References
- Ahas, R., S. Silm, O. Järv, E. Saluveer, and M. Tiru. 2010. Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology 17 (1):3–27. doi: https://doi.org/10.1080/10630731003597306.
- Cao, J., Q. Li, W. Tu, and F. Wang. 2019. Characterizing preferred motif choices and distance impacts. PLoS ONE 14 (4):e0215242. doi: https://doi.org/10.1371/journal.pone.0215242.
- Chechik, G., E. Oh, O. Rando, J. Weissman, A. Regev, and D. Koller. 2008. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nature Biotechnology 26 (11):1251–59. doi: https://doi.org/10.1038/nbt.1499.
- Chen, J., S. L. Shaw, H. Yu, F. Lu, Y. Chai, and Q. Jia. 2011. Exploratory data analysis of activity diary data: A space-time GIS approach. Journal of Transport Geography 19 (3):394–404. doi: https://doi.org/10.1016/j.jtrangeo.2010.11.002.
- Chi, G., and J. Zhu. 2019. Spatial regression models for the social sciences. Thousand Oaks, CA: Sage.
- Chicago Metropolitan Agency for Planning. 2019. Land use inventory for northeast Illinois, 2013 from Chicago Metropolitan Agency for Planning (CMAP). Accessed October 11, 2019. https://datahub.cmap.illinois.gov/dataset/land-use-inventory-for-northeast-illinois-2013.
- Cho, E., S. A. Myers, and J. Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining—KDD ’11, 1082–90. New York: ACM. doi: https://doi.org/10.1145/2020408.2020579.
- Cordella, L. P., P. Foggia, C. Sansone, and M. Vento. 2001. An improved algorithm for matching large graphs. In Proceedings of the 3rd IAPR-TC15 Workshop on Graph-Based Representations in Pattern Recognition, ed. J. Michel Jolion, M. Vento, and W. Kropatsch, 149–59. Ischia, Italy: C. U. E. N.
- Crawford, K., and M. Finn. 2015. The limits of crisis data: Analytical and ethical challenges of using social and mobile data to understand disasters. GeoJournal 80 (4):491–502. doi: https://doi.org/10.1007/s10708-014-9597-z.
- Davies, G., J. Dixon, C. G. Tredoux, J. D. Whyatt, J. J. Huck, B. Sturgeon, B. T. Hocking, N. Jarman, and D. Bryan. 2019. Networks of (dis) connection: Mobility practices, tertiary streets, and sectarian divisions in North Belfast. Annals of the American Association of Geographers 109 (6):1729–47. doi: https://doi.org/10.1080/24694452.2019.1593817.
- Frank, L. D., and P. O. Engelke. 2001. The built environment and human activity patterns: Exploring the impacts of urban form on public health. Journal of Planning Literature 16 (2):202–18. doi: https://doi.org/10.1177/08854120122093339.
- Frank, M. R., L. Mitchell, P. S. Dodds, and C. M. Danforth. 2013. Happiness and the patterns of life: A study of geolocated tweets. Scientific Reports 3 (1):2625. doi: https://doi.org/10.1038/srep02625.
- González, M. C., C. A. Hidalgo, and A. L. Barabási. 2008. Understanding individual human mobility patterns. Nature 453 (7196):779–82. doi: https://doi.org/10.1038/nature06958.
- Greenwood, S., A. Perrin, and M. Duggan. 2016. Social media update 2016. Washington, DC: Pew Research Center.
- Hagberg, A. A., D. A. Schult, and P. J. Swart. 2008. Exploring network structure, dynamics, and function using NetworkX. In 7th Python in Science Conference (SciPy 2008), ed. G. Varoquaux, T. Vaught, and J. Millman, 11–16. Pasadena, CA: Scipy Conference.
- Hawelka, B., I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti. 2014. Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science 41 (3):260–71. doi: https://doi.org/10.1080/15230406.2014.890072.
- Huang, Q., and D. W. S. Wong. 2016. Activity patterns, socioeconomic status and urban spatial structure: What can social media data tell us? International Journal of Geographical Information Science 30 (9):1873–98. doi: https://doi.org/10.1080/13658816.2016.1145225.
- Jenkins, A., A. Croitoru, A. T. Crooks, and A. Stefanidis. 2016. Crowdsourcing a collective sense of place. PLoS ONE 11 (4):e0152932. doi: https://doi.org/10.1371/journal.pone.0152932.
- Jiang, S., A. Alves, F. Rodrigues, J. Ferreira, and F. C. Pereira. 2015. Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems 53:36–46. doi: https://doi.org/10.1016/j.compenvurbsys.2014.12.001.
- Jiang, S., J. Ferreira, and M. C. González. 2012a. Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery 25 (3):478–510. doi: https://doi.org/10.1007/s10618-012-0264-z.
- Jiang, S., J. Ferreira, and M. C. González. 2012b. Discovering urban spatial–temporal structure from human activity patterns. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing—UrbComp ’12, ed. Y. Zheng and O. E. Wolfson, 95–102. New York: ACM. doi: https://doi.org/10.1145/2346496.2346512.
- Jiang, S., J. Ferreira, and M. C. González. 2017. Activity-based human mobility patterns inferred from mobile phone data: A case study of Singapore. IEEE Transactions on Big Data 3 (2):208–19. doi: https://doi.org/10.1109/TBDATA.2016.2631141.
- Jiang, S., Y. Yang, S. Gupta, D. Veneziano, S. Athavale, and M. C. González. 2016. The TimeGeo modeling framework for urban motility without travel surveys. Proceedings of the National Academy of Sciences of the United States of America 113 (37):E5370–78. doi: https://doi.org/10.1073/pnas.1524261113.
- Jurdak, R., K. Zhao, J. Liu, M. AbouJaoude, M. Cameron, and D. Newth. 2015. Understanding human mobility from Twitter. PLoS ONE 10 (7):e0131469. doi: https://doi.org/10.1371/journal.pone.0131469.
- Kung, K. S., K. Greco, S. Sobolevsky, and C. Ratti. 2014. Exploring universal patterns in human home-work commuting from mobile phone data. PLoS ONE 9 (6):e96180. doi: https://doi.org/10.1371/journal.pone.0096180.
- Kwan, M. 2016. Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the American Association of Geographers 106 (2):274–82.
- Liu, Y., X. Liu, S. Gao, L. Gong, C. Kang, Y. Zhi, G. Chi, and L. Shi. 2015. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers 105 (3):512–30. doi: https://doi.org/10.1080/00045608.2015.1018773.
- Lu, X., E. Wetter, N. Bharti, A. J. Tatem, and L. Bengtsson. 2013. Approaching the limit of predictability in human mobility. Scientific Reports 3 (1):2923. doi: https://doi.org/10.1038/srep02923.
- Luo, F., G. Cao, K. Mulligan, and X. Li. 2016. Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago. Applied Geography 70:11–25. doi: https://doi.org/10.1016/j.apgeog.2016.03.001.
- Lynch, K. 1960. The image of the city. Vol. 11. Cambridge, MA: MIT Press.
- Masoudi-Nejad, A., F. Schreiber, and Z. R. M. Kashani. 2012. Building blocks of biological networks: A review on major network motif discovery algorithms. IET Systems Biology 6 (5):164–74. doi: https://doi.org/10.1049/iet-syb.2011.0011.
- MassGIS. 2019. MassGIS (Bureau of Geographic Information) Level 3 assessors’ parcel mapping data set. Accessed November 30, 2019. https://docs.digital.mass.gov/dataset/level-3-assessors-parcels-all-items.
- Milo, R., S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. Network motifs: Simple building blocks of complex networks. Science 298 (5594):824–27. doi: https://doi.org/10.1126/science.298.5594.824.
- Newman, M. 2018. Networks. London: Oxford University Press.
- Paranjape, A., A. R. Benson, and J. Leskovec. 2017. Motifs in temporal networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining—WSDM ’17, 601–10. New York: ACM. doi: https://doi.org/10.1145/3018661.3018731
- Schneider, C. M., V. Belik, T. Couronné, Z. Smoreda, and M. C. González. 2013. Unravelling daily human mobility motifs. Journal of the Royal Society, Interface 10 (84):20130246. doi: https://doi.org/10.1098/rsif.2013.0246.
- Schneider, C. M., C. Rudloff, D. Bauer, and M. C. González. 2013. Daily travel behavior: Lessons from a week-long survey for the extraction of human mobility motifs related information. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing—UrbComp ’13, ed. Y. Zheng, S. E. Koonin, and O. E. Wolfson, 1–7. New York: ACM Press. doi: https://doi.org/10.1145/2505821.2505829.
- Shao, H., Y. Zhang, and W. Li. 2017. Extraction and analysis of city’s tourism districts based on social media data. Computers, Environment and Urban Systems 65:66–78. doi: https://doi.org/10.1016/j.compenvurbsys.2017.04.010.
- Soliman, A., K. Soltani, J. Yin, A. Padmanabhan, and S. Wang. 2017. Social sensing of urban land use based on analysis of Twitter users’ mobility patterns. PLoS ONE 12 (7):e0181657. doi: https://doi.org/10.1371/journal.pone.0181657.
- Soliman, A., J. Yin, K. Soltani, A. Padmanabhan, and S. Wang. 2015. Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users. In Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics—UrbanGIS’15, 55–58. New York: ACM. doi: https://doi.org/10.1145/2835022.2835032.
- Song, C., Z. Qu, N. Blumm, and A.-L. Barabasi. 2010. Limits of predictability in human mobility. Science 327 (5968):1018–21. doi: https://doi.org/10.1126/science.1177170.
- Sun, Y., H. Fan, M. Li, and A. Zipf. 2016. Identifying the city center using human travel flows generated from location-based social networking data. Environment and Planning B: Planning and Design 43 (3):480–98. doi: https://doi.org/10.1177/0265813515617642.
- U.S. Census Bureau. 2020. American Community Survey, 2012–2016 5-year estimates. Accessed January 29, 2020. https://www.census.gov/programs-surveys/acs.
- Xi, W., C. A. Calder, and C. R. Browning. 2020. Beyond activity space: Detecting communities in ecological metworks. Annals of the American Association of Geographers 110 (6):1787–20. doi: https://doi.org/10.1080/24694452.2020.1715779.
- Yin, J., G. Chi, and J. Van Hook. 2018. Evaluating the representativeness in the geographic distribution of Twitter user population. In Proceedings of the 12th Workshop on Geographic Information Retrieval—GIR’18, ed. R. S. Purves and C. B. Jones, 1–2. New York: ACM. doi: https://doi.org/10.1145/3281354.3281360.
- Yin, J., Y. Gao, Z. Du, and S. Wang. 2016. Exploring multi-scale spatiotemporal Twitter user mobility patterns with a visual-analytics approach. ISPRS International Journal of Geo-Information 5 (10):187. doi: https://doi.org/10.3390/ijgi5100187.
- Yin, J., A. Soliman, D. Yin, and S. Wang. 2017. Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data. International Journal of Geographical Information Science 31 (7):1293–313. doi: https://doi.org/10.1080/13658816.2017.1282615.
- Zagheni, E., and I. Weber. 2015. Demographic research with non-representative Internet data. International Journal of Manpower 36 (1):13–25. doi: https://doi.org/10.1108/IJM-12-2014-0261.