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Volunteered Geographic Information

Interpretation, Visualization and Social Context

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About this book

This open access book includes methods for retrieval, semantic representation, and analysis of Volunteered Geographic Information (VGI), geovisualization and user interactions related to VGI, and discusses selected topics in active participation, social context, and privacy awareness. It presents the results of the DFG-funded priority program "VGI: Interpretation, Visualization, and Social Computing" (2016-2023).

The book includes three parts representing the principal research pillars within the program. Part I "Representation and Analysis of VGI" discusses recent approaches to enhance the representation and analysis of VGI. It includes semantic representation of VGI data in knowledge graphs; machine-learning approaches to VGI mining, completion, and enrichment as well as to the improvement of data quality and fitness for purpose. Part II "Geovisualization and User Interactions related to VGI" book explores geovisualizations and user interactions supporting the analysis and presentation of VGI data. When designing these visualizations and user interactions, the specific properties of VGI data, the knowledge and abilities of different target users, and technical viability of solutions need to be considered. Part III "Active Participation, Social Context and Privacy Awareness" of the book addresses the human impact associated with VGI. It includes chapters on the use of wearable sensors worn by volunteers to record their exposure to environmental stressors on their daily journeys, on the collective behavior of people using location-based social media and movement data from football matches, and on the motivation of volunteers who provide important support in information gathering, filtering and analysis of social media in disaster situations.

The book is of interest to researchers and advanced professionals in geoinformation, cartography, visual analytics, data science and machine learning.

Table of Contents

Frontmatter

Representation and Analysis of VGI

Frontmatter

Open Access

Chapter 1. WorldKG: World-Scale Completion of Geographic Information
Abstract
Knowledge graphs provide standardized machine-readable representations of real-world entities and their relations. However, the coverage of geographic entities in popular general-purpose knowledge graphs, such as Wikidata and DBpedia, is limited. An essential source of the openly available information regarding geographic entities is OpenStreetMap (OSM). In contrast to knowledge graphs, OSM lacks a clear semantic representation of the rich geographic information it contains. The generation of semantic representations of OSM entities and their interlinking with knowledge graphs are inherently challenging due to OSM’s large, heterogeneous, ambiguous, and flat schema and annotation sparsity. This chapter discusses recent knowledge graph completion methods for geographic data, comprising entity linking and schema inference for geographic entities, to provide semantic geographic information in knowledge graphs. Furthermore, we present the WorldKG knowledge graph, lifting OSM entities into a semantic representation.
Alishiba Dsouza, Nicolas Tempelmeier, Simon Gottschalk, Ran Yu, Elena Demidova

Open Access

Chapter 2. Analyzing and Improving the Quality and Fitness for Purpose of OpenStreetMap as Labels in Remote Sensing Applications
Abstract
OpenStreetMap (OSM) is a well-known example of volunteered geographic information. It has evolved to one of the most used geographic databases. As data quality of OSM is heterogeneous both in space and across different thematic domains, data quality assessment is of high importance for potential users of OSM data. As use cases differ with respect to their requirements, it is not data quality per se that is of interest for the user but fitness for purpose. We investigate the fitness for purpose of OSM to derive land-use and land-cover labels for remote sensing-based classification models. Therefore, we evaluated OSM land-use and land-cover information by two approaches: (1) assessment of OSM fitness for purpose for samples in relation to intrinsic data quality indicators at the scale of individual OSM objects and (2) assessment of OSM-derived multi-labels at the scale of remote sensing patches (\(1.22 \times 1.22\) km) in combination with deep learning approaches. The first approach was applied to 1000 randomly selected relevant OSM objects. The quality score for each OSM object in the samples was combined with a large set of intrinsic quality indicators (such as the experience of the mapper, the number of mappers in a region, and the number of edits made to the object) and auxiliary information about the location of the OSM object (such as the continent or the ecozone). Intrinsic indicators were derived by a newly developed tool based on the OSHDB (OpenStreetMap History DataBase). Afterward, supervised and unsupervised shallow learning approaches were used to identify relationships between the indicators and the quality score. Overall, investigated OSM land-use objects were of high quality: both geometry and attribute information were mostly accurate. However, areas without any land-use information in OSM existed even in well-mapped areas such as Germany. The regression analysis at the level of the individual OSM objects revealed associations between intrinsic indicators, but also a strong variability. Even if more experienced mappers tend to produce higher quality and objects which underwent multiple edits tend to be of higher quality, an inexperienced mapper might map a perfect land-use polygon. This result indicates that it is hard to predict data quality of individual land-use objects purely on intrinsic data quality indicators. The second approach employed a label-noise robust deep learning method on remote sensing data with OSM labels. As the quality of the OSM labels was manually assessed beforehand, it was possible to control the amount of noise in the dataset during the experiment. The addition of artificial noise allowed for an even more fine-grained analysis on the effect of noise on prediction quality. The noise-tolerant deep learning method was capable to identify correct multi-labels even for situations with significant levels of noise added. The method was also used to identify areas where input labels were likely wrong. Thereby, it is possible to provide feedback to the OSM community as areas of concern can be flagged.
Moritz Schott, Adina Zell, Sven Lautenbach, Gencer Sumbul, Michael Schultz, Alexander Zipf, Begüm Demir

Open Access

Chapter 3. Efficient Mining of Volunteered Trajectory Datasets
Abstract
With the ubiquity of mobile devices that are capable of tracking positions (be it via GPS or Wi-Fi/mobile network localization), there is a continuous stream of location data being generated every second. These location measurements are typically not considered individually but rather as sequences, each of which reflects the movement of one person or vehicle, which we call trajectory. This chapter presents new algorithmic approaches to process and visualize trajectories both in the network-constrained and the unconstrained case.
Axel Forsch, Stefan Funke, Jan-Henrik Haunert, Sabine Storandt

Open Access

Chapter 4. Uncertainty-Aware Enrichment of Animal Movement Trajectories by VGI
Abstract
Combining data from different sources and modalities can unlock novel insights that are not available by analyzing single data sources in isolation. We investigate how multimodal user-generated data, consisting of images, videos, or text descriptions, can be used to enrich trajectories of migratory birds, e.g., for research on biodiversity or climate change. Firstly, we present our work on advanced visual analysis of GPS trajectory data. We developed an interactive application that lets domain experts from ornithology naturally explore spatiotemporal data and effectively use their knowledge. Secondly, we discuss work on the integration of general-purpose image data into citizen science platforms. As part of inter-project cooperation, we contribute to the development of a classifier pipeline to semi-automatically extract images that can be integrated with different data sources to vastly increase the number of available records in citizen science platforms. These works are an important foundation for a dynamic matching approach to jointly integrate geospatial trajectory data and user-generated geo-referenced content. Building on this work, we explore the joint visualization of trajectory data and VGI data while considering the uncertainty of observations. BirdTrace, a visual analytics approach to enable a multi-scale analysis of trajectory and multimodal user-generated data, is highlighted. Finally, we comment on the possibility to enhance prediction models for trajectories by integrating additional data and domain knowledge.
Yannick Metz, Daniel A. Keim

Open Access

Chapter 5. Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection
Abstract
Neural networks have demonstrated great success; however, large amounts of labeled data are usually required for training the networks. In this work, a framework for analyzing the road and traffic situations for cyclists and pedestrians is presented, which only requires very few labeled examples. We address this problem by combining convolutional neural networks and random forests, transforming the random forest into a neural network, and generating a fully convolutional network for detecting objects. Because existing methods for transforming random forests into neural networks propose a direct mapping and produce inefficient architectures, we present neural random forest imitation—an imitation learning approach by generating training data from a random forest and learning a neural network that imitates its behavior. This implicit transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is differentiable, can be used as a warm start for fine-tuning, and enables end-to-end optimization. Experiments on several real-world benchmark datasets demonstrate superior performance, especially when training with very few training examples. Compared to state-of-the-art methods, we significantly reduce the number of network parameters while achieving the same or even improved accuracy due to better generalization.
Christoph Reinders, Michael Ying Yang, Bodo Rosenhahn

Geovisualization and User Interactions Related to VGI

Frontmatter

Open Access

Chapter 6. Toward Visually Analyzing Dynamic Social Messages and News Articles Containing Geo-Referenced Information
Abstract
The number of social media posts and news articles that are being published every day is high. This makes them an attractive source of human-generated information for different domain experts such as journalists and business analysts but also emergency responders, particularly if posts contain references to geolocations. Visual analytics approaches can help to gain insights into such datasets and inform decision-makers. However, the high volume and the veracity of the data, as well as the velocity in the case of streaming data, pose challenges when supporting explorative analysis with interactive visualization. Based on four exemplary approaches, we outline recently proposed strategies to tackle these challenges. We describe how geo-aware filtering and anomaly detection methods can help to inform stakeholders based on geolocated tweets. We show that data-aware tag maps can provide analysts with an overview-first, details-on-demand visual summary of large amounts of text content over time. With space-filling curves, we can visualize the temporal evolution of geolocations in a two-dimensional plot without relying on animations that would impede comparative analyses. Additionally, we discuss the use of an efficient dynamic clustering algorithm for enabling large-scale visual analyses of streaming posts.
Johannes Knittel, Franziska Huth, Steffen Koch, Thomas Ertl

Open Access

Chapter 7. Visually Reporting Geographic Data Insights as Integrated Visual and Textual Representations
Abstract
Geographic information volunteered by the public is usually also of public interest. However, just publishing the data is not enough to make the data accessible and usable for the public. The raw data might need to be abstracted and interpreted, as well as visually presented to be understandable to non-experts. To address this, we propose interactive visual reporting solutions that leverage natural language and visualizations for geo-related data. We present these reports as interactive documents, but also in other media such as virtual reality environments. First, we have studied the interplay of textual and visual content in such reports. To ease the creation of content, we have developed solutions for authoring interactive documents with a close linking of textual contents and visually presented data. Moreover, we propose automatic report generation approaches that specifically support the exploration of the geo-related data starting from an explanatory summary.
Fabian Beck, Shahid Latif

Open Access

Chapter 8. Effects of Landmark Position and Design in VGI-Based Maps on Visual Attention and Cognitive Processing
Abstract
Landmarks play a crucial role in map reading and in the formation of mental spatial models. Especially when following a route to get to a fixed destination, landmarks are crucial orientation aids. Which objects from the multitude of spatial objects in an environment are suitable as landmarks and, for example, can be automatically displayed in navigation systems has hardly been clarified. The analysis of Volunteered Geographic Information (VGI) offers the possibility of no longer having to separate methodologically between active and passive salience of landmarks in order to gain insights into the effect of landmarks on orientation ability or memory performance. Since the users (groups) involved are map producers and map users at the same time, an analysis of the user behavior of user-generated maps provides in-depth insights into cognitive processes and enables the direct derivation of basic methodological principles for map design. The landmarks determined on the basis of the VGI and entered as signs in maps can provide indications of the required choice, number, and position of landmarks that users need in order to orientate themselves in space with the help of maps. The results of several empirical studies show which landmark pictograms from OpenStreetMap (OSM) maps are cognitively processed quickly by users and which spatial position they must have in order to be able to increase memory performance, for example, during route learning.
Julian Keil, Frank Dickmann, Lars Kuchinke

Open Access

Chapter 9. Addressing Landmark Uncertainty in VGI-Based Maps: Approaches to Improve Orientation and Navigation Performance
Abstract
Landmarks, salient spatial objects, play an important role in orientation and navigation. They provide a spatial reference frame that helps to make sense of complex environments. Landmark representations in maps support map matching and orientation, because matching landmarks to their map representations provides information about spatial directions and distances. However, effective landmark-based map matching demands sufficiently accurate georeferencing of the landmarks represented in a map, because spatial inaccuracies of landmark representations cause distortions of the spatial reference frame and derived directions and distances. The requirement of accurate landmark georeferencing imposes difficulties on the use of maps based on Volunteered Geographic Information (VGI) for map matching. Differences of the motivation, competence, and available apparatus of volunteers can cause great variations of the data quality in VGI-based maps, including spatial accuracy of landmark representations. In a series of experiments, we investigated and quantified to what extent spatial inaccuracies of landmark representations in VGI-based maps affect map matching. Based on the findings, we were able to identify critical thresholds for spatial landmark inaccuracies. Furthermore, we explored potential ways to sustain successful map matching at higher degrees of spatial landmark inaccuracies. Through visual communication of spatial uncertainties, we were able to make map users more resilient to potential inaccuracies and sustain successful map matching.
Julian Keil, Frank Dickmann, Lars Kuchinke

Open Access

Chapter 10. Improvement of Task-Oriented Visual Interpretation of VGI Point Data
Abstract
VGI is often generated as point data representing points of interest (POIs) and semantic qualities (such as accident locations) or quantities (such as noise levels), which can lead to geometric and thematic clutter in visual presentations of regions with numerous VGI contributions. As a solution, cartography provides several point generalization operations that reduce the total number of points and therefore increase the readability of a map. However, these operations are applied rather general and could remove specific spatial pattern, possibly leading to false interpretations in tasks where these spatial patterns are of interest. In this chapter, we want to tackle this problem by defining task-oriented sets of map generalization constraints that help to maintain spatial pattern characteristics during the generalization process. Therefore, we conduct a study to analyze the user behavior while solving interpretation tasks and use the findings as constraints in the following point generalization process, which is implemented through agent-based modeling.
Martin Knura, Jochen Schiewe

Active Participation, Social Context, and Privacy Awareness

Frontmatter

Open Access

Chapter 11. Environmental Tracking for Healthy Mobility
Abstract
Environmental stressors in city traffic are a relevant health threat to urban cyclists and pedestrians. These stressors are multifaceted and include noise pollution, heat, and air pollution such as particulate matter. In the present chapter, we describe the use of wearable sensors carried by volunteers to capture their exposure to environmental stressors on their everyday routes. These wearable sensors are becoming increasingly important to capture the spatial and temporal distribution of environmental factors in the city. They also offer the unique opportunity to provide individualized feedback to the person wearing the sensor as well as possibilities to visualize different stressors in their temporal and spatial distribution in a virtual reality environment. We used the option of providing individualized feedback on personal exposure levels in two randomized controlled field studies. In these experiments, we studied the psychological health-related outcomes of carrying a wearable sensor and receiving feedback on one’s individual exposure levels.
Anna Maria Becker, Carolin Helbig, Abdelrhman Mohamdeen, Torsten Masson, Uwe Schlink

Open Access

Chapter 12. Extraction and Visually Driven Analysis of VGI for Understanding People’s Behavior in Relation to Multifaceted Context
Abstract
Volunteered Geographic Information in the form of actively and passively generated spatial content offers great potential to study people’s activities, emotional perceptions, and mobility behavior. Realizing this potential requires methods which take into account the specific properties of such data, for example, its heterogeneity, subjectivity, and spatial resolution but also temporal relevance and bias.
The aim of the chapter is to show how insights into human behavior can be gained from location-based social media and movement data using visual analysis methods. A conceptual behavioral model is introduced that summarizes people’s reactions under the influence of one or more events. In addition, influencing factors are described using a context model, which makes it possible to analyze visitation and mobility patterns with regard to spatial, temporal, and thematic-attribute changes. Selected generic methods are presented, such as extended time curves and the co-bridge metaphor to perform comparative analysis along time axes. Furthermore, it is shown that emojis can be used as contextual indicants to analyze sentiment and emotions in relation to events and locations.
Application-oriented workflows are presented for activity analysis in the field of urban and landscape planning. It is shown how location-based social media can be used to obtain information about landscape objects that are collectively perceived as valuable and worth preserving. The mobility behavior of people is analyzed using the example of multivariate time series from football data. Therefore, topic modeling and pattern analyzes were utilized to identify average positions and area of movements of the football teams.
Dirk Burghardt, Alexander Dunkel, Eva Hauthal, Gota Shirato, Natalia Andrienko, Gennady Andrienko, Maximilian Hartmann, Ross Purves

Open Access

Chapter 13. Digital Volunteers in Disaster Management
Abstract
During disaster situations, social media is used extensively by the affected population for communication and collaboration, but there is also increased public sharing of important disaster-related information about the current situation. With the goal of utilizing this data and Volunteered Geographic Information (VGI) for disaster management, digital volunteers organized themselves into so-called Volunteer and Technical Communities (V&TC). In addition, professionalized digital volunteers have institutionalized Virtual Operations Support Teams (VOST) in established Emergency Management Agencies (EMA). While technical issues have dominated research in this area in recent years, questions about the motivation, organization, and impact of the analytical work of these volunteers have remained unanswered. In this chapter, we present five studies that address questions about the motivation of digital volunteers, organization, and collaboration requirements, the analytical impact of VOST, data biases in Crisis Information Management (CIM), and privacy-related topics. Overall, it could be shown that digital volunteers make a significant contribution during disaster management, in which they effectively process their analytical results and VGI for the management of disaster situations. However, human limitations and privacy-related methods need to receive greater attention in the future, both in research and in practice.
Ramian Fathi, Frank Fiedrich

Open Access

Chapter 14. Protecting Privacy in Volunteered Geographic Information Processing
Abstract
Social media data is used for analytics, e.g., in science, authorities, or the industry. Privacy is often considered a secondary problem. However, protecting the privacy of social media users is demanded by laws and ethics. In order to prevent subsequent abuse, theft, or public exposure of collected datasets, privacy-aware data processing is crucial. In this chapter, we show a set of concepts to process social media data with social media user’s privacy in mind. We present a data storage concept based on the cardinality estimator HyperLogLog to store social media data, so that it is not possible to extract individual items from it, but only to estimate the cardinality of items within a certain set, plus running set operations over multiple sets to extend analytical ranges. Applying this method requires to define the scope of the result before even gathering the data. This prevents the data from being misused for other purposes at a later point in time and thus follows the privacy by design principles. We further show methods to increase privacy through the implementation of abstraction layers. As another additional instrument, we introduce a method to implement filter lists on the incoming data stream. A conclusive case study demonstrates our methods to be protected against adversarial actors.
Marc Löchner, Alexander Dunkel, Dirk Burghardt
Metadata
Title
Volunteered Geographic Information
Editors
Dirk Burghardt
Elena Demidova
Daniel A. Keim
Copyright Year
2024
Electronic ISBN
978-3-031-35374-1
Print ISBN
978-3-031-35373-4
DOI
https://doi.org/10.1007/978-3-031-35374-1

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