Bernd Resch

People as Sensors and Collective Sensing-Contextual Observations Complementing Geo-Sensor Network Measurements

Ubiquitous sensor networks and Location-based Services can potentially assist in taking decisions in near real time in a variety of application areas such as public safety, traffic management, environmental monitoring or in public health. Yet, analysing our surroundings in real time is still a major challenge due to sparsely available data sources for real-time monitoring. The innovative concept of People as Sensors defines a measurement model, in which measurements are not only taken by calibrated hardware sensors, but in which also humans can contribute their individual ‘measurements’ such as their subjective sensations, current perceptions or personal observations. This chapter contains a disambiguation between the terms People as Sensors (people contributing subjective observations), Collective Sensing (analysing aggregated anonymised data coming from collective networks) and Citizen Science (exploiting and elevating expertise of citizens and their personal, local experiences). Then, the particular significance of integrating the People as Sensors concept with established LBS, data analysis and visualisation systems is elaborated. Finally, the paper discusses current challenges, points out possible solutions, and pin-points directions for future research areas.

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data

Abstract Detailed knowledge regarding the whereabouts of people and their social activities in urban areas with high spatial and temporal resolution is still widely unexplored. Thus, the spatiotemporal analysis of Location Based Social Networks (LBSN) has great potential regarding the ability to sense spatial processes and to gain knowledge about urban dynamics, especially with respect to collective human mobility behavior. The objective of this paper is to explore the semantic association between georeferenced tweets and their respective spatiotemporal whereabouts. We apply a semantic topic model classification and spatial autocorrelation analysis to detect tweets indicating specific human social activities. We correlated observed tweet patterns with official census data for the case study of London in order to underline the significance and reliability of Twitter data. Our empirical results of semantic and spatiotemporal clustered tweets show an overall strong positive correlation in comparison with workplace population census data, being a good indicator and representative proxy for analyzing workplace-based activities.

Urban Emotions—Geo-Semantic Emotion Extraction from Technical Sensors, Human Sensors and Crowdsourced Data

How people in the city perceive their surroundings depends on a variety of dynamic and static context factors such as road traffic, the feeling of safety, urban architecture, etc. Such subjective and context-dependent perceptions can trigger different emotions, which enable additional insights into the spatial and temporal configuration of urban structures. This paper presents the Urban Emotions concept that proposes a human-centred approach for extracting contextual emotional information from human and technical sensors. The methodology proposed in this paper consists of four steps: (1) detecting emotions using wristband sensors, (2) “ground-truthing” these measurements using a People as Sensors location-based service, (3) extracting emotion information from crowdsourced data like Twitter, and (4) correlating the measured and extracted emotions. Finally, the emotion information is mapped and fed back into urban planning for decision support and for evaluating ongoing planning processes.

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.

Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics

Ubiquitous geo-sensing enables context-aware analyses of physical and social phenomena, i.e., analyzing one phenomenon in the context of another. Although such context-aware analysis can potentially enable a more holistic understanding of spatio-temporal processes, it is rarely documented in the scientific literature yet. In this paper we analyzed the collective human behavior in the context of the weather. We therefore explored the complex relationships between these two spatio-temporal phenomena to provide novel insights into the dynamics of urban systems. Aggregated mobile phone data, which served as a proxy for collective human behavior, was linked with the weather data from climate stations in the case study area, the city of Udine, Northern Italy. To identify and characterize potential patterns within the weather-human relationships, we developed a hybrid approach which integrates several spatio-temporal statistical analysis methods. Thereby we show that explanatory factor analysis, when applied to a number of meteorological variables, can be used to differentiate between normal and adverse weather conditions. Further, we measured the strength of the relationship between the ‘global’ adverse weather conditions and the spatially explicit effective variations in user-generated mobile network traffic for three distinct periods using the Maximal Information Coefficient (MIC). The analyses result in three spatially referenced maps of MICs which reveal interesting insights into collective human dynamics in the context of weather, but also initiate several new scientific challenges.

Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities

In this article we critically discuss the challenge of integrating contextual information, in particular spatiotemporal contextual information, with human and technical sensor information, which we approach from a geospatial perspective. We start by highlighting the significance of context in general and spatiotemporal context in particular and introduce a smart city model of interactions between humans, the environment, and technology, with context at the common interface. We then focus on both the intentional and the unintentional sensing capabilities of today’s technologies and discuss current technological trends that we consider have the ability to enrich human and technical geo-sensor information with contextual detail. The different types of sensors used to collect contextual information are analyzed and sorted into three groups on the basis of names considering frequently used related terms, and characteristic contextual parameters. These three groups, namely technical in situ sensors, technical remote sensors, and human sensors are analyzed and linked to three dimensions involved in sensing (data generation, geographic phenomena, and type of sensing). In contrast to other scientific publications, we found a large number of technologies and applications using in situ and mobile technical sensors within the context of smart cities, and surprisingly limited use of remote sensing approaches. In this article we further provide a critical discussion of possible impacts and influences of both technical and human sensing approaches on society, pointing out that a larger number of sensors, increased fusion of information, and the use of standardized data formats and interfaces will not necessarily result in any improvement in the quality of life of the citizens of a smart city. This article seeks to improve our understanding of technical and human geo-sensing capabilities, and to demonstrate that the use of such sensors can facilitate the integration of different types of contextual information, thus providing an additional, namely the geo-spatial perspective on the future development of smart cities.

Web-based 4D visualization of marine geo-data using WebGL

For marine logistics and maintenance of extensive and expensive marine engineering projects in the coastal zone, it is essential that institutions provide the public with relevant information in an easily understandable yet comprehensive manner over the web. A perennial challenge, however, has been the development of spatio-temporal (four-dimensional (4D)) geo-visualization algorithms to enable the integration of time-varying geo-information in map-based visualizations on the Internet. In this paper, we address the challenge of visualizing marine spatial data in web-based applications through a 4D visualization concept, focusing on usability criteria, performance parameters, the required implementation effort, and delivering a breath of spatial information that supports decision-making on multiple levels. We used Web Graphic Library (WebGL) to validate our concept through a prototypical implementation. In our technology evaluation, WebGL proved highly suitable for the development of interactive, responsive, efficient, and mobile web-based Geographic Information applications, including 2D, 3D, and 4D (spatiotemporal) content. During our research, we identified a number of open research questions, including mapping graphic variables to thematic expressivity, representation of the time dimension in 4D systems, generic temporal generalization, and integration of (pseudo-)photorealistic illustrations in web-based geo-visualization systems.

Pervasive Monitoring - An Intelligent Sensor Pod Approach for Standardised Measurement Infrastructures

Geo-sensor networks have traditionally been built up in closed monolithic systems, thus limiting trans-domain usage of real-time measurements. This paper presents the technical infrastructure of a standardised embedded sensing device, which has been developed in the course of the Live Geography approach. The sensor pod implements data provision standards of the Sensor Web Enablement initiative, including an event-based alerting mechanism and location-aware Complex Event Processing functionality for detection of threshold transgression and quality assurance. The goal of this research is that the resultant highly flexible sensing architecture will bring sensor network applications one step further towards the realisation of the vision of a “digital skin for planet earth”. The developed infrastructure can potentially have far-reaching impacts on sensor-based monitoring systems through the deployment of ubiquitous and fine-grained sensor networks. This in turn allows for the straight-forward use of live sensor data in existing spatial decision support systems to enable better-informed decision-making.

Towards 4D Cartography - Four-dimensional Dynamic Maps for Understanding Spatio-temporal Correlations in Lightning Events

Abstract While graphic variables in 2D maps have been extensively investigated, 4D cartography is still a widely unexplored field. In this paper, we investigate the usefulness of 4D maps (three spatial dimensions plus time) for cartographically illustrating spatio-temporal environmental phenomena. The presented approach focuses mostly on explorative research rather than on enhancement and extension of existing methods and principles. The user study described in the paper shows that 4D cartography is not a well-explored research area and that many experienced map users try to apply their knowledge from 2D maps to 4D dynamic visualisations. Thus, in order to foster the discussion within the community, we formulated several basic research questions for the area of 4D cartography, which range from methods for representing time in 4D visualisations and understanding the temporal context to finding generic methods to achieve optimized temporal generalisation and a consistent definition of graphical variables for 3D and 4D.