This paper aims to improve the identification of taxi hotspots in spatio-temporal space. We propose an approach that combines ensemble-weighted degree and node entropy measures within a dominating set to enhance the identification of taxi hotspots. First, we construct a graph representation of the spatio-temporal space, where nodes represent potential taxi hotspots. The weighted degree of each node is computed by considering the weights of its adjacent edges, which correspond to the distances between the two adjacent nodes on the road network. This measure quantifies the importance and connectivity of a node. We calculate node entropy to capture the level of uncertainty and randomness associated with each node. The entropy measure provides insights into the diversity of each potential hotspot, helping to distinguish between highly active and less predictable areas. We then integrate the ensemble weighted degree and node entropy measures into a dominating set approach. By iteratively selecting the nodes with the highest combined scores, we construct an optimal set of taxi hotspots that effectively cover the spatio-temporal space while considering both connectivity and uncertainty. The proposed approach enables a more comprehensive identification of taxi hotspots, taking into account the connectivity and importance of potential locations and their level of uncertainty. To evaluate the effectiveness of the proposed approach, we conducted experiments using New York taxi trip data. The results demonstrate that the ensemble weighted degree and node entropy measures enhance the identification of taxi hot spots compared to traditional dominating set methods.