Rozprawy doktorskie: „Geographical data mining”

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Demšar, Urška. "Exploring geographical metadata by automatic and visual data mining". Licentiate thesis, KTH, Infrastructure, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1779.

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Metadata are data about data. They describe characteristicsand content of an original piece of data. Geographical metadatadescribe geospatial data: maps, satellite images and othergeographically referenced material. Such metadata have twocharacteristics, high dimensionality and diversity of attributedata types, which present a problem for traditional data miningalgorithms.

Other problems that arise during the exploration ofgeographical metadata are linked to the expertise of the userperforming the analysis. The large amounts of metadata andhundreds of possible attributes limit the exploration for anon-expert user, which results in a potential loss ofinformation that is hidden in metadata.

In order to solve some of these problems, this thesispresents an approach for exploration of geographical metadataby a combination of automatic and visual data mining.

Visual data mining is a principle that involves the human inthe data exploration by presenting the data in some visualform, allowing the human to get insight into the data and torecognise patterns. The main advantages of visual dataexploration over automatic data mining are that the visualexploration allows a direct interaction with the user, that itis intuitive and does not require complex understanding ofmathematical or statistical algorithms. As a result the userhas a higher confidence in the resulting patterns than if theywere produced by computer only.

In the thesis we present the Visual data mining tool (VDMtool), which was developed for exploration of geographicalmetadata for site planning. The tool provides five differentvisualisations: a histogram, a table, a pie chart, a parallelcoordinates visualisation and a clustering visualisation. Thevisualisations are connected using the interactive selectionprinciple called brushing and linking.

In the VDM tool the visual data mining concept is integratedwith an automatic data mining method, clustering, which finds ahierarchical structure in the metadata, based on similarity ofmetadata items. In the thesis we present a visualisation of thehierarchical structure in the form of a snowflake graph.

Keywords:visualisation, data mining, clustering, treedrawing, geographical metadata.

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Sandell, Anna. "GIS, data mining and wild land fire data within Räddningstjänsten". Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-543.

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Geographical information systems (GIS), data mining and wild land fire would theoretically be suitable to use together. However, would data mining in reality bring out any useful information from wild land fire data stored within a GIS? In this report an investigation is done if GIS and data mining are used within Räddningstjänsten today in some municipalities of the former Skaraborg. The investigation shows that neither data mining nor GIS are used within the investigated municipalities. However, there is an interest in using GIS within the organisations in the future but also some kind of analysis tool, for example data mining. To show how GIS and data mining could be used in the future within Räddningstjänsten some examples on this were constructed.

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Dong, Zheng. "Automated Extraction and Retrieval of Metadata by Data Mining : a Case Study of Mining Engine for National Land Survey Sweden". Thesis, University of Gävle, Department of Technology and Built Environment, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-6811.

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Metadata is the important information describing geographical data resources and their key elements. It is used to guarantee the availability and accessibility of the data. ISO 19115 is a metadata standard for geographical information, making the geographical metadata shareable, retrievable, and understandable at the global level. In order to cope with the massive, high-dimensional and high-diversity nature of geographical data, data mining is an applicable method to discover the metadata.

This thesis develops and evaluates an automated mining method for extracting metadata from the data environment on the Local Area Network at the National Land Survey of Sweden (NLS). These metadata are prepared and provided across Europe according to the metadata implementing rules for the Infrastructure for Spatial Information in Europe (INSPIRE). The metadata elements are defined according to the numerical formats of four different data entities: document data, time-series data, webpage data, and spatial data. For evaluating the method for further improvement, a few attributes and corresponding metadata of geographical data files are extracted automatically as metadata record in testing, and arranged in database. Based on the extracted metadata schema, a retrieving functionality is used to find the file containing the keyword of metadata user input. In general, the average success rate of metadata extraction and retrieval is 90.0%.

The mining engine is developed in C# programming language on top of the database using SQL Server 2005. Lucene.net is also integrated with Visual Studio 2005 to build an indexing framework for extracting and accessing metadata in database.

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Brindley, Paul. "Generating vague geographic information through data mining of passive web data". Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33722/.

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Vagueness is an inherent property of geographic data. This thesis develops a geocomputational method that demonstrates that vague information has the potential to be incorporated within GIS in straightforward manner. This method applies vagueness to the elements of place: types, names and spatial boundaries, generating vague geographic objects by extracting and filtering the differing opinions and perceptions held within web derived data. The aim of the research is threefold: (1) to investigate an approach to automatically generate vague, probabilistic geographical information concerning place by mining differing perspectives from passive web data; (2) to assure the quality of the vague information produced and test the hypothesis that its results are indistinguishable from directly surveying public opinion; and (3) to demonstrate the value of integrating vague information into geospatial applications via examples of its use. To achieve the first aim, the thesis develops methods to extract differing perspectives of place from web data - constructing (i) vague place type settlement classification and (ii) vague place names and boundaries for ‘neighbourhood’ level units. The methods developed are automated, suitable for generating output at a national scale and use a wide range of different source data to collect the differing opinions. The second aim assesses the quality of the data produced, determining if output extracted from the web was representative of that obtained from asking people directly. Statistical analysis of regression models demonstrates that data were representative of that collected through asking people directly both for vague settlement classifications and vague urban locale boundaries. Importantly, the validation data, drawn from public opinion, also supported the notion that vagueness was omnipresent within geographic information concerning place. The third aim was addressed through the use of case studies in order to demonstrate the added value of such data and subsequent integration of vague geographic objects within other socio-economic data. Critically, the incorporation of vagueness within place models not only add value to geographic data but also improve the accuracy of real-world representations within GIS.

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Adu-Prah, Samuel. "GEOGRAPHIC DATA MINING AND GEOVISUALIZATION FOR UNDERSTANDING ENVIRONMENTAL AND PUBLIC HEALTH DATA". OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/657.

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Within the theoretical framework of this study it is recognized that a very large amount of real-world facts and geospatial data are collected and stored. Decision makers cannot consider all the available disparate raw facts and data. Problem-specific variables, including complex geographic identifiers have to be selected from this data and be validated. The problems associated with environmental- and public-health data are that (1) geospatial components of the data are not considered in analysis and decision making process, (2) meaningful geospatial patterns and clusters are often overlooked, and (3) public health practitioners find it difficult to comprehend geospatial data. Inspired by the advent of geographic data mining and geovisualization in public and environmental health, the goal of this study is to unveil the spatiotemporal dynamics in the prevalence of overweight and obesity in United States youths at regional and local levels over a twelve-year study period. Specific objectives of this dissertation are to (1) apply regionalization algorithms effective for the identification of meaningful clusters that are in spatial uniformity to youth overweight and obesity, and (2) use Geographic Information System (GIS), spatial analysis techniques, and statistical methods to explore the data sets for health outcomes, and (3) explore geovisualization techniques to transform discovered patterns in the data sets for recognition, flexible interaction and improve interpretation. To achieve the goal and the specific objectives of this dissertation, we used data sets from the National Longitudinal Survey of Youth 1997 (NLSY'97) early release (1997-2004), NLSY'97 current release (2005 - 2008), census 2000 data and yearly population estimates from 2001 to 2008, and synthetic data sets. The NLSY97 Cohort database range varied from 6,923 to 8,565 individuals during the period. At the beginning of the cohort study the age of individuals participating in this study was between 12 and 17 years, and in 2008, they were between 24 and 28 years. For the data mining tool, we applied the Regionalization with Dynamically Constrained Agglomerative clustering and Partitioning (REDCAP) algorithms to identify hierarchical regions based on measures of weight metrics of the U.S. youths. The applied algorithms are the single linkage clustering (SLK), average linkage clustering (ALK), complete linkage clustering (CLK), and the Ward's method. Moreover, we used GIS, spatial analysis techniques, and statistical methods to analyze the spatial varying association of overweight and obesity prevalence in the youth and to geographically visualize the results. The methods used included the ordinary least square (OLS) model, the spatial generalized linear mixed model (GLMM), Kulldorff's Scan space-time analysis, and the spatial interpolation techniques (inverse distance weighting). The three main findings for this study are: first, among the four algorithms ALK, Ward and CLK identified regions effectively than SLK which performed very poorly. The ALK provided more promising regions than the rest of the algorithms by producing spatial uniformity effectively related to the weight variable (body mass index). The regionalization algorithm-ALK provided new insights about overweight and obesity, by detecting new spatial clusters with over 30% prevalence. New meaningful clusters were detected in 15 counties, including Yazoo, Holmes, Lincoln, and Attala, in Mississippi; Wise, Delta, Hunt, Liberty, and Hardin in Texas; St Charles, St James, and Calcasieu in Louisiana; Choctaw, Sumter, and Tuscaloosa in Alabama. Demographically, these counties have race/ethnic composition of about 75% White, 11.6% Black and 13.4% others. Second, results from this study indicated that there is an upward trend in the prevalence of overweight and obesity in United States youths both in males and in females. Male youth obesity increased from 10.3% (95% CI=9.0, 11.0) in 1999 to 27.0% (95% CI=26.0, 28.0) in 2008. Likewise, female obesity increased from 9.6% (95% CI=8.0, 11.0) in 1999 to 28.9% (95% CI=27.0, 30.0) during the same period. Youth obesity prevalence was higher among females than among males. Aging is a substantial factor that has statistically highly significant association (p < 0.001) with prevalence of overweight and obesity. Third, significant cluster years for high rates were detected in 2003-2008 (relative risk 1.92, 3.4 annual prevalence cases per 100000, p < 0.0001) and that of low rates in 1997-2002 (relative risk 0.39, annual prevalence cases per 100000, p < 0.0001). Three meaningful spatiotemporal clusters of obesity (p < 0.0001) were detected in counties located within the South, Lower North Eastern, and North Central regions. Counties identified as consistently experiencing high prevalence of obesity and with the potential of becoming an obesogenic environment in the future are Copiah, Holmes, and Hinds in Mississippi; Harris and Chamber, Texas; Oklahoma and McCain, Oklahoma; Jefferson, Louisiana; and Chicot and Jefferson, Arkansas. Surprisingly, there were mixed trends in youth obesity prevalence patterns in rural and urban areas. Finally, from a public health perspective, this research have shown that in-depth knowledge of whether and in what respect certain areas have worse health outcomes can be helpful in designing effective community interventions to promote healthy living. Furthermore, specific information obtained from this dissertation can help guide geographically-targeted programs, policies, and preventive initiatives for overweight and obesity prevalence in the United States.

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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.
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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Demšar, Urška. "Data mining of geospatial data: combining visual and automatic methods". Doctoral thesis, KTH, School of Architecture and the Built Environment (ABE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3892.

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Most of the largest databases currently available have a strong geospatial component and contain potentially useful information which might be of value. The discipline concerned with extracting this information and knowledge is data mining. Knowledge discovery is performed by applying automatic algorithms which recognise patterns in the data.

Classical data mining algorithms assume that data are independently generated and identically distributed. Geospatial data are multidimensional, spatially autocorrelated and heterogeneous. These properties make classical data mining algorithms inappropriate for geospatial data, as their basic assumptions cease to be valid. Extracting knowledge from geospatial data therefore requires special approaches. One way to do that is to use visual data mining, where the data is presented in visual form for a human to perform the pattern recognition. When visual mining is applied to geospatial data, it is part of the discipline called exploratory geovisualisation.

Both automatic and visual data mining have their respective advantages. Computers can treat large amounts of data much faster than humans, while humans are able to recognise objects and visually explore data much more effectively than computers. A combination of visual and automatic data mining draws together human cognitive skills and computer efficiency and permits faster and more efficient knowledge discovery.

This thesis investigates if a combination of visual and automatic data mining is useful for exploration of geospatial data. Three case studies illustrate three different combinations of methods. Hierarchical clustering is combined with visual data mining for exploration of geographical metadata in the first case study. The second case study presents an attempt to explore an environmental dataset by a combination of visual mining and a Self-Organising Map. Spatial pre-processing and visual data mining methods were used in the third case study for emergency response data.

Contemporary system design methods involve user participation at all stages. These methods originated in the field of Human-Computer Interaction, but have been adapted for the geovisualisation issues related to spatial problem solving. Attention to user-centred design was present in all three case studies, but the principles were fully followed only for the third case study, where a usability assessment was performed using a combination of a formal evaluation and exploratory usability.

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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data". ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.

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KINSEY, MICHAEL LOY. "PRIVACY PRESERVING INDUCTION OF DECISION TREES FROM GEOGRAPHICALLY DISTRIBUTED DATABASES". University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1123855448.

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Sengstock, Christian [Verfasser], i 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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Alam, Mohammad Tanveer. "Image Classification for Remote Sensing Using Data-Mining Techniques". Youngstown State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.

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Ivanovic, Stefan. "Quality based approach for updating geographic authoritative datasets from crowdsourced GPS traces". Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1068/document.

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Ces dernières années, le besoin de données géographiques de référence a significativement augmenté. Pour y répondre, il est nécessaire de mettre jour continuellement les données de référence existantes. Cette tâche est coûteuse tant financièrement que techniquement. Pour ce qui concerne les réseaux routiers, trois types de voies sont particulièrement complexes à mettre à jour en continu : les chemins piétonniers, les chemins agricoles et les pistes cyclables. Cette complexité est due à leur nature intermittente (elles disparaissent et réapparaissent régulièrement) et à l’hétérogénéité des terrains sur lesquels elles se situent (forêts, haute montagne, littoral, etc.).En parallèle, le volume de données GPS produites par crowdsourcing et disponibles librement augmente fortement. Le nombre de gens enregistrant leurs positions, notamment leurs traces GPS, est en augmentation, particulièrement dans le contexte d’activités sportives. Ces traces sont rendues accessibles sur les réseaux sociaux, les blogs ou les sites d’associations touristiques. Cependant, leur usage actuel est limité à des mesures et analyses simples telles que la durée totale d’une trace, la vitesse ou l’élévation moyenne, etc. Les raisons principales de ceci sont la forte variabilité de la précision planimétrique des points GPS ainsi que le manque de protocoles et de métadonnées (par ex. la précision du récepteur GPS).Le contexte de ce travail est l’utilisation de traces GPS de randonnées pédestres ou à vélo, collectées par des volontaires, pour détecter des mises à jours potentielles de chemins piétonniers, de voies agricoles et de pistes cyclables dans des données de référence. Une attention particulière est portée aux voies existantes mais absentes du référentiel. L’approche proposée se compose de trois étapes : La première consiste à évaluer et augmenter la qualité des traces GPS acquises par la communauté. Cette qualité a été augmentée en filtrant (1) les points extrêmes à l’aide d’un approche d’apprentissage automatique et (2) les points GPS qui résultent d’une activité humaine secondaire (en dehors de l’itinéraire principal). Les points restants sont ensuite évalués en termes de précision planimétrique par classification automatique. La seconde étape permet de détecter de potentielles mises à jour. Pour cela, nous proposons une solution d’appariement par distance tampon croissante. Cette distance est adaptée à la précision planimétrique des points GPS classifiés pour prendre en compte la forte hétérogénéité de la précision des traces GPS. Nous obtenons ainsi les parties des traces n’ayant pas été appariées au réseau de voies des données de référence. Ces parties sont alors considérées comme de potentielles voies manquantes dans les données de référence. Finalement nous proposons dans la troisième étape une méthode de décision multicritère visant à accepter ou rejeter ces mises à jour possibles. Cette méthode attribue un degré de confiance à chaque potentielle voie manquante. L’approche proposée dans ce travail a été évaluée sur un ensemble de trace GPS multi-sources acquises par crowdsourcing dans le massif des Vosges. Les voies manquantes dans les données de références IGN BDTOPO® ont été détectées avec succès et proposées comme mises à jour potentielles
Nowadays, the need for very up to date authoritative spatial data has significantly increased. Thus, to fulfill this need, a continuous update of authoritative spatial datasets is a necessity. This task has become highly demanding in both its technical and financial aspects. In terms of road network, there are three types of roads in particular which are particularly challenging for continuous update: footpath, tractor and bicycle road. They are challenging due to their intermittent nature (e.g. they appear and disappear very often) and various landscapes (e.g. forest, high mountains, seashore, etc.).Simultaneously, GPS data voluntarily collected by the crowd is widely available in a large quantity. The number of people recording GPS data, such as GPS traces, has been steadily increasing, especially during sport and spare time activities. The traces are made openly available and popularized on social networks, blogs, sport and touristic associations' websites. However, their current use is limited to very basic metric analysis like total time of a trace, average speed, average elevation, etc. The main reasons for that are a high variation of spatial quality from a point to a point composing a trace as well as lack of protocols and metadata (e.g. precision of GPS device used).The global context of our work is the use of GPS hiking and mountain bike traces collected by volunteers (VGI traces), to detect potential updates of footpaths, tractor and bicycle roads in authoritative datasets. Particular attention is paid on roads that exist in reality but are not represented in authoritative datasets (missing roads). The approach we propose consists of three phases. The first phase consists of evaluation and improvement of VGI traces quality. The quality of traces was improved by filtering outlying points (machine learning based approach) and points that are a result of secondary human behaviour (activities out of main itinerary). Remained points are then evaluated in terms of their accuracy by classifying into low or high accurate (accuracy) points using rule based machine learning classification. The second phase deals with detection of potential updates. For that purpose, a growing buffer data matching solution is proposed. The size of buffers is adapted to the results of GPS point’s accuracy classification in order to handle the huge variations in VGI traces accuracy. As a result, parts of traces unmatched to authoritative road network are obtained and considered as candidates for missing roads. Finally, in the third phase we propose a decision method where the “missing road” candidates should be accepted as updates or not. This decision method was made in multi-criteria process where potential missing roads are qualified according to their degree of confidence. The approach was tested on multi-sourced VGI GPS traces from Vosges area. Missing roads in IGN authoritative database BDTopo® were successfully detected and proposed as potential updates

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Hammal, Mohamed Ali. "Contribution à la découverte de sous-groupes corrélés : Application à l’analyse des systèmes territoriaux et des réseaux alimentaires". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI024.

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Mieux nourrir les villes en quantité et en qualité, notamment les grandes agglomérations, constitue un défi majeur dont la résolution passe par une meilleure compréhension des relations entre les populations urbaines et leur alimentation. A l’échelle des systèmes alimentaires urbains, on a besoin de diagnostics ciblant la disponibilité des ressources alimentaires croisée avec les profils socio-économiques des territoires et l’on manque d’outils et de méthodes pour appréhender de façon systématique les relations entre les bassins de consommation, l’offre et les comportements alimentaires. L’objectif de cette thèse est de contribuer à l’élaboration de nouveaux outils informatiques pour traiter des données temporelles, hétérogènes et multi-sources afin d’identifier et de caractériser des comportements propres à une zone géographique. Pour cela, nous nous appuyons sur l’exploration conjointe de motifs graduels, identifiant des corrélations de rang, et de sous-groupes afin de découvrir des contextes pour lesquels les corrélations décrites par les motifs graduels sont exceptionnellement fortes par rapport au reste des données. Nous proposons un algorithme d’énumération s’appuyant sur des propriétés d’élagage avec des bornes supérieures, ainsi qu’un autre algorithme qui échantillonne les motifs selon la mesure de qualité. Ces approches sont validées non seulement sur des jeux de données de référence, mais aussi à travers une étude empirique de laformation des déserts alimentaires sur l’agglomération lyonnaise
Better feeding cities in quantity and quality, especially large cities, is a major challenge, whose resolution requires a better understanding of the relationships between urban populations and their food. On the scale of urban food systems, we need to understand the availability of food resources crossed with the socio-economic profiles of the territories. But we lack tools and methods to systematically understand the relationships between consumption basins, supply and eating habits. The objective of this thesis is to contribute to the development of new IT tools to process temporal, heterogeneous and multi-sources data in order to identify and characterize behaviors specific to a geographic area. For this, we rely on the joint exploration of gradual patterns, to discover rank correlations, and subgroups in order to find contexts for which the correlations described by the gradual patterns are exceptionally strong compared to the remaining of the data. We propose an enumeration algorithm based on pruning properties with upper bounds, as well as another algorithm which samples the patterns according to the quality measure. These approaches are validated not only on benchmark datasets, but also through an empirical study of the formation of food deserts in the Lyon urban area

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Katerattanakul, Nitsawan. "A pilot study in an application of text mining to learning system evaluation". Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Katerattanakul_09007dcc807b614f.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).

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Liew, Jie Shi. "Using Social Media Data Mining To Understand The Public Perception of Coal In The United States". OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2744.

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Coal is a highly controversial energy source that has been widely perceived as environmentally harmful but socially important to regions with historical ties to coal production. Gauging public perception of coal is important to contemporary matters in energy policies like energy transition and the future of coal mining. Previous studies have demonstrated that public perception of coal can be affected by a multitude of geographic and social factors such as the distance to coal mining areas and political ideology. These studies predominantly relied on traditional survey approaches, which are typically cost prohibitive. With its growing popularity in public communication, social media has been recognized as an essential means of crowd-sourcing public perception and opinions. However, there is a general paucity of energy perception studies underpinned by social media, especially public perception of coal. Based on the Twitter data downloaded in August 2019, this thesis mapped the patterns of public perception of coal in the contexts of geographic spaces and social media network using data mining approaches. Generalized linear models were used to examine the quantitative relationship between public perception and explanatory geographic and social variables. The results demonstrate the geographic distance to coal mining regions, social network clusters, and certain social identities (i.e., environmental/renewable communities, Republicans, news and experts) have significant effects on coal-related sentiments by Twitter users, which are consistent with the results from other survey-based studies. The coal-related sentiments are found to be generally more similar among those Twitter users who are geographically distant, and socially close based on Twitter conversation network. This work suggests that social media may be a robust approach for future energy research in social science.

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Pivato, Marina Abichabki. "Mineração de regras de associação em dados georreferenciados". Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18092006-104657/.

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Sistemas de informações geográficas permitem armazenar, manipular e armazenar, manipular e analisar dados espaciais e aspectos descritivos desses dados. A análise de dados espaciais pode ser realizada por meio de técnicas de extração de regras de associação, ou seja, regras que descrevem relacionamentos entre os dados. Porém, a mineração de regras de associação não considera as relações topológicas existentes entre dados georreferenciados. Para solucionar esse problema, Koperski and Han (1995) e Malerba et al. (2001) propuseram um processo de extração de regras integrado ao algoritmo de mineração e utilizavam predicados lógicos para representar as regras. Como alternativa a essa solução, este trabalho propõe pré-processar os dados referenciados para encontrar relações topológicas em separado e aplicar um algoritmo de mineração de regras de associação disponí?vel pela comunidade acadêmica. As regras geradas devem apresentar características descritivas dos dados e relações topológicas. Para atingir esse objetivo foi especificado um processo de extração de regras em dados georreferenciados e implementado um módulo de pré-processamento que extrai relações topológicas. O módulo foi avaliado por meio de um estudo de caso utilizando o sistema de informação geográfica da cidade de Jaboticabal, no contexto de planejamento urbano. As regras encontradas foram analisadas por um especialista utilizando as medidas de suporte e confiança. Além disso, uma análise sobre o tempo de processamento e consumo de memória para encontrar as relações topológicas foi realizada, mostrando que é possível extrair padrões utilizando o processo e o módulo proposto neste trabalho.
Geographic information systems are used to store, manipulate, and analyze spatial data and its descriptive aspects. Spatial data analysis can be done by searching association rules that describe relationships between the data. However, georeferenced data present topological relations unknown to traditional mining association rule algorithms. To solve this problem, Koperski and Han (1995) and Malerba et al. (2001) proposed a topological relation extraction process integrated to a mining association rule algorithm. This process requires all data to be translated as logical predicates. As an alternative to this solution, this work proposes to break down this process by pre-processing the georeferenced data to find topological relations, then executing traditional mining association rule algorithms. The resulting rules must present descriptive characteristics of the data and topological relations. To reach this objective, a process of rule extraction in georeferenced data was specified, in addition to a pre-processing module implementation. This module was evaluated by using a case study that uses a geographic information system of the city of Jaboticabal, in the context of urban planning. The generated rules were analyzed by a specialist using the measures of support and confidence. In addition, an analysis regarding the processing time and memory consumption was provided to find the topological relations, which shows that it is possible to extract the patterns with the proposed process and module.

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17

Salap, Seda. "Development Of A Gis-based Monitoring And Management System For Underground Mining Safety". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609815/index.pdf.

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Mine safety is of paramount concern to the mining industry. The generation of a Geographic Information Systems (GIS) which can administrate relevant spatial data and metadata of underground mining safety efficiently is a very vital issue in this sense. In an effort to achieve a balance of safety and productivity, GIS can contribute to the creation of a safe working environment in underground (U/G) mining. Such a system should serve to a continuous risk analysis and be designed for applications in case of emergency. Concept for safety should require three fundamental components, namely (i) constructive safety
(ii) surveillance and maintenance
and (iii) emergency. The implementation has to be carried out in a Web-Based Geographic Information System. The process comprises first the safety concept as the application domain model and then a conceptual model was generated in terms of Entity- Relationship Diagrams. After the implementation of the logical model a user interface was developed and GIS was tested. Finally, one should deal with the question if it is possible to extend the method of resolution used to a national GIS infrastructure.

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18

Braga, Reinaldo. "LIDU : Location-based approach to IDentify similar interests between Users in social networks". Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM055/document.

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Grâce aux technologies web et mobiles, le partage de données entre utilisateurs a considérablement augmenté au cours des dernières années. Par exemple, les utilisateurs peuvent facilement enregistrer leurs trajectoires durant leurs déplacements quotidiens avec l'utilisation de récepteurs GPS et les mettre en relation avec les trajectoires d'autres utilisateurs. L'analyse des trajectoires des utilisateurs au fil du temps peut révéler des habitudes et préférences. Cette information peut être utilisée pour recommander des contenus à des utilisateurs individuels ou à des groupes d'utilisateurs avec des trajectoires ou préférences similaires. En revanche, l'enregistrement de points GPS génère de grandes quantités de données. Par conséquent, les algorithmes de clustering sont nécessaires pour analyser efficacement ces données. Dans cette thèse, nous nous concentrons sur l'étude des différentes solutions pour analyser les trajectoires, extraire les préférences et identifier les intérêts similaires entre les utilisateurs. Nous proposons un algorithme de clustering de trajectoires GPS. En outre, nous proposons un algorithme de corrélation basée sur les trajectoires des points proches entre deux ou plusieurs utilisateurs. Les résultats finaux ouvrent des perspectives intéressantes pour explorer les applications des réseaux sociaux basés sur la localisation
Sharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this thesis, we focus on investigating ways of efficiently analyzing user trajectories, extracting user preferences from them and identifying similar interests between users. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The final results provided interesting avenues for exploring Location-based Social Network (LBSN) applications

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Prananto, Agnes Kristina. "The use of remotely sensed data to analyse spatial and temporal trends in vegetation patchiness within rehabilitated bauxite mines in the Darling Range, W.A. /". Connect to this title, 2005. http://theses.library.uwa.edu.au/adt-WU2006.0012.

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Shakeel, Mohammad Danish. "Land Cover Classification Using Linear Support Vector Machines". Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1231812653.

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Lu, Hongwei Marketing Australian School of Business UNSW. "Small area market demand prediction in the automobile industry". Publisher:University of New South Wales. Marketing, 2008. http://handle.unsw.edu.au/1959.4/43027.

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The general aim of this research is to investigate approaches to: improve small area market demand (i.e. SAMD) prediction accuracy for the purchase of automobiles at the level of each Census Collection District (i.e. CCD); and enhance understanding of meso-level marketing phenomena (i.e. geographically aggregated phenomena) relating to SAMD. Given the importance of SAMD prediction, and the limitations posed by current methods, four research questions are addressed: What are the key challenges in meso-level SAMD prediction? What variables affect SAMD prediction? What techniques can be used to improve SAMD prediction? What is the value of integrating these techniques to improve SAMD prediction? To answer these questions, possible solutions from two broad areas are examined: spatial analysis and data mining. The research is divided into two main studies. In the first study, a seven-step modelling process is developed for SAMD prediction. Several sets of models are analysed to examine the modelling techniques effectiveness in improving the accuracy of SAMD prediction. The second study involves two cases to: 1) explore the integration of these techniques and their advantages in SAMD prediction; and 2) gain insights into spatial marketing issues. The case study of Peugeot in the Sydney metropolitan area shows that urbanisation and geo-marketing factors can have a more important role in SAMD prediction than socio-demographic factors. Furthermore, results show that modelling spatial effects is the most important aspect of this prediction exercise. The value of the integration of techniques is in compensating for the weaknesses of conventional techniques, and in providing complementary and supplementary information for meso-level marketing analyses. Substantively, significant spatial variation and continuous patterns are found with the influence of key studied variables. The substantive implications of these findings have a bearing on both academic and managerial understanding. Also, the innovative methods (e.g. the SAMD modelling process and the model cube based technique comparison) developed from this research make significant contributions to marketing research methodology.

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Marques, Delano Brandes. "SISTEMA INTEGRADO DE MONITORAMENTO E CONTROLE DA QUALIDADE DE COMBUSTÍVEL". Universidade Federal do Maranhão, 2004. http://tedebc.ufma.br:8080/jspui/handle/tede/348.

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This work aims the implantation of an Integrated System that, besides allowing a better, more efficient and more practical monitoring, makes possible the control and optimization of problems related to the oil industry. In order to guarantee fuel s quality and normalization, the development of efficient tools that allow it s monitoring of any point (anywhere) and for any type of fuel is indispensable. Considering the variety of criteria, a decision making should be based on the evaluation of the most varied types of space data and not space data. In this sense, Knowledge Discovery in Databases process is used, where the Data Warehouse and Data Mining steps allied to a Geographic Information System are emphasized. This system presents as objective including several fuel monitoring regions. From different information obtained in the ANP databases, an analysis was carried out and a Data Warehouse model proposed. In the sequel, Data Mining techniques (Principal Component Analysis, Clustering Analysis and Multiple Regression) were applied to the results in order to obtain knowledge (patterns).
O presente trabalho apresenta estudos que visam a implantação de um Sistema Integrado que, além de permitir um melhor monitoramento, praticidade e eficiência, possibilite o controle e otimização de problemas relacionados à indústria de petróleo. Para garantir qualidade e normalização do combustível, é indispensável o desenvolvimento de ferramentas eficientes que permitam o seu monitoramento de qualquer ponto e para qualquer tipo de combustível. Considerando a variedade dos critérios, uma tomada de decisão deve ser baseada na avaliação dos mais variados tipos de dados espaciais e não espaciais. Para isto, é utilizado o Processo de Descoberta de Conhecimento, onde são enfatizadas as etapas de Data Warehouse e Data Mining aliadas ao conceito de um Sistema de Informação Geográfica. O sistema tem por objetivo abranger várias regiões de monitoramento de combustíveis. A partir do levantamento e análise das diferentes informações usadas nos bancos de dados da ANP foi proposto um modelo de data warehouse. Na seqüência foram aplicadas técnicas de mineração de dados (Análise de Componentes Principais, Análise de Agrupamento e Regressão) visando à obtenção de conhecimento (padrões).

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Duarte, Mariana de Luna Freire. "Mineração de dados usando programação genética". Universidade Federal da Paraíba, 2012. http://tede.biblioteca.ufpb.br:8080/handle/tede/6094.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Data mining has become an important activity for decision-making in large and small companies since it allows the extraction of relevant and non-trivial information so that corrections and adjustment in administrative and economic strategies could be selected. Consequently, an increase in the geographical data storage is seen in such a way that conventional data mining cannot carry out the extraction of knowledge from a high dimension database. According to the current literature, there are few tools capable of extracting knowledge from geographical data, mainly if the database is made of conventional (numeral and textual) and geographical (point, line and polygon) data. The aim of this study is to present a new algorithm for spatial data mining DMGP using the two types of data to carry out the information extraction from a determined base. This algorithm is based on the DMGeo algorithm which also seeks to extract knowledge from the two types of data. These algorithms are based on Genetic Programming and were developed to obtain classification rules of patterns existing in the numeral and geographical attributes. To obtain a better performance for the DMGeo, the use of meta-heuristic GRASP and ILS in the performance of DMGP algorithm was proposed to improve the individuals from the generated population . GRASP and ILS were used to generate the initial population and disturb some individuals aiming at finding better solutions.
A mineração de dados tornou-se uma importante atividade para o processo de tomada de decisão para grandes ou pequenas corporações, pois a partir dela é possível extrair informações relevantes e não triviais de forma que correções e ajustes em estratégias econômicas e administrativas possam ser selecionadas. Assim, vê-se um aumento no armazenamento de dados geográficos, de tal maneira que a mineração de dados convencionais não suporta realizar a extração de conhecimento em um banco de dados de elevada dimensão. De acordo com a literatura atual, poucas ferramentas capazes de extrair conhecimento a partir de dados geográficos são encontradas, principalmente, quando a base de dados é composta por dados convencionais (numéricos e textuais) e geográficos (ponto, linha e polígono). Este trabalho tem como objetivo principal apresentar um novo algoritmo, chamado DMGP, para a atividade de mineração de dados espaciais utilizando os dois tipos de dados para realizar a extração de informações de uma determinada base. O algoritmo em questão tem como base o algoritmo DMGeo que, por sua vez, também visa extrair conhecimento a partir dos dois tipos de dados. Estes algoritmos são baseados na Programação Genética e foram desenvolvidos a fim de obter regras de classificação de padrões existentes nos atributos numéricos e geográficos. Visando obter um melhor desempenho para o DMGeo, foi proposto a utilização das meta-heuríticas GRASP e ILS no funcionamento do algoritmo DMGP para aperfeiçoar os indivíduos das populações geradas. Tais meta-heurísticas foram usadas para gerar a população incial e para realizar uma perturbação de alguns indivíduos, com o intuito de encontrar soluções melhores.

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Prananto, Agnes Kristina. "The use of remotely sensed data to analyse spatial and temporal trends in vegetation patchiness within rehabilitated bauxite mines in the Darling Range, W.A". University of Western Australia. School of Earth and Geographical Sciences, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0012.

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[Truncated abstract] The assessment of rehabilitation success is time consuming and costly for bauxite miners because large areas of land (~550 ha per year) are involved. In some cases, rehabilitation results in patches of bare or sparsely vegetated soil. This study uses remote sensing imagery to evaluate the growth of vegetation in rehabilitated bauxite mines in the Darling Range, W.A. This work has five aims, which are to (1) compare vegetation biomass within rehabilitated areas and nearby native forest; (2) analyse temporal changes in vegetation growth within the selected rehabilitated areas, in particular rehabilitated areas with patches of bare soil; (3) compare vegetation growth pre- and post- mining; (4) identify the best type of remotely sensed data for this particular study area, and (5) develop an index, which can classify the degree of vegetation patchiness within rehabilitated mine sites. This information will enable rehabilitation workers to identify patches in rehabilitated areas that may require further remediation. The study used RADARSAT, nine years of Normalised Difference Vegetation Index (NDVI) maps (extracted from LANDSAT TM multivariate imagery and Quickbird imagery) and aerial photographs to evaluate forty-seven ~1 ha study sites. Image and map analyses were conducted mainly using ESRI’s software ArcGIS 8.3 and ER Mapper 6.4. Ground truthing was carried out to confirm and recognise the causes of bare patches within the rehabilitated mine sites ... The results indicate that differences in rehabilitation management do not affect this index but the extent of bare patches does. Due to the sensitivity of radar imagery to surface roughness, rehabilitated areas cannot be distinguished from the native forest using radar images. A building (crusher) appears to be the same as mature vegetation. Knowledge of the features in an area is therefore crucial when utilising RADARSAT. The beam elevation angle and profile of the RADARSAT image used, made superimposition of radar and optical imageries impossible. Speckle noise in RADARSAT images made it impossible to detect relatively small bare patches. In addition, the many cloud free days in Western Australia make optical imaging possible so that the ability of radar imagery to penetrate cloud is redundant for this type of study.

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25

Alatrista-Salas, Hugo. "Extraction de relations spatio-temporelles à partir des données environnementales et de la santé". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2013. http://tel.archives-ouvertes.fr/tel-00997539.

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Face à l'explosion des nouvelles technologies (mobiles, capteurs, etc.), de grandes quantités de données localisées dans l'espace et dans le temps sont désormais disponibles. Les bases de données associées peuvent être qualifiées de bases de données spatio-temporelles car chaque donnée est décrite par une information spatiale (e.g. une ville, un quartier, une rivière, etc.) et temporelle (p. ex. la date d'un événement). Cette masse de données souvent hétérogènes et complexes génère ainsi de nouveaux besoins auxquels les méthodes d'extraction de connaissances doivent pouvoir répondre (e.g. suivre des phénomènes dans le temps et l'espace). De nombreux phénomènes avec des dynamiques complexes sont ainsi associés à des données spatio-temporelles. Par exemple, la dynamique d'une maladie infectieuse peut être décrite par les interactions entre les humains et le vecteur de transmission associé ainsi que par certains mécanismes spatio-temporels qui participent à son évolution. La modification de l'un des composants de ce système peut déclencher des variations dans les interactions entre les composants et finalement, faire évoluer le comportement global du système.Pour faire face à ces nouveaux enjeux, de nouveaux processus et méthodes doivent être développés afin d'exploiter au mieux l'ensemble des données disponibles. Tel est l'objectif de la fouille de données spatio-temporelles qui correspond à l'ensemble de techniques et méthodes qui permettent d'obtenir des connaissances utiles à partir de gros volumes de données spatio-temporelles. Cette thèse s'inscrit dans le cadre général de la fouille de données spatio-temporelles et l'extraction de motifs séquentiels. Plus précisément, deux méthodes génériques d'extraction de motifs sont proposées. La première permet d'extraire des motifs séquentiels incluant des caractéristiques spatiales. Dans la deuxième, nous proposons un nouveau type de motifs appelé "motifs spatio-séquentiels". Ce type de motifs permet d'étudier l'évolution d'un ensemble d'événements décrivant une zone et son entourage proche. Ces deux approches ont été testées sur deux jeux de données associées à des phénomènes spatio-temporels : la pollution des rivières en France et le suivi épidémiologique de la dengue en Nouvelle Calédonie. Par ailleurs, deux mesures de qualité ainsi qu'un prototype de visualisation de motifs sont été également proposés pour accompagner les experts dans la sélection des motifs d'intérêts.

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Gomes, Eduardo Luis. "Arquitetura RF-Miner: uma solução para localização em ambientes internos". Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2898.

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A utilização de etiquetas RFID UHF passivas para localização indoor vem sendo amplamente estudada devido ao seu baixo custo. Porém ainda existe uma grande dificuldade em obter bons resultados, principalmente devido à variação de rádio frequência em ambientes que possuem materiais reflexivos, como por exemplo, metais e vidros. Esta pesquisa propõe uma arquitetura de localização para ambientes indoor utilizando etiquetas RFID UHF passivas e técnicas de mineração de dados. Com a aplicação da arquitetura em ambiente real foi possível identificar a posição exata de objetos com a precisão de aproximadamente cinco centímetros e em tempo real. A arquitetura se demonstrou uma eficiente alternativa para implantação de sistemas de localização indoor, além de apresentar uma técnica de derivação de atributos diretos que contribui efetivamente para os resultados finais.
The use of passive UHF RFID tags for indoor location has been widely studied due to its low cost. However, there is still a great difficulty to reach good results, mainly due the radio frequency variation in environments that have materials with reflective surfaces, such as metal and glass. This research proposes a localization architecture for indoor environments using passive UHF RFID tags and data mining techniques. With the application of the architecture in real environment, it was possible to identify the exact position of objects with the precision of approximately five centimeters and in real time. The architecture has demonstrated an efficient alternative for the implantation of indoor localization systems, besides presenting a derivation technique of direct attributes that contributes effectively to the final results.

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Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining". Thesis, 2002. http://hdl.handle.net/1959.13/25012.

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With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques become essential tools for successful analysis of large spatial datasets. Spatial clustering is fundamental and central to geographical data mining. It partitions a dataset into smaller homogeneous groups due to spatial proximity. Resulting groups represent geographically interesting patterns of concentrations for which further investigations should be undertaken to find possible causal factors. In this thesis, we propose a spatial-dominant generalization approach that mines multivariate causal associations among geographical data layers using clustering analysis. First, we propose a generic framework of multi-purpose exploratory spatial clustering in the form of the Template-Method Pattern. Based on an object-oriented framework, we design and implement an automatic multi-purpose exploratory spatial clustering tool. The first instance of this framework uses the Delaunay diagram as an underlying proximity graph. Our spatial clustering incorporates the peculiar characteristics of spatial data that make space special. Thus, our method is able to identify high-quality spatial clusters including clusters of arbitrary shapes, clusters of heterogeneous densities, clusters of different sizes, closely located high-density clusters, clusters connected by multiple chains, sparse clusters near to high-density clusters and clusters containing clusters within O(n log n) time. It derives values for parameters from data and thus maximizes user-friendliness. Therefore, our approach minimizes user-oriented bias and constraints that hinder exploratory data analysis and geographical data mining. Sheer volume of spatial data stored in spatial databases is not the only concern. The heterogeneity of datasets is a common issue in data-rich environments, but left open by exploratory tools. Our spatial clustering extends to the Minkowski metric in the absence or presence of obstacles to deal with situations where interactions between spatial objects are not adequately modeled by the Euclidean distance. The genericity is such that our clustering methodology extends to various spatial proximity graphs beyond the default Delaunay diagram. We also investigate an extension of our clustering to higher-dimensional datasets that robustly identify higher-dimensional clusters within O(n log n) time. The versatility of our clustering is further illustrated with its deployment to multi-level clustering. We develop a multi-level clustering method that reveals hierarchical structures hidden in complex datasets within O(n log n) time. We also introduce weighted dendrograms to effectively visualize the cluster hierarchies. Interpretability and usability of clustering results are of great importance. We propose an automatic pattern spotter that reveals high level description of clusters. We develop an effective and efficient cluster polygonization process towards mining causal associations. It automatically approximates shapes of clusters and robustly reveals asymmetric causal associations among data layers. Since it does not require domain-specific concept hierarchies, its applicability is enhanced.
PhD Doctorate

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Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining". 2002. http://hdl.handle.net/1959.13/25012.

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With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques become essential tools for successful analysis of large spatial datasets. Spatial clustering is fundamental and central to geographical data mining. It partitions a dataset into smaller homogeneous groups due to spatial proximity. Resulting groups represent geographically interesting patterns of concentrations for which further investigations should be undertaken to find possible causal factors. In this thesis, we propose a spatial-dominant generalization approach that mines multivariate causal associations among geographical data layers using clustering analysis. First, we propose a generic framework of multi-purpose exploratory spatial clustering in the form of the Template-Method Pattern. Based on an object-oriented framework, we design and implement an automatic multi-purpose exploratory spatial clustering tool. The first instance of this framework uses the Delaunay diagram as an underlying proximity graph. Our spatial clustering incorporates the peculiar characteristics of spatial data that make space special. Thus, our method is able to identify high-quality spatial clusters including clusters of arbitrary shapes, clusters of heterogeneous densities, clusters of different sizes, closely located high-density clusters, clusters connected by multiple chains, sparse clusters near to high-density clusters and clusters containing clusters within O(n log n) time. It derives values for parameters from data and thus maximizes user-friendliness. Therefore, our approach minimizes user-oriented bias and constraints that hinder exploratory data analysis and geographical data mining. Sheer volume of spatial data stored in spatial databases is not the only concern. The heterogeneity of datasets is a common issue in data-rich environments, but left open by exploratory tools. Our spatial clustering extends to the Minkowski metric in the absence or presence of obstacles to deal with situations where interactions between spatial objects are not adequately modeled by the Euclidean distance. The genericity is such that our clustering methodology extends to various spatial proximity graphs beyond the default Delaunay diagram. We also investigate an extension of our clustering to higher-dimensional datasets that robustly identify higher-dimensional clusters within O(n log n) time. The versatility of our clustering is further illustrated with its deployment to multi-level clustering. We develop a multi-level clustering method that reveals hierarchical structures hidden in complex datasets within O(n log n) time. We also introduce weighted dendrograms to effectively visualize the cluster hierarchies. Interpretability and usability of clustering results are of great importance. We propose an automatic pattern spotter that reveals high level description of clusters. We develop an effective and efficient cluster polygonization process towards mining causal associations. It automatically approximates shapes of clusters and robustly reveals asymmetric causal associations among data layers. Since it does not require domain-specific concept hierarchies, its applicability is enhanced.
PhD Doctorate

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Ribeiro, Vítor. "Mining Geographic Data for Fuel Consumption Estimation". Dissertação, 2013. http://hdl.handle.net/10216/75540.

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Ribeiro, Vítor Daniel Ferreira da Cunha. "Mining Geographic Data for Fuel Consumption Estimation". Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/66841.

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Ribeiro, Vítor Daniel Ferreira da Cunha. "Mining Geographic Data for Fuel Consumption Estimation". Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/66841.

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Abdelkareem, Nourhan Khalifa. "Analysis and visualization of energy use for university campus". Master's thesis, 2015. http://hdl.handle.net/10362/14567.

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The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

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33

Guo, Yunyong. "A Cloud Computing Based Platform for Geographically Distributed Health Data Mining". Thesis, 2013. http://hdl.handle.net/1828/4890.

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With cloud computing emerging in recent years, more and more interest has been sparked from a variety of institutions, organizations and individual users, as they intend to take advantage of web applications to share a huge amount of public and private data and information in a more affordable way and using a reliable IT architecture. In the area of healthcare, medical and health information systems based on cloud computing are desired, in order to realize the sharing of medical data and health information, coordination of clinical service, along with effective and cost-contained clinical information system infrastructure via the implementation of a distributed and highly-integrated platform. The objective of this study is to discuss the challenges of adopting cloud computing for collaborative health research information management and provide recommendations to deal with corresponding challenges. More specially, the study will propose a cloud computing based platform according to recommendations. The platform can be used to bring together health informatics researchers from the different geographical locations to share medical data for research purposes, for instance, data mining used for improving liver cancer early detection and treatment. Finding from a literature review will be discussed to highlight challenges of applying cloud computing in a wide range of areas, and recommendations will be paired with each challenge. A proof of concept prototype research methodology will be employed to illustrate the proposed cross national cloud computing model for geographically distributed health data mining applied to a health informatics research.
Graduate
0573

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34

"spatiotemporal data mining, analysis, and visualization of human activity data". Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.15915.

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abstract: This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.
Dissertation/Thesis
Ph.D. Geography 2012

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35

Chung, Chi Wei, i 鍾志偉. "Post office location analysis using geographic information system and data mining techniques". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58620853081761708899.

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碩士
國立政治大學
資訊科學學系
99
The amount of postal mail declines in recent years due to the efforts of paper-reduce policies implemented by the government, the industries, and the general publics. It becomes one of the important issues of the Chunghwa Post Company, to compete with other companies in domestic freight and mail services and to achieve the desired profits. Traditionally, the location of post offices were decided according to the government policies as well as the company regulations. The issues involved in the site selection analysis were seldom considered. Hence, developing an effective and fair mechanism to find the new post office locations that could improve the company’s surplus becomes an important problem to be solved. The purpose of this thesis is to provide recommendations to the post office site selection which will yield high profit to the company. We proposed a method to evaluate the effective profits that could be produced by a particular post office through the data mining techniques and the related GIS information. We first collect various data, such as neighborhood population, traffic flow, postal mail received at particular post office, competitor’s information, etc., and analyze these data using data mining techniques in order to establish prediction models. The most appropriate model was chosen to find the new post office sites. The Metropolitan Taipei area was chosen to illustrate our idea. The best sites for new post offices were selected through the buffering analysis as well as the data mining techniques. The experimental results show that our method can successfully find eleven locations which could generate most profit to Chunghwa Post Company if the new post offices were located in these places.

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Wu, Pei-Hua, i 吳珮華. "ATM Location Set-up Analysis by Using Geographic Information System and Data Mining Method". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/44458260609632619451.

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碩士
國立政治大學
資訊科學學系
98
The convenience of the ATM banking facilities caused rapidly increasing in ATM demands during the past decades. The expenses for installation and maintenance of the ATMs are considerably high. However, there are no effective methods to evaluate the economic benefits on ATMs’ locations. Traditionally, the decision for ATM installation is based on policymaker’s experiences and subjective demands. The cost-effective issues and the spatial factors involved in location finding were seldom considered. Hence, develop a reasonable and effective mechanism to find the ATM locations that could improve economic efficiency become an important problem to be solved. The purpose of this thesis is to provide suggestion on the cost-effective ATM installation locations to help the policymaker in making decisions. We combine the techniques in geographical information system (GIS) as well as data mining for the cost-effective ATM installation location analysis. Using the ATM utilization factors for various ATMs, we can associate the attribute data with the spatial provided by GIS. Then, we use data mining techniques to analyze the factors that could influence the cost-effective installation location of ATMs. From this information, we can summarize the association rules that have the most impacts to localize the ATM installation locations. Finally, using these association rules, we can reach conclusion on ATMs’ installation locations. We use our local bank data to illustrate our idea. Experimental results show that we can successfully find the key factors that influence the cost-effective ATM installation locations. The range and the quantities of these events can be identified clearly, hence, making it possible to suggest whether an ATM should be removed or be relocated. Furthermore, we can suggest installing a new ATM at a particular location for potential customers or not.

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Veiga, Pedro Guilherme Ribeiro. "Determine the potential and the extent to which geographic socio-demographic data impacts retail performance revenue and consumer behavior and determine how much discounts impact revenue". Master's thesis, 2021. http://hdl.handle.net/10362/129685.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
The objective of this thesis is to describe and analyze the sales and revenue of a food distribution company, Tasty and Sweet, by product or product type and relate it to geographic socio-demographic data provided by National Bureau of Statistics (INE). Tasty and Sweet operates and covers the entire Portuguese national territory, distributing and selling their products to any retailer company who is willing to resell them. The goal of this work is to develop an analytical model that allows the tracking of all the sales and revenue, by group item or by item, relating them, eventually, to the social and demographic characterization of a specific geography so that patterns may be (or not) identified. Another goal of the thesis is to determine the extent to which sales promotions have an impact on retailer sales. In order to achieve these objectives several methods of data analysis will be developed, supported and backed up by software from SAS Institute (Sas Guide and Sas Miner). It was possible to come to the following conclusions: firstly there are relevant socio-demographic variables that impact, or are more related, with de retailer’s revenue like: Indicators of enterprises by municipality N. º/km2 - enterprise density, 2016; Territorial structure by municipality - weight of resident population, 2011; Distribution of declared gross income less individual tax income paid of tax households by municipality (less 5k), 2016 are in fact more impacting; Secondly revenue volume is in fact impacted by discounts and promotions.

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Cowan, Terri. "A Framework for Investigating Volunteered Geographic Information Relevance in Planning". Thesis, 2013. http://hdl.handle.net/10012/7475.

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Advances in information and communication technology and the ready availability of Global Positioning Systems (GPS) have made it possible for citizens to create information on the internet expressing their personal perceptions in the form of pictures, videos and text narratives associated with geographic locations. The term Volunteered Geographic information (VGI) was coined to describe the processes whereby non-professionals or “citizen scientists” participate directly in spatial data creation, editing and shared use. VGI offers promise as an innovative way for members of the public to participate directly in the use, production and sharing of spatial information that is relevant to issues of personal or community concern and as a means of addressing some of the issues associated with traditional public participation methods. Planners can find meaning in the heterogeneous, time-sensitive, geo-social geographic information created by citizen volunteers in a bottom-up participation process where planners give up some control over what data is collected and from whom. However, uncertainties associated with volunteered geographic information include relevance, credibility, representativeness and quality of the geographic information. This thesis investigates the opportunities and barriers to the use of volunteered geographic information as public participation in planning. A framework and methodology for collaborative quality control of VGI through multi-criteria subjective relevance ratings of the VGI by its producers and users is put forward in this thesis. The relevance rating framework for quality control of VGI is based on the use of relevance in information retrieval in information science to improve the relevance of search engine results. This concept is transferred to the quality control of VGI contributions to determine the best VGI contributions to be used in planning as public participation. A VGI web application prototype, including the subjective relevance rating system, was created and a methodology and demonstration of its use for public participation was presented.

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Yu, Chih-ting, i 余致廷. "Where to open a clinic? Analysis of optimal clinic location using geographic information system and data mining techniques". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/bzu6rw.

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碩士
國立中央大學
資訊管理學系
102
In the past 10 years, the number of clinics in Taiwan has increased gradually, and the scale of hospitals has become larger, making the market of medical care in Taiwan more competitive. Therefore, to find an optimum location effectively and quickly is critical for new entrants to develop the business. This issue is not well addressed in the literature. To fill this knowledge gap, this study proposes a model to help select the most suitable site for a medical clinic, combining Geographic Information System (GIS) and data mining. Using data from a primary survey of clinics and various secondary sources in Taipei, we analyzed the critical determinants of clinic location choice. We expect to provide information showing where to open a clinic, which would help clinic owners reduce operation costs.

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Antunes, Jorge Manuel Alves. "Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon". Master's thesis, 2016. http://hdl.handle.net/10362/17446.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.

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Penedos, Pedro Pais. "Precision Agriculture Using Unmanned Aerial Systems: Mapping Vigor’s Spatial Variability On Low Density Agricultures Using a Canopy Pixel Classification And Interpolation Model". Master's thesis, 2018. http://hdl.handle.net/10362/33277.

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Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
It is becoming more present in agriculture’s practices the use of Unmanned Aerial Systems with sensors capable of capturing light, in the visible and in longer wavelengths of the electromagnetic spectrum once reflected on the field. These sensors have been used to perform Remote Sensing also in other knowledge fields, describing phenomenon without the risk, cost and the time consuming processes associated with in site samples collection and analysis by a technician or satellite imagery acquisition. The Vegetation Indexes developed can explain the vigor of the cultivation and its data collection processes are more cost and time efficient, allowing farmers to monitor plant grow in every critical stage. These Vegetation Indexes started by being calculated from satellite and airborne imagery, one of the main source for crop management tools, however UAS is becoming more present in Precision Agriculture, achieving better spatial and temporal resolution. This gap in spatial resolution when studying low density cultivations like olive groves and vineyards, creates Vegetation Index’s maps polluted with noise caused by the soil and therefore difficult to interpret and analyse. Hence, when the agriculture has spaced and low density vegetation becomes challenging to understand and extract information from these vegetation index’s maps regarding different spatial variability patterns of the tree canopy vigor. In these cases, where vegetation is spaced it is important to filter this noise. A Classification Model was developed with the objective of extracting just the vegetation’s canopy data. The soil was filtered and the canopy data interpolated using spatial analysis tools. The final interpolated maps produced can provide meaningful information regarding the spatial variability and be used to support decision making, identifying critical areas to be intervened and managed, or be used as an input for Variable Rate Technology applications.

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42

Šećerov, Milan. "Analysis of panoramio photo tags in order to extract land use information". Master's thesis, 2015. http://hdl.handle.net/10362/14549.

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In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

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Dlamini, Wisdom Mdumiseni Dabulizwe. "Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques". Thesis, 2016. http://hdl.handle.net/10500/20692.

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Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.
Environmental Sciences
D. Phil. (Environmental Science)

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"Developing a Cohesive Space-Time Information Framework for Analyzing Movement Trajectories in Real and Simulated Environments". Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.9514.

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abstract: In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
Dissertation/Thesis
Ph.D. Geography 2011

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João, Paulo Abel de Almeida. "Modelo preditivo da criminalidade – georeferenciação ao concelho de Lisboa". Master's thesis, 2010. http://hdl.handle.net/10362/3424.

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Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Estatística e Gestão de Informação
Pretende-se elaborar um modelo preditivo ou processo analítico e sistemático de descoberta do conhecimento, orientado segundo os princípios da pertinência e da oportunidade, que detecte os hot spots da criminalidade, que faça uma previsão e propensão de ocorrência e ainda, que faça uma previsão da sua evolução, estagnação ou redução, sendo realizado a partir do estabelecimento de correlações entre conjuntos de dados criminais ocorridos no primeiro semestre do ano de 2007 no concelho de Lisboa. Este modelo poderá posteriormente ser aplicado a outras regiões do país.

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Pileththuwasan, Gallege Lahiru Sandakith. "Design, development and experimentation of a discovery service with multi-level matching". Thesis, 2013. http://hdl.handle.net/1805/3695.

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Indiana University-Purdue University Indianapolis (IUPUI)
The contribution of this thesis focuses on addressing the challenges of improving and integrating the UniFrame Discovery Service (URDS) and Multi-level Matching (MLM) concepts. The objective was to find enhancements for both URDS and MLM and address the need of a comprehensive discovery service which goes beyond simple attribute based matching. It presents a detailed discussion on developing an enhanced version of URDS with MLM (proURDS). After implementing proURDS, the thesis includes details of experiments with different deployments of URDS components and different configurations of MLM. The experiments and analysis were carried out using proURDS produced MLM contracts. The proURDS referred to a public dataset called QWS dataset. This dataset includes actual information of software components (i.e., web services), which were harvested from the Internet. The proURDS implements the different matching operations as independent operators at each level of matching (i.e., General, Syntactic, Semantic, Synchronization, and QoS). Finally, a case study was carried out with the deployed proURDS. The case study addresses real world component discovery requirements from the earth science domain. It uses the contracts collected from public portals which provide geographical and weather related data.

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