Next Article in Journal
Accuracy Analysis of a 3D Model of Excavation, Created from Images Acquired with an Action Camera from Low Altitudes
Previous Article in Journal
A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2019, 8(2), 82; https://doi.org/10.3390/ijgi8020082

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

1
Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran
2
GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden
*
Author to whom correspondence should be addressed.
Received: 31 December 2018 / Revised: 24 January 2019 / Accepted: 11 February 2019 / Published: 13 February 2019
Full-Text   |   PDF [10135 KB, uploaded 23 February 2019]   |  

Abstract

Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method. View Full-Text
Keywords: spatial clustering; density-based clustering; spatial heterogeneity; text Similarity; twitter spatial clustering; density-based clustering; spatial heterogeneity; text Similarity; twitter
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ghaemi, Z.; Farnaghi, M. A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data. ISPRS Int. J. Geo-Inf. 2019, 8, 82.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top