Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 15th international conference on Multimedia
Mining geographic knowledge using location aware topic model
Proceedings of the 4th ACM workshop on Geographical information retrieval
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Tag-geotag correlation in social networks
Proceedings of the 2008 ACM workshop on Search in social media
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Proceedings of the 18th international conference on World wide web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Event detection from flickr data through wavelet-based spatial analysis
Proceedings of the 18th ACM conference on Information and knowledge management
An agenda for the next generation gazetteer: geographic information contribution and retrieval
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Latent geographic feature extraction from social media
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A probablistic model for spatio-temporal signal extraction from social media
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Given the steadily increasing amount of geographic information on the Web, there is a strong need for suitable methods in exploratory data analysis that can be used to efficiently describe the characteristics of such large-scale, often noisy datasets. Existing methods in spatial data mining focus primarily on mining patterns describing spatial proximity relationships such as co-location patterns or spatial associations rules. In this paper, we present a novel approach to describe the spatial characteristics of geographic information sources comprised of instances of geographic features. Using the concept of interaction characteristics of geographic features, similarities in how features are distributed in space can be computed and interesting patterns of similar features in the datasets regarding their geographic semantics (landmark, local, regional, global) can be determined. For this, we employ clustering techniques of spatial distance statistics. We discuss the properties of our method and detail a comprehensive evaluation using publicly available datasets (Flickr, Twitter, OpenStreeMap). We demonstrate the feasibility of identifying groups of geographic features with distinct geographic semantics, which then can be used to select subsets of features for subsequent learning tasks or to compare different datasets.