A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty

Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. When a detailed digital representation of the region is to be created, domain experts are required to make best-guess decisions about each object. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by military, simulation, earth sciences and gaming communities. Many of these applications are increasingly using digitally synthesized visuals and require detailed digital 3D representations to be generated quickly after acquiring the necessary initial data. Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the semantic inferencing system and is extended with a data extraction framework. This framework enables the extraction of implicit property information using data and image analysis techniques. The data extraction framework supports extraction of spatial relationship values and attribution of uncertainties to inferred details. Uncertainty is recorded per property and used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services. Previous semantics based research in this domain has concentrated more on improving represented details through the addition of artifacts like lights, signage, crosswalks, etc. Previous attempts regarding uncertainty in assertions use a modified reasoner expressivity and calculus. Our work differs in that separating formal knowledge from data processing allows fusion of different heterogeneous data sources which share the same context. Imprecision is modeled through uncertainty on assertions without defining a new expressivity as long as KB explanation services are available for the used expressivity. We also believe that in our use case, this simplifies uncertainty calculations. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our method’s components, discuss advantages and limitations, and show sample results for the transportation domain

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer

Formalising cartographic generalisation knowledge in an ontology to support on-demand mapping

This thesis proposes that on-demand mapping - where the user can choose the geographic features to map and the scale at which to map them - can be supported by formalising, and making explicit, cartographic generalisation knowledge in an ontology. The aim was to capture the semantics of generalisation, in the form of declarative knowledge, in an ontology so that it could be used by an on-demand mapping system to make decisions about what generalisation algorithms are required to resolve a given map condition, such as feature congestion, caused by a change in scale. The lack of a suitable methodology for designing an application ontology was identified and remedied by the development of a new methodology that was a hybrid of existing domain ontology design methodologies. Using this methodology an ontology that described not only the geographic features but also the concepts of generalisation such as geometric conditions, operators and algorithms was built. A key part of the evaluation phase of the methodology was the implementation of the ontology in a prototype on-demand mapping system. The prototype system was used successfully to map road accidents and the underlying road network at three different scales. A major barrier to on-demand mapping is the need to automatically provide parameter values for generalisation algorithms. A set of measure algorithms were developed to identify the geometric conditions in the features, caused by a change in scale. From this a Degree of Generalisation (DoG) is calculated, which represents the “amount” of generalisation required. The DoG is used as an input to a number of bespoke generalisation algorithms. In particular a road network pruning algorithm was developed that respected the relationship between accidents and road segments. The development of bespoke algorithms is not a sustainable solution and a method for employing the DoG concept with existing generalisation algorithms is required. Consideration was given to how the ontology-driven prototype on-demand mapping system could be extended to use cases other than mapping road accidents and a need for collaboration with domain experts on an ontology for generalisation was identified. Although further testing using different uses cases is required, this work has demonstrated that an ontological approach to on-demand mapping has promise

Personalized City Tours - An Extension of the OGC OpenLocation Specification

A business trip to London last month , a day visit in Cologne next saturday and romantic weekend in Paris in autumn – this example exhibits one of the central characteristics of today’s tourism. People in the western hemisphere take much pleasure in frequent and repeated short term visits of cities. Every city visitor faces the general problems of where to go and what to see in the diverse microcosm of a metropolis. This thesis presents a framework for the generation of personalized city tours - as extension of the Open Location Specification of the Open Geospatial Consortium. It is founded on context-awareness and personalization while at the same time proposing a combined approach to allow for adaption to the user. This framework considers TimeGeography and its algorithmic implementations to be able to cope with spatio-temporal constraints of a city tour. Traveling salesmen problems - for which a heuristic approache is proposed – are subjacent to the tour generation. To meet the requirements of today’s distributed and heterogeneous computing environments, the tour framework comprises individual services that expose standard-compliant interfaces and allow for integration in service oriented architectures

Localizing the media, locating ourselves: a critical comparative analysis of socio-spatial sorting in locative media platforms (Google AND Flickr 2009-2011)

In this thesis I explore media geocoding (i.e., geotagging or georeferencing), the process of inscribing the media with geographic information. A process that enables distinct forms of producing, storing, and distributing information based on location. Historically, geographic information technologies have served a biopolitical function producing knowledge of populations. In their current guise as locative media platforms, these systems build rich databases of places facilitated by user-generated geocoded media. These geoindexes render places, and users of these services, this thesis argues, subject to novel forms of computational modelling and economic capture. Thus, the possibility of tying information, people and objects to location sets the conditions to the emergence of new communicative practices as well as new forms of governmentality (management of populations). This project is an attempt to develop an understanding of the socio-economic forces and media regimes structuring contemporary forms of location-aware communication, by carrying out a comparative analysis of two of the main current location-enabled platforms: Google and Flickr. Drawing from the medium-specific approach to media analysis characteristic of the subfield of Software Studies, together with the methodological apparatus of Cultural Analytics (data mining and visualization methods), the thesis focuses on examining how social space is coded and computed in these systems. In particular, it looks at the databases’ underlying ontologies supporting the platforms' geocoding capabilities and their respective algorithmic logics. In the final analysis the thesis argues that the way social space is translated in the form of POIs (Points of Interest) and business-biased categorizations, as well as the geodemographical ordering underpinning the way it is computed, are pivotal if we were to understand what kind of socio-spatial relations are actualized in these systems, and what modalities of governing urban mobility are enabled

Social Space and Social Media: Analyzing Urban Space with Big Data

This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes solutions to two common barriers to the adoption of big data in the social sciences: accessing and collecting such data and, subsequently, meaningful analysis. These methods and the theoretical foundation are combined in three case studies that show the successful integration of a quantitative research methodology with social theories on space. The case studies demonstrate how such an approach can create new and alternative understandings of urban space. In doing so it answers three specific research questions: (1) How can big data facilitate the integration of social theory on space with quantitative research methodology? (2) What are the practical challenges and solutions to moving “beyond the geotag” when utilizing big data in geographical research? (3) How can the quantitative analysis of big data provide new and useful insight in the complex character of social space? More specifically, what insights does such an analysis of relational social space provide about urban mobility and cognitive neighborhoods

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier