A Scale-Invariant Spatial Graph Model

Information wird räumlich genannt, wenn sie Referenzen zum Raum beinhaltet. Die vorliegende Dissertation zielt darauf ab, die Charakterisierung räumlicher Information auf ein strukturelles Level zu heben. Toblers erstes Gesetz der Geographie und die Skaleninvarianz werden weithin zur Charakterisierung räumlicher Information verwendet. Ihre formale Beschreibung basiert jedoch auf expliziten Referenzen zum Raum, was einer Verwendung für die strukturelle Charakterisierung räumlicher Information entgegensteht. Der Autor führt daher ein Graphenmodell ein, welches im Falle einer Einbettung des Graphen in einen Raum typische Eigenschaften räumlicher Information aufweist, d.h. unter anderem Toblers Gesetz befolgt und skaleninvariant ist. Das Graphenmodell weist die Auswirkungen dieser typischen Eigenschaften auf seine Struktur auch dann auf, wenn es als abstrakter Graph interpretiert wird. Daher ist es zur Diskussion dieser typischen Eigenschaften auf einem strukturellen Level geeignet. Ein Vergleich des Modells mit verschiedenen räumlichen und nicht-räumlichen Datensätzen in der vorliegenden Dissertation legt nahe, dass räumliche Datensätze durch eine gemeinsame Struktur gekennzeichnet sind, weil die betrachteten räumlichen Datensätze im Gegensatz zu den nicht-räumlichen Gemeinsamkeiten mit dem Modell aufweisen. Dies lässt das Konzept einer räumlichen Struktur sinnvoll erscheinen. Das eingeführte Modell ist ein Modell dieser räumlichen Struktur. Die Dimension des Raumes wirkt sich auf räumliche Information und somit auch auf die räumliche Struktur aus. Die Dissertation untersucht, wie die Eigenschaften des Modells, insbesondere im Falle einer Gleichverteilung der Knoten im Raum, von der Dimension des Raumes abhängen und zeigt, wie eine Schätzung der Dimension aus der räumlichen Struktur eines Datensatzes gefolgert werden kann. Die Ergebnisse der Dissertation, insbesondere das Konzept einer räumlichen Struktur und das Graphenmodell, stellen einen grundlegenden Beitrag für die Diskussion räumlicher Information auf einem strukturellen Level dar: Auf räumlichen Daten operierende Algorithmen können unter Berücksichtigung der räumlichen Struktur verbessert werden; eine statistische Evaluation von Überlegungen zu räumlichen Daten wird möglich, da das Graphenmodell beliebig viele Testdatensätze mit kontrollierbaren Eigenschaften generieren kann; und das Erkennen von räumlichen Strukturen sowie die Schätzung der Dimension und weiterer Parameter kann zum langfristigen Ziel beitragen, Daten mit unvollständiger oder fehlender Semantik zu verwenden.Information is called spatial if it contains references to space. The thesis aims at lifting the characterization of spatial information to a structural level. Tobler's first law of geography and scale invariance are widely used to characterize spatial information, but their formal description is based on explicit references to space, which prevents them from being used in the structural characterization of spatial information. To overcome this problem, the author proposes a graph model that exposes, when embedded in space, typical properties of spatial information, amongst others Tobler's law and scale invariance. The graph model, considered as an abstract graph, still exposes the effect of these typical properties on the structure of the graph and can thus be used for the discussion of these typical properties at a structural level. A comparison of the proposed model to several spatial and non-spatial data sets in this thesis suggests that spatial data sets can be characterized by a common structure, because the considered spatial data sets expose structural similarities to the proposed model but the non-spatial data sets do not. This proves the concept of a spatial structure to be meaningful, and the proposed model to be a model of spatial structure. The dimension of space has an impact on spatial information, and thus also on the spatial structure. The thesis examines how the properties of the proposed graph model, in particular in case of a uniform distribution of nodes in space, depend on the dimension of space and shows how to estimate the dimension from the structure of a data set. The results of the thesis, in particular the concept of a spatial structure and the proposed graph model, are a fundamental contribution to the discussion of spatial information at a structural level: algorithms that operate on spatial data can be improved by paying attention to the spatial structure; a statistical evaluation of considerations about spatial data is rendered possible, because the graph model can generate arbitrarily many test data sets with controlled properties; and the detection of spatial structures as well as the estimation of the dimension and other parameters can contribute to the long-term goal of using data with incomplete or missing semantics.von Franz-Benjamin MocnikZusammenfassung in deutscher SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersTechnische Universität Wien, Dissertation, 2016OeBB(VLID)164200

Introduction to the Second International Symposium on Platial Information Science

People ‘live’ and constitute places every day through recurrent practices and experience. Our everyday lives, however, are complex, and so are places. In contrast to abstract space, the way people experience places includes a range of aspects like physical setting, meaning, and emotional attachment. This inherent complexity requires researchers to investigate the concept of place from a variety of viewpoints. The formal representation of place – a major goal in GIScience related to place – is no exception and can only be successfully addressed if we consider geographical, psychological, anthropological, sociological, cognitive, and other perspectives. This year’s symposium brings together place-based researchers from different disciplines to discuss the current state of platial research. Therefore, this volume contains contributions from a range of fields including geography, psychology, cognitive science, linguistics, and cartography

Open source data mining infrastructure for exploring and analysing OpenStreetMap

OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too

A grounding-based ontology of data quality measures

Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic information (VGI), while also applying to other geographical data and data in general. An evaluation of the ontology in the context of data quality measures for OpenStreetMap (OSM) data, a well-known example of VGI, provides insights about which types of quality measures for OSM data have and which have not yet been considered in literature

OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data

Abstract OpenStreetMap (OSM) is a collaborative project collecting geographical data of the entire world. The level of detail of OSM data and its data quality vary much across different regions and domains. In order to analyse such variations it is often necessary to research the history and evolution of the OSM data. The OpenStreetMap History Database (OSHDB) is a new data analysis tool for spatio-temporal geographical vector data. It is specifically optimized for working with OSM history data on a global scale and allows one to investigate the data evolution and user contributions in a flexible way. Benefits of the OSHDB are for example: to facilitate accessing OSM history data as a research subject and to assess the quality of OSM data by using intrinsic measures. This article describes the requirements of such a system and the resulting technical implementation of the OSHDB: the OSHDB data model and its application programming interface

Linked Open Data Vocabularies for Semantically Annotated Repositories of Data Quality Measures (Short Paper)

The fitness for purpose concerns many different aspects of data quality. These aspects are usually assessed independently by different data quality measures. However, for the assessment of the fitness for purpose, a holistic understanding of these aspects is needed. In this paper we discuss two Linked Open Data vocabularies for formally describing measures and their relations. These vocabularies can be used to semantically annotate repositories of data quality measures, which accordingly adhere to common standards even if being distributed on multiple servers. This allows for a better understanding of how data quality measures relate and mutually constrain. As a result, it becomes possible to improve intrinsic data quality measures by evaluating their effectivity and by combining them

On the Cartographic Communication of Places (Short Paper)

Maps are excellent as a medium for communicating spatial configurations at geographical scales. However, the communication of thematic qualities of geographical features is constrained by the traditionally assumed strict classification of features on the map and the strong focus on spatial representation. This is despite the fact that places are central aspects of everyday life that we use to structure our experiences and thus the need to include them in many maps. This paper explores how places can be communicated through the map medium. In particular, it addresses the question of the extent to which places are mediated or merely referenced, and the extent to which maps already communicate places through its inherent spatial and thematic aspects. This is followed by a discussion of how maps not only communicate but also shape places. In perspective, this contributes to a better and more targeted representation of places, especially through maps, but also advances our understanding of how places are conceptually entangled with spatial and thematic aspects

Ein skaleninvariantes räumliches Graphenmodell

Zusammenfassung in deutscher SpracheAbweichender Titel nach Übersetzung der Verfasserin/des VerfassersInformation wird räumlich genannt, wenn sie Referenzen zum Raum beinhaltet. Die vorliegende Dissertation zielt darauf ab, die Charakterisierung räumlicher Information auf ein strukturelles Level zu heben. Toblers erstes Gesetz der Geographie und die Skaleninvarianz werden weithin zur Charakterisierung räumlicher Information verwendet. Ihre formale Beschreibung basiert jedoch auf expliziten Referenzen zum Raum, was einer Verwendung für die strukturelle Charakterisierung räumlicher Information entgegensteht. Der Autor führt daher ein Graphenmodell ein, welches im Falle einer Einbettung des Graphen in einen Raum typische Eigenschaften räumlicher Information aufweist, d.h. unter anderem Toblers Gesetz befolgt und skaleninvariant ist. Das Graphenmodell weist die Auswirkungen dieser typischen Eigenschaften auf seine Struktur auch dann auf, wenn es als abstrakter Graph interpretiert wird. Daher ist es zur Diskussion dieser typischen Eigenschaften auf einem strukturellen Level geeignet. Ein Vergleich des Modells mit verschiedenen räumlichen und nicht-räumlichen Datensätzen in der vorliegenden Dissertation legt nahe, dass räumliche Datensätze durch eine gemeinsame Struktur gekennzeichnet sind, weil die betrachteten räumlichen Datensätze im Gegensatz zu den nicht-räumlichen Gemeinsamkeiten mit dem Modell aufweisen. Dies lässt das Konzept einer räumlichen Struktur sinnvoll erscheinen. Das eingeführte Modell ist ein Modell dieser räumlichen Struktur. Die Dimension des Raumes wirkt sich auf räumliche Information und somit auch auf die räumliche Struktur aus. Die Dissertation untersucht, wie die Eigenschaften des Modells, insbesondere im Falle einer Gleichverteilung der Knoten im Raum, von der Dimension des Raumes abhängen und zeigt, wie eine Schätzung der Dimension aus der räumlichen Struktur eines Datensatzes gefolgert werden kann. Die Ergebnisse der Dissertation, insbesondere das Konzept einer räumlichen Struktur und das Graphenmodell, stellen einen grundlegenden Beitrag für die Diskussion räumlicher Information auf einem strukturellen Level dar: Auf räumlichen Daten operierende Algorithmen können unter Berücksichtigung der räumlichen Struktur verbessert werden; eine statistische Evaluation von Überlegungen zu räumlichen Daten wird möglich, da das Graphenmodell beliebig viele Testdatensätze mit kontrollierbaren Eigenschaften generieren kann; und das Erkennen von räumlichen Strukturen sowie die Schätzung der Dimension und weiterer Parameter kann zum langfristigen Ziel beitragen, Daten mit unvollständiger oder fehlender Semantik zu verwenden.Information is called spatial if it contains references to space. The thesis aims at lifting the characterization of spatial information to a structural level. Tobler's first law of geography and scale invariance are widely used to characterize spatial information, but their formal description is based on explicit references to space, which prevents them from being used in the structural characterization of spatial information. To overcome this problem, the author proposes a graph model that exposes, when embedded in space, typical properties of spatial information, amongst others Tobler's law and scale invariance. The graph model, considered as an abstract graph, still exposes the effect of these typical properties on the structure of the graph and can thus be used for the discussion of these typical properties at a structural level. A comparison of the proposed model to several spatial and non-spatial data sets in this thesis suggests that spatial data sets can be characterized by a common structure, because the considered spatial data sets expose structural similarities to the proposed model but the non-spatial data sets do not. This proves the concept of a spatial structure to be meaningful, and the proposed model to be a model of spatial structure. The dimension of space has an impact on spatial information, and thus also on the spatial structure. The thesis examines how the properties of the proposed graph model, in particular in case of a uniform distribution of nodes in space, depend on the dimension of space and shows how to estimate the dimension from the structure of a data set. The results of the thesis, in particular the concept of a spatial structure and the proposed graph model, are a fundamental contribution to the discussion of spatial information at a structural level: algorithms that operate on spatial data can be improved by paying attention to the spatial structure; a statistical evaluation of considerations about spatial data is rendered possible, because the graph model can generate arbitrarily many test data sets with controlled properties; and the detection of spatial structures as well as the estimation of the dimension and other parameters can contribute to the long-term goal of using data with incomplete or missing semantics.15

The OpenStreetMap folksonomy and its evolution

The comprehension of folksonomies is of high importance when making sense of Volunteered Geographic Information (VGI), in particular in the case of OpenStreetMap (OSM). So far, only little research has been conducted to understand the role and the evolution of folksonomies in VGI and OSM, which is despite the fact that without a comprehension of the folksonomies the thematic dimension of data can hardly be used. This article examines the history of the OSM folksonomy, with the aim to predict its future evolution. In particular, we explore how the documentation of the OSM folksonomy relates to its actual use in the data, and we investigate the historical and future scope and granularity of the folksonomy. Finally, a visualization technique is proposed to examine the folksonomy in more detail