SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI

Development and evaluation of a geographic information retrieval system using fine grained toponyms

Geographic information retrieval (GIR) is concerned with returning information in response to an information need, typically expressed in terms of a thematic and spatial component linked by a spatial relationship. However, evaluation initiatives have often failed to show significant differences between simple text baselines and more complex spatially enabled GIR approaches. We explore the effectiveness of three systems (a text baseline, spatial query expansion, and a full GIR system utilizing both text and spatial indexes) at retrieving documents from a corpus describing mountaineering expeditions, centred around fine grained toponyms. To allow evaluation, we use user generated content (UGC) in the form of metadata associated with individual articles to build a test collection of queries and judgments. The test collection allowed us to demonstrate that a GIR-based method significantly outperformed a text baseline for all but very specific queries associated with very small query radii. We argue that such approaches to test collection development have much to offer in the evaluation of GIR

Web-based discovery and dissemination of multidimensional geographic information

A spatial data clearinghouse is an electronic facility for searching, viewing, transferring, ordering, advertising, and disseminating spatial data from numerous sources via the Internet. Governments and other institutions have been implementing spatial data clearinghouses to minimise data duplication and thus reduce the cost of spatial data acquisition. Underlying these clearinghouses are geoportals and databases of geospatial metadata.A geoportal is an access point of a spatial data clearinghouse and metadata is data that describes data. The success of a clearinghouse's spatial data discovery system is dependent on its ability to communicate the contents of geospatial metadata by providing both visual and analytical assistancet o a user. The model currently adopted by the geographic information community was inherited from generic information systems and thus to an extent ignores spatial characteristics of geographic data. Consequently, research in Geographic Information Retrieval (GIR) has focussed on spatial aspects of webbased data discovery and acquisition. This thesis considers how the process of GIR from geoportals can be enhanced through multidimensional visualisation served by web-based geographic data sources. An approach is proposed for the presentation of search results in ontology assisted GIR. Also proposed is an approach for the visualisation of multidimensional geographic data from web-based data sources. These approaches are implemented in two prototypes, the Geospatial Database Online Visualisation Environment (GeoDOVE) and the Spatio-Temporal Ontological Relevance Model (STORM). A discussion of their design, implementation and evaluation is presented. The results suggest that ontology-assisted visualisation can improve a user's ability to identify the most relevant multidimensional geographic datasets from a set of search results. Additional results suggest that it is possible to offer the proposed visualisation approaches on existing geoportal frameworks. The implication of the results is that multidimensional visualisation should be considered by the wider geographic information community as an alternative to historic approaches for presenting search results on geoportals, such as the textual ranked list and two-dimensional maps.EThOS - Electronic Theses Online ServiceUniversity of Newcastle upon TyneGBUnited Kingdo

Open Infrared : enhancing environmental monitoring through accessible remote sensing, in Indonesia and beyond

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.As the human landscape changes ever more rapidly, environmental change accelerates. Much environmental information is publicly available as infrared satellite data. However, for the general user, this information is difficult to obtain, and even more difficult to interpret. With this in mind, my team and I launched OpenIR (Open Infrared), an ICT (Information Communication Technology) that provides geo-located IR (infrared) satellite data as ondemand map layers, automates environmental feature classification, experiments with flood risk mapping, and interfaces IR data with crowd- and citizen-maps. OpenIR's initial use case is emergency management and environmental monitoring in the economically developing and ecologically vulnerable archipelago of Indonesia, where we conduced initial usability tests in January 2013.by Arlene Ducao.S.M

Using Geographic Relevance (GR) to contextualize structured and unstructured spatial data

Geographic relevance is a concept that has been used to improve spatial information retrieval on mobile devices, but the idea of geographic relevance has several potential applications outside of mobile computing. Geographic relevance is used measure how related two spatial entities are using a set of criteria such as distance between features, the semantic similarity of feature names or clustering pattern of features. This thesis examines the use of geographic relevance to organize and filter web based spatial data such as framework data from open data portals and unstructured volunteer geographic information generated from social media or map-based surveys. There are many new users and producers of geographic information and it is unclear to new users which data sets they should use to solve a given problem. Governments and organizations also have access to a growing volume of volunteer geographic information but current models for matching citizen generated information to locations of concern to support filtering and reporting are inadequate. For both problems, there is an opportunity to develop semi-automated solutions using geographic relevance metrics such as topicality, spatial proximity, cluster and co-location. In this thesis, two geographic relevance models were developed using Python and PostgreSQL to measure relevance and identify relationships between structured framework data and unstructured VGI in order to support data organization, retrieval and filtering. This idea was explored through two related case studies and prototype applications. The first study developed a prototype application to retrieve spatial data from open data portals using four geographic relevance criteria which included topicality, proximity, co-location and cluster co-location. The second study developed a prototype application that matches VGI data to authoritative framework data to dynamically summarize and organize unstructured VGI data. This thesis demonstrates two possible approaches for using GR metrics to evaluate spatial relevance between large data sets and individual features. This thesis evaluates the effectiveness of GR metrics for performing spatial relevance analysis and it demonstrates two potential use cases for GR

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Planning for Urban Ecosystem Services: Generating Actionable Knowledge for Reducing Environmental Inequities in Santiago de Chile

abstract: Cities are hubs for economic and social development, but they are increasingly becoming hotspots of environmental problems and socio-economic inequalities. Because cities result from complex interactions among ecological, social and economic factors, environmental problems and socio-economic inequalities are often spatially interconnected, generating emergent environmental inequity issues due to the unfair distribution of environmental quality among socioeconomic groups. Since urban environmental quality is tightly related to the capacity of urban landscapes to provide ecosystem services, optimizing the allocation of ecosystem services within cities is a main goal for moving towards more equitable and sustainable cities. Nevertheless, we often lack the empirical data and specific methods for planning urban landscapes to optimize the provision of ecosystem services. Therefore, the development of knowledge and methods to optimize the provision of ecosystem services is essential for tackling urban environmental problems, reducing environmental inequities, and promoting sustainable cities. The main goal of this dissertation is to generate actionable knowledge for helping decision-makers to optimize the allocation of urban vegetation for reducing environmental inequities through the provision of ecosystem services. The research uses the city of Santiago de Chile as a case study from a Latin-American city. To achieve this goal, I framed my dissertation in four linked research chapters, each of them providing methodological approaches to help link environmental inequity problems with the development of urban planning interventions promoting an equitable provision of urban ecosystem services. These chapters are specifically aimed at providing actionable knowledge for: (1) Identifying the level, distribution, and spatial scales at which environmental inequities are more relevant; (2) Identifying the areas and administrative units where environmental inequities interventions should be prioritized; (3) Identifying optimal areas to allocate vegetation for increasing the provision of urban ecosystem services; (4) Evaluating the role that planned urban vegetation may have in the long-term provision of ecosystem services by natural remnants within the urban landscape. Thus, this dissertation contributes to urban sustainability science by proposing methods and frameworks to address urban environmental inequities through the provision of ecosystem services, but it also provides place-based information that can be readily used for planning urban vegetation in Santiago.Dissertation/ThesisDoctoral Dissertation Sustainability 201

Domain-sensitive Temporal Tagging for Event-centric Information Retrieval

Temporal and geographic information is of major importance in virtually all contexts. Thus, it also occurs frequently in many types of text documents in the form of temporal and geographic expressions. Often, those are used to refer to something that was, is, or will be happening at some specific time and some specific place – in other words, temporal and geographic expressions are often used to refer to events. However, so far, event-related information needs are not well served by standard information retrieval approaches, which motivates the topic of this thesis: event-centric information retrieval. An important characteristic of temporal and geographic expressions – and thus of two components of events – is that they can be normalized so that their meaning is unambiguous and can be placed on a timeline or pinpointed on a map. In many research areas in which natural language processing is involved, e.g., in information retrieval, document summarization, and question answering, applications can highly benefit from having access to normalized information instead of only the words as they occur in documents. In this thesis, we present several frameworks for searching and exploring document collections with respect to occurring temporal, geographic, and event information. While we rely on an existing tool for extracting and normalizing geographic expressions, we study the task of temporal tagging, i.e., the extraction and normalization of temporal expressions. A crucial issue is that so far most research on temporal tagging dealt with English news-style documents. However, temporal expressions have to be handled in different ways depending on the domain of the documents from which they are extracted. Since we do not want to limit our research to one domain and one language, we develop the multilingual, cross-domain temporal tagger HeidelTime. It is the only publicly available temporal tagger for several languages and easy to extend to further languages. In addition, it achieves state-of-the-art evaluation results for all addressed domains and languages, and lays the foundations for all further contributions developed in this thesis. To achieve our goal of exploiting temporal and geographic expressions for event-centric information retrieval from a variety of text documents, we introduce the concept of spatio-temporal events and several concepts to "compute" with temporal, geographic, and event information. These concepts are used to develop a spatio-temporal ranking approach, which does not only consider textual, temporal, and geographic query parts but also two different types of proximity information. Furthermore, we adapt the spatio-temporal search idea by presenting a framework to directly search for events. Additionally, several map-based exploration frameworks are introduced that allow a new way of exploring event information latently contained in huge document collections. Finally, an event-centric document similarity model is developed that calculates document similarity on multilingual corpora solely based on extracted and normalized event information

Search improvement within the geospatial web in the context of spatial data infrastructures

El trabajo desarrollado en esta tesis doctoral demuestra que es posible mejorar la búsqueda en el contexto de las Infraestructuras de Datos Espaciales mediante la aplicación de técnicas y buenas prácticas de otras comunidades científicas, especialmente de las comunidades de la Web y de la Web Semántica (por ejemplo, Linked Data). El uso de las descripciones semánticas y las aproximaciones basadas en el contenido publicado por la comunidad geoespacial pueden ayudar en la búsqueda de información sobre los fenómenos geográficos, y en la búsqueda de recursos geoespaciales en general. El trabajo comienza con un análisis de una aproximación para mejorar la búsqueda de las entidades geoespaciales desde la perspectiva de geocodificación tradicional. La arquitectura de geocodificación compuesta propuesta en este trabajo asegura una mejora de los resultados de geocodificación gracias a la utilización de diferentes proveedores de información geográfica. En este enfoque, el uso de patrones estructurales de diseño y ontologías en esta aproximación permite una arquitectura avanzada en términos de extensibilidad, flexibilidad y adaptabilidad. Además, una arquitectura basada en la selección de servicio de geocodificación permite el desarrollo de una metodología de la georreferenciación de diversos tipos de información geográfica (por ejemplo, direcciones o puntos de interés). A continuación, se presentan dos aplicaciones representativas que requieren una caracterización semántica adicional de los recursos geoespaciales. El enfoque propuesto en este trabajo utiliza contenidos basados en heurísticas para el muestreo de un conjunto de recursos geopesaciales. La primera parte se dedica a la idea de la abstracción de un fenómeno geográfico de su definición espacial. La investigación muestra que las buenas prácticas de la Web Semántica se puede reutilizar en el ámbito de una Infraestructura de Datos Espaciales para describir los servicios geoespaciales estandarizados por Open Geospatial Consortium por medio de geoidentificadores (es decir, por medio de las entidades de una ontología geográfica). La segunda parte de este capítulo desglosa la aquitectura y componentes de un servicio de geoprocesamiento para la identificación automática de ortoimágenes ofrecidas a través de un servicio estándar de publicación de mapas (es decir, los servicios que siguen la especificación OGC Web Map Service). Como resultado de este trabajo se ha propuesto un método para la identificación de los mapas ofrecidos por un Web Map Service que son ortoimágenes. A continuación, el trabajo se dedica al análisis de cuestiones relacionadas con la creación de los metadatos de recursos de la Web en el contexto del dominio geográfico. Este trabajo propone una arquitectura para la generación automática de conocimiento geográfico de los recursos Web. Ha sido necesario desarrollar un método para la estimación de la cobertura geográfica de las páginas Web. Las heurísticas propuestas están basadas en el contenido publicado por os proveedores de información geográfica. El prototipo desarrollado es capaz de generar metadatos. El modelo generado contiene el conjunto mínimo recomendado de elementos requeridos por un catálogo que sigue especificación OGC Catalogue Service for the Web, el estandar recomendado por deiferentes Infraestructuras de Datos Espaciales (por ejemplo, the Infrastructure for Spatial Information in the European Community (INSPIRE)). Además, este estudio determina algunas características de la Web Geoespacial actual. En primer lugar, ofrece algunas características del mercado de los proveedores de los recursos Web de la información geográfica. Este estudio revela algunas prácticas de la comunidad geoespacial en la producción de metadatos de las páginas Web, en particular, la falta de metadatos geográficos. Todo lo anterior es la base del estudio de la cuestión del apoyo a los usuarios no expertos en la búsqueda de recursos de la Web Geoespacial. El motor de búsqueda dedicado a la Web Geoespacial propuesto en este trabajo es capaz de usar como base un motor de búsqueda existente. Por otro lado, da soporte a la búsqueda exploratoria de los recursos geoespaciales descubiertos en la Web. El experimento sobre la precisión y la recuperación ha demostrado que el prototipo desarrollado en este trabajo es al menos tan bueno como el motor de búsqueda remoto. Un estudio dedicado a la utilidad del sistema indica que incluso los no expertos pueden realizar una tarea de búsqueda con resultados satisfactorios

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities