La production et la maintenance de contenus géographiques se fait souvent grâce à la mise en commun de contributions diverses. La mise à jour des données de l'IGN s'appuie ainsi sur l'intégration de données de partenaires ou la prise en compte d'alertes d'évolution du terrain. C'est également le cas des contenus libres produits par des projets communautaires comme OpenStreetMap. Un aspect problématique est la gestion de la qualité d'un contenu géographique collaboratif, particulièrement de leur cohérence afin de permettre que des prises de décision s'appuient dessus. Cette cohérence est liée à l'homogénéité de la représentation de l'espace, ainsi qu'à la préservation d'informations importantes non explicites mais qui peuvent être retrouvées sur les entités décrites grâce à leurs géométries. Ce travail de thèse propose un modèle baptisé Coalla pour l'édition collaborative d'un contenu géographique avec gestion de la cohérence. Ce modèle comporte trois contributions : 1) l'identification et la définition des éléments que doit comporter un vocabulaire formel visant à faciliter la construction d'un contenu géographique collaboratif ; 2) un processus d'aide à la construction à la volée d'un vocabulaire formel à partir de spécifications formelles des bases de données IGN et à des vocabulaires collaboratifs existants, et 3) une stratégie d'évaluation et de réconciliation des contributions afin de les intégrer d'une façon cohérente au contenu central. Notre modèle Coalla a été implémenté dans un prototype / Geographic content production and maintenance is often done through a combination of various contributions. Thus, updating IGN geographic data relies on integrating data from partners or by involving field change alerts. This is also the case of free content produced by community projects such as OpenStreetMap. An important problem is quality management of collaboratively produced geographic content, in particular consistency management. This allows for decision-making which is based on this content. Data consistency depends on how homogenous space representation is. Likewise, it depends on preserving important non-explicit information that can be found on the geometries of the entities described in the content. This Thesis proposes a model baptized Coalla for collaborative editing of geographic content with consistency management. The model has three contributions: 1) identifying and defining elements that should be included in a formal vocabulary to facilitate the construction of collaborative geographic content, 2) user assistance process to help users build on the fly a formal vocabulary extracted from formal IGN databases specifications and existing collaborative vocabularies, and 3) a strategy for evaluating and reconciling user contributions in order to coherently integrate them into the content. Our model Coalla has been implemented in a prototype

Aujourd’hui, les recherches autour du stockage et de l’intégration des données spatiales constituent un maillon important qui redynamise les recherches sur la qualité des données. La prise en compte de l’imperfection des données géographiques, particulièrement l’imprécision, ajoute une réelle complexification. Parallèlement à l’augmentation des exigences de qualité centrées sur les données (précision, exhaustivité, actualité), les besoins en information intelligible ne cessent d’augmenter. Sous cet angle, nous sommes intéressés aux bases de données géographiques imprécises (BDGI) et leur cohérence. Ce travail de thèse présente des solutions pour la modélisation et la construction des BDGI et cohérentes pour les SGBD SQL et NoSQL.Les méthodes de modélisation conceptuelle de données géographiques imprécises proposées ne permettent pas de répondre de façon satisfaisante aux besoins de modélisation du monde réel. Nous présentons une version étendue de l’approche F-Perceptory pour la conception de BDGI. Afin de construire la BDGI dans un système relationnel, nous présentons un ensemble de règles de transformation automatique de modèles pour générer à partir du modèle conceptuel flou le modèle physique. Nous implémentons ces solutions sous forme d’un prototype baptisé FPMDSG.Pour les systèmes NoSQL type document. Nous présentons un modèle logique baptisé Fuzzy GeoJSON afin de mieux cerner la structure des données géographiques imprécises. En plus, ces systèmes manquent de pertinence pour la cohérence des données ; nous présentons une méthodologie de validation pour un stockage cohérent. Les solutions proposées sont implémentées sous forme d'un processus de validation. / Today, research on the storage and the integration of spatial data is an important element that revitalizes the research on data quality. Taking into account the imperfection of geographic data particularly the imprecision adds a real complexity. Along with the increase in the quality requirements centered on data (accuracy, completeness, topicality), the need for intelligible information (logically consistent) is constantly increasing. From this point of view, we are interested in Imprecise Geographic Databases (IGDBs) and their logical coherence. This work proposes solutions to build consistent IGDBs for SQL and NoSQL database systems.The design methods proposed to imprecise geographic data modeling do not satisfactorily meet the modeling needs of the real world. We present an extension to the F-Perceptory approach for IGDBs design. To generate a coherent definition of the imprecise geographic objects and built the IGDB into relational system, we present a set of rules for automatic models transformation. Based on these rules, we develop a process to generate the physical model from the fuzzy conceptual model. We implement these solutions as a prototype called FPMDSG.For NoSQL document oriented databases, we present a logical model called Fuzzy GeoJSON to better express the structure of imprecise geographic data. In addition, these systems lack relevance for data consistency; therefore, we present a validation methodology for consistent storage. The proposed solutions are implemented as a schema driven pipeline based on Fuzzy GeoJSON schema and semantic constraints.

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.

The aim of the master's thesis is to describe the map products of the State Administration of Land Surveying and Cadastre and their applicability in planning the Land consolidation. This master's thesis consists of two parts. In literary research are explained basic terms and describe all the map series, administered by the State Administration of Land Surveying and Cadastre. There are described contents and possibilities of using individual maps, including their availability. In practical part of master's thesis are describes the maps used in the Land consolidation from different viewpoints.

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.

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.

Les champs continus sont des types de représentations spatiales utilisées pour modéliser des phénomènes tels que la température, la pollution ou l’altitude. Ils sont définis selon une fonction de mapping f qui affecte une valeur du phénomène étudié à chaque localisation p du domaine d’étude. Par ailleurs, la représentation des champs continus à différentes échelles ou résolutions est souvent essentielle pour une analyse spatiale efficace. L’avantage des champs continus réside dans le niveau de détails généré par la continuité, ainsi que la qualité de l’analyse spatiale fournie par la multi-résolution. L’inconvénient de ce type de représentations dans l’analyse spatio-multidimensionnelle est le coût des performances d’analyse et de stockage. Par ailleurs, les entrepôts de données spatiaux et les systèmes OLAP spatiaux (EDS et SOLAP) sont des systèmes d’aide à la décision qui permettent l’analyse spatio-multidimensionnelle de grands volumes de données spatiales et non spatiales. L’analyse des champs continus dans l’architecture SOLAP représente un défi de recherche intéressant. Différents travaux se sont intéressés à l’intégration de ce type de représentations dans le système SOLAP. Cependant, celle-ci est toujours au stade embryonnaire. Cette thèse s’intéresse à l’intégration des champs continus incomplets représentés par une grille régulière de points dans l’analyse spatio-multidimensionnelle. Cette intégration dans le système SOLAP implique que l’analyse des champs continus doit supporter : (i) les opérateurs OLAP classiques, (ii) la vue continue des données spatiales, (iii) les opérateurs spatiaux (slice spatial) et (iv) l’interrogation des données à différentes résolutions prédéfinies. Dans cette thèse nous proposons différentes approches pour l’analyse des champs continus dans le SOLAP à différents niveaux de l’architecture relationnelle, de la modélisation conceptuelle à l’optimisation des performances de calcul. Nous proposons un modèle logique FISS qui permet d’optimiser les performances d’analyse à multi-résolution en se basant sur des méthodes d’interpolation. Puis, nous exposons une méthodologie basée sur la méthode d’échantillonnage du Clustering, qui permet d’optimiser les opérations d’agrégation des grilles régulières de points dans l’architecture SOLAP relationnelle en effectuant une estimation des résultats. / Continuous fields are types of spatial representations used to model phenomena such as temperature, pollution or altitude. They are defined according to a mapping function f that assigns a value of the studied phenomenon to each p location of the studied area. Moreover, the representation of continuous fields at different scales or resolutions is often essential for effective spatial analysis. The advantage of continuous fields is the level of details generated by the continuity of the spatial data, and the quality of the spatial analysis provided by the multi-resolution. The downside of this type of spatial representations in the multidimensionnal analysis is the high cost of analysis and storage performances. Moreover, spatial data warehouses and spatial OLAP systems (EDS and SOLAP) are decision support systems that enable multidimensional spatial analysis of large volumes of spatial and non-spatial data. The analysis of continuous fields in SOLAP architecture represents an interesting research challenge. Various studies have focused on the integration of such representations in SOLAP system. However, this integration still at an early stage. Thus, this thesis focuses on the integration of incomplete continuous fields represented by a regular grid of points in the spatio-multidimensional analysis. This integration in the SOLAP system involves that the analysis of continuous fields must support:(i) conventional OLAP operators, (ii) Continuous spatial data, (iii) spatial operators (spatial slice), and (iv) querying data at different predefined levels of resolutions. In this thesis we propose differents approaches for the analysis of continuous fields in SOLAP system at different levels of the relational architecture (from the conceptual modeling to the optimization of computing performance). We propose a logical model FISS to optimize the performances of the multi-resolution analysis, based on interpolation methods. Then, we present a new methodology based on the Clustering sampling method, to optimize aggregation operations on regular grids of points in the relational SOLAP architecture.

The objective of this master thesis is to design and develop a real-time storage system for geographic data from driving school trips. The system provides tools for analysis and evaluation of practice trips. This system is an extension of the DoAutoskoly.cz project which is described in the text. The next part contains an introduction to geographical data, spatial data and available databases with spatial extensions. The understanding to spatial databases is very important for the system design, an explanation of a solution for a database layer and implementation of major parts. Solution for a graphical view of the results and possible extensions of the system are described in the last part of this thesis.

This thesis describes and analyzes the current state of environmental management of geographic data within geoinformation infrastructure of the Czech Republic in response to European legislation. The work is divided into four main parts. The first part deals with geoinformation infrastructure of the Czech Republic in its basic components due to the efficiency of the management of geographic data. Another part deals with the general characteristics of geospatial data underlying the description methods, organization and retrieval in the next section. The final part of its qualitative research describes in detail the management of geospatial data in the environmental sector, which is affected by the phenomenon of the implementation of INSPIRE and draws conclusions and recommendations. The work also supports the analysis of the availability of data sources for the implementation of the INSPIRE directive, the questionnaire used as a guide for research purposes, to illustrate the services Geoportal three pictures. Powered by TCPDF (www.tcpdf.org)

This master’s thesis determines trail suitability in the context of environmental area sensitivity, closely focusing on a proposed Bästeträsk National Park, Gotland, Sweden. The current relative low usage of the area is expected to significantly increase its tourism flow once the proposed national park is established, as the demand for nature-based tourism and recreation is growing. However, due to its pristine landscapes, myriad endemic and red-listed species, and rare geomorphological phenomena, there is a need to closely review the destination’s environmental sensitivity and potential recreational adverse impacts on the area’s ecosystem services. Consequently, the research reviews Volunteered Geographic Data within the studied area and employs Geographic Information Systems-based Multi-Criteria Decision Analysis to determine environmental trail suitability. The research identified that more than half of the studied area exhibits substantial environmental sensitivity. Additionally, it highlights that multiple used and established trails intersect considerably sensitive areas, which must be considered for future tourism planning to attain sustainable destination development. Moreover, the study furthers on the requisite to recognise nature-based activities beyond the means of low impact due to their increasing popularity and anthropogenic impacts.