Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate

Weight of evidence to assess sediment quality

Estuaries are perhaps the most threatened environments in the coastal fringe; the coincidence of high natural value and attractiveness for human use has led to conflicts between conservation and development. These conflicts occur in the Sado Estuary since its location is near the industrialised zone of Peninsula of Setúbal and at the same time, a great part of the Estuary is classified as a Natural Reserve due to its high biodiversity. These facts led us to the need of implementing a model of environmental management and quality assessment, based on methodologies that enable the assessment of the Sado Estuary quality and evaluation of the human pressures in the estuary. These methodologies are based on indicators that can better depict the state of the environment and not necessarily all that could be measured or analysed. Sediments have always been considered as an important temporary source of some compounds or a sink for other type of materials or an interface where a great diversity of biogeochemical transformations occur. For all this they are of great importance in the formulation of coastal management system. Many authors have been using sediments to monitor aquatic contamination, showing great advantages when compared to the sampling of the traditional water column. The main objective of this thesis was to develop an estuary environmental management framework applied to Sado Estuary using the DPSIR Model (EMMSado), including data collection, data processing and data analysis. The support infrastructure of EMMSado were a set of spatially contiguous and homogeneous regions of sediment structure (management units). The environmental quality of the estuary was assessed through the sediment quality assessment and integrated in a preliminary stage with the human pressure for development. Besides the earlier explained advantages, studying the quality of the estuary mainly based on the indicators and indexes of the sediment compartment also turns this methodology easier, faster and human and financial resource saving. These are essential factors to an efficient environmental management of coastal areas. Data management, visualization, processing and analysis was obtained through the combined use of indicators and indices, sampling optimization techniques, Geographical Information Systems, remote sensing, statistics for spatial data, Global Positioning Systems and best expert judgments. As a global conclusion, from the nineteen management units delineated and analyzed three showed no ecological risk (18.5 % of the study area). The areas of more concern (5.6 % of the study area) are located in the North Channel and are under strong human pressure mainly due to industrial activities. These areas have also low hydrodynamics and are, thus associated with high levels of deposition. In particular the areas near Lisnave and Eurominas industries can also accumulate the contamination coming from Águas de Moura Channel, since particles coming from that channel can settle down in that area due to residual flow. In these areas the contaminants of concern, from those analyzed, are the heavy metals and metalloids (Cd, Cu, Zn and As exceeded the PEL guidelines) and the pesticides BHC isomers, heptachlor, isodrin, DDT and metabolits, endosulfan and endrin. In the remain management units (76 % of the study area) there is a moderate impact potential of occurrence of adverse ecological effects and in some of these areas no stress agents could be identified. This emphasizes the need for further research, since unmeasured chemicals may be causing or contributing to these adverse effects. Special attention must be taken to the units with moderate impact potential of occurrence of adverse ecological effects, located inside the natural reserve. Non-point source pollution coming from agriculture and aquaculture activities also seem to contribute with important pollution load into the estuary entering from Águas de Moura Channel. This pressure is expressed in a moderate impact potential for ecological risk existent in the areas near the entrance of this Channel. Pressures may also came from Alcácer Channel although they were not quantified in this study. The management framework presented here, including all the methodological tools may be applied and tested in other estuarine ecosystems, which will also allow a comparison between estuarine ecosystems in other parts of the globe

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

Factors Impacting Observation-Based Estimates of Urban Greenhouse Gas Emissions

Urban areas are responsible for a large and increasing fraction of anthropogenic greenhouse gas emissions. Accurate methods for quantifying and monitoring those emissions are needed to suggest and evaluate mitigation policies, as well as for fundamental carbon cycle science as anthropogenic carbon dioxide emissions become a dominant source of uncertainty in closing the global carbon budget. I present investigations into several factors that can impact our ability to characterize urban greenhouse gas emissions using observations in the atmosphere. An automated method is developed for estimating the mixing depth, a key meteorological variable affecting the sensitivity of mole fraction observations to emissions fluxes, using optical remote sensing instruments. In a long time series of mixing depth estimates in Pasadena, California, day-to-day variability is shown to be large in comparison to seasonal trends. Significant mixing depth biases are demonstrated in meteorological models, and the likely impacts on emissions estimation are discussed. Optimized estimates of methane emissions in the South Coast Air Basin, California, are made using several flux inversion or regularization methods, with four sources of meteorological information, and with all or some of the mole fraction observations taken at nine within-basin observing sites associated with the LA Megacities Carbon Project. Using the full observational dataset in a geostatistical inversion, the capability to detect seasonal and event-driven emissions changes is demonstrated with generic meteorology, opening the door to near-real-time monitoring. Differences in absolute methane emissions flux magnitude according to the source of driving meteorological information are shown to be largely removable by calibration to a trusted model. The choice of inversion or regularization method is shown to have substantial impacts both on the estimated emissions time series and on the capacity to detect emissions changes, especially when the observational constraint is reduced.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145986/1/johnware_1.pd

Spatial pattern of dolphin watching in the Sado Estuary

Este trabalho de projeto mapeia e analisa dados GPS recolhidos de embarcações marítimo turísticas quando estavam em observação ativa dos golfinhos-roazes (Tursiops truncates) no estuário do Sado e no oceano Atlântico. O pequeno grupo de 27 golfinhos do estuário do Sado é um dos poucos grupos na Europa que não regista movimentos migratórios, mas tem um habitat permanente. O conjunto de dados inclui 292 pontos de dados, que foram recolhidos em um esforço de observação de 130 horas no total em 22 dias diferentes durante o verão de 2020. A coleção de dados foi influenciada pela pandemia de Covid19. Os resultados de uma análise exploratória de dados mostram que a observação de golfinhos se concentrou no oceano ao longo da península de Tróia no verão de 2020. O número máximo de localizações registadas por dia foram 33. Número elevado de localizações principalmente, mas não sempre, coincidem com densidades altos de barcos, que foram quantificadas por meio de uma estimativa Kernel Density. Os operadores marítimo-turísticos com licença para observação de golfinhos estavam localizados principalmente em profundidades de água até 20 m. A distância de viagem até o porto de origem fica entre cinco e 11 km. Não registámos nenhuma infração sistemática das diretrizes de observação de golfinhos. A estimativa da Kernel Density provou ser uma ferramenta útil para mapear a extensão e intensidade das operações de observação de golfinhos. Os resultados podem ajudar a melhorar a base de informações para a definição de uma capacidade de carga adequada da população de golfinhos do estuário. Concluímos que a capacidade de carga das áreas marinhas deve ser vista como um instrumento flexível que deve considerar também o comportamento dos operadores. Devido à pandemia Covid19 observamos apenas 23 do total de 46 embarcações autorizadas. Os resultados mostram que medidas adicionais de conservação podem ser necessárias para o funcionamento sustentável da observação de golfinhos no estuário do Sado durante períodos sem pandemia.This project work maps and analyses GPS data of touristic dolphin watching boats while they were in active observation of bottlenose dolphins (Tursiops truncates) in the Sado estuary and the adjacent ocean. The small group of 27 dolphins in the Sado estuary is one of very few groups in Europe, which do not travel but have a permanent habitat. The data set includes 292 data points which were collected within an observation effort of 130 hours in total on 22 different days during the summer of 2020. The results of an exploratory data analysis show, that dolphin watching was concentrated in the ocean along the peninsula of Tróia in summer 2020. The number of registered localizations per day went up to 33. High numbers of localizations mainly, but not always, coincide with high boat densities, which were quantified via a kernel density estimation. Marine touristic operators with a dolphin watching license were mainly localized in water depths till 20 m. The travel distance to their home port lies mainly in between five and 11 km. We registered no systematic infraction of the dolphin watching guidelines. Kernel density estimation proved to be a useful tool to map the extension and intensity of dolphin watching operations. The results may help to improve the information basis for the definition of an appropriate carrying capacity of the estuary’s dolphin population. We conclude that the carrying capacity of marine areas should be seen as a flexible instrument which also should consider the operators behaviour. Due to the Covid19 pandemic we only observed 23, of the total of 46 authorized boats. The results show that further conservation measures might be necessary for the sustainable operation of dolphin watching in the Sado estuary during times with no pandemic

Development of an advanced geostatistical model for shear wave velocity profiles to manage uncertainties and variabilities in Ground Response Analyses

L'abstract è presente nell'allegato / the abstract is in the attachmen

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed