A visualization approach for discovering colocation patterns

ABSTRACT

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.

KEYWORDS:

• Spatial data mining
• colocation
• visualization
• local analysis
• colocation rule map

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable suggestions and comments that helped improve the quality of this article a lot.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [2017YFB0503500]; National Natural Science Foundation of China [41531180].

Notes on contributors

Mengjie Zhou

Mengjie Zhou is a lecturer in College of Resources and Environmental Science at Hunan Normal University.She received the BS degree in geographic information system, the MS degree in surveying and mapping engineering, and the PhD degree in cartography and geographic information system from Wuhan University, China. Her research interests include cartographic visualization and spatial data mining.

Tinghua Ai

Tinghua Ai is a professor of cartography in School of Resource and Environmental Science at Wuhan University. His research interests include map generalization, spatial cognition and spatial data mining. He is currently the editorial board member of Computers, Environment and Urban Systems. He published more than 100 papers on the application of computational geometry in map generalization, cartographic visualization and spatial data analysis. He used to lead a team developing a generalization system which is now widely applied in map production lines in China; the system also played an important role in the engineering project of Chinese 1:50000 map updating.

Chao Wu

Chao Wu is a lecturer in School of Geographic and Biologic Information at Nanjing University of Posts and Telecommunications. She holds a PhD in cartography and geographical information system from Wuhan University. Her research interests are spatial-temporal modelling and analysis and social geographical computing.

Yuli Gu

Yuli Gu is an undergraduate student in College of Resources and Environmental Science at Hunan Normal University. Her research interests focus on spatial analysis and spatial data mining.

Na Wang

Na Wang is an undergraduate student in College of Resources and Environmental Science at Hunan Normal University. Her research interests are spatial planning and spatial data mining.