A review of the use of geosocial media data in agent-based models for studying urban systems

References

• Abar, S., Theodoropoulos, G. K., Lemarinier, P., & O’Hare, G. M. (2017). Agent based modelling and simulation tools: A review of the state-of-art software. Computer Science Review, 24, 13–33.
   Google Scholar
• Anagnostopoulos, C. B., Tsounis, A., & Hadjiefthymiades, S. (2007). Context awareness in mobile computing environments. Wireless Personal Communications, 42(3), 445–464.
   Google Scholar
• Bao, J., Lian, D., Zhang, F., & Yuan, N. J. (2016). Geosocial media data analytic for user modeling and location-based services. The SIGSPATIAL Special, 7(3), 11–18.
   Google Scholar
• Barbati, M., Bruno, G., & Genovese, A. (2012). Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications, 39(5), 6020–6028.
   Google Scholar
• Batty, M. (2013). The new science of cities. Cambridge, Massachusetts: MIT press.
   Google Scholar
• Bazzan, A. L. C., & Klügl, F. (2013). A review on agent-based technology for traffic and transportation. The Knowledge Engineering Review, 29, 1–29.
   Google Scholar
• Beigi, G., & Liu, H. (2020). A survey on privacy in social media: Identification, mitigation, and applications. ACM Transactions on Data Science, 1(1), 1–38.
   Google Scholar
• Bellifemine, F., Poggi, A., & Rimassa, G. (1999). JADE–A FIPA-compliant agent framework. Proceedings of PAAM, 99(97–108), 33.
   Google Scholar
• Berger, T. (2001). Agent-based spatial models applied to agriculture: A simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics, 25(2–3), 245–260.
   Google Scholar
• Bettencourt, L. M., Lobo, J., Strumsky, D., & West, G. B. (2010). Urban scaling and its deviations: Revealing the structure of wealth, innovation and crime across cities. PloS One, 5(11), e13541.
   Google Scholar
• Bohensky, E., Smajgl, A., & Herr, A. (2007). Calibrating behavioural variables in agent-based models: Insights from a case study in East Kalimantan, Indonesia. In MODSIM 2007 International Congress on Modelling and Simulation. Christchurch, New Zealand: Modelling and Simulation Society of Australia and New Zealand.
   Google Scholar
• Bonabeau, E. (2002). Agent-based Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the USA, 99(10), 7280–7287.
   Google Scholar
• Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In M. Kennedy, G. W. Winch, R. S. Langer, J. I. Rowe, & J. M. Yanni (Eds.), Proceedings of the 22nd international conference of the system dynamics society (Vol. 22, p. 45). Oxford.
   Google Scholar
• Bretagnolle, A., Daudé, E., & Pumain, D. (2006). From theory to modelling: Urban systems as complex systems. Cybergeo: European Journal of Geography, 1–23. doi:10.4000/cybergeo.2420
   Google Scholar
• Cocks, D. (2006). How Should We Use the Term “ System of Systems “ and Why Should We Care? Introduction to the Problem. INCOSE International Symposium, 16(1), 427–438.
   Google Scholar
• Coffey, W. J. (1998). Urban systems research: An overview. Canadian Journal of Regional Science, 21(3), 327–364.
   Google Scholar
• Cohen, B. (2006). Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society, 28(1–2), 63–80.
   Google Scholar
• Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In Agent-Based Models of Geographical Systems, 164, 85–105.
   Google Scholar
• Darvishi, M., & Ahmadi, G. (2014). Validation techniques of agent based modelling for geospatial simulations. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 40/2W3, 91–95. 10.5194/isprsarchives-XL-2-W3-91-2014
   Google Scholar
• Davis, J. P., Eisenhardt, K. M., & Bigham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32(2), 480–499.
   Google Scholar
• Durak, M., & Till, R. (2015). Optimizing an agent-based traffic evacuation model using genetic algorithms. In L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, & M. D. Rossetti (Eds.), Proceedings of the 2015 Winter Simulation Conference (pp. 288–299). Huntington Beach: IEEE Xplore.
   Google Scholar
• Ebrahimpour, Z., Wan, W., Velázquez García, J. L., Cervantes, O., & Hou, L. (2020). Analyzing social-geographic human mobility patterns using large-scale social media data. ISPRS International Journal of Geo-Information, 9(2), 125.
   Google Scholar
• Facebook Inc. (2019a). Facebook annual report. Retrieved June 19, 2020, from. https://investor.fb.com/financials/?section=annualreports
   Google Scholar
• Facebook Inc. (2019b). The graph API. Retrieved June 19, 2020, from. https://developers.facebook.com/docs/graph-api
   Google Scholar
• Fortino, G., Guerrieri, A., Russo, W., & Savaglio, C. (2014). Integration of agent-based and cloud computing for the smart objects-oriented IoT. In Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design (CSCWD) (pp. 493–498). Hsinchu, Taiwan, China: IEEE.
   Google Scholar
• França, U., Sayama, H., McSwiggen, C., Daneshvar, R., & Bar‐Yam, Y. (2016). Visualizing the “heartbeat” of a city with tweets. Complexity, 21(6), 280–287.
   Google Scholar
• Frias-Martinez, E., Williamson, G., & Frias-Martinez, V. (2011). An agent-based model of epidemic spread using human mobility and social network information. 2011 IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom), Boston, MA, 49–56. https://doi.org/10.1109/PASSAT/SocialCom.2011.142
   Google Scholar
• Fujita, M., Krugman, P., & Mori, T. (1999). On the evolution of hierarchical urban systems. European Economic Review, 43(2), 209–251.
   Google Scholar
• Fujita, M., & Mori, T. (1997). Structural stability and evolution of urban systems. Regional Science and Urban Economics, 27(4–5), 399–442.
   Google Scholar
• Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223(4), 120–123.
   Google Scholar
• Ge, X., & Kremers, E. (2015). Optimization applied with agent based modelling in the context of urban energy planning. 2015 Winter Simulation Conference (WSC), 3096–3097. 10.1109/WSC.2015.7408417
   Google Scholar
• Ghavami, S. M., Taleai, M., & Arentze, T. (2016). Socially rational agents in spatial land use planning: A heuristic proposal based negotiation mechanism. Computers, Environment and Urban Systems, 60, 67–78.
   Google Scholar
• Goh, G., Koh, J. Y., & Zhang, Y. (2019). Twitter-informed crowd flow prediction. IEEE International Conference on Data Mining Workshops, ICDMW, 2018-November, 624–631. 10.1109/ICDMW.2018.00097
   Google Scholar
• Gomide, J., Veloso, A., Meira, W., Almeida, V., Benevenuto, F., Ferraz, F., & Teixeira, M. (2011). Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. Proceedings of the 3rd International Web Science Conference (1–8). 10.1145/2527031.2527049
   Google Scholar
• Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768.
   Google Scholar
• Haer, T., Botzen, W. J. W., & Aerts, J. C. J. H. (2016). The effectiveness of flood risk communication strategies and the influence of social networks—Insights from an agent-based model. Environmental Science & Policy, 60, 44–52.
   Google Scholar
• Hall, A., & Virrantaus, K. (2016). Visualizing the workings of agent-based models: Diagrams as a tool for communication and knowledge acquisition. Computers, Environment and Urban Systems, 58, 1–11.
   Google Scholar
• Heppenstall, A., Malleson, N., & Crooks, A. (2016). “Space, the final frontier”: How good are agent-based models at simulating individuals and space in cities? Systems, 4(1), 9.
   Google Scholar
• Hristova, D., Williams, M. J., Musolesi, M., Panzarasa, P., & Mascolo, C. (2016). Measuring urban social diversity using interconnected geosocial networks. Proceedings of the 25th International Conference on World Wide Web, 21–30. 10.1145/2872427.2883065
   Google Scholar
• Hua, T., Zhao, L., Chen, F., Lu, C.-T., & Ramakrishnan, N. (2016, November). How events unfold: Spatiotemporal mining in social media. The SIGSPATIAL Special, 7(3), 19–25.
   Google Scholar
• Huang, W., & Li, S. (2016). Understanding human activity patterns based on space-time-semantics. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 1–10.
   Google Scholar
• Huang, W., Li, S., Liu, X., & Ban, Y. (2015). Predicting human mobility with activity changes. International Journal of Geographical Information Science, 29(9), 1569–1587.
   Google Scholar
• Huber, M., Knottnerus, J. A., Green, L., van der Horst, H., Jadad, A. R., Kromhout, D., … Schnabel, P. (2011). How should we define health? BMJ, 343, 1–3.
   Google Scholar
• Immonen, A., Pääkkönen, P., & Ovaska, E. (2015). Evaluating the quality of social media data in big data architecture. IEEE Access, 3, 2028–2043.
   Google Scholar
• Ingalls, R. G. (2008). Introduction to Simulation. In S. Mason, R. Hill, M. Lars, & O. Rose (Eds.), Proceedings of the 40th Conference on Winter Simulation (pp. 17–26). Retrieved from http://www.informs-sim.org/wsc08papers/263.pdf
   Google Scholar
• Jiang, W., Wang, Y., Tsou, M. H., & Fu, X. (2016). Using geo-targeted social media data to detect outdoor air pollution. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41, 553–554.
   Google Scholar
• Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and Geographic Information Science, 46(3), 228–242.
   Google Scholar
• Johnson, B., & Hernandez, A. (2016). Exploring Engineered Complex Adaptive Systems of Systems. In C. H. Dagli (Ed.). Procedia Computer Science, 95, 58–65. .
   Google Scholar
• Kavak, H., Padilla, J. J., Lynch, C. J., & Diallo, S. Y. (2018, April). Big data, agents, and machine learning: Towards a data-driven agent-based modeling approach. In Proceedings of the Annual Simulation Symposium (pp. 1–12). Baltimore, Maryland.
   Google Scholar
• Kim, H. S. (2016). What drives you to check in on Facebook? Motivations, privacy concerns, and mobile phone involvement for location-based information sharing. Computers in Human Behavior, 54, 397–406.
   Google Scholar
• Law, A. M. (2008). How to build valid and credible simulation models. In S. Mason, R. Hill, L. Monch, & O. Rose (Eds.), Proceedings of the 40th Conference on Winter Simulation (pp. 39–47). Retrieved from http://dl.acm.org/citation.cfm?id=1516758
   Google Scholar
• Levy, S., Martens, K., & van der Heijden, R. (2016). Agent-based models and self-organisation: Addressing common criticisms and the role of agent-based modelling in urban planning. Town Planning Review, 87(3), 321–338.
   Google Scholar
• Li, Q., Shah, S., Thomas, M., Anderson, K., & Liu, X. (2016b). How much data do you need? Twitter decahose data analysis. In The 9th International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction. Washington, DC, USA.
   Google Scholar
• Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., … Cheng, T. (2016a). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133.
   Google Scholar
• Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014). Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4), 46–50.
   Google Scholar
• Luo, W., Gao, P., & Cassels, S. (2018, July). A large-scale location-based social network to understanding the impact of human geosocial interaction patterns on vaccination strategies in an urbanized area. Computers, Environment and Urban Systems, 72, 78–87.
   Google Scholar
• Macal, C. M., & North, M. J. (2008). Agent-Based Modeling and Simulation: AMBS Examples. In S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, & J. W. Fowler (Eds.), Proceedings of the 2009 Winter Simulation Conference (pp. 101–112). 10.1109/WSC.2008.4736060
   Google Scholar
• Macnamara, J., & Zerfass, A. (2012). Social Media Communication in Organizations: The Challenges of Balancing Openness, Strategy, and Management. International Journal of Strategic Communication, 6(4), 287–308.
   Google Scholar
• Markovic, A., & Zornic, N. (2016). Trends in the application of agent-based modeling and simulation. Central European Conference on Information and Intelligent Systems, 65–70. Varazdin, Croatia.
   Google Scholar
• Masoumzadeh, A., & Joshi, J. (2011, November). Anonymizing geo-social network datasets. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS (pp. 25–32). Chicago, IL.
   Google Scholar
• McPhearson, T., Haase, D., Kabisch, N., & Gren, Å. (2016). Advancing understanding of the complex nature of urban systems. Ecological Indicators, 70, 566–573.
   Google Scholar
• Men, L. R., & Tsai, W. H. S. (2013). Beyond liking or following: Understanding public engagement on social networking sites in China. Public Relations Review, 39(1), 13–22.
   Google Scholar
• Mitchell, M. (2009). Complexity: A Guided Tour. New York, NY: Oxford University Press. doi:10.1063/1.3326990
   Google Scholar
• Morstatter, F., & Liu, H. (2017). Discovering, assessing, and mitigating data bias in social media. Online Social Networks and Media, 1, 1–13.
   Google Scholar
• Morstatter, F., Pfeffer, J., & Liu, H. (2014). When is it biased? Assessing the representativeness of twitter’s streaming API. In Proceedings of the 23rd international conference on world wide web (pp. 555–556). Seoul, Korea.
   Google Scholar
• Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., … Schwarz, N. (2013). Describing human decisions in agent-based models - ODD+D, an extension of the ODD protocol. Environmental Modelling and Software, 48, 37–48.
   Google Scholar
• Netto, V. M., Meirelles, J., & Ribeiro, F. L. (2017). Social interaction and the city: The effect of space on the reduction of entropy. Complexity, 2017.
   Google Scholar
• O’Brien, P. D., & Nicol, R. C. (1998). FIPA—towards a standard for software agents. BT Technology Journal, 16(3), 51–59.
   Google Scholar
• O’Connor, T., & Wong, H. Y. (2002). Emergent Properties. In The Stanford Encyclopedia of Philosophy (pp. 1–23). Retrieved from http://plato.stanford.edu/archives/fall2012/entries/social-institutions/
   Google Scholar
• Ostermann, F. O., Huang, H., Andrienko, G., Andrienko, N., Capineri, C., Farkas, K., & Purves, R. S. (2015). Extracting and Comparing Places Using geosocial Media. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3/W5, 311–316. doi:10.5194/isprsannals-II-3-W5-311-2015
   Google Scholar
• Parker, D. C., & Meretsky, V. (2004). Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agriculture. Ecosystems & Environment, 101(2–3), 233–250.
   Google Scholar
• Perrin, A. (2015). Social media usage. Pew Research Center, 52–68.
   Google Scholar
• Pinto, A., Gonçalo Oliveira, H., & Oliveira Alves, A. (2016). Comparing the performance of different NLP toolkits in formal and social media text. In 5th Symposium on Languages, Applications and Technologies (SLATE’16). Maribor, Slovenia: Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
   Google Scholar
• Poslad, S., Buckle, P., and Hadingham, R. 2000. The FIPA-OS agent platform: Open source for open standards. Proceedings of the 5th international conference and exhibition on the practical application of intelligent agents and multi-agents, (Vol. 355, No. 20).
   Google Scholar
• Prager, S. D., & Wiegand, R. P. (2014). Modeling Use of Space from Social Media Data Using a Biased Random Walker. Transactions in GIS, 18(6), 817–833.
   Google Scholar
• Qi, H., Xu, Y., & Wang, X. (2003). Mobile-agent-based collaborative signal and information processing in sensor networks. Proceedings of the IEEE, 91(8), 1172–1183.
   Google Scholar
• Qi, L., Li, J., Wang, Y., & Gao, X. (2019). Urban Observation: Integration of Remote Sensing and Social Media Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–13. doi:10.1109/jstars.2019.2908515
   Google Scholar
• Rama, G. M., & Kak, A. (2015). Some structural measures of API usability. Software - Practice and Experience, 45(1), 75–110.
   Google Scholar
• Rand, W., Herrmann, J., Schein, B., & Vodopivec, N. (2015). An Agent-Based Model of Urgent Diffusion in Social Media. Journal of Artificial Societies & Social Simulation, 18(2), 1.
   Google Scholar
• Ranneries, S. B., Kalør, M. E., Nielsen, S. A., Dalgaard, L. N., Christensen, L. D., & Kanhabua, N. (2016). Wisdom of the local crowd: Detecting Local Events Using Social Media Data. In W. Nejdl, W. Hall, P. Parigi, & S. Staab (Eds.), Proceedings of the 8th ACM Conference on Web Science - WebSci ’16 (pp. 352–354). Hannover, Germany. doi:10.1145/2908131.2908197
   Google Scholar
• Scheutz, M., & Mayer, T. (2016). Combining agent-based modeling with big data methods to support architectural and urban design. In Understanding Complex Urban Systems (pp. 15–31). Cham: Springer.
   Google Scholar
• Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017). The spread of fake news by social bots. arXiv Preprint arXiv:1707.07592, 96, 104.
   Google Scholar
• Singh, A., & Malhotra, M. (2012). Agent based framework for scalability in cloud computing. International Journal of Computer Science & Engineering Technology (IJCSET), 3(4), 41–45.
   Google Scholar
• Smith, L., Liang, Q., James, P., & Lin, W. (2017). Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework. Journal of Flood Risk Management, 10(3), 370–380.
   Google Scholar
• Smith, M., Szongott, C., Henne, B., & Von Voigt, G. (2012). Big data privacy issues in public social media. In 2012 6th IEEE international conference on digital ecosystems and technologies (DEST) (pp. 1–6). Campione d'Italia, Italy: IEEE.
   Google Scholar
• Storper, M., Van Marrewijk, C., & Van Oort, F. G. (2012). Introduction: Processes of change in urban systems. Journal of Regional Science, 52(1), 1–9.
   Google Scholar
• Twitter Inc. (2019a). Annual Report 2019. Retrieved from https://investor.twitterinc.com/annuals-proxies.cfm
   Google Scholar
• Twitter Inc. (2019b). Twitter Developer Documentation. Retrieved June 19, 2020, from. https://dev.twitter.com/docs
   Google Scholar
• United Nations. (2018). World Urbanization Prospects: The 2018 Revision. Retrieved from https://population.un.org/wup/Publications/Files/WUP2018-KeyFacts.pdf
   Google Scholar
• Wang, Q., & Taylor, J. E. (2016). Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS ONE, 11(1), 1–14.
   Google Scholar
• Weller, K. (2015). Trying to understand social media users and usage: The forgotten features of social media platforms. Online Information Review, 40(2), 256–264.
   Google Scholar
• Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115–152.
   Google Scholar
• Wu, F. (2002). Complexity and urban simulation: Towards a computational laboratory. Geography Research Forum, 22, 22–40.
   Google Scholar
• Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014). Intra-urban human mobility and activity transition: Evidence from social media check-in data. PLoS ONE, 9(5), 1–13.
   Google Scholar
• Xu, S., Li, S., & Huang, W. (2020). A Spatial-temporal-semantic Approach for Detecting Local Events using Geosocial Media Data. Transactions in GIS, 24(1), 142–173.
   Google Scholar
• Xu, S., Li, S., & Wen, R. (2018). Sensing and Detecting Traffic Events using Geosocial Media Data: A Review. Computers. Environment and Urban Systems, 72(2018), 146–160.
   Google Scholar
• Zhang, H., Vorobeychik, Y., Letchford, J., & Lakkaraju, K. (2016). Data-driven agent-based modeling, with application to rooftop solar adoption. Autonomous Agents and Multi-Agent Systems, 30(6), 1023–1049.
   Google Scholar
• Zhang, J.-D., & Chow, C.-Y. (2015). Point-of-Interest Recommendations in Location-Based Social Networks. The SIGSPATIAL Special, 7, 26–33.
   Google Scholar