Relevant bibliographies by topics / Geographic knowledge discovery

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Geographic knowledge discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Geographic knowledge discovery"

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Peuquet, Donna J., and Menno-Jan Kraak. "Geobrowsing: Creative Thinking and Knowledge Discovery Using Geographic Visualization." Information Visualization 1, no. 1 (2002): 80–91. http://dx.doi.org/10.1057/palgrave.ivs.9500007.

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In the modern computing context, the map is no longer just a final product. Maps are now being used in a fundamentally different way – as a self-directed tool for deriving the desired information from geographic data. This, along with developments in GIScience and computer graphics, have led to the new field of geographic visualization. A central issue is how to design visualization capabilities that, as a process, facilitate creative thinking for discovering previously new information from large databases. The authors propose the term ‘geobrowsing’ to designate this process. A number of interrelated ways that visualization can be used to spark the imagination in order to derive new insights are discussed and a brief example provided. Based upon the cognitive literature, specific properties of a visual image that promote discovery and insight are discussed. These are known as preinventive properties, and include; novelty, incongruence, abstraction, and ambiguity. All of these properties, either individually or in combination, tend to produce features that are unanticipated by the viewer, and often not explicitly created or anticipated by the person generating the visual display. While traditional (i.e. non-computer generated) images can also possess these properties, as shown in the historical examples in this discussion, it is the capability of the viewer to directly and quickly manipulate these properties that provides the real power of ‘geobrowsing’ for uncovering new insights.
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Peuquet, Donna J., and Menno-Jan Kraak. "Geobrowsing: creative thinking and knowledge discovery using geographic visualization." Information Visualization 1, no. 1 (2002): 80–91. http://dx.doi.org/10.1057/palgrave/ivs/9500007.

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Mennis, Jeremy, and Diansheng Guo. "Spatial data mining and geographic knowledge discovery—An introduction." Computers, Environment and Urban Systems 33, no. 6 (2009): 403–8. http://dx.doi.org/10.1016/j.compenvurbsys.2009.11.001.

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Mennis, Jeremy, and Donna J. Peuquet. "The Role of Knowledge Representation in Geographic Knowledge Discovery: A Case Study." Transactions in GIS 7, no. 3 (2003): 371–91. http://dx.doi.org/10.1111/1467-9671.00151.

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Guo, Diansheng, Mark Gahegan, Alan M. MacEachren, and Biliang Zhou. "Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach." Cartography and Geographic Information Science 32, no. 2 (2005): 113–32. http://dx.doi.org/10.1559/1523040053722150.

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Lee, Ickjai, and Christopher Torpelund-Bruin. "Geographic knowledge discovery from Web Map segmentation through generalized Voronoi diagrams." Expert Systems with Applications 39, no. 10 (2012): 9376–88. http://dx.doi.org/10.1016/j.eswa.2012.02.129.

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Gahegan, Mark, Monica Wachowicz, Mark Harrower, and Theresa-Marie Rhyne. "The Integration of Geographic Visualization with Knowledge Discovery in Databases and Geocomputation." Cartography and Geographic Information Science 28, no. 1 (2001): 29–44. http://dx.doi.org/10.1559/152304001782173952.

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Janowicz, Krzysztof, Song Gao, Grant McKenzie, Yingjie Hu, and Budhendra Bhaduri. "GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond." International Journal of Geographical Information Science 34, no. 4 (2019): 625–36. http://dx.doi.org/10.1080/13658816.2019.1684500.

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Ganapathy, Jayanthi, and Uma V. "Reasoning Temporally Attributed Spatial Entity Knowledge Towards Qualitative Inference of Geographic Process." International Journal of Intelligent Information Technologies 15, no. 2 (2019): 32–53. http://dx.doi.org/10.4018/ijiit.2019040103.

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Knowledge discovery with geo-spatial information processing is of prime importance in geomorphology. The temporal characteristics of evolving geographic features result in geo-spatial events that occur at a specific geographic location. Those events when consecutively occur result in a geo-spatial process that causes a phenomenal change over the period of time. Event and process are essential constituents in geo-spatial dynamism. The geo-spatial data acquired by remote sensing technology is the source of input for knowledge discovery of geographic features. This article performs qualitative inference of geographic process by identifying events causing geo-spatial deformation over time. The evolving geographic features and their types have association with spatial and temporal factors. Event calculus-based spatial knowledge formalism allows reasoning over intervals of time. Hence, representation of Event Attributed Spatial Entity (EASE) Knowledge is proposed. Logical event-based queries are evaluated on the formal representation of EASE Knowledge Base. Event-based queries are executed on the proposed knowledge base and when experimented on, real data sets yielded comprehensive results. Further, the significance of EASE-based spatio-temporal reasoning is proved by evaluating with respect to query processing time and accuracy. The enhancement of EASE with a direction for further development to explore its significance towards prediction is discussed towards the end.
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Karl, Jason W. "Mining location information from life- and earth-sciences studies to facilitate knowledge discovery." Journal of Librarianship and Information Science 51, no. 4 (2018): 1007–21. http://dx.doi.org/10.1177/0961000618759413.

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Location information in published studies represents an untapped resource for literature discovery, applicable to a range of domains. The ability to easily discover scientific articles from specific places, nearby locales, or similar (but geographically separate) areas worldwide is important for advancing science and addressing global sustainability challenges. However, the thematic and not geographic nature of current search tools makes location-based searches challenging and inefficient. Manually geolocating studies is labor intensive, and place-name recognition algorithms have performed poorly due to prevalence of irrelevant place names in scientific articles. These challenges have hindered past efforts to create map-based literature search tools. Thus, automated approaches are needed to sustain article georeferencing efforts. Common pattern-matching algorithms (parsers) can be used to identify and extract geographic coordinates from the text of published articles. Pattern-matching algorithms (geoparsers) were developed using regular expressions and lexical parsing and tested their performance against sets of full-text articles from multiple journals that were manually scanned for coordinates. Both geoparsers performed well at recognizing and extracting coordinates from articles with accuracy ranging from 85.1% to 100%, and the lexical geoparser performing marginally better. Omission errors (i.e. missed coordinates) were 0% to 14.9% for the regular expression geoparser and 0% to 10.3% for the lexical geoparser. Only a single commission error (i.e. erroneous coordinate) was encountered with the lexical geoparser. The ability to automatically identify and extract location information from published studies opens new possibilities for transforming scientific literature discovery and supporting novel research.
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Dissertations / Theses on the topic "Geographic knowledge discovery"

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Tao, Jia. "Exploring Massive Volunteered Geographic Information for Geographic Knowledge Discovery." Licentiate thesis, KTH, Geoinformatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-27034.

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Conventionally geographic data produced and disseminated by the national mapping agencies are used for studying various urban issues. These data are not commonly available or accessible, but also are criticized for being expensive. However, this trend is changing along with the rise of Volunteered Geographic Information (VGI). VGI, known as user generated content, is the geographic data collected and disseminated by individuals at a voluntary basis. So far, a huge amount of geographic data has been collected due to the increasing number of contributors and volunteers. More importantly, they are free and accessible to anyone.   There are many formats of VGI such as Wikimapia, Flickr, GeoNames and OpenStreetMap (OSM). OSM is a new mapping project contributed by volunteers via a wiki-like collaboration, which is aimed to create free, editable map of the entire world. This thesis adopts OSM as the main data source to uncover the hidden patterns around the urban systems. We investigated some fundamental issues such as city rank size law and the measurement of urban sprawl. These issues were conventionally studied using Census or satellite imagery data.   We define the concept of natural cities in order to assess city size distribution. Natural cities are generated in a bottom up manner via the agglomeration of individual street nodes. This clustering process is dependent on one parameter called clustering resolution. Different clustering resolutions could derive different levels of natural cities. In this respect, they show little bias compared to city boundaries imposed by Census bureau or extracted from satellite imagery. Based on the investigation, we made two findings about rank size distributions. The first one is that all the natural cities in US follow strictly Zipf’s law regardless of the clustering resolutions, which is different from other studies only investigating a few largest cities. The second one is that Zipf’s law is not universal at the state level, e.g., Zipf’s law for natural cities within individual states does not hold valid.   This thesis continues to detect the sprawling based on natural cities. Urban sprawl devours large amount of open space each year and subsequently leads to many environmental problems. To curb urban sprawl with proper policies, a major problem is how to objectively measure it. In this thesis, a new approach is proposed to measure urban sprawl based on street nodes. This approach is based on the fact that street nodes are significantly correlated with population in cities. Specifically, it is reported that street nodes have a linear relationship with city sizes with correlation coefficient up to 0.97. This linear regression line, known as sprawl ruler, can partition all cities into the sprawling, compact and normal cities. This study verifies this approach with some US census data and US natural cities. Based on the verification, this thesis further applies it to three European countries: France, Germany and UK, and consequently categorizes all natural cities into three classes: sprawling, compact and normal. This categorization provides a new insight into the sprawling detection and sets a uniform standard for cross comparing sprawling level across an entire country.<br>QC 20101206
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Christopoulou, K. "A geographic knowledge discovery approach to property valuation." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/14871/.

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This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality.
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Jia, Tao. "Geospatial Knowledge Discovery using Volunteered Geographic Information : a Complex System Perspective." Doctoral thesis, KTH, Geodesi och geoinformatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104783.

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The continuous progression of urbanization has resulted in an increasing number of people living in cities or towns. In parallel, advancements in technologies, such as the Internet, telecommunications, and transportation, have allowed for better connectivity among people. This has engendered drastic changes in urban systems during the recent decades. From a social geographic perspective, the changes in urban systems are primarily characterized by intensive contacts among people and their interactions with the surrounding urban environment, which further leads to subsequent challenging problems such as traffic jams, environmental pollution, urban sprawl, etc. These problems have been reported to be heterogeneous and non-deterministic. Hence, to cope with them, massive amounts of geographic data are required to create new knowledge on urban systems. Due to the thriving of Volunteer Geographic Information (VGI) in recent years, this thesis presents knowledge on urban systems based on extensive VGI datasets from three sources: highway dataset from the OpenStreetMap (OSM) project, photo location dataset from the Flickr website, and GPS tracking datasets from volunteers, taxicabs, and air flights. The knowledge primarily relates to two issues of urban systems: the urban space and the corresponding human dynamics. In accordance, on one hand, urban space acts as a carrier for associated geographic activities and knowledge of it benefits our understanding of current social and economic problems in urban systems. On the other hand, human dynamics reflect human behavior in urban space, which leads to complex mobility or activity patterns. Its investigation allows a derivation of the underlying driving force that is very instructive to urban planning, traffic management, and infectious disease control. Therefore, to fully understand the two issues, this thesis conducts a thorough investigation from multiple aspects. The first issue is investigated from four aspects. First, at the city level, the controversial topic of city size regularity is investigated in terms of natural cities, and the conclusion is that Zipf’s law holds stably for all US cities. Second, at the sub-city level, the size distribution of spatial units within different cities in terms of the clusters formed by street nodes, photo locations, and taxi static points are explored, and the result shows a remarkable scaling property of these spatial units. Third, enlightened by the scaling property of the urban space at the city or sub-city level, this thesis devises a novel tool that can demarcate the cities into three categories: compact cities, normal cities, and sprawling cities. The tool is then applied to cities in both the US and three European countries. In the last, another representation of urban space is taken into account, namely the transportation network. The findings report that the US airport network displays the properties of scale-free, small-world, and disassortative mixing and that the individual natural airports show heterogeneous patterns that are probably subject to geographic constraints and socioeconomic factors. The second issue is examined from four perspectives. First, at the city level, the movement flow contributed by agents using two types of behavior is investigated through an agent-based simulation, and the result conjectures that the human mobility behavior is mainly shaped by the underlying street network. Second, at the country level, this thesis reports that the human travel length by air can be approximated well by an exponential distribution, and subsequent simulations indicate that human mobility behavior is largely constrained by the underlying airport network. Third, at the regional level, the length that humans travel by car is demonstrated to agree well with a power law with exponential cutoff distribution, and subsequent simulation further reproduces this levy flight characteristic. Based on the simulation, human mobility behavior is again revealed to be primarily shaped by the underlying hierarchical spatial structure. Finally, taxicab static points are adopted to explore human activity patterns, which can be characterized as the regularities in space and time, the heterogeneity and predictability in space. From a complex system perspective, this thesis presents the knowledge discovered in urban systems using massive volumes of geographic data. Together with new knowledge from empirical findings, the development of methods, and the design of theoretic models, this thesis also shares the research community with geographic data generated from extensive VGI datasets and the corresponding source codes. Moreover, this study is aligned with a paradigm shift in that it analyzes large-size datasets using high processing power as opposed to analyzing small-size datasets with low processing power.<br><p>QC 20121113</p>
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McCollister, Christopher Michael. "Geographic knowledge discovery techniques for exploring historical weather and avalanche data." Thesis, Montana State University, 2004. http://etd.lib.montana.edu/etd/2004/mccollister/McCollisterC0805.pdf.

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Sengstock, Christian [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data / Christian Sengstock ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180395662/34.

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Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.

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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial.<br>The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
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Lindsey, Daniel Clayton. "A Geospatial Analysis of the Northeastern Plains Village Complex: An Exploration of a GIS-Based Multidisciplinary Method for the Incorporation of Western and Traditional Ecological Knowledge into the Discovery of Diagnostic Prehistoric Settlement Patterns." Thesis, North Dakota State University, 2019. https://hdl.handle.net/10365/31623.

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This thesis research analyzes how Traditional Ecological Knowledge (TEK) can be used to understand extant Northeastern Plains Village (NEPV) settlement strategies in aggregate for the purposes of subjoining a subsequent verification metric to the current archaeological classification system used to describe NEPV associated sites. To accomplish this task, I extracted Traditional Ecological Knowledge from ethnographic sources for comparison to geospatial, geostatistical, and statistical analyses. My results show that the hierarchical clustering exhibited among NEPV sites is congruent with first person narratives of habitation and resource collection activities occurring in the pre-Reservation period (before AD 1880) within the research area. This study emphasizes the importance of the incorporation of Traditional Ecological Knowledge into material typological classification schemes for archaeological sites which are convoluted by a high rates of cultural transmission.
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Pedrosa, Klebber de Araújo. "GeoMiningVisualQL: uma linguagem de consulta visual para mineração de dados geográficos." Universidade Federal da Paraí­ba, 2010. http://tede.biblioteca.ufpb.br:8080/handle/tede/6146.

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Made available in DSpace on 2015-05-14T12:36:56Z (GMT). No. of bitstreams: 1 parte1.pdf: 1854774 bytes, checksum: 9564eb94b101d580f9879bf9c9422f98 (MD5) Previous issue date: 2010-08-10<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Several areas of knowledge domain, such as remote sensing systems, transportation, telecommunication, digital mapping, among others, make use of large amounts of geographic data. Typically, these data are stored in Management Systems Geographic Database (SGBDGeo), through which can be often manipulated by Geographic Information Systems (GIS). However, these systems are not able to extract new information, previously unknown to users, which may be embedded within the database field analysed and that, somehow, represent new and userful knowledge, for example, for decision making. In this case, it is necessary to make use of specific techniques of Knowledge Discovery in Databases (KDD). Moreover, spatial data present inherently visual characteristics that, often, can be associated with geometric and pictographic visual representations. In this context, there are few visual query languages for spatial data. However, few of this treat mining methods among the spatial data. Thus, this paper proposes the construction of an environment for data mining tasks performed under certain geographical areas, beyond the formal specification of a visual query language to be used in this environment. These queries are formulated through pictorial representations of geographic features, operators, and spatial relationships between these data. To this end, we use metaphorical abstractions on the metadata of the geographical environment, and the approach defined as "flowing stream" in which the user focuses attention on certain stages of the mining process, facilitating the construction of these consultations a number of them. Thus, the proposed environment aims to simplify the tasks of consultations on mining spatial data, making them more user friendly, providing more efficiency and speed when compared to textual queries scripts.<br>Diversas áreas de domínio de conhecimento, tais como os sistemas de sensoriamente remoto, transportes, telecomunicações, cartografia digital, entre outras, fazem uso de uma grande quantidade de dados geográficos. Normalmente, esses dados são armazenados em Sistemas Gerenciadores de Banco de Dados Geográficos (SGBDGeo), através dos quais, muitas vezes, podem ser manipulados por Sistemas de Informações Geográficas (SIG). Entretanto, esses sistemas não são capazes de extrair novas informações, previamente desconhecidas pelos usuários, as quais podem estar embutidas dentro da base de dados do domínio analisado e que, de certo modo, representam algum conhecimento novo e de grande utilidade, por exemplo, para tomadas de decisões. Neste caso, é necessário fazer uso de técnicas específicas de Descoberta de Conhecimento em Banco de Dados (DCBD ou KDD, Knowledge Discovery in Database). Além disso, os dados geográficos apresentam características inerentemente visuais que, muitas vezes, podem ser associados a representações visuais geométricas ou pictográficas. Nesse contexto, existem algumas linguagens de consultas visuais para dados geográficos. Todavia, poucas delas tratam métodos de mineração espacial entre os dados. Desta forma, este trabalho propõe a construção de um ambiente para as tarefas de mineração de dados realizada sob certos domínios geográficos, além da especificação formal de uma linguagem de consulta visual a ser usada neste ambiente. Estas consultas são formuladas através de representações pictóricas de feições geográficas, operadores e relacionamentos espaciais existentes entre estes dados. Para tal, utilizam-se abstrações metafóricas sobre os metadados do ambiente geográfico, além da abordagem definida como fluxo corrente na qual o usuário foca a sua atenção em determinadas etapas do processo de mineração, facilitando a construção destas consultas por parte dos mesmos. Desta forma, o ambiente proposto tem como objetivo simplificar as consultas sobre tarefas de mineração de dados geográficos, tornando-as mais amigáveis aos usuários, concedendo mais eficiência e rapidez quando se comparado aos scripts textuais de consultas.
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"GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53688.

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abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.<br>Dissertation/Thesis<br>Doctoral Dissertation Geography 2019
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Chen, Chih-Yuan, and 陳致元. "Applying Geovisualization Techniques in Enhancing Knowledge Discovery Framework for Geographical Databases-The Case Study of Traffic Flow in Taipei City." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/25095610779799100060.

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博士<br>國立臺灣大學<br>地理環境資源學研究所<br>99<br>Geovisualization is a method of exploring spatial knowledge hidden in multidimensional geographic and temporal data via interaction with map and graph. The visualization of Self-Organizing Map (SOM) is one of the most effective methods but still has problems of what size the network should be. This research proposed a novel method called “Divide and Regroup”, to integrate clustering analysis and two SOM algorithms (SOM and Geo-SOM) interactively and dynamically for finding hidden data relations and spatial patterns. Two different rush-hour traffic flow data of Taipei City were selected and two cases were done to demonstrate the effectiveness of this novel method. In the first case, the correlation coefficient and coefficient of determination of the unclassified data were low. Two major groups of traffic flow data were recognized using the Geovisualization approach. The correlation coefficients and coefficients of determination of the classified data have improved significantly. In the second case, six major spatial clusters and liner feature groups were recognized. Furthermore, these groups showed different data patterns indicate that the Geovisualization approach is useful for identifying spatial and data characteristics hidden in geographic data. The results demonstrated the effectiveness of the novel method of Geovisualization.
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Books on the topic "Geographic knowledge discovery"

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Miller, Harvey J., and Jiawei Han. Geographic Data Mining and Knowledge Discovery. Taylor & Francis, 2001. http://dx.doi.org/10.4324/9780203468029.

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Sui, Daniel. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. Springer Netherlands, 2013.

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Lakshmanan, Valliappa. Automating the Analysis of Spatial Grids: A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications. Springer Netherlands, 2012.

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Places, Center for American, ed. Erikson, Eskimos & Columbus: Medieval European knowledge of America. Johns Hopkins University Press, 2002.

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La información colombina y el descubrimiento de América. Fundación CITEMA, 1992.

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Esparza, José Ruiz de. América 500 años. Salvat, ciencia y cultura latinoamericana, 1992.

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Cuenca, Luis Miguel Coín. Una travesía de 20 días a dos rumbos que cambió el mundo. Universidad de Cádiz, Servicio de Publicaciones, 2003.

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Christopher Columbus, cosmographer: A history of metrology, geodesy, geography, and exploration from antiquity to the Columbian era. Landmark Enterprises, 1987.

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Columbus und seine Zeit. Beck, 2006.

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Wey-Gómez, Nicolás. The tropics of empire: Why Columbus sailed south to the Indies. MIT Press, 2008.

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Book chapters on the topic "Geographic knowledge discovery"

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Miller, Harvey J. "Geographic Knowledge Discovery." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_468-2.

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Miller, Harvey J. "Geographic Knowledge Discovery." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_468.

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Miller, Harvey J. "Geographic Knowledge Discovery." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_468.

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Wang, Shuliang, and Wenzhong Shi. "Data Mining and Knowledge Discovery." In Springer Handbook of Geographic Information. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72680-7_5.

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Lan, Rongqin. "Spatial Data Mining and Knowledge Discovery." In Advances in Cartography and Geographic Information Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0614-4_14.

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Brito, Jaqueline Joice, Thiago Luís Lopes Siqueira, Valéria Cesário Times, Ricardo Rodrigues Ciferri, and Cristina Dutra de Ciferri. "Efficient Processing of Drill-across Queries over Geographic Data Warehouses." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23544-3_12.

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Zhuang, Honglei, Alvin Chin, Sen Wu, Wei Wang, Xia Wang, and Jie Tang. "Inferring Geographic Coincidence in Ephemeral Social Networks." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33486-3_39.

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Salleb, Ansaf, and Christel Vrain. "An Application of Association Rules Discovery to Geographic Information Systems." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_74.

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Wachowicz, M., A. Ligtenberg, C. Renso, and S. Gürses. "Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process." In Mobility, Data Mining and Privacy. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_3.

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Silva, Carolina, Cirano Iochpe, and Paulo Engel. "Applying the Process of Knowledge Discovery in Databases to Identify Analysis Patterns for Reuse in Geographic Database Design." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36127-8_28.

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Conference papers on the topic "Geographic knowledge discovery"

1

Giannotti, Fosca, Dino Pedreschi, and Yannis Theodoridis. "Geographic privacy-aware knowledge discovery and delivery." In the 12th International Conference. ACM Press, 2009. http://dx.doi.org/10.1145/1516360.1516504.

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Torpelund-Bruin, Christopher, and Ickjai Lee. "Geographic Knowledge Discovery from Geo-referenced Web 2.0." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.209.

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Glorio, O., J. Zubcoff, and J. Trujillo. "A model driven framework for geographic knowledge discovery." In 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5293412.

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Appice, Annalisa, Anna Ciampi, Antonietta Lanza, Donato Malerba, Antonella Rapolla, and Luisa Vetturi. "Geographic Knowledge Discovery in INGENS: An Inductive Database Perspective." In 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.120.

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"APPLYING ONTOLOGIES IN THE KNOWLEDGE DISCOVERY IN GEOGRAPHIC DATABASES." In 6th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002634505090512.

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Bernabe Loranca, Maria Beatriz, David Pinto Avendano, Elias Olivares Benitez, Javier Ramirez Rodriguez, and Jose Luis Martinez Flores. "Simulated Annealing and Variable Neighborhood Search Hybrid Metaheuristic for the Geographic Clustering Simulated Annealing and Variable Neighborhood Search Hybrid Metaheuristic for the Geographic Clustering Simulated Annealing and Variable Neighborhood Search Hybrid Metaheuristic for the Geographic." In Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Atlantis Press, 2013. http://dx.doi.org/10.2991/.2013.17.

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de Araujo, Adelson, João Marcos do Valle, and Nélio Cacho. "Geographic Feature Engineering with Points-of-Interest from OpenStreetMap." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010155101160123.

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de Araujo, Adelson, João Marcos do Valle, and Nélio Cacho. "Geographic Feature Engineering with Points-of-Interest from OpenStreetMap." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010155101100117.

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Rolnick, David, Kevin Aydin, Jean Pouget-Abadie, Shahab Kamali, Vahab Mirrokni, and Amir Najmi. "Randomized Experimental Design via Geographic Clustering." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2019. http://dx.doi.org/10.1145/3292500.3330778.

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Luo, XianGang, Qi Ma, and Zhong Xie. "The Organization and Scheduling of Massive Grid Geographic Data." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.229.

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