Signature-Based Methods for Data Streams
Data Mining and Knowledge Discovery
Data Signatures and Visualization of Scientific Data Sets
IEEE Computer Graphics and Applications
Discovering Homogeneous Regions in Spatial Data through Competition
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
REG^2: a regional regression framework for geo-referenced datasets
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A dissimilarity function for clustering geospatial polygons
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Towards region discovery in spatial datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Unsupervised clustering of multidimensional distributions using earth mover distance
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of interesting regions in spatial data sets using supervised clustering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Computing with Spatial Trajectories
Computing with Spatial Trajectories
Answering top-k similar region queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
On the shape of a set of points in the plane
IEEE Transactions on Information Theory
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Discriminative Polygonal Place Scoping
Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place
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Cities all around the world are in constant evolution due to numerous factors, such as fast urbanization and new ways of communication and transportation. Since understanding the composition of cities is the key to intelligent urbanization, there is a growing need to develop urban computing and analysis tools to guide the orderly development of cities, as well as to enhance their smooth and beneficiary evolution. This paper presents a spatial clustering approach to discover interesting regions and regions which serve different functions in cities. Spatial clustering groups the objects in a spatial dataset and identifies contiguous regions in the space of the spatial attributes. We formally define the task of finding uniform regions in spatial data as a maximization problem of a plug-in measure of uniformity and introduce a prototype-based clustering algorithm named CLEVER to find such regions. Moreover, polygon models which capture the scope of a spatial cluster and histogram-style distribution signatures are used to annotate the content of a spatial cluster in the proposed methodology; they play a key role in summarizing the composition of a spatial dataset. Furthermore, algorithms for identifying popular distribution signatures and approaches for identifying regions which express a particular distribution signature will be presented. The proposed methodology is demonstrated and evaluated in a challenging real-world case study centering on analyzing the composition of the city of Strasbourg in France.