Name
Abstract | ||
|---|---|---|
AOI(Attribute-Oriented Induction) is one of the data mining techniques with which phenomena of complicated spatial relationships can be expressed as specific rules by summarizing spatial or non-spatial data in accordance with super ordinate concepts. In addition to such AOI technique, if GIS(Geographic Information System) that has an advantage to solve spacerelated problems is combined, they can be used to expect the issues of urban growth. Combining the AOI technique and GIS, the study draws out spatial association rules focusing on a physical urban growth, and those rules are applied to urban growth models during the period from the 1960's to the 1990's. The results and analysis of the urban growth modes combined with data mining are compared with those of Clarke Keith's UGM(Urban Growth Model). |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74819-9_44 | KES (1) |
Keywords | Field | DocType |
spatial association rule,data mining,urban growth,urban growth probability model,aoi technique,physical urban growth,non-spatial data,urban growth model,complicated spatial relationship,data mining technique,urban growth mode,spatial relationships,association rule,spatial data,geographic information system | Geographic information system,Data mining,Cellular automaton,Probability model,Growth model,Ordinate,Computer science,Association rule learning | Conference |
Volume | ISSN | Citations |
4692 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seonghwi Cho | 1 | 0 | 0.34 |
| Sungeon Hong | 2 | 5 | 1.82 |
| Jungyeop Kim | 3 | 16 | 2.75 |
| Soohong Park | 4 | 72 | 22.26 |