Publish/subscribe is a messaging pattern where message producers, called publishers, publish messages which they want to be distributed to message consumers, called subscribers. Subscribers are required to subscribe to messages of interest in advance to be able to receive them upon publishing. In this paper, we discuss a special type of publish/subscribe systems, namely geospatial publish/subscribe systems, in which both published messages (i.e., publications) and subscriptions include a geospatial object. Such an object is used to express both the location information of a publication and the location of interest of a subscription. We argue that there is a great potential for using geospatial publish/subscribe systems for the Internet of Things and Sensor Web applications. However, existing geospatial publish/subscribe systems are not applicable for this purpose since they are centralized and cannot cope with multiple highly frequent incoming publications containing geospatial data streams. To overcome this limitation, we present a distributed geospatial publish/subscribe system in the cluster which efficiently matches incoming publications in real-time with a set of stored subscriptions. Additionally, we propose four different (distributed) algorithms for matchinging publications and subscriptions in our distributed geospatial publish/subscribe system. Finally, we present results of an extensive experimental evaluation in which we compare the throughput, latency, and memory consumption of these algorithms. These results clearly show that they are both efficient and scalable to larger clusters. The comparison with centralized state-of-the-art approaches shows that the processing overhead of our distributed algorithms introduced by the Apache Spark platform is almost negligible.