The purpose of this research is to analyse pedestrian volumes on multi-use trails in Winnipeg, Canada. The research methodology consisted of collecting continuous automated pedestrian count volumes at seven locations on four multi-use trails in Winnipeg from January 1, 2014 to December 31, 2014. An average pedestrian volume was calculated for each count site over annual, seasonal, and monthly time periods. Pedestrian volumes were found to vary consistently by month of year and hour of day. Day-of-week patterns were not consistent in terms of pedestrian volume. There was a negative relationship between pedestrian volume and rainfall volume and duration, and average daily wind speed. There was a positive non-linear relationship between pedestrian volume and maximum daily temperature. While pedestrian volume correlates with weather factors, variability remains. This suggests that weather analysis may be useful as a complement, but not a replacement of traditional temporal analysis for estimation of pedestrian volumes. / May 2016

The Long Point wetland complex is one of the most significant coastal wetland systems in the Great Lakes, containing a diverse mosaic of wetland vegetation communities that have developed in response to water level fluctuations due to natural climate variability. Natural short-term water level variations are important for promoting wetland productivity and diversity, but long-term water level changes resulting from human-induced climate change can have serious and long-term consequences on the integrity and health of wetlands. The historical response of the wetland to water level fluctuations was quantified and modelled to provide an indication of how the wetland may respond to future projected water level changes - water level fluctuations are used as a surrogate for climate change. A spatiotemporal trend analysis was conducted within a geographic information system (GIS) to determine the effects of water level conditions on wetland vegetation and land cover at the wetland complex at Long Point, Ontario for seven years from 1945 to 1999. The spatiotemporal trend analysis documented changes in the structure and composition of the wetland complex in response to declining and rising water level conditions. During drier periods, there were significant increases in the amount of drier emergent and meadow vegetation, especially within the Inner Bay and northern portion of the outer peninsula. There was less fragmentation and complexity in the wetland as these drier communities expanded forming larger continuous patches of vegetation. During wetter periods, open water increased and there was a predominance of wetter emergent and meadow communities in the wetland. Drier vegetation communities became interspersed with water creating a more fragmented convoluted wetland landscape. The historical response of the wetland vegetation and land cover to water level fluctuations was then simulated with three different wetland models developed in the GIS. A rule-based model, a probability model, and a transition model were developed to assess wetland response to future water level changes. The models were evaluated using simple statistical methods. The transition and rule-based models performed the best and were successful in predicting over 80 % of the wetland vegetation distribution correctly. The probability model was the least successful, predicting only 55 % of the response correctly. The GIS proved successful in documenting wetland response to historical water level fluctuations and providing insight into the potential impacts of future climate change though water level fluctuations on the Long Point coastal wetland complex. The spatiotemporal analysis and wetland modelling advance the role of GIS in wetland management and analysis. They are practical methods within a GIS that can be used to assess the impacts of climate change on wetland systems and to document and model wetland change in other coastal wetlands of the Great Lakes.

The Long Point wetland complex is one of the most significant coastal wetland systems in the Great Lakes, containing a diverse mosaic of wetland vegetation communities that have developed in response to water level fluctuations due to natural climate variability. Natural short-term water level variations are important for promoting wetland productivity and diversity, but long-term water level changes resulting from human-induced climate change can have serious and long-term consequences on the integrity and health of wetlands. The historical response of the wetland to water level fluctuations was quantified and modelled to provide an indication of how the wetland may respond to future projected water level changes - water level fluctuations are used as a surrogate for climate change. A spatiotemporal trend analysis was conducted within a geographic information system (GIS) to determine the effects of water level conditions on wetland vegetation and land cover at the wetland complex at Long Point, Ontario for seven years from 1945 to 1999. The spatiotemporal trend analysis documented changes in the structure and composition of the wetland complex in response to declining and rising water level conditions. During drier periods, there were significant increases in the amount of drier emergent and meadow vegetation, especially within the Inner Bay and northern portion of the outer peninsula. There was less fragmentation and complexity in the wetland as these drier communities expanded forming larger continuous patches of vegetation. During wetter periods, open water increased and there was a predominance of wetter emergent and meadow communities in the wetland. Drier vegetation communities became interspersed with water creating a more fragmented convoluted wetland landscape. The historical response of the wetland vegetation and land cover to water level fluctuations was then simulated with three different wetland models developed in the GIS. A rule-based model, a probability model, and a transition model were developed to assess wetland response to future water level changes. The models were evaluated using simple statistical methods. The transition and rule-based models performed the best and were successful in predicting over 80 % of the wetland vegetation distribution correctly. The probability model was the least successful, predicting only 55 % of the response correctly. The GIS proved successful in documenting wetland response to historical water level fluctuations and providing insight into the potential impacts of future climate change though water level fluctuations on the Long Point coastal wetland complex. The spatiotemporal analysis and wetland modelling advance the role of GIS in wetland management and analysis. They are practical methods within a GIS that can be used to assess the impacts of climate change on wetland systems and to document and model wetland change in other coastal wetlands of the Great Lakes.

Η έρευνα για την ασφάλεια της κυκλοφορίας γίνεται για να αποτρέψει τα ατυχήματα και να μειώσει τη δριμύτητα των τραυματισμών στο δρόμο, και αποτελεί σημαντικό θέμα στον τομέα της εφαρμοσμένης μηχανικής των μεταφορών δεδομένου ότι στοχεύει να σώσει ανθρώπινες ζωές. Η παρούσα εργασία έχει ως σκοπό την εύρεση μιας συνάρτησης που εκτιμά σε πραγματικό χρόνο μία συγκεκριμένη κυκλοφοριακή μεταβλητή, τον χρονικό διαχωρισμό, η οποία μπορεί να χρησιμοποιηθεί ως πρόδρομος ατυχήματος ή παρ’ ολίγον ατυχήματος. Η έρευνα αυτή έγινε σε τμήμα του αυτοκινητοδρόμου Ι-94, που αποτελεί σύνδεσμο μεταξύ των πόλεων St. Paul και Minneapolis και παρουσιάζει φόρτο 80.000 οχήματα ανά κατεύθυνση και κυκλοφοριακή συμφόρηση τουλάχιστον για 5 ώρες καθημερινά. Εξετάστηκαν τέσσερις ανεξάρτητες μεταβλητές και διερευνήθηκε η συσχέτιση του χρονικού διαχωρισμού με τις τρέχουσες και παρελθούσες τιμές των μεταβλητών αυτών. Με την μέθοδο της παλινδρόμησης υπολογίζεται η σημαντικότητα κάθε μεταβλητής. Επίσης υπολογίζεται το βάθος χρόνου στο οποίο η πιθανή συσχέτιση είναι σημαντική. Συμπεραίνεται ότι είναι δυνατή η εκτίμηση του χρονικού διαχωρισμού 2–5 λεπτά πριν παρατηρηθεί, αναλόγως της κυκλοφοριακής κατάστασης. / The research on traffic safety seeks to prevent accidents and reduce the severity of injuries on the road, and is an important area in the field of transportation engineering as it aims to save lives. This study aims to identify a function that estimates, in real time, a specific traffic variable, time headway, that can be used as a precursor to an accident or near accident. The method is applied in a section of freeway I-94 between St. Paul and Minneapolis that carries 80,000 vehicles in each stream and experiences traffic congestion for at least 5 hours daily. Four independent variables were considered and the relationship between time headway and current and past values of these variables was examined. Using regression, the significance of each variable was determined. Further, the extent in time for which such relationship is significant was also determined. It is concluded that estimation of time headway is feasible, 2-5 minutes prior to occurrence, depending on traffic conditions.

Knowledge discovery on spatio-temporal datasets has attracted<br />growing interests. Recent advances on remote sensing technology mean<br />that massive amounts of spatio-temporal data are being collected,<br />and its volume keeps increasing at an ever faster pace. It becomes<br />critical to design efficient algorithms for identifying novel and<br />meaningful patterns from massive spatio-temporal datasets. Different<br />from the other data sources, this data exhibits significant<br />space-time statistical dependence, and the assumption of i.i.d. is<br />no longer valid. The exact modeling of space-time dependence will<br />render the exponential growth of model complexity as the data size<br />increases. This research focuses on the construction of efficient<br />and effective approaches using approximate inference techniques for<br />three main mining tasks, including spatial outlier detection, robust<br />spatio-temporal prediction, and novel applications to real world<br />problems.<br /><br />Spatial novelty patterns, or spatial outliers, are those data points<br />whose characteristics are markedly different from their spatial<br />neighbors. There are two major branches of spatial outlier detection<br />methodologies, which can be either global Kriging based or local<br />Laplacian smoothing based. The former approach requires the exact<br />modeling of spatial dependence, which is time extensive; and the<br />latter approach requires the i.i.d. assumption of the smoothed<br />observations, which is not statistically solid. These two approaches<br />are constrained to numerical data, but in real world applications we<br />are often faced with a variety of non-numerical data types, such as<br />count, binary, nominal, and ordinal. To summarize, the main research<br />challenges are: 1) how much spatial dependence can be eliminated via<br />Laplace smoothing; 2) how to effectively and efficiently detect<br />outliers for large numerical spatial datasets; 3) how to generalize<br />numerical detection methods and develop a unified outlier detection<br />framework suitable for large non-numerical datasets; 4) how to<br />achieve accurate spatial prediction even when the training data has<br />been contaminated by outliers; 5) how to deal with spatio-temporal<br />data for the preceding problems.<br /><br />To address the first and second challenges, we mathematically<br />validated the effectiveness of Laplacian smoothing on the<br />elimination of spatial autocorrelations. This work provides<br />fundamental support for existing Laplacian smoothing based methods.<br />We also discovered a nontrivial side-effect of Laplacian smoothing,<br />which ingests additional spatial variations to the data due to<br />convolution effects. To capture this extra variability, we proposed<br />a generalized local statistical model, and designed two fast forward<br />and backward outlier detection methods that achieve a better balance<br />between computational efficiency and accuracy than most existing<br />methods, and are well suited to large numerical spatial datasets.<br /><br />We addressed the third challenge by mapping non-numerical variables<br />to latent numerical variables via a link function, such as logit<br />function used in logistic regression, and then utilizing<br />error-buffer artificial variables, which follow a Student-t<br />distribution, to capture the large valuations caused by outliers. We<br />proposed a unified statistical framework, which integrates the<br />advantages of spatial generalized linear mixed model, robust spatial<br />linear model, reduced-rank dimension reduction, and Bayesian<br />hierarchical model. A linear-time approximate inference algorithm<br />was designed to infer the posterior distribution of the error-buffer<br />artificial variables conditioned on observations. We demonstrated<br />that traditional numerical outlier detection methods can be directly<br />applied to the estimated artificial variables for outliers<br />detection. To the best of our knowledge, this is the first<br />linear-time outlier detection algorithm that supports a variety of<br />spatial attribute types, such as binary, count, ordinal, and<br />nominal.<br /><br />To address the fourth and fifth challenges, we proposed a robust<br />version of the Spatio-Temporal Random Effects (STRE) model, namely<br />the Robust STRE (R-STRE) model. The regular STRE model is a recently<br />proposed statistical model for large spatio-temporal data that has a<br />linear order time complexity, but is not best suited for<br />non-Gaussian and contaminated datasets. This deficiency can be<br />systemically addressed by increasing the robustness of the model<br />using heavy-tailed distributions, such as the Huber, Laplace, or<br />Student-t distribution to model the measurement error, instead of<br />the traditional Gaussian. However, the resulting R-STRE model<br />becomes analytical intractable, and direct application of<br />approximate inferences techniques still has a cubic order time<br />complexity. To address the computational challenge, we reformulated<br />the prediction problem as a maximum a posterior (MAP) problem with a<br />non-smooth objection function, transformed it to a equivalent<br />quadratic programming problem, and developed an efficient<br />interior-point numerical algorithm with a near linear order<br />complexity. This work presents the first near linear time robust<br />prediction approach for large spatio-temporal datasets in both<br />offline and online cases. / Ph. D.

No description available.

Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185&#965;m). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type. / Ph. D.

Identification of an individual from discriminating features of the friction ridge surface is one of the oldest and most commonly used biometric techniques. Methods for identification span from tedious, although highly accurate, manual examination to much faster Automated Fingerprint Identification Systems (AFIS). While automatic fingerprint recognition has grown in popularity due to the speed and accuracy of matching minutia features of good quality plain-to-rolled prints, the performance is less than impressive when matching partial fingerprints. For some applications, including forensic analysis where partial prints come in the form of latent prints, it is not always possible to obtain high-quality image samples. Latent prints, which are lifted from a surface, are typically of low quality and low fingerprint surface area. As a result, the overlapping region in which to find corresponding features in the genuine matching ten-print is reduced; this in turn reduces the identification performance. Image quality also can vary substantially during image capture in applications with a high throughput of subjects having limited training, such as in border control. The rushed image capture leads to an overall acceptable sample being obtained where local image region quality may be low. We propose an improvement to the reliability of features detected in exemplar prints in order to reduce the likelihood of an unreliable overlapping region corresponding with a genuine partial print. A novel approach is proposed for detecting minutiae in low quality image regions. The approach has demonstrated an increase in match performance for a set of fingerprints from a well-known database. While the method is effective at improving match performance for all of the fingerprint images in the database, a more significant improvement is observed for a subset of low quality images. In addition, a novel method for fingerprint analysis using a sequence of fingerprint images is proposed. The approach uses the sequence of images to extract and track minutiae for temporal analysis during a single impression, reducing the variation in image quality during image capture. Instead of choosing a single acceptable image from the sequence based on a global measure, we examine the change in quality on a local level and stitch blocks from multiple images based on the optimal local quality measures. / Ph. D.

Eastern Equine Encephalitis virus is a vector-borne virus amplified by the Culiseta melanura mosquito in an enzootic avian cycle, causing high morbidity and mortality to horses and humans when contracted as incidental hosts. The virus is distributed across most of the eastern United States, Canada, and Gulf coast, and has been expanding in geographic range and season of activity over time. Spatial-temporal trends in Cs. melanura abundance were correlated with available meteorological (temperature and precipitation) and remotely sensed environmental data for the period of 1997-2012 in Connecticut. The effects of inter-annual changes in precipitation, temperature, and groundwater levels on Cs. melanura abundances using time-series linear regression and cross-correlation analyses were inconclusive. Habitat modeling using logistic regression and landscape-based predictive variables demonstrated strong efficiency (46.2%) and acceptable sensitivity and specificity (65.6 and 78.6%, respectively) using NDVI difference and distance from palustrine areas as predictive factors. Remotely sensed data can improve the understanding of vector abundance patterns, helping to forecast future outbreaks and regional expansions by guiding surveillance efforts.

OBJETIVO: Investigar a tendência temporal na atividade física de lazer em adultos paulistanos entre os anos de 2006 a 2016. MÉTODOS: Estudo de análise de série temporal. Inicialmente fez-se o download dos bancos de dados das variáveis de atividade física no tempo de lazer (se praticava ou não, tipo de modalidade, frequência semanal e duração diária), do sexo, da idade e da escolaridade diretamente na base dados do Sistema de Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico (n=21.357). Foram realizadas análises gerais e estratificadas por sexo, idade e escolaridade utilizando-se o método descritivo através das prevalências e seus respectivos intervalos de confiança (IC 95%). RESULTADOS: Nos onze anos de observação, a prevalência de atividade física no tempo de lazer em adultos paulistanos aumentou em 8,6 pontos percentuais. Houve aumento significativo no grupo de mulheres. Maiores prevalências foram observadas em pessoas até 34 anos e com nove anos ou mais de escolaridade. As três modalidades mais praticadas foram a caminhada, o futebol e a musculação. A maioria das pessoas praticou de uma a duas vezes por semana e por trinta minutos ou mais por dia. CONCLUSÃO: Ao longo dos onze anos de observação, verificou-se um aumento na prevalência de atividade física durante o tempo de lazer em adultos residentes na cidade de São Paulo, principalmente em mulheres / OBJECTIVE: To investigate the temporal trend in leisure physical activity among adults from São Paulo between 2006 and 2016. METHODS: Time series analysis. Initially, the databases of physical activity variables were downloaded in leisure time ( practice: yes or not, type of modality, weekly frequency, and daily duration), sex, age and schooling directly in the database of the Surveillance System of Risk Factors and Protection for Chronic Diseases by Telephone Inquiry (n = 21,357). General and stratified analyzes were performed by sex, age and schooling using the descriptive method through prevalence and their respective confidence intervals (95% CI). RESULTS: In the eleven years of observation, the prevalence of physical activity in leisure time in adults from São Paulo increased by 8.6 percentage points. There was a significant increase in the group of women. Higher prevalence was observed in people up to 34 years of age and with nine years or more of education. The three most practiced modalities were walking, soccer and weight training. . Most people practiced once or twice a week for thirty minutes or more a day. CONCLUSION: Over the eleven years of observation, there was an increase in the prevalence of physical activity during leisure time in adults living in the city of São Paulo, especially in women