An Approach to Updating Vector Features by Clustering Algorithm

Abstract

Abstract. The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to update its data. One way of updating GIS vector data is by deleting and inserting data to the databases by manually defined unique value. Other one is by deleting and adding data manually. These methods all require manual predefined unique values or manual operations which is not suitable for updating data in a timely and fast way, especially large numbers of data are updated. Therefore, an automated vector data updating method has the potential to significantly increase productivity, particularly as existing GIS vector data application increase in size, the data become outdated more quickly. In this paper, we consider the difference between GIS vector data and other data that GIS vector data have spatial coordinates and topology relationships, propose an approach to updating of vector features based on clustering algorithm. First, the minimum bounding box and its center point of vector features are calculated, and then clusters the center points with DBSCAN to get some clusters. In the end, extract the smallest bounding box of every cluster and update vector features with the boxes. This approach uses smallest bounding box to update features is to reduce database query cost and improves the data update efficiency. Experiments show that the method is effective and feasible.