Examining the effects of proximity to rail transit on travel to non-work destinations: Evidence from Yelp data for cities in North America and Europe
AbstractUrban planners often seek to establish land use patterns around transit stations that encourage non-auto travel. However, the willingness of travelers to use different modes in the vicinity of transit remains understudied, in part because of the lack of spatially-precise data on destination and mode choices. Using transportation content extracted from Yelp, a location-based social network (LBSN), we investigate how travel mode to non-work destinations is influenced by proximity to transit. We use textual analysis to analyze travel for non-work activities in seven cities across North America and Europe. Mixed-effect and binomial logistic models show how reported travel by mode varies by distance to rail transit stations. We find that for most non-work activity purposes, reported rail use is highly sensitive to proximity to stations, but some purposes are more amenable to rail use overall. Additionally, compared to non-US cities, US cities are far more parking-dependent near rail stations. The results suggest that not all activities elicit the same levels of non-auto travel, and transit-oriented planning should account for specific activities and regional factors that may modify willingness to travel by different modes. While subject to limitations, LBSNs can illuminate local travel with greater spatial specificity than traditional surveys.
Andrienko, G., Andrienko, N., Bosch, H., Ertl, T., Fuchs, G., Jankowski, P., & Thom, D. (2013). Thematic patterns in georeferenced tweets through space-time visual analytics. Computing in Science Engineering, 15(3), 72–82. doi: 10.1109/MCSE.2013.70
Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., … Green, P. (2017). lme4: Linear mixed-effects models using “Eigen” and S4 (Version 1.1-13). Retrieved from https://cran.r-project.org/web/packages/lme4/index.html
Boarnet, M. G., & Compin, N. S. (1999). Transit-oriented development in San Diego County: The incremental implementation of a planning idea. Journal of the American Planning Association, 65(1), 80–95. doi: 10.1080/01944369908976035
Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. doi: 10.1080/1369118X.2012.678878
Chaniotakis, E., Antoniou, C., Aifadopoulou, G., & Dimitriou, L. (2017). Inferring activities from social media data. Transportation Research Record: Journal of the Transportation Research Board, 2666(1), 29–37. doi: 10.3141/2666-04
Chatman, D. G. (2008). Deconstructing development density: Quality, quantity and price effects on household non-work travel. Transportation Research Part A: Policy and Practice, 42(7), 1008–1030. doi: 10.1016/j.tra.2008.02.003
Chatman, D. G. (2013). Does TOD need the T? Journal of the American Planning Association, 79(1), 17–31. doi: 10.1080/01944363.2013.791008
Chen, C., Gong, H., Lawson, C., & Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A: Policy and Practice, 44(10), 830–840. doi: 10.1016/j.tra.2010.08.004
Choe, Y., Kim, J., & Fesenmaier, D. R. (2017). Use of social media across the trip experience: An application of latent transition analysis. Journal of Travel & Tourism Marketing, 34(4), 431–443. doi: 10.1080/10548408.2016.1182459
Chung, N., & Koo, C. (2015). The use of social media in travel information search. Telematics and Informatics, 32(2), 215–229. doi: 10.1016/j.tele.2014.08.005
Clifton, K. J., Currans, K. M., Cutter, A. C., & Schneider, R. (2012). Household travel surveys in context-based approach for adjusting ITE trip generation rates in urban contexts. Transportation Research Record: Journal of the Transportation Research Board, 2307(1), 108–119. doi: 10.3141/2307-12
Cole, T. J. (1991). Applied logistic regression. In D. W. Hosmer & S. Lemeshow (Eds.), New York: John Wiley & Sons. https://doi.org/10.1002/sim.4780100718
Collins, C., Hasan, S., & Ukkusuri, S. (2013). A novel transit rider satisfaction metric: Rider sentiments measured from online social media data — National Center for Transit Research. Journal of Public Transportation, 16(2), 21–45.
Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013). Beyond the geotag: Situating ‘big data’ and leveraging the potential of the geoweb. Cartography and Geographic Information Science, 40(2), 130–139. doi: 10.1080/15230406.2013.777137
Curtin, K. M. (2007). Network analysis in geographic information science: Review, assessment, and projections. Cartography and Geographic Information Science, 34(2), 103–111. doi: 10.1559/152304007781002163
Czepiel, S. A. (2002). Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation. Retrieved from czep. net/stat/mlelr.pdf
Dittmar, H., & Ohland, G. (2012). The new transit town: Best practices in transit-oriented development. Washington, DC: Island Press.
Dziak, J. J., Coffman, D. L., Lanza, S. T., & Li, R. (2017). Sensitivity and specificity of information criteria. Manuscript submitted for publication. doi.org/10.7287/peerj.preprints.1103v3
ESRI. (2018). ArcGIS network analyst. Retrieved from https://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/what-is-network-analyst-.htm
Evans, L., & Saker, M. (2017). Location-based social media: Space, time and identity. New York: Springer.
Ewing, R., Tian, G., Lyons, T., & Terzano, K. (2017). Trip and parking generation at transit-oriented developments: Five US case studies. Landscape and Urban Planning, 160, 69–78. doi: 10.1016/j.landurbplan.2016.12.002
Faraway, J. J. (2016). Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models, second edition. Boca Raton, FL: CRC Press.
Forrest, T., & Pearson, D. (2005). Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transportation Research Record: Journal of the Transportation Research Board, 1917, 63–71. doi: 10.3141/1917-08
Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27.
Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 211–220). New York: ACM. doi: 10.1145/1518701.1518736
Greenwald, M. J., & Boarnet, M. G. (2001). The built environment as a determinant of walking behavior: Analyzing non-work pedestrian travel in Portland, Oregon. Retrieved from https://escholarship.org/uc/item/9gn7265f#metrics
Gruen, V. (1964). The heart of our cities: The urban crisis, diagnosis and cure. New York: Simon and Schuster.
Guerra, E., Cervero, R., & Tischler, D. (2012). Half-mile circle. Transportation Research Record: Journal of the Transportation Research Board, 2276, 101–109. doi: 10.3141/2276-12
Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites. The ANNALS of the American Academy of Political and Social Science, 659(1), 63–76. doi: 10.1177/0002716215570866
Hargittai, E. (2018). Potential biases in big data: Omitted voices on social media. Social Science Computer Review, 089443931878832. doi: 10.1177/0894439318788322
Higuchi, K. (2012). Quantitative content analysis or text mining by KH Coder. Retrieved from https://sourceforge.net/p/khc/wiki/KWIC%20Concordance/
Higuchi, K. (2014). KH Coder (Version 2.00 beta. 32). Retrieved from http://khcoder.net/en/
Hoback, A., Anderson, S., & Dutta, U. (2008). True walking distance to transit. Transportation Planning and Technology, 31(6), 681–692. doi: 10.1080/03081060802492785
Hong, A., Boarnet, M. G., & Houston, D. (2016). New light rail transit and active travel: A longitudinal study. Transportation Research Part A: Policy and Practice, 92, 131–144. doi: 10.1016/j.tra.2016.07.005
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Hoboken, NJ: Wiley.
Hu, X., & Liu, H. (2012). Text analytics in social media. In Mining text data (pp. 385–414). New York: Springer. doi: 10.1007/978-1-4614-3223-4_12
Ignatow, G., & Mihalcea, R. (2016). Text mining: A guidebook for the social sciences. Thousand Oaks, CA: SAGE Publications.
Jockers, M. (2014). Text analysis with R for students of literature. New York: Springer.
Kelley, M. J. (2013). The emergent urban imaginaries of geosocial media. GeoJournal, 78(1), 181–203. doi: 10.1007/s10708-011-9439-1
King, G., & Zeng, L. (2001). Explaining rare events in international relations. International Organization, 55(3), 693–715. doi: 10.1162/00208180152507597
Kovacs-Györi, A., Ristea, A., Kolcsar, R., Resch, B., Crivellari, A., & Blaschke, T. (2018). Beyond spatial proximity—classifying parks and their visitors in London based on spatiotemporal and sentiment analysis of Twitter data. ISPRS International Journal of Geo-Information, 7(9), 378. doi: 10.3390/ijgi7090378
Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Thousand Oaks, CA: SAGE.
Kurkcu, A., Ozbay, K., & Morgul, E. F. (2016). Evaluating the usability of geo-located Twitter as a tool for human activity and mobility patterns: A case study for New York City. In TRB 95th Annual Meeting Compendium of Papers. Retrieved from https://trid.trb.org/view/1393445
Lierop, D., Maat, K., & El-Geneidy, A. (2017). Talking TOD: Learning about transit-oriented development in the United States, Canada, and the Netherlands. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 10(1), 49–62. doi: 10.1080/17549175.2016.1192558
Litman, T. (2011). Evaluating accessibility for transportation planning: Measuring people’s ability to reach desired goods and activities. Victoria, BC: Victoria Transport Policy Institute.
Lüdecke, D. (2018). sjPlot-package: Data visualization for statistics in social science in sjPlot. Retrieved from https://CRAN.R-project.org/package=sjPlot
Manca, M., Boratto, L., Morell Roman, V., Martori i Gallissà, O., & Kaltenbrunner, A. (2017). Using social media to characterize urban mobility patterns: State-of-the-art survey and case-study. Online Social Networks and Media, 1, 56–69. doi: 10.1016/j.osnem.2017.04.002
Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the Digital Humanities (pp. 460–475). Minneapolis: University of Minnesota Press. doi: 10.5749/minnesota/9780816677948.003.0047
Markov, Z., & Larose, D. T. (2007). Data mining the web: Uncovering patterns in Web content, structure, and usage. Hoboken, NJ: John Wiley & Sons.
Mcculloch, C., & Neuhaus, J. (2001). Generalized linear mixed models. Hoboken, NJ: John Wiley & Sons.
Mjahed, L. B., Mittal, A., Elfar, A., Mahmassani, H. S., & Chen, Y. (2017). Exploring the role of social media platforms in informing trip planning. Transportation Research Record: Journal of the Transportation Research Board, 2666, 1–9. doi: 10.3141/2666-01
Mondschein, A. (2015). Five-star transportation: Using online activity reviews to examine mode choice to non-work destinations. Transportation, 42(4), 707–722. doi: 10.1007/s11116-015-9600-7
Munar, A. M., & Jacobsen, J. K. S. (2013). Trust and involvement in tourism social media and web-based travel information sources. Scandinavian Journal of Hospitality and Tourism, 13(1), 1–19. doi: 10.1080/15022250.2013.764511
Murray, A. T., Davis, R., Stimson, R. J., & Ferreira, L. (1998). Public transportation access. Transportation Research Part D: Transport and Environment, 3(5), 319–328. doi: 10.1016/S1361-9209(98)00010-8
Nelson, D., & Niles, J. (1999). Essentials for transit-oriented development planning: Analysis of non-work activity patterns and a method for predicting success. Proceedings of the 7th TRB Conference on the Application of Transportation Planning Methods, Boston, Massachusetts. Retrieved from http://docs.trb.org/00939750.pdf
Nikšič, M., Campagna, M., Massa, P., Caglioni, M., & Nielsen, T. (2017). Opportunities for volunteered geographic information use in spatial planning. In Mapping and the citizen sensor (pp. 327–349). London: Ubiquity Press. Retrieved from https://www.ubiquitypress.com/site/chapters/10.5334/bbf.n/
Noland, R. B., Weiner, M. D., DiPetrillo, S., & Kay, A. I. (2017). Attitudes towards transit-oriented development: Resident experiences and professional perspectives. Journal of Transport Geography, 60, 130–140. doi: 10.1016/j.jtrangeo.2017.02.015
Olszewski, P., & Wibowo, S. (2005). Using equivalent walking distance to assess pedestrian accessibility to transit stations in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 1927, 38–45. doi: 10.3141/1927-05
O’Sullivan, S., & Morrall, J. (1996). Walking distances to and from light-rail transit stations. Transportation Research Record: Journal of the Transportation Research Board, 1538, 19–26. doi: 10.3141/1538-03
Quantcast. (2017). Yelp audience insights and demographic analytics. Retrieved from https://www.quantcast.com/yelp.com/demographics/WEB?country=US
R Core Team. (2017). R: A language and environment for statistical computing. Retrieved from https://www.r-project.org/
Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S., & Waller, T. S. (2017). Exploring the capacity of social media data for modelling travel behavior: Opportunities and challenges. Transportation Research Part C: Emerging Technologies, 75, 197–211. doi: 10.1016/j.trc.2016.12.008
Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 532–538). New York: Springer. doi: 10.1007/978-0-387-39940-9_565
Reilly, M. K., O’Mara, M. P., & Seto, K. C. (2009). From Bangalore to the Bay Area: Comparing transportation and activity accessibility as drivers of urban growth. Landscape and Urban Planning, 92(1), 24–33. doi: 10.1016/j.landurbplan.2009.02.001
Rissel, C., Curac, N., Greenaway, M., & Bauman, A. (2012). Physical activity associated with public transport use—a review and modelling of potential benefits. International Journal of Environmental Research and Public Health, 9(7), 2454–2478. doi: 10.3390/ijerph9072454
Rybarczyk, G., Banerjee, S., Starking-Szymanski, M. D., & Shaker, R. R. (2018). Travel and us: The impact of mode share on sentiment using geo-social media and GIS. Journal of Location Based Services, 12(1), 40–62. doi: 10.1080/17489725.2018.1468039
Sedera, D., Lokuge, S., Atapattu, M., & Gretzel, U. (2017). Likes—the key to my happiness: The moderating effect of social influence on travel experience. Information & Management, 54(6), 825–836. doi: 10.1016/j.im.2017.04.003
Senaratne, H., Mobasheri, A., Ali, A. L., Capineri, C., & Haklay, M. (2017). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1), 139–167. doi: 10.1080/13658816.2016.1189556
Statistics Canada. (2016). Data products, 2016 Census. Retrieved from https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-eng.cfm
Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics — challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168. doi: 10.1016/j.ijinfomgt.2017.12.002
Stopher, P., Jiang, Q., & FitzGerald, C. (2005). Processing GPS data from travel surveys. Australasian Transport Research Forum (ATRF), 28th, 2005, Sydney, New South Wales, Australia.
Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381. doi: 10.1016/j.tra.2006.09.005
Trajdos, P., & Kurzynski, M. (2018). Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix. Pattern Recognition Letters, 103, 60–67. doi:org/10.1016/j.patrec.2018.01.012
Tung, E. (2015, September 2). Automatically categorizing Yelp businesses. Retrieved from https://engineeringblog.yelp.com/2015/09/automatically-categorizing-yelp-businesses.html
U.S. Census Bureau. (2016). US Census Bureau: American community survey, 2016 5-year estimates. Suitland, MD: US Census Bureau.
Vinithra, S. N., Selvan, S. J. A., Kumar, M. A., & Soman, K. P. (2015). Simulated and self-sustained classification of Twitter data based on its sentiment. Indian Journal of Science and Technology, 8(24), 1–7. doi: 10.17485/ijst/2015/v8i24/80205
Vuchic, V. R. (2017). Urban transit: Operations, planning and economics. Hoboken, NJ: J. Wiley & Sons.
Walle, S., & Steenberghen, T. (2006). Space and time related determinants of public transport use in trip chains. Transportation Research Part A: Policy and Practice, 40(2), 151–162. doi: 10.1016/j.tra.2005.05.001
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. doi: 10.1109/TKDE.2013.109
Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. doi: 10.1016/j.tourman.2009.02.016
Yelp. (2017a). API 2.0: All category list. Yelp for developers. Retrieved from https://www.yelp.com/developers/documentation/v3/all_category_list
Yelp. (2017b, January). Yelp dataset challenge. Retrieved from https://www.yelp.com/dataset_challenge
Yelp. (2017c, March). Yelp factsheet. Retrieved from https://www.yelp.com/factsheet
Copyright (c) 2019 Zhiqiu Jiang, Andrew Mondschein
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.