QuCOM:k nearest features neighborhood based qualitative spatial co-location patterns mining algorithm
Abstract-Spatial Co-location patterns ard similar to association rules but explore relying spatial quto-correlation. They represent subsets of Boolean spatial features whose instances are often located in close geographic proximity. Existing co-location patterns mining researches only concern the spatial attributes, and few of them can handle the huge amount of non-spatial attributes in spatial datasets. Also, they use distance threshold to define spatial neighborhood.However, it is hard to decide the distance threshold for each spatial dataset without specific prior knowledge. Moreover,spatial datasets are not usually even distributed, so a unique distance value cannot fit an irregularly distributed spatial dataset well. Here, we proposed a qualitative spatial co-location pattern, which contains both spatial and non-spatial information. And the knearest features (k-NF)neighbourhood relation was defined to set the spatial relation between different kinds of spatial features. The k-NF set of one featuresinstaces was used to evaluate close relationship to the other features. To find qualitative co-location patterns in large spatial datasets, some formal definitions were given,and a QuCOM (Qualitative spatial CO-location patterns Mining)algorithm was proposed. Experimental results on the USA thesismap data prove that QuCOM algorithm is accurate and efficient, and the patterns founded contain more interesting information.
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You Wan Chenghu Zhou
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China State Key Lab of Resources and Environment Information System,Institute of Geography Sciences and Na
国际会议
福州
英文
54-59
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）