Using location-based social network data for activity intensity analysis: A case study of New York City


Location-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on disaggregate patterns or aggregate analysis of social and temporal attributes, no attempt has been made to relate the data to transportation planning outcomes. To that extent, the current study employs LBSN service-based data for an aggregate-level transportation planning exercise by developing land-use planning models. Specifically, we employ check-in data aggregated at the census tract level to develop a quantitative model for activity intensity as a function of land use and built-environment attributes for the New York City (NYC) region. A statistical exercise based on clustering of census tracts and negative binomial regression analyses are adopted to analyze the aggregated data. We demonstrate the implications of the estimated models by presenting the spatial aggregation profiling based on the model estimates. The findings provide insights on relative differences of activity engagements across the urban region. The proposed approach thus provides a complementary analysis tool to traditional transportation planning exercises.

Author Biographies

Haluk Laman, University of Central Florida
Research Assistant Department of Civil, Environmental & Construction Engineering
Shamsunnahar Yasmin, University of Central Florida
Postdoctoral Associate Department of Civil, Environmental & Construction Engineering
Naveen Eluru, University of Central Florida
Associate Professor Department of Civil, Environmental and Construction Engineering


Ahas, R., Aasa, A., Yuan, Y., Raubal, M., Smoreda, X., Liu, Y., … Zook, M. (2015). Everyday space–time geographies: Using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. International Journal of Geographical Information Science, 29(11), 2017–2039.

Ahmed, A., Hong, L., & Smola, A. J. (2013). Hierarchical geographical modeling of user locations from social media posts. Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, May 13-17.

Anderberg, M. R. (2014). Cluster analysis for applications: Probability and mathematical statistics: A series of monographs and textbooks, vol. 19. Cambridge, MA: Academic Press.

Bawa-Cavia, A. (2011). Sensing the urban: Using location-based social network data in urban analysis. Pervasive PURBA Workshop, San Francisco, June 12-15.

Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., & Soltani, K. (2015). A scalable framework for spatiotemporal analysis of location-based social media data Computers, Environment and Urban Systems, 51, 70–82.

Cheng, Z., Caverlee, J., Lee, K., & Sui, D. Z. (2011). Exploring millions of footprints in location sharing services. ICWSM, 2010, 81–88.

Cranshaw, J., Hong, J. I., & Sadeh, N. (2012). The livehoods project: Utilizing social media to understand the dynamics of a city. The 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, June 4–7.

Do, T. M. T, & Gatica-Perez, D. (2013). The places of our lives: Visiting patterns and automatic labeling from longitudinal smartphone data. IEEE Transportation Mobility Computations, 1, 1.

Frias-Martinez, V., Soto, V., Hohwald, H., & Frias-Martinez, E. (2012). Characterizing urban landscapes using geolocated tweets. Proceedings 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust & 2012 ASE/IEEE International Conference on Social Computing, SocialCom/PASSAT 2012, Washington DC, September 3–5.

Frias-Martinez, V., & Frias-Martinez, E. (2014). Spectral clustering for sensing urban land use using Twitter activity. Engineering Applications of Artificial Intelligence, 35, 237–245.

Gordon, E., & de Souza e Silva, A. (2011). Net locality: Why location matters in a networked world. Hoboken, NJ: John Wiley & Sons.

Hasan, S., & Ukkusuri, S. V. (2014). Urban activity pattern classification using topic models from online geo-location data, Transportation Research Part C: Emerging Technologies, 44, 363–381.

Hu., Y., Gao, S., Janowicz, K., Yu, B., Li, W., & Prasad, S. (2015). Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240–254.

Karlaftis, M. G., & Tarko, A. P. (1998). Heterogeneity considerations in accident modeling. Accident Analysis & Prevention, 30(4), 425–433.

Klin, F. & Pozdnoukhov, A. (2012). When a city tells a story: Urban topic analysis. Proceedings of the 20th International Conference on Advances in Geographic Information Systems-SIGSPATIAL '12, 482-485. New York: ACM.

Li, L., Goodchild, M. F., & Xu, B. (2013). Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr, Cartography and Geographic Information Science, 40(2), 61–77.

Miller, H. J. (2014). Activity-based analysis. Handbook of regional science (pp. 705724). New York: Springer.

Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., & Mascolo, C. (2012). A tale of many cities: Universal patterns in human urban mobility. PLoS One, 7(5): e37027.

Pew Research Center. (2017). Social media fact sheet, Pew Res. Cent. Internet, Sci. Tech. Retrieved from

Pinjari, A., Eluru, N., Bhat, C., Pendyala, R., & Spissu, E. (2008). Joint model of choice of residential neighborhood and bicycle ownership: Accounting for self-selection and unobserved heterogeneity. Transportation Research Record, 2082, 17–26.

Pinjari, A. R., & Bhat, C. R. (2011). Activity-based travel demand analysis. In A handbook of transport Economics. Cheltenham, UK: Edward Elgar Publishing.

Rzeszewski, M. (2018). Geosocial capta in geographical research–a critical analysis, Cartography and Geographic Information Science, 45(1), 18–30.

Sengstock, C., & Gertz, M. (2012). Latent geographic feature extraction from social media. Proceedings of the 20th International Conference on Advances in Geographic Information Systems-SIGSPATIAL '12, 149–158. New York: ACM.

Sloan, L., Morgan, J., Burnap, P., & Williams, M. (2015). Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS One, 10(3), e0115545.

Wakamiya, S., Lee, R., & Sumiya, K. (2011). Urban area characterization based on semantics of crowd activities in Twitter (Including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Lecture Notes in Computer Science, 6631, 108–123.

Winkelmann, R. (2008). Econometric analysis of count data. Berlin, Germany: Springer Science & Business Media.

Zhan, X., Ukkusuri, S. V., & Zhu, F. (2014). Inferring urban land use using large-scale social media check-in data. Networks and Spatial Economics, 14(3–4), 647–667.

How to Cite
Laman, H., Yasmin, S., & Eluru, N. (2019). Using location-based social network data for activity intensity analysis: A case study of New York City. Journal of Transport and Land Use, 12(1).