A conceptual framework and taxonomy of techniques for analyzing movement

Movement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining. We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks. Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake

Capturing time in space : Dynamic analysis of accessibility and mobility to support spatial planning with open data and tools

Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning

Understanding movement data quality

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results. Investigation of quality of movement data, due to their spatio-temporal nature, requires consideration from multiple perspectives at different scales. We review the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identify these types of problems

A semantic model for human mobility in an urban region

The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Today, very large trajectory datasets are often publicly generated thanks to the availability of many positioning sensors and location-based services. However, the successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; second, significant patterns can be derived at the database manipulation level using some specifically developed query functions

Un modèle spatio-temporel sémantique pour la modélisation de mobilités en milieu urbain

The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Today, very large trajectory datasets are often publicly generated thanks to the availability of many positioning sensors and location-based services. However, the successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; second, significant patterns can be derived at the database manipulation level using some specifically developed query functions

Group Detection on GNSS Based Tracks Using Minimum Spanning Tree

I denne oppgaven er det anvendt gruppedetektering på GNSS-spor fra et orienteringsløp. Ved å betrakte bevegelseshistorikk som punktbevegelse, er problemet redusert til å finne gjentagende romlig naboskap mellom to eller flere punkter som kan antas å være i gruppe. Det ble implementert et program som utfører iterativ clustering gitt ved minimum spenntre, og oppdaterer datasettet med felles attributt for hvert cluster. Basert på dette attributtet ble det utført manuell filtrering i SQL for å bestemme clustere som varte lenge nok til å kunne regnes som grupper. Det ble også laget en visualisering av strekninger hvor de antatte gruppene var i bevegelse. Resultatene viser at en kan identifisere mulige grupper av individer, selv på et datasett hvor det totalt sett var tett bevegelse. Det ble også funnet at metoden er svak på å kartlegge vekslinger og brudd i gruppene.In this thesis, an application of group detection has been tested on GNSS-tracks from orienteering. By considering historic movement as moving points, the problem is generalized to finding two or more neighbouring points who maintain their neighbourhood repeatedly over time. A program was developed for iteratively determining clusters using minimum spanning tree and updating the data set with a common attribute for cluster members. This was followed by a filtering step in SQL to find the clusters with duration long enough to be considered as groups. For groups in movement a visualization were also made. The results show that it is possible to identify possible groups of people, even when the data is about overall dense movement. The method was found to be weak in identifying splits and transitions between groups.M-GEO

Group Detection on GNSS Based Tracks Using Minimum Spanning Tree

I denne oppgaven er det anvendt gruppedetektering på GNSS-spor fra et orienteringsløp. Ved å betrakte bevegelseshistorikk som punktbevegelse, er problemet redusert til å finne gjentagende romlig naboskap mellom to eller flere punkter som kan antas å være i gruppe. Det ble implementert et program som utfører iterativ clustering gitt ved minimum spenntre, og oppdaterer datasettet med felles attributt for hvert cluster. Basert på dette attributtet ble det utført manuell filtrering i SQL for å bestemme clustere som varte lenge nok til å kunne regnes som grupper. Det ble også laget en visualisering av strekninger hvor de antatte gruppene var i bevegelse. Resultatene viser at en kan identifisere mulige grupper av individer, selv på et datasett hvor det totalt sett var tett bevegelse. Det ble også funnet at metoden er svak på å kartlegge vekslinger og brudd i gruppene.In this thesis, an application of group detection has been tested on GNSS-tracks from orienteering. By considering historic movement as moving points, the problem is generalized to finding two or more neighbouring points who maintain their neighbourhood repeatedly over time. A program was developed for iteratively determining clusters using minimum spanning tree and updating the data set with a common attribute for cluster members. This was followed by a filtering step in SQL to find the clusters with duration long enough to be considered as groups. For groups in movement a visualization were also made. The results show that it is possible to identify possible groups of people, even when the data is about overall dense movement. The method was found to be weak in identifying splits and transitions between groups.M-GEO

Análise espacial de grandes quantidades de dados de movimento usando técnicas de clustering baseadas em densidade

Dissertação de mestrado em Engenharia e Gestão de Sistemas de InformaçãoA análise de entidades em movimento, representados através de Moving Point Objects (MPO), é útil nas mais variadas áreas, desde o estudo de migrações de animais, até ao estudo do comportamento de multidões. Grandes quantidades de dados sobre movimento continuam a ser recolhidas utilizando tecnologias como o Global Gositioning Systems (GPS) e informação geográfica voluntária baseado na Internet. Um grande desafio no estudo de dados sobre movimento é o tamanho cada vez mais avultado das bases de dados que são passiveis de serem analisadas. Para analisar grandes quantidades de dados, com o objetivo de identificar padrões ou tendências nos mesmos, podem ser utilizados algoritmo de clustering. Estes podem ser de diferentes tipos. Dentre os mesmos, e dadas as características dos dados a analisar, foram selecionados os algoritmos baseados em densidade de pontos. Os algoritmos de clustering cujos resultados se têm mostrado mais satisfatórios, como o “sheared nearest neighbour”, tendem a não ser aplicáveis a bases de dados massivas, devido à sua complexidade ser quadrática, o que apresenta custos em termos de tempo de execução. Este trabalho propõe-se a identificar e avaliar alternativas que possam ser adotadas no sentido de diminuir a complexidade e consequentemente o tempo de execução do algoritmo. A otimizações identificadas e implementadas baseiam-se na redução muito significativa do número de cálculos de proximidade necessários para definir as listas de vizinhos mais próximos de cada ponto. Isto foi conseguido através da divisão dos pontos, através das suas coordenadas espaciais, por uma matriz, e comparando os pontos de cada célula dessa matriz com os pontos de células vizinhas. Foram atingidos resultados relevantes quando se tornou possível reduzir o tamanho dessas células sem nenhuma restrição ao nível da ocorrência de erros de clustering. O algoritmo resultante foi implementado numa ferramenta preparada para facilitar a análise de dados sobre movimento, e permitir o uso da estratégia desenvolvida neste trabalho noutros fins, diferentes do uso do algoritmo SNN.The analysis of bodies in motion, represented through Moving Point Objects (MPO), is useful in various areas, from the study of migrations of animals up to the study of behavior of crowds. Large amounts of movement data continue to be collected using technologies such as Global Gositioning Systems (GPS) and geographic information-based voluntary Internet. A major challenge in the study of movement data is the increasingly large size of databases that are ready for analysis. To analyze large amounts of data in order to identify patterns or trends in them, a clustering algorithm can be used. These can be of different types. Among them, and given the characteristics of the data to be analyzed, density-based algorithms were selected. The clustering algorithms whose results have proved most satisfactory, as the "sheared nearest neighbor", tend not to be applicable to massive data bases because of their quadratic complexity, which has costs in terms of runtime. This study proposes to identify and evaluate alternatives that can be adopted to reduce the complexity and thus the running time of the algorithm. The optimizations identified and implemented are based on the significant reduction in the number of calculations needed to determine the nearest neighbors lists of each point. This was accomplished by dividing points through their spatial coordinates, into a matrix, and comparing the points of each cell of this array points to neighboring cells. Significant results were achieved when it became possible to reduce the size of these cells without any restriction in terms of the occurrence of errors in clustering. The resulting algorithm has been implemented in a tool equipped to facilitate analysis of movement data, and enable the use of the strategy developed in this work for other purposes, different from the use of SNN algorithm