When Cluster Analysis Meets Geography: The Spatial Agglomeration of the MICE Industry

Abstract

In this study, we investigate the spatial agglomeration of the Meetings, Incentives, Conferences, and Exhibitions (MICE) industry. We use detailed information on the convention venues characteristics and their spatial distribution to identify clusters by using the spatially constrained clustering algorithm. This approach enables the inclusion of geographical and non-geographical information in the clustering algorithm. In particular, the non-geographical aspects are measured by the Krugman sectoral dissimilarity index based on convention centers’ seat places; beside, a spatial contiguity matrix is used as geographical information. Our findings show the existence of developed and developing convention clusters, with different MICE offer. Using convention production's data for each cluster, we were able to conclude the cluster’s profile and propose managerial strategies.

Keywords: spatial cluster; spatially constrained clustering; Krugman index; MICE industry; demand and supply data.