The types of data available have changed in the last decade. While, historically, data were gathered in batches and distributed as such, e.g. as a database or shapefile, today we are dealing increasingly with real-time data. This data is produced and consumed continuously in real time. The phenomenon is most commonly known as streaming data. Traditionally, software for spatial analysis, such as a Geographical Information System (GIS) or spatial database, was created and optimized for the batch processing of data. However, the inherent characteristics of streaming data provide new challenges for data-stream processing systems, which have not yet been solved. In this paper, we propose enhancing systems for the handling and analysis of streaming data through the use of spatial operators. We identify Complex Event Processing (CEP) as a promising underlying concept for such a system and use the (open source) self-service IoT toolbox ‘StreamPipes’ as a representative for this. On the basis of a review of the literature, we selected 6 core types of spatial operator and implemented 33 basic spatial operators in 11 groups. These can be combined with the existing non-spatial operators for in-depth analysis of streaming data that involves spatial dimensions.