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Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis
Open AccessArticle

Spatial-Temporal Event Detection from Geo-Tagged Tweets

Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Libraries, Purdue University, West Lafayette, IN 47907, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(4), 150;
Received: 21 February 2018 / Revised: 7 April 2018 / Accepted: 9 April 2018 / Published: 15 April 2018
PDF [32426 KB, uploaded 3 May 2018]


As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events. View Full-Text
Keywords: social media; Twitter; spatial-temporal data; data mining; event detection social media; Twitter; spatial-temporal data; data mining; event detection

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Huang, Y.; Li, Y.; Shan, J. Spatial-Temporal Event Detection from Geo-Tagged Tweets. ISPRS Int. J. Geo-Inf. 2018, 7, 150.

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