Geotagged Digital Traces

This dataset, divided into files by city, contains geotagged digital traces collected from different social media platforms, detailed below.

• Tweets - Cheng et al. [1]

• Gowalla [2]

• Tweets - Lamsal [3]

• YELP[4]

• Tweets - Kejriwal et al. [5]

• Geotagged Tweets [6]

• UrbanActivity, [7]

• Brightkite [8]

• Weeplaces [8]

• Flickr [9]

• Foursquare [10]

Each file is named according to the city to which the digital traces were associated and contains the columns:

• Source: contains the name of the source platform
• Event_date: contains the date associated with the digital trace
• Lat: latitude of the digital trace
• Lng: length of the digital trace

The definition of city/town used is provided by Simplemaps [11], which considers a city/town any inhabited place as determined by U.S. government agencies. The location of cities and their respective centers were obtained from the World Cities Database provided by the same company.

A specific group of these cities was utilized for the research presented in the article submitted to Sensors Journal:

Muñoz-Cancino, R., Rios, S. A., & Graña, M. (2023). Clustering cities over features extracted from multiple virtual sensors measuring micro-level activity patterns allows to discriminate large-scale city characteristics. Sensors, Under Review. 

Comprehensive guidelines and the selection criteria can be found in the abovementioned article.

References

[1] Zhiyuan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: A content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM '10, page 759{768, New York, NY, USA, 2010. Association for Computing Machinery.
[2] Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 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, page 1082{1090, New York, NY, USA, 2011. Association for Computing Machinery.
[3] Yunhe Feng and Wenjun Zhou. Is working from home the new norm? an observational study based on a large geo-tagged covid-19 twitter dataset, 2020.
[4] Yelp Inc. Yelp Open Dataset, 2021. Retrieved from https://www.yelp.com/dataset. Accessed October 26, 2021.
[5] Mayank Kejriwal and Sara Melotte. A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas, January 2021.
[6] Rabindra Lamsal. Design and analysis of a large-scale covid-19 tweets dataset. Applied Intelligence, 51(5):2790{2804, 2021.
[7] Geraud Le Falher, Aristides Gionis, and Michael Mathioudakis. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In 9th AAAI Conference on Web and Social Media - ICWSM 2015, Oxford, United Kingdom, May 2015.
[8] Yong Liu, WeiWei, Aixin Sun, and Chunyan Miao. Exploiting geographical neighborhood characteristics for location  recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, page 739{748, New York, NY,USA, 2014. Association for Computing Machinery.
[9] Hatem Mousselly-Sergieh, Daniel Watzinger, Bastian Huber, Mario Doller, Elood Egyed-Zsigmond, and Harald Kosch. World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging. In Proceedings of the 5th ACM Multimedia Systems Conference, MMSys '14, page 47{52, New York, NY, USA, 2014. Association for Computing Machinery.
[10] Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129{142, 2015.
[11] Simple Maps. Basic World Cities Database, 2021. Retrieved from https://simplemaps.com/data/world-cities. Accessed September 3, 2021.