Analysis of human mobility patterns from GPS trajectories and contextual information

This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe

Combining landscape genetics and movement ecology to assess functional connectivity for red deer (Cervus elaphus) in Schleswig-Holstein, Germany

Die anthropogen bedingte Zerschneidung der Landschaft stellt eine wichtige Herausforderung für den Natur- und Artenschutz dar. Große Säugetiere, wie zum Beispiel der Rothirsch (Cervus elaphus) sind durch die Fragmentierung einer Verkleinerung und zunehmenden Isolierung der Lebensräume ausgesetzt. Dies kann weitreichende Folgen wie einen verringerten Austausch an Individuen und damit langfristig an Genen mit sich ziehen. Um diesen Folgen entgegenzuwirken und den genetischen Austausch zu verbessern sind objektive Beurteilungsverfahren über die Konnektivität der Landschaft notwendig. Die Erfassung und Modellierung der funktionellen Landschaftskonnektivität für eine Zielart basiert häufig auf Grundlagen wie Expertenwissen, Habitatmodellen oder Bewegungsdaten. Allerdings werden diese Methoden hinsichtlich ihrer Repräsentativität für tatsächliche Abwanderungen oder effektivem Genfluss diskutiert. Im Rahmen von landschaftsgenetischen Analysen werden Informationen über den genetischen Austausch zwischen Populationen oder einzelnen Individuen mit entsprechenden Ausprägungen der Landschaft korreliert. Genetische Daten haben dabei den Vorteil, dass sie sowohl eine erfolgreiche Wanderung zwischen Verbreitungsgebieten als auch die anschließende Reproduktion mit anderen Individuen, widerspiegeln können. Daher stellt die Landschaftsgenetik eine innovative Ansatzmöglichkeit zur Beurteilung der funktionellen Landschaftskonnektivität dar. Ziel der Dissertation ist die Konzipierung und Evaluierung von artspezifischen Modellen der Landschaftskonnektivität mit Hilfe von Gendaten und Telemetrie-Ergebnissen. Der Rothirsch in Schleswig-Holstein dient dabei als Beispielart, mit der die Unterschiede bezüglich der methodischen und konzeptionellen Herangehensweisen demonstriert werden sollen. Insbesondere für die naturschutzfachliche Praxis und Korridorplanung ist dies von grundlegender Bedeutung. 8 Im ersten Kapitel wird zunächst eine generelle Einleitung in die Problematik der Landschaftszerschneidung gegeben und anhand des Rothirschs in Schleswig-Holstein verdeutlicht. Anschließend werden die verschiedenen Ansatzmöglichkeiten der Landschaftsgenetik als auch der Bewegungsökologie zur Beurteilung der Landschaftskonnektivität dargestellt. Die Bewegungsökologie setzt sich unter anderem damit auseinander, welche Faktoren die Bewegungen von Organismen in ihrem Lebensraum beeinflussen. Durch die Verknüpfung von Bewegungsdaten mit Landschaftsvariablen lassen sich so wichtige Erkenntnisse über die Lebensraumansprüche einer Zielart gewinnen. Dabei können unter anderem die Habitatpräferenzen während unterschiedlicher Bewegungsmuster, wie zum Beispiel der Abwanderung in neue Gebiete, differenziert betrachtet werden. Das zweite Kapitel befasst sich mit der genetischen Diversität und Differenzierung der lokalen Rothirschvorkommen in Schleswig-Holstein. Anhand der genetischen Daten wird dabei verdeutlicht, dass die regionalen Managementeinheiten (Hegeringe) nicht immer in sich geschlossene Populationen darstellen. Die Rothirschpopulationen weisen vielmehr eine hierarchische Struktur auf. Zum Beispiel ist der Genfluss, je nach Dichte der benachbarten Populationen, unterschiedlich stark ausgeprägt. Insgesamt konnte für mehrere Populationen eine im europäischen Vergleich geringe genetische Diversität festgestellt werden. Dies unterstreicht, dass ein besseres Verständnis über die Auswirkungen der Landschaftszerschneidung sowie eine Bewertung der Landschaftskonnektivität aus Sicht des Rothirschs notwendig ist, um dem Verlust an genetischer Vielfalt entgegenzuwirken. Eine Möglichkeit die Landschaftskonnektivität zu bewerten stellt die Analyse von Telemetrie-Daten dar. Für die Auswertung von solchen Bewegungsdaten stehen eine Vielzahl an Methoden zur Verfügung. Im dritten Kapitel werden die verschiedenen Ansätze zur Differenzierung unterschiedlicher Bewegungsmuster aus Telemetrie-Daten zusammengestellt. Durch eine umfangreiche Methodenübersicht werden Entscheidungshilfen für die Anwendung solcher Pfad-Segmentierungen zur Beantwortung bestimmter Fragestellungen in der Bewegungsökologie gegeben. Das vierte Kapitel greift unter anderem auf eine solche Methode der Pfad-Segmentierung zurück, um potentielle Ausbreitungsbewegungen innerhalb der Telemetrie-Daten von besenderten Rothirschen zu ermitteln. Diese Bewegungsdaten 9 werden anschließend mit Landschaftsvariablen verknüpft und ein Modell abgeleitet, welches den Widerstand für Wanderbewegungen darstellt (Widerstandsmodell). Darüber hinaus werden in dieser Studie weitere methodische Ansätze zur Modellierung der funktionellen Landschaftskonnektivität verglichen. Diese basieren unter anderem auf Expertenwissen und Habitatmodellen sowie weiteren Auswertungsansätzen der Bewegungsdaten. Für den Vergleich der resultierenden Widerstandsmodelle wird die Landschaftsgenetik hinzugezogen. Dabei werden effektive Distanzen basierend auf den jeweiligen Modellen den genetischen Distanzmaßen gegenübergestellt. Die Modelle mit der höchsten Übereinstimmung werden ferner genutzt, um methodische Unterschiede in der Ausweisung von Korridoren darzustellen. Es zeigte sich, dass für weitreichende Abwanderungen die Rothirsche auf geeignete Habitatverhältnisse innerhalb der Landschaftsmatrix angewiesen sind. Die Auswertung der Bewegungsdaten ergab hingegen, dass für kürzere Distanzen auch suboptimale Gebiete durchquert werden können. Abschließend werden im fünften Kapitel die Ergebnisse zusammengefasst und diskutiert. Besonderer Schwerpunkt liegt dabei auf dem Beitrag der Anwendung von Landschaftsgenetik und Bewegungsökologie im angewandten Naturschutz und welche Erkenntnisse für die Ausweisung und Effektivität von Korridoren gewonnen werden können.Human-caused restrictions like the fragmentation of the landscape poses a major challenge to wildlife conservation. Large and mobile species such as red deer (Cervus elaphus) are subject to increasing effects of isolation and a decrease of primary habitats. This can result in a reduction of the exchange of individuals or even a long-term loss of gene flow. In order to counteract these negative effects and to promote genetic exchange, suitable approaches for estimating functional connectivity of the landscape are necessary. In most cases, landscape models of functional connectivity for a given study species are based on expert knowledge, habitat suitability, or movement data. However, there is an ongoing debate whether these methods are representative of actual dispersal or effective gene flow. Landscape genetic analyses correlate estimates of genetic differentiation between populations or individuals with landscape composition. The advantage of genetic data is that it reflects both successful dispersal between populations, as well as subsequent reproduction with other individuals. Therefore, landscape genetics represent an innovative approach for assessing functional connectivity of the landscape matrix. The aim of this dissertation is to compare different species-specific models of functional connectivity utilizing genetic and movement data. Using red deer in Northern Germany as an example, the methodological and conceptual differences of multiple approaches are demonstrated. Overall, the presented thesis provides important insights for applied conservation of wildlife and planning of corridors. The first chapter provides a general introduction to the issue of landscape fragmentation and illustrates the effects on red deer in the study area of Schleswig-Holstein. Furthermore, the potential applications of landscape genetics and movement ecology to assess landscape connectivity are presented. For example, movement ecology provides an integral framework to explore the potential factors shaping the movements of organisms and the ecological consequences of these movements such as gene flow. The second chapter comprises a study on the genetic diversity and structure of red deer populations in Northern Germany. The results indicate that local populations are best described as an hierarchical network of subpopulations with different levels of gene flow. Overall, genetic diversity of red deer from the study area is quite low compared to other populations from Central Europe. This underlines that a better understanding of the isolation effects caused by landscape fragmentation and species-specific assessment of landscape connectivity for red deer are needed to address the observed loss of genetic diversity. One possible approach for estimating functional connectivity is by linking telemetry data with landscape variables in order to gain insights into the habitat requirements of a target species. However, habitat preferences are very likely to change with different movement behaviors. This represents an important point to consider when studying the effects of landscape composition on actual dispersal movements. The third chapter of this thesis presents an extensive overview on different methods for identifying behavioral patterns from movement data. Furthermore, it provides guidelines for deciding among the available methods of path-segmentation and shows how they can be applied to answer research questions within the movement ecology paradigm. The study described in the fourth chapter utilizes such a path-segmentation method to detect potential dispersal movements from telemetry data of multiple red deer individuals. The observed movements are then linked to landscape variables in order to model functional connectivity based on landscape resistance towards dispersal of red deer throughout the study area. In addition, the study applies and compares different methodological approaches for modeling functional connectivity based on expert knowledge, habitat models and other analyses of movement data. A landscape genetic approach is used as a means to compare the resulting resistance models. Effective distances derived from the models are compared with estimates on genetic distance. The highest ranked models are further used to illustrate methodological differences in the designation of conservation corridors. The results show that for large scale dispersal red deer rely on primary habitat conditions within the landscape matrix. However, connectivity based on the identified dispersal movements showed that areas of poor habitat quality can be traversed by red deer at shorter distances. Finally, in the fifth chapter, the results of the presented studies are summarized and discussed. In particular, the contribution of landscape genetics and movement ecology to applied conservation and landscape planning are elaborated. The results of this thesis could ultimately increase the effectiveness of conservation measures such as the placement of corridors.2021-06-2

SEGMENTATION TECHNIQUES BASED ON CLUSTERING FOR THE ANALYSIS OF MOBILITY DATA

La Tesi riguarda l'analisi e applicazione di metodi di segmentazione per il partizionamento delle traiettorie spaziali in sotto-traiettorie semanticamente significative, e il loro utilizzo per l'analisi del comportamento di oggetti in movimento. Le traiettorie spaziali sono dati strutturati complessi costituiti da sequenze ordinate di punti spazio-temporali che campionano il movimento continuo di un oggetto in uno spazio di riferimento. Le tecniche di segmentazione sono essenziali per l'analisi delle traiettorie spaziali. In generale, l'attivit\ue0 di segmentazione divide una sequenza di punti dati in una serie di sottosequenze disgiunte basate su criteri di omogeneit\ue0. La Tesi si focalizza, in particolare, sulle tecniche di segmentazione basate su \u201cdensity based clustering\u201d. A differenza dei processi di clustering tradizionali, che sono applicati ad \u201cinsiemi\u201d di punti, le tecniche di segmentazione basate su clustering partizionano \u201csequenze\u201d in una serie di \u201cclusters\u201d temporalmente separati. Possibili applicazioni includono l'analisi del movimento di individui in ambito urbano e lo studio del comportamento di animali. Alcune tecniche di segmentazione basate su \u201ccluster\u201d sono descritte in letteratura, tuttavia nessuna di queste soluzioni permette di gestire in modo efficace i punti non strutturati (noise). Inoltre, le metodologie adottate per validare queste tecniche soffrono di gravi limitazioni, ad esempio le verifiche sperimentale utilizzano dati molto semplici che non riflettono la complessit\ue0 del movimento reale, come pure non permettono di effettuare un confronto con ground truth. Questa Tesi si focalizza su una recente tecnica per la segmentazione basata su cluster con noise, chiamata SeqScan, proposta in un lavoro precedente. In particolare, la ricerca ha affrontato i seguenti problemi: i) definizione di un framework rigoroso per l' analisi delle propriet\ue0 del modello di segmentazione; ii) validazione del metodo attraverso un'ampia sperimentazione che prevede il confronto con la ground truth; iii) estensione dell'approccio per consentire la individuazione di gatherings. Il gathering \ue9 un gruppo di oggetti mobili che condividono la stessa zona, per un certo periodo di tempo con la possibilit\ue0 di assenze occasionali; iv) sviluppo di una piattaforma software che integra i diversi algoritmi ed ulteriori strumenti a supporto dell'analisi dei dati di mobilit\ue0.The Thesis focuses on segmentation methods for the partitioning of spatial trajectories in semantically meaningful sub-trajectories and their application to the analysis of mobility behavior. Spatial trajectories are complex structured data consisting of sequences of temporally ordered spatio-temporal points sampling the continuous movement of an object in a reference space. Spatial trajectories can reveal behavioral information about individuals and groups of individuals, and that motivates the concern for data analysis techniques. Segmentation techniques are key for the analysis of spatial trajectories. In general, the segmentation task partitions a sequence of data points in a series of disjoint sub-sequences based on some homogeneity criteria. The Thesis focuses, in particular, on the use of clustering methods for the segmentation of spatial trajectories. Unlike the traditional clustering task, which is applied to sets of data points, the goal of this class of techniques is to partition sequential data in temporally separated clusters. Such techniques can be utilized for example to detect the sequences of places or regions visited by moving objects. While a number of techniques for the cluster-based segmentation are proposed in literature, none of them is really robust again noise, while the methodologies put in place to validate those techniques suffer from severe limitations, e.g., simple datasets, no comparison with ground truth. This Thesis focuses on a recent cluster-based segmentation method, called SeqScan, proposed in previous work. This technique promises to be robust against noise, nonetheless the approach is empirical and lacks a formal and theoretical framework. The contribution of this research is twofold. First it provides analytical support to SeqScan, defining a rigorous framework for the analysis of the properties of the model. The method is validated through an extensive experimentation conducted in an interdisciplinary setting and contrasting the segmentation with ground truth. The second contribution is the proposal of a technique for the discovery of a collective pattern, called gathering. The gathering pattern describes a situation in which a significant number of moving objects share the same region, for enough time periods with possibility of occasional absences, e.g. a concert, an exhibition. The technique is built on SeqScan. A platform, called MigrO, has been finally developed, including not only the algorithms but also a variety of tools facilitating data analysis

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer

Mobility Modelling through Trajectory Decomposition and Prediction

The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage of regularity within the movement trajectories. The evaluation of our approach is done using three large-scale spatio-temporal datasets, from three different cities: San Francisco, Porto, and Beijing. Two of these datasets contain only cab traces and one contains all modes of transportation. Therefore, our approach is solely dependent on the inherent regularity within the trajectories regardless of the city or transportation mode

Visuelle Analyse großer Partikeldaten

Partikelsimulationen sind eine bewährte und weit verbreitete numerische Methode in der Forschung und Technik. Beispielsweise werden Partikelsimulationen zur Erforschung der Kraftstoffzerstäubung in Flugzeugturbinen eingesetzt. Auch die Entstehung des Universums wird durch die Simulation von dunkler Materiepartikeln untersucht. Die hierbei produzierten Datenmengen sind immens. So enthalten aktuelle Simulationen Billionen von Partikeln, die sich über die Zeit bewegen und miteinander interagieren. Die Visualisierung bietet ein großes Potenzial zur Exploration, Validation und Analyse wissenschaftlicher Datensätze sowie der zugrundeliegenden Modelle. Allerdings liegt der Fokus meist auf strukturierten Daten mit einer regulären Topologie. Im Gegensatz hierzu bewegen sich Partikel frei durch Raum und Zeit. Diese Betrachtungsweise ist aus der Physik als das lagrange Bezugssystem bekannt. Zwar können Partikel aus dem lagrangen in ein reguläres eulersches Bezugssystem, wie beispielsweise in ein uniformes Gitter, konvertiert werden. Dies ist bei einer großen Menge an Partikeln jedoch mit einem erheblichen Aufwand verbunden. Darüber hinaus führt diese Konversion meist zu einem Verlust der Präzision bei gleichzeitig erhöhtem Speicherverbrauch. Im Rahmen dieser Dissertation werde ich neue Visualisierungstechniken erforschen, welche speziell auf der lagrangen Sichtweise basieren. Diese ermöglichen eine effiziente und effektive visuelle Analyse großer Partikeldaten