Name
Abstract | ||
|---|---|---|
Geotagged images (e.g., Flickr) could indicate some social events (e.g., festival, parade, protest, sports, etc.) with spatial, temporal and semantic information. Previous researches relied much on tag frequency, thus some images which do not have clearly tag indicating the occurrence of social event would be missing in this case. One potential way to address this problem or enhance the event identification is to make more use of the spatial and temporal information. In this chapter, we take into consideration the underlying spatio-temporal pattern of social events. Particularly, the influence of urban land use and road on the occurrence of event is considered. Specifically, with a spatio-temporal cluster detection method, we firstly detected spatio-temporal clusters composed of geotagged images. Among these detected S-T clusters, we furthermore attempted to identify social events in terms of a classification model. Specifically, land use and road were considered to generate new kinds of spatial characteristics used as dependent variables incorporated into the classification model. In addition to this, user characteristics (i.e., the number of images and the number of users), spatial and temporal range of images, and the heterogeneity of temporal distribution of images were considered as the other dependent variables for the classification model. Consequently, with a binary logistic regression (BLR) method, we estimated the categories (i.e., 'event' or 'non-event' one) of the S-T clusters (cases). Experimental results demonstrated the good performance of the method with a total accuracy of 71%. With the variable selection process of the BLR method, empirical result also indicates that (1) some characteristics (e.g., the distance to the road and the heterogeneity of temporal distribution of images) do not have considerable influence on the occurrence of 'event'; and (2) compared to the other urban land categories (i.e., residential and recreational land), commercial land has a relatively high influence on the occurrence of 'event'. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-03611-3_5 | Lecture Notes in Geoinformation and Cartography |
Keywords | Field | DocType |
Volunteered geographic information,Event detection,Flickr,Classification model,Spatial environment | Computer vision,Feature selection,Computer science,Georeference,Knowledge management,Semantic information,Volunteered geographic information,Variables,Artificial intelligence,Logistic regression,Cartography,Land use | Conference |
ISSN | Citations | PageRank |
1863-2246 | 0 | 0.34 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Sun | 1 | 71 | 8.99 |
| Hongchao Fan | 2 | 17 | 7.44 |