4,495 research outputs found

    Methodology for high resolution spatial analysis of the physical flood susceptibility of buildings in large river floodplains

    Get PDF
    The impacts of floods on buildings in urban areas are increasing due to the intensification of extreme weather events, unplanned or uncontrolled settlements and the rising vulnerability of assets. There are some approaches available for assessing the flood damage to buildings and critical infrastructure. To this point, however, it is extremely difficult to adapt these methods widely, due to the lack of high resolution classification and characterisation approaches for built structures. To overcome this obstacle, this work presents: first, a conceptual framework for understanding the physical flood vulnerability and the physical flood susceptibility of buildings, second, a methodological framework for the combination of methods and tools for a large-scale and high-resolution analysis and third, the testing of the methodology in three pilot sites with different development conditions. The conceptual framework narrows down an understanding of flood vulnerability, physical flood vulnerability and physical flood susceptibility and its relation to social and economic vulnerabilities. It describes the key features causing the physical flood susceptibility of buildings as a component of the vulnerability. The methodological framework comprises three modules: (i) methods for setting up a building topology, (ii) methods for assessing the susceptibility of representative buildings of each building type and (iii) the integration of the two modules with technological tools. The first module on the building typology is based on a classification of remote sensing data and GIS analysis involving seven building parameters, which appeared to be relevant for a classification of buildings regarding potential flood impacts. The outcome is a building taxonomic approach. A subsequent identification of representative buildings is based on statistical analyses and membership functions. The second module on the building susceptibility for representative buildings bears on the derivation of depth-physical impact functions. It relates the principal building components, including their heights, dimensions and materials, to the damage from different water levels. The material’s susceptibility is estimated based on international studies on the resistance of building materials and a fuzzy expert analysis. Then depth-physical impact functions are calculated referring to the principal components of the buildings which can be affected by different water levels. Hereby, depth-physical impact functions are seen as a means for the interrelation between the water level and the physical impacts. The third module provides the tools for implementing the methodology. This tool compresses the architecture for feeding the required data on the buildings with their relations to the building typology and the building-type specific depth-physical impact function supporting the automatic process. The methodology is tested in three flood plains pilot sites: (i) in the settlement of the Barrio Sur in MaganguĂ© and (ii) in the settlement of La Peña in Cicuco located on the flood plain of Magdalena River, Colombia and (iii) in a settlement of the city of Dresden, located on the Elbe River, Germany. The testing of the methodology covers the description of data availability and accuracy, the steps for deriving the depth-physical impact functions of representative buildings and the final display of the spatial distribution of the physical flood susceptibility. The discussion analyses what are the contributions of this work evaluating the findings of the methodology’s testing with the dissertation goals. The conclusions of the work show the contributions and limitations of the research in terms of methodological and empirical advancements and the general applicability in flood risk management.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – MaganguĂ©, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203In vielen StĂ€dten nehmen die Auswirkungen von Hochwasser auf GebĂ€ude aufgrund immer extremerer Wetterereignisse, unkontrollierbarer Siedlungsbauten und der steigenden VulnerabilitĂ€t von BesitztĂŒmern stetig zu. Es existieren zwar bereits AnsĂ€tze zur Beurteilung von WasserschĂ€den an GebĂ€uden und Infrastrukturknotenpunkten. Doch ist es bisher schwierig, diese Methoden großrĂ€umig anzuwenden, da es an einer prĂ€zisen Klassifizierung und Charakterisierung von GebĂ€uden und anderen baulichen Anlagen fehlt. Zu diesem Zweck sollen in dieser Arbeit erstens ein Konzept fĂŒr ein genaueres VerstĂ€ndnis der physischen VulnerabilitĂ€t von GebĂ€uden gegenĂŒber Hochwasser dargelegt, zweitens ein methodisches Verfahren zur Kombination der bestehenden Methoden und Hilfsmittel mit dem Ziel einer großrĂ€umigen und hochauflösenden Analyse erarbeitet und drittens diese Methode an drei Pilotstandorten mit unterschiedlichem Ausbauzustand erprobt werden. Die Rahmenbedingungen des Konzepts grenzen die Begriffe der VulnerabilitĂ€t, der physischen VulnerabilitĂ€t und der physischen AnfĂ€lligkeit gegenĂŒber Hochwasser ein und erörtern deren Beziehung zur sozialen und ökonomischen VulnerabilitĂ€t. Es werden die Merkmale der physischen AnfĂ€lligkeit von GebĂ€uden gegenĂŒber Hochwasser als Bestandteil der VulnerabilitĂ€t definiert. Das methodische Verfahren umfasst drei Module: (i) Methoden zur Erstellung einer GebĂ€udetypologie, (ii) Methoden zur Bewertung der AnfĂ€lligkeit reprĂ€sentativer GebĂ€ude jedes GebĂ€udetyps und (iii) die Kombination der beiden Module mit Hilfe technologischer Hilfsmittel. Das erste Modul zur GebĂ€udetypologie basiert auf der Klassifizierung von Fernerkundungsdaten und GIS-Analysen anhand von sieben GebĂ€udeparametern, die sich fĂŒr die Klassifizierung von GebĂ€uden bezĂŒglich ihres Risikopotenzials bei Hochwasser als wichtig erweisen. Daraus ergibt sich ein Ansatz zur GebĂ€udeklassifizierung. Die anschließende Ermittlung reprĂ€sentativer GebĂ€ude beruht auf statistischen Analysen und Zugehörigkeitsfunktionen. Das zweite Modul zur AnfĂ€lligkeit reprĂ€sentativer GebĂ€ude beruht auf der Ableitung von Funktion von Wasserstand und physischer Einwirkung. Es setzt die relevanten GebĂ€udemerkmale, darunter Höhe, Maße und Materialien, in Beziehung zum erwartbaren Schaden bei unterschiedlichen WasserstĂ€nden. Die MaterialanfĂ€lligkeit wird aufgrund internationaler Studien zur Festigkeit von Baustoffen sowie durch Anwendung eines Fuzzy-Logic-Expertensystems eingeschĂ€tzt. Anschließend werden Wasserstand-Schaden-Funktionen unter Einbeziehung der HauptgebĂ€udekomponenten berechnet, die durch unterschiedliche WasserstĂ€nde in Mitleidenschaft gezogen werden können. Funktion von Wasserstand und physischer Einwirkung dienen hier dazu, den jeweiligen Wasserstand und die physischen Auswirkung in Beziehung zueinander zu setzen. Das dritte Modul stellt die zur Umsetzung der Methoden notwendigen Hilfsmittel vor. Zur UnterstĂŒtzung des automatisierten Verfahrens dienen Hilfsmittel, die die GebĂ€udetypologie mit der Funktion von Wasserstand und physischer Einwirkung fĂŒr GebĂ€ude in Hochwassergebieten kombinieren. Die Methoden wurden anschließend in drei hochwassergefĂ€hrdeten Pilotstandorten getestet: (i) in den Siedlungsgebieten von Barrio Sur in MaganguĂ© und (ii) von La Pena in Cicuco, zwei Überschwemmungsgebiete des Magdalenas in Kolumbien, und (iii) im Stadtgebiet von Dresden, das an der Elbe liegt. Das Testverfahren umfasst die Beschreibung der DatenverfĂŒgbarkeit und genauigkeit, die einzelnen Schritte zur Analyse der. Funktion von Wasserstand und physischer Einwirkung reprĂ€sentativer GebĂ€ude sowie die Darstellung der rĂ€umlichen Verteilung der physischen AnfĂ€lligkeit fĂŒr Hochwasser. In der Diskussion wird der Beitrag dieser Arbeit zur Beurteilung der Erkenntnisse der getesteten Methoden anhand der Ziele dieser Dissertation analysiert. Die Folgerungen beleuchten abschließend die Fortschritte und auch Grenzen der Forschung hinsichtlich methodischer und empirischer Entwicklungen sowie deren allgemeine Anwendbarkeit im Bereich des Hochwasserschutzes.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – MaganguĂ©, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203El impacto de las inundaciones sobre los edificios en zonas urbanas es cada vez mayor debido a la intensificaciĂłn de los fenĂłmenos meteorolĂłgicos extremos, asentamientos no controlados o no planificados y su creciente vulnerabilidad. Hay mĂ©todos disponibles para evaluar los daños por inundaciĂłn en edificios e infraestructuras crĂ­ticas. Sin embargo, es muy difĂ­cil implementar estos mĂ©todos sistemĂĄticamente en grandes ĂĄreas debido a la falta de clasificaciĂłn y caracterizaciĂłn de estructuras construidas en resoluciones detalladas. Para superar este obstĂĄculo, este trabajo se enfoca, en primer lugar, en desarrollar un marco conceptual para comprender la vulnerabilidad y susceptibilidad fĂ­sica de edificios por inudaciones, en segundo lugar, en desarrollar un marco metodolĂłgico para la combinaciĂłn de los mĂ©todos y herramientas para una anĂĄlisis de alta resoluciĂłn y en tercer lugar, la prueba de la metodologĂ­a en tres sitios experimentales, con distintas condiciones de desarrollo. El marco conceptual se enfoca en comprender la vulnerabilidad y susceptibility de las edificaciones frente a inundaciones, y su relaciĂłn con la vulnerabilidad social y econĂłmica. En Ă©l se describen las principales caracterĂ­sticas fĂ­sicas de la susceptibilidad de edificicaiones como un componente de la vulnerabilidad. El marco metodolĂłgico consta de tres mĂłdulos: (i) mĂ©todos para la derivaciĂłn de topologĂ­a de construcciones, (ii) mĂ©todos para evaluar la susceptibilidad de edificios representativos y (iii) la integraciĂłn de los dos mĂłdulos a travĂ©s herramientas tecnolĂłgicas. El primer mĂłdulo de topologĂ­a de construcciones se basa en una clasificaciĂłn de datos de sensoramiento rĂ©moto y procesamiento SIG para la extracciĂłn de siete parĂĄmetros de las edficaciones. Este mĂłdulo parece ser aplicable para una clasificaciĂłn de los edificios en relaciĂłn con los posibles impactos de las inundaciones. El resultado es una taxonomĂ­a de las edificaciones y una posterior identificaciĂłn de edificios representativos que se basa en anĂĄlisis estadĂ­sticos y funciones de pertenencia. El segundo mĂłdulo consiste en el anĂĄlisis de susceptibilidad de las construcciones representativas a travĂ©s de funciones de profundidad del impacto fĂ­sico. Las cuales relacionan los principales componentes de la construcciĂłn, incluyendo sus alturas, dimensiones y materiales con los impactos fĂ­sicos a diferentes niveles de agua. La susceptibilidad del material se calcula con base a estudios internacionales sobre la resistencia de los materiales y un anĂĄlisis a travĂ©s de sistemas expertos difusos. AquĂ­, las funciones de profundidad de impacto fĂ­sico son considerados como un medio para la interrelaciĂłn entre el nivel del agua y los impactos fĂ­sicos. El tercer mĂłdulo proporciona las herramientas necesarias para la aplicaciĂłn de la metodologĂ­a. Estas herramientas tecnolĂłgicas consisten en la arquitectura para la alimentaciĂłn de los datos relacionados a la tipologĂ­a de construcciones con las funciones de profundidad del impacto fĂ­sico apoyado en procesos automĂĄticos. La metodologĂ­a es probada en tres sitios piloto: (i) en el Barrio Sur en MaganguĂ© y (ii) en la barrio de La Peña en Cicuco situado en la llanura inundable del RĂ­o Magdalena, Colombia y (iii) en barrio Kleinzschachwitz de la ciudad de Dresden, situado a orillas del rĂ­o Elba, en Alemania. Las pruebas de la metodologĂ­a abarca la descripciĂłn de la disponibilidad de los datos y la precisiĂłn, los pasos a seguir para obtener las funciones profundidad de impacto fĂ­sico de edificios representativos y la presentaciĂłn final de la distribuciĂłn espacial de la susceptibilidad fĂ­sica frente inundaciones El discusiĂłn analiza las aportaciones de este trabajo y evalua los resultados de la metodologĂ­a con relaciĂłn a los objetivos. Las conclusiones del trabajo, muestran los aportes y limitaciones de la investigaciĂłn en tĂ©rminos de avances metodolĂłgicos y empĂ­ricos y la aplicabilidad general de gestiĂłn del riesgo de inundaciones.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – MaganguĂ©, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 20

    Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling

    Get PDF
    The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stress-minimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA

    Effective Method of Image Retrieval Using BTC with Gabor Wavelet Matrix

    Get PDF
    emergence of multimedia technology and the rapidly expanding image collections on the database have attracted significant research efforts in providing tools for effective retrieval and management of visual data. The need to find a desired image from a large collection. Image retrieval is the field of study concerned with searching and retrieving digital image from a collection of database .In real images, regions are often homogenous; neighboring pixels usually have similar properties (shape, color, texture). In this paper we proposed novel image retrieval based on Block Truncation Coding (BTC) with Gabor wavelet co-occurrence matrix. For image retrieval the features like shape, color, texture, spatial relation, and correlation and Eigen values are considered. BTC can be used for grayscale as well as for color images. The average precision and recall of all queries are computed and considered for performance analysis

    Lightweight Machine Learning with Brain Signals

    Full text link
    Electroencephalography(EEG) signals are gaining popularity in Brain-Computer Interface(BCI) systems and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks(CNNs) or graph neural networks(GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account; 3) navigating and training deep learning model with the most critical EEG channels; 4) detecting most similar EEG segments with target subject to reduce the cost of computation as well as inter-subject variability. Specifically, we construct a task-adaptive graph representation of brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. Lastly, contributory EEG segments are detected by several similarity estimation metrics, we then evaluate and train our proposed framework upon detected EEG segments to compare the performance of different metrics in EEG BCI tasks. We empirically show that our proposed approach, SIFT-EEG, outperforms state-of-the-art, with around 4% and 7% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data. Moreover, the best-performed metric can achieve a high level of accuracy with less than 9% training data, suggesting a possible shift in direction for future works other than simply scaling up the model

    Context-sensitive interpretation of natural language location descriptions : a thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy in Information Technology at Massey University, Auckland, New Zealand

    Get PDF
    People frequently describe the locations of objects using natural language. Location descriptions may be either structured, such as 26 Victoria Street, Auckland, or unstructured. Relative location descriptions (e.g., building near Sky Tower) are a common form of unstructured location description, and use qualitative terms to describe the location of one object relative to another (e.g., near, close to, in, next to). Understanding the meaning of these terms is easy for humans, but much more difficult for machines since the terms are inherently vague and context sensitive. In this thesis, we study the semantics (or meaning) of qualitative, geospatial relation terms, specifically geospatial prepositions. Prepositions are one of the most common forms of geospatial relation term, and they are commonly used to describe the location of objects in the geographic (geospatial) environment, such as rivers, mountains, buildings, and towns. A thorough understanding of the semantics of geospatial relation terms is important because it enables more accurate automated georeferencing of text location descriptions than use of place names only. Location descriptions that use geospatial prepositions are found in social media, web sites, blogs, and academic reports, and georeferencing can allow mapping of health, disaster and biological data that is currently inaccessible to the public. Such descriptions have unstructured format, so, their analysis is not straightforward. The specific research questions that we address are: RQ1. Which geospatial prepositions (or groups of prepositions) and senses are semantically similar? RQ2. Is the role of context important in the interpretation of location descriptions? RQ3. Is the object distance associated with geospatial prepositions across a range of geospatial scenes and scales accurately predictable using machine learning methods? RQ4. Is human annotation a reliable form of annotation for the analysis of location descriptions? To address RQ1, we determine the nature and degree of similarity among geospatial prepositions by analysing data collected with a human subjects experiment, using clustering, extensional mapping and t-stochastic neighbour embedding (t-SNE) plots to form a semantic similarity matrix. In addition to calculating similarity scores among prepositions, we identify the senses of three groups of geospatial prepositions using Venn diagrams, t-sne plots and density-based clustering, and define the relationships between the senses. Furthermore, we use two text mining approaches to identify the degree of similarity among geospatial prepositions: bag of words and GloVe embeddings. By using these methods and further analysis, we identify semantically similar groups of geospatial prepositions including: 1- beside, close to, near, next to, outside and adjacent to; 2- across, over and through and 3- beyond, past, by and off. The prepositions within these groups also share senses. Through is recognised as a specialisation of both across and over. Proximity and adjacency prepositions also have similar senses that express orientation and overlapping relations. Past, off and by share a proximal sense but beyond has a different sense from these, representing on the other side. Another finding is the more frequent use of the preposition close to for pairs of linear objects than near, which is used more frequently for non-linear ones. Also, next to is used to describe proximity more than touching (in contrast to other prepositions like adjacent to). Our application of text mining to identify semantically similar prepositions confirms that a geospatial corpus (NCGL) provides a better representation of the semantics of geospatial prepositions than a general corpus. Also, we found that GloVe embeddings provide adequate semantic similarity measures for more specialised geospatial prepositions, but less so for those that have more generalised applications and multiple senses. We explore the role of context (RQ2) by studying three sites that vary in size, nature, and context in London: Trafalgar Square, Buckingham Palace, and Hyde Park. We use the Google search engine to extract location descriptions that contain these three sites with 9 different geospatial prepositions (in, on, at, next to, close to, adjacent to, near, beside, outside) and calculate their acceptance profiles (the profile of the use of a preposition at different distances from the reference object) and acceptance thresholds (maximum distance from a reference object at which a preposition can acceptably be used). We use these to compare prepositions, and to explore the influence of different contexts. Our results show that near, in and outside are used for larger distances, while beside, adjacent to and at are used for smaller distances. Also, the acceptance threshold for close to is higher than for other proximity/adjacency prepositions such as next to, adjacent to and beside. The acceptance threshold of next to is larger than adjacent to, which confirms the findings in ‎Chapter 2 which identifies next to describing a proximity rather than touching spatial relation. We also found that relatum characteristics such as image schema affect the use of prepositions such as in, on and at. We address RQ3 by developing a machine learning regression model (using the SMOReg algorithm) to predict the distance associated with use of geospatial prepositions in specific expressions. We incorporate a wide range of input variables including the similarity matrix of geospatial prepositions (RQ1); preposition senses; semantic information in the form of embeddings; characteristics of the located and reference objects in the expression including their liquidity/solidity, scale and geometry type and contextual factors such as the density of features of different types in the surrounding area. We evaluate the model on two different datasets with 25% improvement against the best baseline respectively. Finally, we consider the importance of annotation of geospatial location descriptions (RQ4). As annotated data is essential for the successful study of automated interpretation of natural language descriptions, we study the impact and accuracy of human annotation on different geospatial elements. Agreement scores show that human annotators can annotate geospatial relation terms (e.g., geospatial prepositions) with higher agreement than other geospatial elements. This thesis advances understanding of the semantics of geospatial prepositions, particularly considering their semantic similarity and the impact of context on their interpretation. We quantify the semantic similarity of a set of 24 geospatial prepositions; identify senses and the relationships among them for 13 geospatial prepositions; compare the acceptance thresholds of 9 geospatial prepositions and describe the influence of context on them; and demonstrate that richer semantic and contextual information can be incorporated in predictive models to interpret relative geospatial location descriptions more accurately
    • 

    corecore