Network-based spatial clustering technique for exploring features in regional industry

Tien-Yin Chou; Pi-Hui Huang; Lung-Shih Yang; Wen-Tzu Lin

doi:10.1117/12.812740

5 November 2008 Network-based spatial clustering technique for exploring features in regional industry

Tien-Yin Chou, Pi-Hui Huang, Lung-Shih Yang, Wen-Tzu Lin

Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 714419 (2008) https://doi.org/10.1117/12.812740
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

In the past researches, industrial cluster mainly focused on single or particular industry and less on spatial industrial structure and mutual relations. Industrial cluster could generate three kinds of spillover effects, including knowledge, labor market pooling, and input sharing. In addition, industrial cluster indeed benefits industry development. To fully control the status and characteristics of district industrial cluster can facilitate to improve the competitive ascendancy of district industry. The related researches on industrial spatial cluster were of great significance for setting up industrial policies and promoting district economic development. In this study, an improved model, GeoSOM, that combines DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SOM (Self-Organizing Map) was developed for analyzing industrial cluster. Different from former distance-based algorithm for industrial cluster, the proposed GeoSOM model can calculate spatial characteristics between firms based on DBSCAN algorithm and evaluate the similarity between firms based on SOM clustering analysis. The demonstrative data sets, the manufacturers around Taichung County in Taiwan, were analyzed for verifying the practicability of the proposed model. The analyzed results indicate that GeoSOM is suitable for evaluating spatial industrial cluster.

Citation Download Citation

Tien-Yin Chou, Pi-Hui Huang, Lung-Shih Yang, and Wen-Tzu Lin "Network-based spatial clustering technique for exploring features in regional industry", Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 714419 (5 November 2008); https://doi.org/10.1117/12.812740

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