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.

Edge computing emerged as an attractive method of distributing computational resources in a network. When compared with cloud computing, edge computing presents a number of key benefits which include improved response times, scalability, privacy, and redundancy. This makes edge computing desirable for use in mobile robotics, in which low response times and redundancy are key issues. This thesis work will cover the design and implementation of a general-purpose edge orchestrator, that can support a wide range of domains due to being built around the concept of modularity. An edge orchestrator is a program that manages an edge network by analyzing the edge network and the requirements of devices within that network, then optimizing how the computational resources are distributed within the devices in the network. Modules have been designed and implemented on top of the orchestrator that allow for optimizations specific to mobile robotics. A proof of concept module was designed to optimize for latency which was compared with an external algorithm that seeks to optimize for latency as well. Both were implemented on the orchestrator and an evaluation was performed to compare both approaches. It was found that the module designed in this thesis is better suited for optimizing for latency. LXD was chosen to be used for software packaging which is a container-based software packaging solution. A software packaging solution is used to package software which would be deployed by the orchestrator. The choice of LXD is analyzed through an evaluation procedure that compares it with Docker, which is another container-based software packaging solution. It was found that LXD produces containers of smaller size but required more time to generate those containers, when compared with Docker. It was also found that LXD container images exhibited better performance than the Docker ones for software which is not I/O heavy. It was decided through this evaluation that LXD was a better choice for the orchestrator. / Edge computing är en attraktiv metod för distribution av beräkningsresurser i ett nätverk. Jämfört med molnberäkningar har edge computing ett antal viktiga fördelar som inkluderar förbättrade svarstider, skalbarhet, integritet och redundans. Detta gör edge computing önskvärt för användning i mobil robotik, där låga svarstider och redundans är viktiga frågor. Detta examensarbete täcker min design och implementering av en generell edge-orkestrerare, som kan stödja ett brett spektrum av domäner eftersom den är byggd på ett modulärt sätt. En edge-orkestrerare är ett program som hanterar ett edge-nätverk genom att analysera edge-nätverket och kraven på enheter inom det nätverket, för att sedan optimera hur beräkningsresurserna fördelas över enheterna i nätverket. Jag har utformat och implementerat moduler ovanpå orkestratorn som möjliggör optimeringar specifika för mobil robotik. Jag designade också en koncepttest-modul för att optimera för latens, vilken jag jämförde med en extern algoritm som även den försöker optimera för latens. Jag implementerade båda på orkestratorn och utförde en utvärdering för att jämföra båda metoderna. Resultaten visar att modulen utformad i detta examensarbete är bättre lämpad för att optimera för latens. För mjukvarupaketering valde jag att använda LXD, vilket är en containerbaserad mjukvarupaketeringslösning. Dess syfte är att paketera programvara som ska distribueras av orkestratorn. Jag analyserade valet av LXD genom ett utvärderingsförfarande som jämför det med Docker, som är en annan containerbaserad mjukvarupaketeringslösning. Jag fann att LXD producerar mindre containrar, men krävde mer tid för att generera dessa containrar jämfört med Docker. Jag fann också att LXD-containerbilder visade bättre prestanda än Docker-bilderna för programvara som inte är I/O-intensiv. Jag fann genom denna utvärdering att LXD var ett bättre val för orkestratorn.