Many researches focus on clustering location or time series. In time series data, similarity metric are often used to measure the similar data. Many works use different algorithms to calculate similarity between two subsequences. Also, in location clustering, the well-known algorithm k-means and k-NN propose excellent results, and many works focus on how to increase efficient and accuracy of these algorithms. However, in some cases, we need to consider both similarities between locations and time series. Like greenhouse or wildfire detection, key points in these cases play an important status. This paper addresses how to cluster both location points and time series data together. We propose a new algorithm to consider location and time series together. We further show the algorithm we proposed can solve the problem of real cases well.