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Geospatial Clustering: Methods, Applications and Future Directions

Geospatial clustering is used in the detection of areas with similar land use patterns in a geospatial database, as well as in the merging of regions with similar land use patterns in a geographic information system. Because of the wide range of applications it provides in a wide range of domains, geospatial clustering has been a very active issue in machine learning research. The purpose of this work is to investigate the use of geospatial data in the clustering method. It makes an attempt to investigate a few clustering approaches using geospatial data. This can be accomplished by using some of the prominent clustering techniques, such as K-means, K-medoids and DBSCAN to the given starbucks location geospatial data set and Canada Weather data set. It also compares to K-means, K-medoids and DBSCAN. This work also opens the door to the possibility of a variety of additional clustering algorithms that may be used to geospatial data in order to gain insights that can be applied in real-world applications.