Residential burglaries are increasing. By visualizing patterns as spatial hotspots, law-enforcement agents can get a better understanding of crime distributions and trends. Two aspects are investigated, first, measuring the accuracy and performance of the KDE algorithm using small data sets. Secondly, investigation of the amount of crime data needed to compute accurate and reliable hotspots. The Prediction Accuracy Index is used to effectively measure the accuracy of the algorithm. The data from three geographical areas in Sweden, including Stockholm, Gothenburg and Malmö are analyzed and evaluated over a one year. The results suggest that the usage of the KDE algorithm to predict residential burglaries performs well overall when having access to enough crimes, but is capable with small data sets as well