In GIS-projects the data capture is one of the most time consuming processes. Both how to collect the data and the quality of the collected data is of high importance. Common methods for data capture are GPS, LiDAR, Total Station and Aerial Photogrammetry. Unmanned Aerial Vehicles, UAVs, have become more common in recent years and the number of applications continues to increase. As the technique develops there are more ways that UAV technique can be used for collection of geographic data. One of these techniques is the UAV photogrammetry that entails using an UAV equipped with a camera combined with photogrammetric software in order to create three dimensional models and orthophotos of the ground surface. This thesis contains a comparison between different geographic data capture methods such as terrestrial and aerial methods as well as UAV photogrammetry. The aim is to investigate how UAVs are used to collect geographic data today as well how the techniques involving UAVs can replace or be used as a complement to traditional methods. This study is based on a literature study and interviews. The literature study aims to give a deeper insight in where and how UAVs are used today for geographic data capturing with focus on three main areas: environmental monitoring, urban environment and infrastructure, and natural resources. Regarding the interviews companies and other participants using UAVs for geographic data collection in Sweden have been interviewed to get an accurate overview of the current status regarding the use of UAVs in Sweden. Advantages, disadvantages, limitations, economical aspects, accuracy and possible future use or development are considered as well as different areas of applications. The study is done in collaboration with the geographic IT company Digpro Solutions AB. The goal is to be able to present suggestions of how UAV data can be applied in Digpros applications. Information from the literature study and the interviews show that using a UAV makes it possible to cover a large range between terrestrial and aerial methods, and that it can replace or complement other methods for surveying and data collection. The use gives the possibility to get close to the object without being settle to the ground, as well as work environment profits since dangerous, difficult areas can be accessed from distance. The data can be collected faster, quicker, cheaper and more frequent. Time savings occurs in the measurement stage but compared to terrestrial methods more time is required for the post-processing of the data. The use in Sweden is limited due to difficulties linked to Swedish legislation regarding camera surveillance, as well as long waiting times for the permissions that is required to fly. However, a change in the camera surveillance law is expected which means that UAVs will be excluded from the law. That may result in great benefits for everyone within the industry as well as a continued development of the technique and the use of UAVs. / Inom GIS ar datainsamling en av de mest tidskrävande processerna. Både hur data samlas in samt kvaliteten ar av hög vikt. Några av de vanligaste metoderna för datainsamling idag är GPS, LiDAR, totalstation och fotogrammetri. Obemannade flygfarkoster, UAVs, har de senaste åren blivit allt vanligare och användningsområdena fortsätter att öka. I takt med att tekniken hela tiden utvecklas finns idag flertalet satt att med hjälp av UAVs samla in geografisk data. Med kamerautrustade obemannade flygfarkoster och fotogrammetriska programvaror ar det bland annat möjligt att skapa tredimensionella modeller samt ortofoton av markytan. Detta kandidatexamensarbete innehaller en jämförelse mellan terrestra- samt flygburna metoder för datainsamling och obemannade flygburna metoder. Syftet är att undersöka hur UAVs kan anvandas för att samla in geografisk data samt möjligheten att ersätta eller komplettera existerande metoder, samt att presentera en overgripande bild av UAVs anvandningsomåden. Denna studie bygger pa en litteraturstudie samt intervjuer. Litteraturstudien syftar till en djupare inblick i anvandningsområden för UAV tekniken med fokus på tre huvudområden: miljöövervakning, urbana miljöer och infrastruktur samt naturliga resurser. Under intervjuerna intervjuades företag och andra aktörer inom branschen med syftet att göra en nulägesanalys av hur UAVs används för insamling av geografisk data i Sverige. Det insamlade materialet analyserades med avseende pa användningsområden, för- och nackdelar, hinder, kostnader, noggrannhet samt möjlig framtida användning och utveckling av tekniken. Studien är gjord i samarbete med företaget Digpro Solutions AB som är verksamma inom geografisk IT. Målet är att efter studien kunna ge förslag på hur data insamlad med UAV kan appliceras på Digpros applikationer. Information fran intervjuerna och litteraturen har visat att UAV täcker ett stort spann mellan terrestra- och flygburna metoder, och att den kan ersätta eller utgöra ett komplement till många mät- och datainsamlingsmetoder. Användningen av UAVs innebär möjlighet till att samla in data på ett nära avstånd till objekt utan att vara bunden till marken. Den medför även arbetsmiljövinster då farliga, svårtillgängliga områden kan nås från avstånd. Data kan samlas in snabbare, enklare, billigare och mer frekvent. Tisdbesparingar sker i inmätningsskedet men jämfört med terrestra mätmetoder krävs dock mer tid för efterbearbetning av mätdatat. Användningen i Sverige begränsas av svårigheter kopplade till Svensk lagstiftning gällande kameraövervakning, samt långa väntetider på de tillstånd som kravs för att få flyga. Dock väntas en ändring i kameraövervakningslagen som innebär att drönare inte innefattas i lagen. Detta kan komma att medföra stora fördelar för samtliga inom branschen samt en fortsatt utveckling av tekniken samt användningen av UAVs.

This research demonstrated the effectiveness of Transit Signal Priority (TSP) in improving bus corridor travel time in a simulated environment using real world data. TSP is a technology that provides preferential treatment to buses at signalized intersections. By considering different scenarios of activating bus signal priority when a bus is 3 or 5 minutes behind schedule, it was demonstrated that bus travel times improved significantly while there is little effect on delays for crossing street traffic. The case of providing signal priority for buses unconditionally resulted in significant crossing street delays for some signalized intersections with only minor improvement to bus travel time over both scenarios of Conditional priority. Evaluation was conducted by using micro-simulation and statistical analysis to compare Unconditional and Conditional TSP with the No TSP scenario. This evaluation looked at performance metrics (for buses and all vehicles) including average speed profiles, average travel times, average number of stops, and crossing street delay. Different Conditional TSP scenarios of activating TSP when a bus is 3 or 5 minutes behind schedule were considered. The simulation demonstrated that Conditional TSP significantly improved bus travel times with little effect on crossing street delays. The results also showed that utilizing TSP technology reduced the environmental emissions in the I-Drive corridor. Furthermore, field data was used to calculate actual passenger travel time savings and benefit cost ratio (7.92) that resulted from implementing conditional TSP. Conditional TSP 3 minutes behind schedule was determined to be the most beneficial and practical TSP scenario for real world implementation at both the corridor and regional levels.

The global positioning system (GPS) makes use of extremely accurate measures of the time to determine position. The times required for electronic signals to travel at the speed of light from at least four orbiting satellites to a receiver on earth is measured precisely and used to calculate the distances from the satellites to the receiver. The calculated distances are used to determine the position of the receiver through triangulation. This research takes an approach opposite the original GPS research, focusing on the use of position to determine the time at which events occur. Specifically, this work addresses the question: Can the information pertaining to position and speed contained in a GPS record be used to autonomously identify the times at which critical events occur within a production cycle? The research question was answered by determining the hardware needs for collecting the desired data in a useable format an developing a unique data collection tool to meet those needs. The tool was field evaluated and the data collected was used to determine the software needs for automated reduction of the data to the times at which key events occurred. The software tools were developed in the form of Time Identification Modules (TIMs). The TIMs were used to reduce data collected from a load and haul earthmoving operation to duration measures for the load, haul, dump, and return activities. The value of the developed system was demonstrated by investigating correlations between performance times in construction operations and by using field data to verify the results obtained from productivity estimating tools. Use of the system was shown to improve knowledge and provide additional insight into operations analysis studies. / Ph. D.

Understanding police use of lethal force requires the collection of reliable data. Due to bias present in police-use-of-lethal-force databases, researchers typically triangulate using multiple data sources to compensate for this bias; however, triangulation is restricted when the bias present in each database is unknown. This study investigates three government-funded and three independent police-use-of-lethal-force databases to identify methodological sources of bias present in the major U.S. data-collection systems. Bias was coded based on nine categories, including misclassification bias, broad conceptualization, narrow conceptualization, overlap bias, coverage bias, voluntary response bias, observer bias, gatekeeping bias, and self-report response bias. Findings suggest that all six databases had at least three different types of methodological bias present. Generally, public, government-sponsored databases exhibit bias through data self-reporting by law enforcement and varying victim race determination methods. Private databases reveal bias through media-based reporting and the triangulation of data from multiple sources, which is further complicated by lack of transparency in the databases' design and administrative procedures. All six databases have a unique position to the State, which should also inform researcher data selection. I argue that selecting data sources that complement each other based on these identified biases will produce a more complete image of police-use-of-lethal-force and enhance finding accuracy in future research. / Master of Science / Understanding incidents where a civilian dies due to the actions of police officers requires the collection of reliable data. Due to bias—flaws in the data collection methods or data presentation—which lead to varying results when using different databases, researchers typically use multiple data sources to make up for these flaws; however, this method is restricted when the bias present in each database is unknown. This study investigates three government-funded and three independent police-use-of-lethal-force databases to identify sources of bias present in the major U.S. data-collection systems. Findings suggest that all six databases had at least three different types of flaws present. Generally, public, government-sponsored databases exhibit bias through police self-reporting lethal force, where an officer's department reports the officer's actions and there is no individual or group outside of the police reporting these incidents. Additionally, there is a flaw in how police record the race of a victim, who dies through police use of lethal force; Varying procedures in how race is recorded, whether recorded based on the officer's opinion or where a victim self-reports their own race prior to death on a government data system such as the Department of Motor Vehicles, also impacts the race data included in public databases. Private databases reveal bias through collecting incident data from news reports and using data from multiple sources such as law enforcement reports, medical examiner reports, and media reports simultaneously; this is further complicated by lack of transparency in the databases' design and administrative procedures, where there are no documents detailing the steps databases take in collecting and presenting data. All six databases have a unique position to the U.S. Government, where some are funded by the Government and where some are motivated by recent high profile police killings, which should impact researcher data selection. Ideally, the databases used should hold multiple perspectives or positions to the Government to provide an more complete image of lethal force. I argue that selecting data sources that complement each other based on these identified biases will produce a more complete image of police-use-of-lethal-force and enhance finding accuracy in future research.

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The era of modern computing has been the stage for numerous innovations that have led to cutting edge applications and systems. The characteristics of these systems and applications have been described and quantified by many, however such information is fragmented between various repositories of system and component information. In an effort to collate these disparate collections of information we propose iLORE, an extensible data framework for representing computer systems and their components. We describe the iLORE framework and the pipeline used to aggregate, clean, and insert system and component information into a database that uses iLORE's framework. Additionally, we demonstrate how the database can be used to analyze trends in computing by validating the collected data using previous works, and by showcasing new analyses that were created with said data. Analyses and visualizations created via iLORE are available at csgenome.org. / Master of Science / The era of modern computing has been the stage for numerous innovations that have led to cutting edge applications and computer systems. The characteristics of these systems and applications have been described and quantified by many, however such information is fragmented amongst different websites and databases. We propose iLORE, an extensible data framework for representing computer systems and their components. We describe the iLORE framework and the steps taken to create an iLORE database: aggregation, standardization, and insertion. Additionally, we demonstrate how the database can be used to analyze trends in computing by validating the collected data using previous works, and by showcasing new analyses that were created with said data. Analyses and visualizations created via iLORE are available at csgenome.org.