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Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users.

, , , , and . UrbanGIS@SIGSPATIAL, page 55-58. ACM, (2015)

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Building an Automated and Self-Aware Anomaly Detection System., , , , , and . IEEE BigData, page 1465-1475. IEEE, (2020)Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users., , , , and . UrbanGIS@SIGSPATIAL, page 55-58. ACM, (2015)Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment., , , and . CoRR, (2020)A scalable framework for spatiotemporal analysis of location-based social media data., , , , , and . Comput. Environ. Urban Syst., (2015)Root Cause Detection Among Anomalous Time Series Using Temporal State Alignment., , , and . ICMLA, page 523-528. IEEE, (2019)FluMapper: a cyberGIS application for interactive analysis of massive location-based social media., , , , , , , and . Concurr. Comput. Pract. Exp., 26 (13): 2253-2265 (2014)A multidimensional spatial scan statistics approach to movement pattern comparison., , , , and . Int. J. Geogr. Inf. Sci., 32 (7): 1304-1325 (2018)Consistency of Social Sensing Signatures Across Major US Cities., , , and . CoRR, (2016)A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data., , , , , and . CoRR, (2014)Parallel cartographic modeling: a methodology for parallelizing spatial data processing., , , , , , and . Int. J. Geogr. Inf. Sci., 30 (12): 2355-2376 (2016)