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Digital street and trail maps are typically represented by an interconnected network of path segments. These spatial networks are used in map creation, route planning, and geo-location. Consumer GPS devices have become popular as a method of collecting data for use in spatial networks. Existing methods for creating spatial networks either require extensive hand editing or use inefficient algorithms that require re-computation when adding new data to an existing network. I demonstrate a method for creating and maintaining spatial networks that allows for incremental updates without complete re-computation. I also demonstrate how spatial limits on data set growth allows networks to be updated in linear time after initial path discovery. This approach allows networks to be rapidly and accurately updated using data from consumer GPS devices.

Cycling, and micromobility tools like bike share, have increasingly been recognized for their health, economic and environmental benefits, and municipalities have recently made encouraging the use of these modes of urban transportation both a policy and a financial priority. Many studies, using varying methods, have identified and confirmed an association between an increased presence and connectivity of cycling infrastructure (bike lanes, cycle tracks, etc.) and cycling or bike share ridership. Determining a more explicit causal link between infrastructure and ridership, however, often proves challenging to researchers, due to data limitations and a variety of simultaneous, exogenous, factors that abound within complex urban transportation systems. Given the financial impacts of capital investment in infrastructure, more closely establishing this causal link, and identifying infrastructure’s ability to generate cycling and bike share traffic, is of growing importance to municipal governments and taxpayers. Using Hamilton Bike Share (HBS) trip logs and GPS trajectories occurring between January, 2019 and August, 2022 (n = 741,369 and 609,746, respectively), this thesis constructs individual shapefiles of each HBS trip for GIS analysis through Dalumpines and Scott’s (2011) GIS-Based Map-Matching Algorithm. It investigates the impact of ten separated cycling infrastructure projects in Hamilton, constructed between 2019 and 2022, on HBS ridership along the respective intervention segments. The thesis also holistically analyzes the spatial and ridership impacts of one infrastructure intervention, the Victoria Avenue cycle track, on the distribution of riders using the segment of interest, a more precise classification of post-intervention trip natures (‘induced’ or ‘diverted’) using a novel categorization process, and maps the impact of the iv segment on trip diversion to use the cycle track. Results indicate that five of the ten interventions have had significant, positive, impacts on monthly HBS ridership along their respective segments, with others having nearly statistically significant results as well. Moreover, the Victoria Avenue cycle track lessened the cost of distance associated with using Victoria Avenue, and 46.9% of trips along the cycle track post-intervention, were determined to be ‘induced’ trips. Finally, of the streets in the Victoria Avenue cycle track’s neighborhood, the cycle track segments were the only segments to experience ridership increases post-intervention, which indicates a significant level of trip diversion and funneling of trips to use the cycle track. These results enhance findings from the literature and more concretely quantify the direct impacts of infrastructure investments. Investments in infrastructure appear to make a significant difference in increasing ridership and serve to benefit more than just existing riders. This thesis can have an important impact on municipal active transportation planning, policy, and financing, through its results and by providing a methodological foundation for future research into infrastructure’s impacts on a variety of users. / Thesis / Master of Science (MSc)

Surveillance cameras are integral in assisting law enforcement by collecting video information that may help officers detect people for whom they are looking. While surveillance cameras record the area covered by the camera, unlike humans, they cannot "understand" what is happening. My research uses multiple curvilinear regression models to accurately place differentially corrected GPS points with submeter accuracy onto a camera image. Optimal results were achieved after splitting the image into four zones with the focus on calibrating each area separately. This resulted in adjusted R2 values as high as 99.8 percent, indicating that high quality GPS points can form a good manual camera calibration. To ascertain whether or not a lesser quality GPS point associated with a social media application would allow location of the person sending the message, I used an iPhone 5s to do a follow up. Using the zone-based calibration equations on GPS point locations from an iPhone 5s show that the locations collected are less accurate than differentially corrected GPS locations, but there is still a decent chance of being able to locate the correct person in an image based off that person's location. That chance, however, depends on the population density inside the image. Pedestrian density tests show that about 70-80 percent of the phone locations in a low-density environment could be used to locate the correct person that sent a message while 30-60 percent of the phone locations could be used in that manner in a high-density environment. / Master of Science

Data from a Coarse Acquisition (C/A) Global Positing System (GPS) map-grade receiver were evaluated to assess the accuracy of differentially corrected points. Many studies have focused on the accuracy of GPS units under ideal data collection conditions. Ideal conditions allow the collection of data with four satellites (3D mode), yet field data conditions are often less than ideal. Four satellites may not always be in view because of mountainous topography, heavy forest cover, or other obstructions which block satellite signals from the receiver. This study examines GPS accuracy when four satellites are not available, instead collecting data with only three satellites (2D mode). 3D GPS points compute four unknowns: x, y , z, and clock error. In comparison, 2D GPS points are less accurate as only three unknowns are calculated: x, y, and clock error. Elevation (or z) is not computed for 2D points, causing increased error in the horizontal (x, y) measurement. The effect of elevation was evaluated on 234 2D GPS data points. These points were collected and corrected at elevation intervals of true elevation, +-25 meters, +- 50 meters, and +-75 meters. These 2D points were then compared to surveyed points to measure the effect vertical error has on horizontal accuracy. In general, the more error in the vertical estimate during correction, the greater the horizontal error. / Master of Science

Cyber-attack on synchrophasor data has become a widely explored area. However, GPS-spoofing and FDIA attacks require different responsive actions. State-estimation based attack detection method works similar way for both types of attacks. It implies that using state-estimation based detection alone doesn’t give the control center enough information about the attack type. This scenario is specifically more critical for those attack detection methods which consider GPS-spoofing attack as another FDIA with falsified phase angle data. Since identifying correct attack type is paramount, we have attempted to develop an algorithm to distinguish these two attacks. Previous researchers exploited low-rank approximation of Hankel Matrix to differentiate between FDIA and physical events. We have demonstrated that, together with angle unwrapping algorithm, low-rank approximation of Hankel Matrix can help us separating GPS-spoofing attack with FDIA. The proposed method is verified with simulation result. It has been demonstrated that the GSA with 3 second time-shift creates a low-rank approximation error 700% higher than that of normal condition, whereas FDIA doesn’t produce any significant change in low-rank approximation error from that of normal condition. Finally, we have proposed a real-time method for successful identification of event, FDIA and GSA. / M.S. / Cyber-attack on synchrophasor data has become a widely explored area. However, GPS-spoofing and FDIA attacks require different responsive actions. State-estimation based attack detection method works similar way for both types of attacks. It implies that using state-estimation based detection alone doesn’t give the control center enough information about the attack type. This scenario is specifically more critical for those attack detection methods which consider GPS-spoofing attack as another FDIA with falsified phase angle data. Since identifying correct attack type is paramount, we have attempted to develop an algorithm to distinguish these two attacks. Previous researchers exploited low-rank approximation of Hankel Matrix to differentiate between FDIA and physical events. We have demonstrated that, together with angle unwrapping algorithm, low-rank approximation of Hankel Matrix can help us separating GPS-spoofing attack with FDIA. The simulation result verifies the next chapter discusses our proposed algorithm on GPS-spoofing attack detection and its ability to distinguish this type of attack from conventional FDIA. The proposed method is verified with simulation result. It has been demonstrated that the GSA with 3 second time-shift creates a low-rank approximation error 700% higher than that of normal condition, whereas FDIA doesn’t produce any significant change in low-rank approximation error from that of normal condition. Finally, we have proposed a real-time method for successful identification of event, FDIA and GSA.

Le Positionnement Ponctuel Précis (PPP) est une technique de positionnement qui utilise un seul récepteur GNSS. Cette approche diffère nettement des méthodes différentielles qui sont largement utilisées et qui nécessitent deux ou plusieurs récepteurs. Le PPP est une technique économique, autonome par station, avec une précision centimétrique qui a ouvert la possibilité à une large gamme d'applications. Cependant, dans les applications qui nécessitent une convergence rapide et une haute précision, la performance du PPP n'est pas encore suffisante. Le PPP peut prendre jusqu’à 30 minutes pour converger vers des solutions avec une précision décimétrique. Cette longue période d’initiation est principalement consacrée à la stabilisation des ambiguïtés de phase vers des valeurs flottantes constantes. Compte tenu de ce problème, il est démontré que la Résolution des Ambigüités (RA) améliore la stabilité du modèle d’estimation, réduit le temps de convergence et améliore la précision des coordonnées. Cette étude vise donc à améliorer la performance du PPP pour obtenir une meilleure précision plus rapidement. À cette fin, les biais de phase des satellites qui perturbent la résolution des ambiguïtés sont estimées du côté producteur et résolues du côté utilisateur. Une modélisation judicieuse de paramètres et une sélection rigoureuse des modèles de correction sont effectuées afin de réduire la propagation des erreurs non-corrigées dans les biais de phase des satellites estimés. Du côté producteur, une nouvelle approche modulaire à deux étapes est proposée et implémentée comprenant l'estimation de biais de phase des satellites à partir de chaque site et l'intégration séquentielle des solutions. Ces deux étapes ont des structures simples et elles permettent d'estimer précisément les biais de phase de chaque satellite. L’algorithme de l’intégration séquentielle garde un bon compromis entre la charge de calcul, la charge et la capacité de mémoire de l’ordinateur, l'efficacité du traitement des paramètres et la précision des estimations. Du coté producteur, chaque observation est modélisée individuellement et intégrée dans le processus d'estimation. Ceci facilite l'intégration des fréquences supplémentaires (par exemple, la troisième fréquence L5 ou des observations multi-GNSS) pour améliorer davantage la performance de PPP en fonction de la modernisation du GNSS. Du côté utilisateur, un modèle d’observation de plein rang au niveau de récepteur mono-fréquence est proposé et implémenté. Cela donne de la flexibilité aux utilisateurs avec les récepteurs mono-fréquence pour utiliser notre solution PPP-RA lorsque les produits d'ionosphère de haute précision sont disponibles. Le modèle proposé est compatible avec les horloges de satellite standards actuelles fournies par IGS, CODE, JPL, par exemple. Le délai ionosphérique est corrigé et estimé en parallèle. Cela permet à l'utilisateur d’utiliser une correction ionosphérique de faible ou de haute précision et obtenir une performance améliorée en ajustant uniquement la précision de l'estimation de ce paramètre. La performance du PPP-RA du côté utilisateur a été comparée au PPP conventionnel en termes du temps de convergence et de la précision des coordonnées. En résumé, on a obtenu une amélioration importante en temps de convergence (jusqu'à 80 %) et en précision planimétrique (jusqu'à 60 %) par rapport au PPP conventionnel. De plus, une étude comparative est effectuée pour distinguer les caractéristiques de notre approche des autres méthodes PPP-RA. L’avantage de notre PPP-RA est aussi démontré par son approche unique à éliminer les défauts de rang et résoudre les ambiguïtés libre-de-biais. / Precise Point Positioning (PPP) is a single receiver GNSS positioning technique developed in contrast to broadly used differential methods that require two or more receivers. Employing a single receiver makes PPP a cost-effective and per-site autonomous technique with centimetre precision that has opened up the possibility of a wide range of applications. In many applications where short time period is required to reach high precisions, the performance of PPP is not yet sufficient. Typically, PPP takes up to 30 minutes in order to converge to coordinate solutions with acceptable precision. This limitation is mostly due to the long period required for stabilizing the float carrier phase ambiguities to constant values. Given this problem, it is showed that Ambiguity Resolution (AR) improves drastically the stability of the estimation model that in turn reduces the convergence time, and increases the precision of estimated coordinates. Thus, this study seeks to enhance the performance of PPP to obtain higher precision at a faster convergence time. For this reason, the hardware dependent satellite phase biases are estimated at the producer-side and by applying them as corrections the integer ambiguities can be resolved at the user-side. A judicious modelling of all parameters and a careful selection of the correction models is accomplished to reduce the impact of error propagation on the precision of parameters estimation. At the producer-side, a novel modular approach is proposed and established including the per-site satellite phase bias estimation and the sequential integration of the solutions. The two-steps of the producer-side have simple structures and allows for estimating the satellite phase biases. The proposed sequential network solution, keeps a good compromise between the computational burden, the computer memory load, the efficiency of handling parameters and the precision of estimations. In addition, all observables are modelled individually and integrated in the estimation process that facilitates the extension of algorithms for integrating additional frequencies or mutli-GNSS observables with respect to the GNSS modernization. At the user side, a novel full rank design matrix in the single frequency level is proposed and implemented. This gives the flexibility to the users with single-frequency receivers to benefit from our PPP-AR while the high precision ionosphere products are available. The proposed model is compatible with the current standard satellite clocks available, for example, from IGS, CODE, and JPL. The ionosphere is corrected and estimated at the same time. This gives the user the possibility to take advantage of low or high precision ionosphere products for obtaining an enhanced performance with our PPP-AR by only adjusting the precision of the ionosphere delay estimation. The performance of our PPP-AR user-side is then compared to the conventional PPP in terms of convergence time and coordinate precision. A substantial improvement has been obtained in terms of planimetric precision (up to 60%) and the convergence time (up to 80%) compared to the conventional PPP method. In addition, the characteristics of our PPP-AR are compared in detail with other PPP-AR methods. The advantage of our method is discussed by its unique approach of removing the rank deficiencies and resolving bias-free ambiguities.

Movement is a core mechanism through which animals interact with their environment. A popular approach for investigating animal movement involves the use of GPS telemetry, which provides insights into both the spatial and temporal patterns exhibited by an individual or population. While many approaches for evaluating movement data have often analyzed intensity of use, most studies focused on a single property of use: the total duration an animal spent in a location. While this can provide insight into which landscape and habitat characteristics an individual may be selecting for, it does not fully account for how that space is being used. Therefore, it could be beneficial to combine more than one aspect of intensity of use to evaluate the behavioral mechanisms leading to patterns in space use, and by extension the drivers of home range structuring. As a territorial, central-place forager, the North American beaver (Castor canadensis) presents an ideal system for investigating drivers of home range structuring. However, while some species are easily tracked through traditional GPS attachment methods (such as GPS collars or backpacks), other species such as beavers present unique challenges given their fusiform shape and tapered neck. The overall objective of my thesis was to better understand fine-scale movement of beavers and the drivers of home range structuring. To do so, in chapter 1, I first developed and tested three different GPS transmitter attachment methods to determine which was most effective in terms of retention time (RT, total number of days a transmitter remains attached) and GPS fix success rate (FSR, % of successful fixes vs. attempted) and investigated to what degree various factors (season, sex, and age class) affected these results. In chapter 2, I analyzed space use in relation to four intensity of use metrics with machine learning to define homogenous types of space use. These metrics included the total number of visits, total and mean duration of visits, and mean interval between visits. GPS transmitters glued to the lower back of beavers provided up to two months of fine-scale data, as well as producing the highest FSR. In addition, longer retention times were found for transmitters attached to males versus females, and for transmitters deployed in the fall versus the spring. Using these data, I was able to capture five distinct population level intensity of use types including, low use – irregular, low use – regular, medium use – short duration, medium use – long duration, and lodge use. These types of use were all generally observed towards the core of the home range. In addition, all types of use were characterized by low intervals between visits except for the low use – irregular cluster. These results suggest highly structured, regular movements occurring in the core of beaver home ranges related primarily to shelter, foraging patches, and the movement paths used to link these locations. Overall, this approach allowed me to delineate between two distinct low use and medium use types and provided insight into the different behavioral mechanisms that may be driving these similar types of use. Capturing these different types of use was only possible by specifically combining multiple movement metrics at once to evaluate space use, as opposed to strictly using the number of GPS fixes to evaluate a location.

The MSU ERC UltraScope system consists of a distributed computing system, custom PCI cards, GPS receivers, and a re-radiation system. The UltraScope system allows precision timestamping of events in a distributed application on a system where the CPU and PCI clocks are phase-locked. The goal of this research is to expand the UltraScope system, using software routines and minimal hardware modifications, to allow precision timestamping of events on an asynchronous distributed system. The timestamp process is similar to the Network Time Protocol (NTP) in that it uses a series of timestamps to improve precision. As expected, the precision is less accurate on an asynchronous system than on a synchronous system. Results show that the precision is improved using this sequence of timestamps, and the major error component is due to operating system delays. The errors associated with this timestamping process are characterized using a synchronous system as a baseline.

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