K-means should be right in this case. Since k-means tries to group based solely on euclidean distance between objects you will get back clusters of locations that are close to each other.

To find the optimal number of clusters you can try making an 'elbow' plot of the within group sum of square distance. This may be helpful (http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)

K-means should be right in this case. Since k-means tries to group based solely on euclidean distance between objects you will get back clusters of locations that are close to each other.

To find the optimal number of clusters you can try making an 'elbow' plot of the within group sum of square distance. This may be helpful