- freely available
ISPRS Int. J. Geo-Inf. 2015, 4(2), 799-814; https://doi.org/10.3390/ijgi4020799
2. Related Work
2.1. The Evaluation of Geographic Relevance
2.2. Potentials of Volunteered Geographic Information
3. Understanding Context
- Temporal. Users tend to have typical mobility patterns over different time periods. Temporal context can be considered on different scales, such as by the season, the days of the week, hours in a day etc. In this paper, we use the last scale, that is, the hours in a day.
- Spatial. Researchers have confirmed that urban regions typically comprise different functional configurations, such as residential, educational and business, which typically influence the user’s behavior . In this paper, we will observe such influences within administrative postal regions.
3.2. Foursquare: Exemplar of Location-Bassed Social Networks
3.3. Revealing Contextual Influences on Mobile Users
4. Contextualized Geographic Relevance Evaluation
4.2. Global and Individual Patterns
4.3. Experimental Results
- Group 1: Learn global patterns from the learning dataset, and predict, without a context, the record to be predicted;
- Group 2: Learn individual patterns from the learning dataset, and predict without a context;
- Group 3: Learn global patterns from the learning dataset, and predict with a context;
- Group 4: Learn individual patterns from the learning dataset, and predict with a context.
5. Conclusions and Future Work
Conflicts of Interest
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