We consider the problem of detecting hotspots in spatial point patterns observed over time while accounting for an inhomogeneous background intensity. For example, in disease surveillance, the interest is often in identifying regions of unusually high incidence rate given a background incidence rate that may be spatially varying due to underlying variation in population density, say. I will present a K-scan method that uses components of the inhomogeneous K function to identify such anomalies or hotspots. The significance of detected hotspots is assessed using either bootstrap or a p value approximation based on a Gumbel distribution. I will show some results from a simulation study, as well as applications of this method to dead bird sighting data from Contra Costa County in California and to fast food restaurant location data in New York City.