GraCT: A Grammar based Compressed representation of Trajectories

We present a compressed data structure to store free trajectories of moving objects (ships over the sea, for example) allowing spatio-temporal queries. Our method, GraCT, uses a $k^2$-tree to store the absolute positions of all objects at regular time intervals (snapshots), whereas the positions between snapshots are represented as logs of relative movements compressed with Re-Pair. Our experimental evaluation shows important savings in space and time with respect to a fair baseline.Comment: This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094

Collaborative Research: Matching Levels of Detail in Descriptions and Depictions of Geographic Space

This collaborative research project focuses on issues related to wayfinding, and is at the interface of geographic information science and human cognition. The research addresses the questions of the relationship between verbal and pictorial representations of geographic information, and how both representation modes can be successfully integrated in systems relating geographic information to users. To address this question, a theory of content and level of geographic detail is constructed that is independent of specific mode. The research is founded upon computational and cognitive theories of geographic information. The main idea is that theories of information granularity can be imposed on a \u27canonical model\u27 of dual mode information, enabling inter-modal comparisons and transformations to be made between levels of detail and information content. The level of detail that should be presented in each of the verbal and pictorial modes, and relationships between levels of detail in each mode, are important areas of investigation. The theory of information granularity should accord with human cognition, and a major part of the work will be the testing of the theory by a graded set of experiments with human subjects. The research will be evaluated through the construction of a demonstrator mobile \u27wayfinding assistant\u27 that will operate using both verbal and pictorial modes. This project is motivated by the task of wayfinding in an unknown city, using a portable, mobile, digital wayfinding assistant, with two interaction modes, verbal and pictorial, and some locational and directional capabilities. With increasing use of multimedia, this research will help to formulate guidelines for effective presentation of pictoral and verbal spatial information. The research addresses fundamental questions such as: What should be the balance between modes of information supplied to the user? Should the information to be represented in each mode be complementary or supplementary? If it is necessary to \u27switch off\u27 a mode, how can this be done seamlessly, with the same level of detail presented? The ability to change flexibly between modalities may be especially appropriate for individuals with sensory impairments, as well as those whose tasks require both modes operating simultaneously, or those who need to switch seamlessly between modes (e.g. a driver of a vehicle who must switch from a visual display to audio cues because of the need to concentrate full visual attention on a traffic situation)

How context influences the segmentation of movement trajectories - an experimental approach for environmental and behavioral context

In the digital information age where large amounts of movement data are generated daily through technological devices, such as mobile phones, GPS, and digital navigation aids, the exploration of moving point datasets for identifying movement patterns has become a research focus in GIScience (Dykes and Mountain 2003). Visual analytics (VA) tools, such as GeoVISTA Studio (Gahegan 2001), have been developed to explore large amounts of movement data based on the contention that VA combine computational methods with the outstanding human capabilities for pattern recognition, imagination, association, and reasoning (Andrienko et al. 2008). However, exploring, extracting and understanding the meaning encapsulated in movement data from a user perspective has become a major bottleneck, not only in GIScience, but in all areas of science where this kind of data is collected (Holyoak et al. 2008). Specifically the inherent complex and multidimensional nature of spatio-temporal data has not been sufficiently integrated into visual analytics tools. To ensure the inclusion of cognitive principles for the integration of space-time data, visual analytics has to consider how users conceptualize and understand movement data (Fabrikant et al. 2008). A review on cognitively motivated work exemplifies the urgent need to identify how humans make inferences and derive knowledge from movement data. In order to enhance visual analytics tools by integrating cognitive principles we have to first ask to what extent cognitive factors influence our understanding, reasoning, and analysis of movement pattern extraction. It is especially important to comprehend human knowledge construction and reasoning about spatial and temporal phenomena and processes. This paper proposes an experimental approach with human subject testing to evaluate the importance of contextual information in visual displays of movement patterns. This research question is part of a larger research project, with two main objectives, namely * getting a better understanding of how humans process spatio-temporal information * and empirically validating guidelines to improve the design of visual analytics tools to enhance visual data exploration

A conceptual framework for studying collective reactions to events in location-based social media

Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events

On the semantics of big Earth observation data for land classification

This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. I argue for sound theories when working with big data. After revising existing classification schemes such as FAO\u27s Land Cover Classification System (LCCS), I conclude that LCCS and similar proposals cannot capture the complexity of landscape dynamics. I then investigate concepts that are being used for analyzing satellite image time series; I show these concepts to be instances of events. Therefore, for continuous monitoring of land change, event recognition needs to replace object identification as the prevailing paradigm. The paper concludes by showing how event semantics can improve data-driven methods to fulfil the potential of big data

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Why are events important and how to compute them in geospatial research?

Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based modeling and to emphasize the epistemological role of events in geospatial information science. Events are essential to understanding the world and communicating the understanding, events provide points of entry for knowledge inquiries and the inquiry processes, and events mediate objects and scaffold causality. We compute events to improve understanding, but event computing and computability depend on event representation. The paper briefly reviews event-based data models in spatial databases and methods to compute events for site understanding and prediction, for spatial impact assessment, and for discovering events\u27 dynamic structures. Concluding remarks summarize key arguments and comment on opportunities to extend event computability

Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation

Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines such as urban planning environmental sciences and health care. Thus remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result we propose a high level conceptual framework based on existing and standardized approaches offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge

Sustainable Population Growth in Low-Density Areas in a New Technological Era: Prospective Thinking on How to Support Planning Policies Using Complex Spatial Models

Urban development is the result of the interaction between anthropogenic and environmental dimensions. From the perspective of its density, it ranges from high-density populated areas, associated with large cities that concentrate the main economic and social thrust of societies, to low-density populated areas (e.g., rural areas, small–medium-sized cities). Against the backdrop of the new technological and environmental era, this commentary offers insights on how to support spatial planning policies for sustainable urban growth in low-density areas. We propose the integration of technological drivers such as Internet networks, telecommuting, distance-learning education, the use of electric cars, etc. into the complex spatial models to project and thus to identify the best locations for urban development in low-density areas. This understanding can help to mitigate the disparities between high- and low-density populated areas, and to reduce the inequality among regions as promoted in the UN 2030 Agenda for Sustainable Development Goals.info:eu-repo/semantics/publishedVersio

Special issue on spatio-temporal theories and models for environmental, urban and social sciences: where do we stand ?

This extensive editorial of this special journal issue follows a workshop organized in conjunction with the 11th International Conference on Spatial Information Theory (COSIT 2013) in September 2013 in Scarborough, UK. The objective of this international workshop was to bring together representatives from these different disciplinary communities, and integrate academics, students, and practitioners for a one-day workshop on spatiotemporal concepts and theories. This editorial introduces the special issue, the research objectives the workshop followed and some of the main contributions as well as the theoretical achievements and research perspectives left