Dissertationen zum Thema „Spatio-temporal trajectories“
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Ishikawa, Yoshiharu. „SPATIO-TEMPORAL DATA MINING FROM MOVING OBJECT TRAJECTORIES“. INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10446.
Der volle Inhalt der QuellePartsinevelos, Panayotis. „Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery“. Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.
Der volle Inhalt der QuelleJin, Meihan. „Un modèle spatio-temporel sémantique pour la modélisation de mobilités en milieu urbain“. Thesis, Brest, 2017. http://www.theses.fr/2017BRES0067/document.
Der volle Inhalt der QuelleMassive trajectory datasets generated in modern cities generate not only novel research opportunities but also important methodological challenges for academics and decision-makers searching for a better understanding of travel patterns in space and time. This PhD research is oriented towards the conceptual and GIS-based modeling of human displacements derived from large sets of urban trajectories. The motivation behind this study originates from the necessity to search for and explore travel patterns that emerge from citizens acting in the city. Our research introduces a conceptual modelling framework whose objective is to integrate and analyze human displacements within a GIS-based practical solution. The framework combines conceptual and logical models that represent travel trajectories of citizens moving in a given city. The whole approach has been implemented in a geographical database system, experimented in the context of transportation data, and enriched by a series of query interface manipulations and specific functions that illustrate the potential of our whole framework for urban studies. The whole framework has been experimented on top of the Geolife project and large trajectories datasets available in the city of Beijing. Overall, the findings are twofold: first, it appears that our modelling framework can appropriately act as an extensible geographical database support for the integration of large trajectory datasets; second the approach shows that several emerging human displacements can be explored from the manipulation of large urban trajectories
Palma, Andrey Luis Tietbohl. „A clustering-based approach for discovering interesting places in trajectories“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/17024.
Der volle Inhalt der QuelleBecause of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have focused on the geometric properties of trajectories, but recently emerged the concepts of semantic trajectories, in which the background geographic information is integrated to trajectory sample points. In this new concept, trajectories are observed as a set of stops and moves, where stops are the most important parts of the trajectory. Stops and moves have been computed by testing the intersection of trajectories with a set of geographic objects given by the user. In this dissertation we present an alternative solution with the capability of finding interesting places that are not expected by the user. The proposed solution is a spatio-temporal clustering method, based on speed, to work with single trajectories. We compare the two different approaches with experiments on real data and show that the computation of stops using the concept of speed can be interesting for several applications.
Wu, Jing. „A qualitative spatio-temporal modelling and reasoning approach for the representation of moving entities“. Thesis, Brest, 2015. http://www.theses.fr/2015BRES0036/document.
Der volle Inhalt der QuelleThe research developed in this thesis introduces a qualitative approach for representing and reasoning on moving entities in a two-dimensional geographical space. Movement patterns of moving entities are categorized based on a series of qualitative spatial models of topological relations between a directed line and a region, and orientation relations between two directed lines, respectively. Qualitative movements are derived from the spatio-temporal relations that characterize moving entities conceptualized as either points or regions in a two-dimensional space. Such a spatio-temporal framework supports the derivation of the basic movement configurations inferred from moving and static entities. The approach is complemented by a tentative qualification of the possible natural language expressions of the primitive movements identified. Complex movements can be represented by a composition of these primitive movements. The notion of conceptual transition that favors the exploration of possible trajectories in the case of incomplete knowledge configurations is introduced and explored.Composition tables are also studied and provide additional reasoning capabilities. The whole approach is applied to the analysis of flight patterns and maritime trajectories
Vercelloni, Julie. „Quantifying the state of populations and effects of disturbances at large spatio-temporal scales: The case of coral populations in the great barrier reef“. Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/87812/1/Julie_Vercelloni_Thesis.pdf.
Der volle Inhalt der QuelleReux, Sara. „Les figures de la discontinuité dans le développement résidentiel périurbain : application à la région Limousin“. Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0019/document.
Der volle Inhalt der QuelleWhile understanding urban areas through continuity of developed land reached its limits,discontinuity of urban fabrics has become a key to understand today's cities and their shaping dynamics. Itraises researchers’ interest especially as GIS development gives new opportunities to measure urbanpatterns. While researches in landscape ecology or geography allow to measure discontinuous patterns, itseems to be important to focus on their economic foundations which are a matter for recent empiricalresearches in economy. The construction of an analytical grid of discontinuous urban patterns allows tounderstand simultaneously peri-urban development and patterns of residential development at the parcellevel. This research is applied to the Limousin region on the 1950-2009 period. The focus on discontinuousurban patterns sheds light on residential trajectories of the Limousin region's communes. The proposal of aspatio-temporal data base allows to understand these trajectories through combined measures of geographical dispersion and morphological dispersion. With these measures, we broach the link betweenfunctional and morphological dynamics thanks to a multitheme data base. To understand household locationand residential dispersion, we analyze the issue of housing production, the interaction between property andpublic regulation at the scale of communes, the influence of amenities and desamenities of urban and ruralspaces
Almuhisen, Feda. „Leveraging formal concept analysis and pattern mining for moving object trajectory analysis“. Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.
Der volle Inhalt der QuelleThis dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Almuhisen, Feda. „Leveraging formal concept analysis and pattern mining for moving object trajectory analysis“. Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.
Der volle Inhalt der QuelleThis dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Strat, Sabin Tiberius. „Analyse et interprétation de scènes visuelles par approches collaboratives“. Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00959081.
Der volle Inhalt der QuelleCouronné, Raphaël. „Modélisation de la progression de la maladie de Parkinson“. Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS363.
Der volle Inhalt der QuelleIn this work, we developed statistical methods to model disease progression from patient’s repeated measurements, with a focus on Parkinson’s Disease (PD). A key challenge lies in the inherent heterogeneity of PD across patients, to the extent that PD is now suspected to encompass multiple subtypes or motor phenotypes. To gain insights on disease progression, research studies propose to gather a broad range of marker measurements, at multiple timepoints for each patients. These data allow to investigate the disease’s patterns of progression via statistical modeling. In a first part, we modeled the progression of scalar markers of PD. We extended on a disease progression model, namely the longitudinal spatiotemporal model. We then proposed to address data missingness, and to model the joint progression of markers of different nature, such as clinical scores, and scalar measurements extracted on imaging modalities. With this method, we modeled early motor progression in PD, and, in a second work, the heterogeneity of idiopathic PD progression, with a focus on sleep symptoms. In a second, independent, part of the manuscript, we tackled the longitudinal modeling of medical images. For these higher dimensionality data, Deep Learning is often used, but mostly in cross sectional setups, ignoring the possible inner dynamics. We proposed to leverage Deep Learning as a dimensionality reduction tool to build a spatiotemporal coordinate system of disease progression. We first took advantage of this flexibility to handle multimodal data. Then we leveraged the self-supervision induced by assuming monotonicity over time, to offer higher flexibility in modeling temporal variability
Bermingham, Luke Leslie. „From spatio-temporal trajectories to succinct and semantically meaningful patterns“. Thesis, 2018. https://researchonline.jcu.edu.au/53636/1/53636-bermingham-2018-thesis.pdf.
Der volle Inhalt der QuelleAkal, Tigabu Dagne. „Spatio-temporal pattern mining from global positioning systems (GPS) trajectories dataset“. Master's thesis, 2015. http://hdl.handle.net/10362/31766.
Der volle Inhalt der QuelleThe increasing frequency of use location-acquisition technology like the Global Positioning System is leading to the collection of large spatio-temporal datasets. The prospect of discovering usable knowledge about movement behavior, which encourages for the discovery of interesting relationships and characteristics users that may exist implicitly in spatial databases. Therefore spatial data mining is emerging as a novel area of research. In this study, the experiments were conducted following the Knowledge Discovery in Database process model. The Knowledge Discovery in Database process model starts from selection of the datasets. The GPS trajectory dataset for this research collected from Microsoft Research Asia Geolife project. After taking the data, it has been preprocessed. The major preprocessing activities include: Fill in missed values and remove outliers; Resolve inconsistencies, integration of data that contains both labeled and unlabeled datasets, Dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 4,273 trajectory dataset are used for training the models. For validating the performance of the selected model a separate 1,018 records are used as a testing set. For building a spatiotemporal model of this study the K-nearest Neighbors (KNN), decision tree and Bayes algorithms have been tasted as supervised approach. The model that was created using 10-fold cross validation with K value 11 and other default parameter values showed the best classification accuracy. The model has a prediction accuracy of 98.5% on the training datasets and 93.12% on the test dataset to classify the new instances as bike, bus, car, subway, train and walk classes. The findings of this study have shown that the spatiotemporal data mining methods help to classify user mobility transportation modes. Future research directions are forwarded to come up an applicable system in the area of the study.
„Spatio-Temporal Data Mining to Detect Changes and Clusters in Trajectories“. Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.15907.
Der volle Inhalt der QuelleDissertation/Thesis
Ph.D. Industrial Engineering 2012
Li, Song. „Knowledge discovery from trajectories“. Master's thesis, 2009. http://hdl.handle.net/10362/2320.
Der volle Inhalt der QuelleAs a newly proliferating study area, knowledge discovery from trajectories has attracted more and more researchers from different background. However, there is, until now, no theoretical framework for researchers gaining a systematic view of the researches going on. The complexity of spatial and temporal information along with their combination is producing numerous spatio-temporal patterns. In addition, it is very probable that a pattern may have different definition and mining methodology for researchers from different background, such as Geographic Information Science, Data Mining, Database, and Computational Geometry. How to systematically define these patterns, so that the whole community can make better use of previous research? This paper is trying to tackle with this challenge by three steps. First, the input trajectory data is classified; second, taxonomy of spatio-temporal patterns is developed from data mining point of view; lastly, the spatio-temporal patterns appeared on the previous publications are discussed and put into the theoretical framework. In this way, researchers can easily find needed methodology to mining specific pattern in this framework; also the algorithms needing to be developed can be identified for further research. Under the guidance of this framework, an application to a real data set from Starkey Project is performed. Two questions are answers by applying data mining algorithms. First is where the elks would like to stay in the whole range, and the second is whether there are corridors among these regions of interest.
„Developing a Cohesive Space-Time Information Framework for Analyzing Movement Trajectories in Real and Simulated Environments“. Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.9514.
Der volle Inhalt der QuelleDissertation/Thesis
Ph.D. Geography 2011