, 2527, 49–57.
Jestico, B., Nelson, T., & Winters, M. (2016). Mapping ridership using crowd-sourced cycling data. Journal of Transport Geography, 52, 90–97.
Keeler, B. L., Wood, S. A., Polasky, S., Kling, C., Filstrup, C. T., & Downing, J. A. (2015). Recreational demand for clean water: Evidence from geotagged photographs by visitors to lakes. Frontiers in Ecology and the Environment, 13(2), 76–81.
Lenormand, M., Tugores, A., Colet, P., & Ramasco, J. J. (2014). Tweets on the road. PLOS ONE, 9(8), e105407.
Lindsey, G., Petesch, M., & Hankey, S. (2015). The Minnesota bicycle and pedestrian counting initiative: Implementation study. MnDOT Report No. 2015-34. Minneapolis, MN: Minnesota Department of Transportation, Office of Transit, Bicycle/Pedestrian Section.
Lindsey, G., Wilson, J., Rubchinskaya, E., Yang, J., & Han, Y. (2007). Estimating urban trail traffic: Methods for existing and proposed trails. Landscape and Urban Planning, 81(4), 299–315.
Metropolitan Council. (2016). Annual Use Estimate of the Metropolitan Regional Parks System for 2014. St. Paul, MN: Metropolitan Council. Retrieved from http://metrocouncil.org/Parks/Publications-And-Resources/PARK-USE-REPORTS/2014-Annual-Use-Estimate-of-the-Regional-Parks-Sys.aspx
Miguéns, J., Baggio, R., & Costa, C. (2008). Social media and tourism destinations: TripAdvisor case study. Advances in Tourism Research, 26(28), 1–6.
Minge, E., Falero, C., Lindsey, G., & Petesch, M. (2015). Bicycle and pedestrian data collection manual (DRAFT). MnDOT Report No. MN/RC 2015-33. Minneapolis, MN: Minnesota Department of Transportation, Office of Transit, Bicycle/Pedestrian Section.
Miranda-Moreno, L. F., Nosal, T., Schneider, R. J., and Proulx, F. (2013). Classification of bicycle patterns in five North American cities. Transportation Research Record, 2339, 68–79. doi: 10.3141/2339-08
Nordback, K., Marshall, W., Janson, B., & Stolz, E. (2013). Estimating annual average daily bicyclists: Error and accuracy. Transportation Research Record, 2339, 90–97.
Richards, D. R., & Friess, D. A. (2015). A rapid indicator of cultural ecosystem service usage at a fine spatial scale: Content analysis of social media photographs. Ecological Indicators, 53, 187–195.
Sessions, C., Wood, S. A., Rabotyagov, S., & Fisher, D. (2016). Measuring recreational visitation at U.S. national parks with crowd-sourced photographs. Journal of Environmental Management, 183, 703–711.
Sharp, R., Tallis, H. T., Ricketts, T., Guerry, A. D., Wood, S. A., Chaplin-Kramer, R., … Bierbower, W. (2016). InVEST +VERSION+ User’s Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and the World Wildlife Fund.
Verma, R., Stock, D., & McCarthy, L. (2012). Customer preferences for online, social media, and mobile innovations in the hospitality industry. Cornell Hospitality Quarterly, 53(3), 183–186.
Wang J., Hankey, S., Wu, X., & Lindsey, G. (2016). Monitoring and modeling urban trail traffic: Validation of direct demand models in Minneapolis, Minnesota, and Columbus, Ohio. Transportation Research Record, 2593, 47–59. doi: 10.3141/2593-06
Wang, X., Lindsey, G., Hankey, S., & Hoff, K. (2014). Estimating mixed-mode urban trail traffic using negative binomial regression models. Journal of Urban Planning and Development, 140(1).
Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M. (2013). Using social media to quantify nature-based tourism and recreation. Scientific Reports, 3, 2976. doi:10.1038/srep02976
