Gotowe bibliografie tematyczne / Geographical data mining

Gotowa bibliografia na temat „Geographical data mining”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 25 stycznia 2023

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Artykuły w czasopismach na temat "Geographical data mining"

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Estivill-Castro, Vladimir, i Ickjai Lee. "Clustering with obstacles for Geographical Data Mining". ISPRS Journal of Photogrammetry and Remote Sensing 59, nr 1-2 (sierpień 2004): 21–34. http://dx.doi.org/10.1016/j.isprsjprs.2003.12.003.

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M. Khedr, Ahmed, Zaher AL Aghbari i Ibrahim Kamel. "Privacy Preserving Decomposable Mining Association Rules on Distributed Data". International Journal of Engineering & Technology 7, nr 3.13 (27.07.2018): 157. http://dx.doi.org/10.14419/ijet.v7i3.13.16343.

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In distributed computing, data sharing is inevitable, however, moving local databases from one site to another should be avoided because of the computational overhead and privacy consideration. Most of the data mining algorithms are designed assuming that data repository is stored locally. This paper presents a scheme and algorithms for mining association rules in geographically distributed data. The proposed scheme preserves data privacy of the different geographical site by passing secure messages between them. The algorithms minimize the communication cost by exchanging statistical summaries of the local databases. We provide a privacy and security analysis that shows the privacy preserving aspects of the proposed algorithms. Moreover, the paper presents extensive simulation experiments to evaluate the efficiency of the proposed scheme.
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Lee, Sang-Moon, i Jeong-Min Seo. "A Spatial Data Mining and Geographical Customer Relationship Management System". Journal of the Korea Society of Computer and Information 15, nr 6 (30.06.2010): 121–28. http://dx.doi.org/10.9708/jksci.2010.15.6.121.

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Lu, Y. L., C. W. Liu, J. W. Li i J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (7.02.2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.

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Abstract. In recent years, with high accuracy, high frequency, considerable coverage of remote sensing images, map tiles, video surveillance, web crawlers, social networking platforms and other types of spatiotemporal data have exploded in geometric progression. Human society has come into the era of big data in time and space. In view of the characteristics of multi-attribute, multi-dimensional, multisource and heterogeneous spatiotemporal big data, how to make use of the emerging information technology means, combined with the geographic information data analysis means, the rapid mining and utilization of spatiotemporal big data has become a key problem to be solved. Built on the background of spatiotemporal big data and the process of geospatial cognition, this paper proposes a "cell-cube" spatiotemporal object data model. This paper constructs a model system of geo-spatiotemporal big data from the aspects of data organization, data storage and data partition, and abstracts the geo-space into an infinite number of geo-cells, and the adjacent geo-cells gather around the core cells to form geographical clusters, and the geographical clusters with similar attributes are clustered into geographical blocks. At the level of data organization, the spatial and temporal characteristics of structured data and unstructured data are considered as organizational dimensions, and a multi-factor extended cube data model is proposed. In the aspect of data storage, the organization model is further abstracted into the cell-cube structure of distributed data warehouse, and then the spatiotemporal data is stored uniformly. At the level of data segmentation, the mathematical table and space calculation method of multi-feature extended cube are proposed, and the geographical cell data division model based on connection is established. It solves the organization and management problem of spatiotemporal big data, provides a more complete data organization framework and solution for the application of geo-spatiotemporal big data, and promotes the development of deep mining of spatiotemporal extensive data in GIS. And to achieve space-time big data in the geographical space microscopic and the macroscopic unification cognition.
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Banerjee, Soumita. "The Role of Global Educational Database in Educational Data Mining". European Journal of Engineering and Technology Research 1, nr 6 (27.07.2018): 16–26. http://dx.doi.org/10.24018/ejeng.2016.1.6.194.

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Educational Data Mining is one of the major on-going research platforms now. Students’ records need to be maintained and analyzed in a manner so that they can be utilized to predict students’ behavior and learning methods. Although students’ academic records need to be processed and analyzed through data mining tools, the primary challenge is to gather individual academic student details. This paper proposes a global database of students irrespective of geographical boundaries. Academic performance of every student from every country will be updated in this platform. Students’ performance on major examinations will be available in the database. Supporting documents and performance details will be readily available and accessible to the evaluators from any geographic location. This will be helpful to standardize the evaluation process and analyze the performance of a student, irrespective of geographic boundaries. The following paper will discuss the available EDM tools and how data can be analyzed to extract information.
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Leon, Florin, Gabriela Maria Atanasiu i Dan Gâlea. "Using Data Mining Techniques for the Management of Seismic Vulnerability". Key Engineering Materials 326-328 (grudzień 2006): 501–4. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.501.

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Natural hazards, and especially earthquakes, are often recurring phenomena. Therefore, there is a permanent need for solutions to reduce earthquake losses by developing technologies, procedures, knowledge, and tools for seismic design and rehabilitation of buildings and infrastructure. A key point to an effective decision making process that aims at mitigating their effects is building a model of the underlying facts. A Geographical Information System (GIS) is a framework able to assemble, keep, process and display specific information, identified by geographical location, which can combine layers of information to give the user a better understanding about that location. By using a Geographical Information System containing geospatial data, one can develop useful scenarios to reduce natural disaster risk and vulnerability of structures. In this paper, we describe a way of applying data mining techniques from the artificial intelligence field to earthquake analysis in order to make a better investigation of the available data. These methods are capable of finding “hidden” correlations among different subsets of data, which cannot be revealed by means of simple statistics.
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Chakri, Sana, Said Raghay i Salah El Hadaj. "Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data". International Journal of Software Engineering and Knowledge Engineering 27, nr 03 (kwiecień 2017): 399–421. http://dx.doi.org/10.1142/s0218194017500140.

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Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.
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Liu, Zhewei, Xiaolin Zhou, Wenzhong Shi i Anshu Zhang. "Towards Detecting Social Events by Mining Geographical Patterns with VGI Data". ISPRS International Journal of Geo-Information 7, nr 12 (17.12.2018): 481. http://dx.doi.org/10.3390/ijgi7120481.

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Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.
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Gadekar, Dr Prof Amit R. "Location based Anomalies Detection on Geographical Map using Data Mining Techniques". International Journal for Research in Applied Science and Engineering Technology 8, nr 4 (30.04.2020): 138–41. http://dx.doi.org/10.22214/ijraset.2020.4021.

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M. Almuttairi, Rafah, Mahdi S. Almhanna, Mohammed Q. Mohammed i Saif Q Muhamed. "Promote Replica Management based on Data Mining Techniques". International Journal of Engineering & Technology 7, nr 4.19 (27.11.2018): 838. http://dx.doi.org/10.14419/ijet.v7i4.19.28006.

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The data grid technique evolved largely in sharing the data in multiple geographical stations across different sites to improve the data access and increases the speed of transmission data. The performance and the availability of the resources is taken into account, when a total of sites holding a copy of files, there is a considerable benefit in selecting the best set of replica sites to be cooperated for increasing data transfer job. In this paper, new selecrtion strategy is proposed to reduce the total transfer time of required files. Pincer-Search algorithm is used to explore the common characteristics of sites to select uncongested replica sites.
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Rozprawy doktorskie na temat "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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Książki na temat "Geographical data mining"

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

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

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Caluwe, Rita. Spatio-Temporal Databases: Flexible Querying and Reasoning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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J, Miller Harvey, i Han Jiawei, red. Geographic data mining and knowledge discovery. London: Taylor & Francis, 2001.

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

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Bienen, Derk. Die politische Oekonomie von Arbeitsmarktreformen in Argentinien. Bern: Peter Lang International Academic Publishers, 2005.

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International Conference on Geospatial Semantics (3rd 2009 Mexico City, Mexico). GeoSpatial semantics: Third international conference, GeoS 2009, Mexico City, Mexico, December 3-4, 2009 : proceedings. Berlin: Springer, 2009.

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Blanuca, Viktor, Leonid Bezrukov, Egor Sherin i Anatoliy Yakobson. Public geography: Digital priorities of the XXI century. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863096.

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Social geography, aimed at understanding the territorial organization of society, has a number of digital priorities that have not been analyzed before. They are related to the objects and methods of research, as well as to the methods of visualization and the areas of application of the results obtained. The monograph analyzes the world experience of socio-geographical study of platform economy, telecommunication networks and "smart cities", the use of data mining, models of spatial diffusion of innovations and zoning, visualization through cartographic image, dendrogram and tag cloud, the implementation of research results in geographical expertise, regional policy and administrative-territorial division. It is intended for specialists, students and postgraduates.
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P, Sheth Amit, i SpringerLink (Online service), red. Geospatial Semantics and the Semantic Web: Foundations, Algorithms, and Applications. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Kevin, Shaw, i Abdelguerfi Mahdi, red. Mining spatio-temporal information systems. Boston: Kluwer Academic Publishers, 2002.

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Części książek na temat "Geographical data mining"

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Rinzivillo, S., F. Turini, V. Bogorny, C. Körner, B. Kuijpers i M. May. "Knowledge Discovery from Geographical Data". W Mobility, Data Mining and Privacy, 243–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_10.

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Nissi, Eugenia, Annalina Sarra, Sergio Palermi i Gaetano De Luca. "The Application of M-Function Analysis to the Geographical Distribution of Earthquake Sequence". W Classification and Data Mining, 271–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28894-4_32.

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Doan, Thanh-Nam, i Ee-Peng Lim. "Modeling Check-In Behavior with Geographical Neighborhood Influence of Venues". W Advanced Data Mining and Applications, 429–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69179-4_30.

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Wang, Fengjiao, Chun-Ta Lu, Yongzhi Qu i Philip S. Yu. "Collective Geographical Embedding for Geolocating Social Network Users". W Advances in Knowledge Discovery and Data Mining, 599–611. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_47.

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He, Yuan, Cheng Wang i Changjun Jiang. "Multi-perspective Hierarchical Dirichlet Process for Geographical Topic Modeling". W Advances in Knowledge Discovery and Data Mining, 811–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_63.

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Marjoribanks, Roger. "GIS Geographical Information Systems and exploration data basesExploration Databases". W Geological Methods in Mineral Exploration and Mining, 165–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-74375-0_10.

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Han, Yuqiang, Yao Wan, Liang Chen, Guandong Xu i Jian Wu. "Exploiting Geographical Location for Team Formation in Social Coding Sites". W Advances in Knowledge Discovery and Data Mining, 499–510. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_39.

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Malerba, Donato, Annalisa Appice i Michelangelo Ceci. "A Data Mining Query Language for Knowledge Discovery in a Geographical Information System". W Database Support for Data Mining Applications, 95–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44497-8_5.

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Mazzola, Luca, Pedro Chahuara, Aris Tsois i Mauro Pedone. "Resolution of Geographical String Name through Spatio-Temporal Information". W Machine Learning and Data Mining in Pattern Recognition, 498–512. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_38.

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Lee, Ickjai, i Vladimir Estivill-Castro. "Polygonization of Point Clusters through Cluster Boundary Extraction for Geographical Data Mining". W Advances in Spatial Data Handling, 27–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56094-1_3.

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Streszczenia konferencji na temat "Geographical data mining"

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Shu, Hong, i Cuihong Qi. "Temporal uncertainty of geographical information". W MIPPR 2005 Geospatial Information, Data Mining, and Applications, redaktorzy Jianya Gong, Qing Zhu, Yaolin Liu i Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651233.

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Zhou, Fang, Q. Claire i Ross D. King. "Predicting the Geographical Origin of Music". W 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.73.

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Li, Zonghua, Mingjun Peng i Wei Fan. "A SOA-based approach to geographical data sharing". W International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, redaktorzy Yaolin Liu i Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838415.

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Gong, Jian, Yaolin Liu, Zhi Zhang i Jianfeng Li. "Urban land space evolution based on geographical simulation systems". W International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, redaktorzy Yaolin Liu i Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838662.

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Cui, Wei, i Deren Li. "The geographical ontology, LDAP, and the space information semantic grid". W MIPPR 2005 Geospatial Information, Data Mining, and Applications, redaktorzy Jianya Gong, Qing Zhu, Yaolin Liu i Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650271.

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Ahuja, Aman, Wei Wei, Wei Lu, Kathleen M. Carley i Chandan K. Reddy. "A Probabilistic Geographical Aspect-Opinion Model for Geo-Tagged Microblogs". W 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.82.

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de Vries, Gerben K. D., Willem Robert van Hage i Maarten van Someren. "Comparing Vessel Trajectories Using Geographical Domain Knowledge and Alignments". W 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.123.

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Hu, Shunguang, Zengxiang Zhang, Xianhu Wei i Fang Liu. "A research on natural geographical factors based on Chinese urban expansion". W International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, redaktorzy Yaolin Liu i Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.837745.

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Cheng, Gang, i Qingyun Du. "Construction of geographical names knowledge base with ontology and production rule". W International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, redaktorzy Yaolin Liu i Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838292.

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Mo, Fan, i Hayato Yamana. "Point of Interest Recommendation by Exploiting Geographical Weighted Center and Categorical Preference". W 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00021.

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Raporty organizacyjne na temat "Geographical data mining"

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Kindt, Roeland, Ian K Dawson, Jens-Peter B Lillesø, Alice Muchugi, Fabio Pedercini i James M Roshetko. The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21001.pdf.

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A systematic approach to tree planting and management globally is hindered by the limited synthesis of information sources on tree uses and species priorities. To help address this, the authors ‘mined’ information from 23 online global and regional databases to assemble a list of the most frequent tree species deemed useful for planting according to database mentions, with a focus on tropical regions. Using a simple vote count approach for ranking species, we obtained a shortlist of 100 trees mentioned in at least 10 of our data sources (the ‘top-100’ species). A longer list of 830 trees that were mentioned at least five times was also compiled. Our ‘top-100’ list indicated that the family Fabaceae (syn. Leguminosae) was most common. The information associated with our mined data sources indicated that the ‘top-100’ list consisted of a complementary group of species of differing uses. These included the following: for wood (mostly for timber) and fuel production, human nutrition, animal fodder supply, and environmental service provision (varied services). Of these uses, wood was most frequently specified, with fuel and food use also highly important. Many of the ‘top-100’ species were assigned multiple uses. The majority of the ‘top-100’ species had weediness characteristics according to ‘attribute’ invasiveness databases that were also reviewed, thereby demonstrating potential environmental concerns associated with tree planting that need to be balanced against environmental and livelihood benefits. Less than half of the ‘top-100’ species were included in the OECD Scheme for the Certification of Forest Reproductive Material, thus supporting a view that lack of germplasm access is a common concern for trees. A comparison of the ‘top-100’ species with regionally-defined tree inventories indicated their diverse continental origins, as would be anticipated from a global analysis. However, compared to baseline expectations, some geographic regions were better represented than others. Our analysis assists in priority-setting for research and serves as a guide to practical tree planting initiatives. We stress that this ‘top-100’ list does not necessarily represent tree priorities for the future, but provides a starting point for also addressing representation gaps. Indeed, our primary concern going forward is with the latter.
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Matenga, Chrispin, i Munguzwe Hichaambwa. A Multi-Phase Assessment of the Effects of COVID-19 on Food Systems and Rural Livelihoods in Zambia. Institute of Development Studies (IDS), grudzień 2021. http://dx.doi.org/10.19088/apra.2021.039.

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COVID-19 was declared a pandemic by the World Health Organization in March 2020. The speed with which the pandemic spread geographically, and the high rate of mortality of its victims prompted many countries around the world to institute ‘lockdowns’ of various sorts to contain it. While the global concern in the early months following the emergence of COVID-19 was with health impacts, the ‘lockdown’ measures put in place by governments triggered global socioeconomic shocks as economies entered recessions due to disruption of economic activity that the ‘lockdown’ measures entailed. Data suggests that the socioeconomic shocks arising from ‘lockdowns’ have been more severe in sub-Saharan Africa countries, generating dire livelihood consequences for most citizens who depend on the informal economy for survival. In Zambia, the effects of COVID-19 combined with a severe drought, and a decline in mining activity to contribute to a downward spiral in Zambia’s economy. This report aims to gain real-time insights into how the COVID-19 crisis was unfolding in Zambia and how rural people and food and livelihood systems were responding. The study focused on documenting and understanding the differential impacts of the pandemic at the household level in terms of changes in participation in farming activities, availability of services for agricultural production, labour and employment, marketing and transport services, food and nutrition security and poverty and wellbeing.
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Henderson, Tim, Mincent Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, kwiecień 2021. http://dx.doi.org/10.36967/nrr-2285306.

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A fundamental responsibility of the National Park Service is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile for this unit. The type or reference section is important both historically and scientifically, and should be recorded such that other researchers may evaluate it in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN, methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Chihuahuan Desert Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...
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Henderson, Tim, Vincent Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, kwiecień 2021. http://dx.doi.org/10.36967/nrr-2285337.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be available for other researchers to evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Northern Colorado Plateau Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...
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Henderson, Tim, Vincent Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, lipiec 2021. http://dx.doi.org/10.36967/nrr-2286915.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be protected and conserved for researchers to study and evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Klamath Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers...
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Henderson, Tim, Vincent Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, grudzień 2021. http://dx.doi.org/10.36967/nrr-2289952.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities that may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the type section or other category of stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit which presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future.. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (e.g., geology, hydrology, climate), biological resources (e.g., flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities such as inventory, monitoring, research, and data management. Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory & Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the Mojave Desert Inventory & Monitoring Network (MOJN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the MOJN shows there are currently no designated stratotypes for Joshua Tree National Park (JOTR) or Manzanar National Historic Site (MANZ); Death Valley...
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Henderson, Tim, Vincet Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, marzec 2022. http://dx.doi.org/10.36967/nrr-2293013.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that form a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies (rock types), bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2021). In most instances, when a new geologic unit (such as a formation) is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit that presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring (I&M) networks established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities (inventory, monitoring, research, and data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the North Coast and Cascades Inventory & Monitoring Network (NCCN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the NCCN shows there are currently no designated stratotypes for Fort Vancouver National Historic Site (FOVA), Lewis and Clark National Historical Park (LEWI), or San Juan...
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Henderson, Tim, Vincent Santucci, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, maj 2022. http://dx.doi.org/10.36967/nrr-2293381.

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Streszczenie:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that form a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies (rock types), bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2021). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit that presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS is centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project (Henderson et al. 2020). Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the Arctic Inventory & Monitoring Network (ARCN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the ARCN shows there are currently no designated stratotypes for Cape Krusenstern National Monument (CAKR) and Kobuk Valley National Park (KOVA)...
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Henderson, Tim, Vincent Santucciq, Tim Connors i Justin Tweet. National Park Service geologic type section inventory: San Francisco Bay Area Inventory & Monitoring Network. National Park Service, maj 2022. http://dx.doi.org/10.36967/nrr-2293533.

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Streszczenie:
A fundamental responsibility of the National Park Service (NPS) is to ensure that the resources of the National Park System are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities that may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that form a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies (rock types), bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2021). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit that presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS is centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory & Monitoring Network (GRYN) as the pilot network for initiating this project (Henderson et al. 2020). Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the San Francisco Bay Area Inventory & Monitoring Network (SFAN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the SFAN shows there are currently no designated stratotypes for Fort Point National Historic Site (FOPO) and Muir Woods National Monument (MUWO)...
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