Discovering Spatial Interaction Patterns
| dc.contributor.author | SHENG, Chang | en_US |
| dc.contributor.author | HSU, Wynne | en_US |
| dc.contributor.author | LEE, Mong Li | en_US |
| dc.date.accessioned | 2007-09-21T01:39:10Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:26Z | |
| dc.date.available | 2007-09-21T01:39:10Z | en_US |
| dc.date.available | 2017-01-23T07:00:26Z | |
| dc.date.issued | 2007-06-29 | en_US |
| dc.description.abstract | Advances in sensing and satellite technologies and the growth of Internet have resulted in a vast amount of spatial data that are easily accessible. Extracting useful knowledge from these data has remained an important and challenging task. Among the various spatial analysis tasks, finding interaction among spatial features is one of the most important problem. Existing works typically adopt a grid-like approach to transform the continuous space to a discrete space. This may lead to some meaningful knowledge being missed. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the distribution of the feature instances. Superimposing the influence maps allows the interaction of the feature types to be quickly determined. Experiments on both synthetic and real world datasets indicate that the proposed approach is scalable and is able to discover patterns that have ... | en_US |
| dc.format.extent | 1172167 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/2574 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRC6/07 | en_US |
| dc.title | Discovering Spatial Interaction Patterns | en_US |
| dc.type | Technical Report | en_US |