- freely available
ISPRS Int. J. Geo-Inf. 2016, 5(10), 193; doi:10.3390/ijgi5100193
- Development of a mining framework: a unified framework is proposed to discover evolving domain-related spatio-temporal patterns in Twitter. Prior knowledge is not required in the new framework.
- Extraction of domain related Twitter events by dynamic query expansion: For the target domain, related tweets can be obtained using a dynamic query expansion strategy. These tweets tagged with geo-location and time information constitute spatio-temporal Twitter events.
- Discovery of evolving spatio-temporal patterns from Twitter events: For the extracted domain related spatio-temporal Twitter events, spatial clusters and outliers are detected by spatial clustering, after which the spatio-temporal patterns are discovered by spatio-temporal clustering as they evolve.
- Experimental evaluation using real Twitter data: The proposed framework was extensively tested for spatio-temporal Twitter events related to ‘civil unrest’ in Mexico. The advantages and effectiveness of the new method are demonstrated by comparing the results with alternative methods and baseline data.
2. Related work
2.1. Twitter Event Extraction
2.2. Cluster, Outlier and Hotspot Detection
3. Motivation and Proposed Strategy
3.2. A New Strategy for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter
4. Domain Related Twitter Event Detection
4.1. Basic Definitions
4.2. Dynamic Query Expansion
4.3. Spatio-Temporal Twitter Events
5. Evolving Spatio-Temporal Patterns Discovery
5.1. Spatial Distribution Patterns Detection
5.1.1. Identification and Removal of I-Long Edges
5.1.2. Identification and Removal of II-Long Edges
5.1.3. Identification and Removal of III-Long Edges
5.1.4. Determination of Spatial Patterns
5.2. Discovery of Evolving Spatio-Temporal Patterns
- all spatio-temporal Twitter events belonging to SNδ(sttei);
- all spatio-temporal Twitter events belonging to TNε(sttei); and
- all spatio-temporal Twitter events corresponding to spatial Twitter events in SNδ(stei’) with IsOccur_Ttwi(twi∈TWε)=1, where stei’ is the spatial Twitter event of sttei.
5.3. The Evolving_Pattern_Discovery Algorithm
- Input: Spatio-temporal Twitter events STTE, projected spatial Twitter events STE, threshold δ and ε
- Output: Evolving spatio-temporal patterns
- Construct the Delaunay triangulation for STE to obtain the initial spatial proximity graph;
- Identify and remove inconsistent long edges, i.e., I-long edges, II-long edges and III-long edges, from the Delaunay triangulation;
- Extract connected sub-graphs and identify spatial clusters and outliers based on the volume of each connected sub-graph.
- Determine the spatial neighborhoods of each spatial Twitter event and the spatial neighborhoods of each spatio-temporal Twitter event based on δ;
- Construct time windows based on ε and determine the temporal neighborhoods of each spatio-temporal Twitter event;
- Determine the spatio-temporal neighborhoods of each spatio-temporal Twitter event; and
- Extract spatio-temporal connected graphs based on the spatio-temporal proximity relationships and identify spatio-temporal clusters and outliers based on the volume of each spatio-temporal connected graph.
6. Experimental Evaluation and Analysis by Visualization
6.1. Dataset and Labels
6.2. Experimental Comparisons
6.2.1. The Results Obtained by the New Method
6.2.2. The Results Obtained by ST-DBSCAN
6.2.3. The Results Obtained by STSNN
6.3. Analysis of Evolving Spatio-Temporal Patterns
6.3.1. Analysis of Spatio-Temporal Clusters by Our Method
6.3.2. Comparison with Labels
Conflicts of Interest
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