A spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in spatial neighborhoods. The existing interesting metrics for spatial co-location pattern mining do not take account of the difference between features and the diversity between instances belonging to the same feature. In addition, using the traditional data-driven spatial co-location pattern mining method, the mining results often contain a lot of useless or uninteresting patterns. In view of the above problems, firstly, a more general study object-spatial instance with utility value was proposed, and the Utility Participation Index (UPI) was defined as the new interesting metric of the spatial high utility co-location patterns. Secondly, the domain knowledge was formalized into three kinds of semantic rules and applied to the mining process, and a new domain-driven iterative mining framework was put forward. Finally, by the extensive experiments, the differences between mined results with different interesting metrics were compared in two aspects of utility ratio and frequency, as well as the changes of the mining results after taking the domain knowledge into account. Experimental results show that the proposed UPI metric is a more reasonable measure in consideration of both frequency and utility, and the domain-driven mining method can effectively find the co-location patterns that users are really interested in.