Comparison and Prediction of Temporal Hotspot Maps

Sammanfattning: Context. To aid law enforcement agencies when coordinating and planningtheir efforts to prevent crime, there is a need to investigate methods usedin such areas. With the help of crime analysis methods, law enforcementare more efficient and pro-active in their work. One analysis method istemporal hotspot maps. The temporal hotspot map is often represented asa matrix with a certain resolution such as hours and days, if the aim is toshow occurrences of hour in correlation to weekday. This thesis includes asoftware prototype that allows for the comparison, visualization and predic-tion of temporal data. Objectives. This thesis explores if multiprocessing can be utilized to im-prove execution time for the following two temporal analysis methods, Aoris-tic and Getis-Ord'. Furthermore, to what extent two temporal hotspotmaps can be compared and visualized is researched. Additionally it wasinvestigated if a naive method could be used to predict temporal hotspotmaps accurately. Lastly this thesis explores how different software packag-ing methods compare to certain aspects defined in this thesis. Methods. An experiment was performed, to answer if multiprocessingcould improve execution time of Getis-Ord' or Aoristic. To explore howhotspot maps can be compared, a case study was carried out. Another ex-periment was used to answer if a naive forecasting method can be used topredict temporal hotspot maps. Lastly a theoretical analysis was executedto extract how different packaging methods work in relation to defined as-pects. Results. For both Getis-Ord' and Aoristic, the sequential implementationsachieved the shortest execution time. The Jaccard measure calculated thesimilarity most accurately. The naive forecasting method created, provednot adequate and a more advanced method is preferred. Forecasting Swedishburglaries with three previous months produced a mean of only 12.1% over-lap between hotspots. The Python package method accumulated the highestscore of the investigated packaging methods. Conclusions. The results showed that multiprocessing, in the languagePython, is not beneficial to use for Aoristic and Getis-Ord' due to thehigh level of overhead. Further, the naive forecasting method did not provepractically useful in predicting temporal hotspot maps.