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A visualization approach for discovering colocation patterns
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AI
DOI：10.1080/13658816.2018.1550784.png)
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摘要
En 中文
Colocation mining is one of the major spatial data mining tasks. When discovering colocation patterns, spatial statistics or data mining approaches are commonly used. Colocation mining results are typically presented in a textual form and do not provide any spatial information; thus, the results lack an intuitive approach to obtain cognition of colocation rules. Here, we propose a visualization approach to discover colocation patterns for two independent point distributions and generate visual results. This approach makes use of the ability of human color perception. For two geographic features, our approach first generates density surfaces of the input features and then visualizes the density surfaces using a red or green light with different intensities. Then, based on the law of additive color mixing, our approach mixes the colors of the two density surfaces to generate a colocation rule map. The visualization approach can also provide local details of colocation and be used for local colocation analysis. Users can detect colocation patterns and their distribution from the colocation rule maps. We use both synthetic data and real data to test the performance of our approach.
Keyword:
Spatial data mining
colocation
visualization
local analysis
colocation rule map
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5.1
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2.7K
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引用论文
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The Colocation Quotient: A New Measure of Spatial Association Between Categorical Subsets of Points
PREAI
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