Dissertations / Theses on the topic '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.
Full textMetadata 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.
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.
Full textGeographical 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.
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.
Full textMetadata 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.
Brindley, Paul. "Generating vague geographic information through data mining of passive web data." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33722/.
Full textAdu-Prah, Samuel. "GEOGRAPHIC DATA MINING AND GEOVISUALIZATION FOR UNDERSTANDING ENVIRONMENTAL AND PUBLIC HEALTH DATA." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/657.
Full textBogorny, 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.
Full textThe 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.
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.
Full textMost 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.
Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.
Full textKINSEY, 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.
Full textSengstock, Christian [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data / Christian Sengstock ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180395662/34.
Full textAlam, Mohammad Tanveer. "Image Classification for Remote Sensing Using Data-Mining Techniques." Youngstown State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.
Full textIvanovic, Stefan. "Quality based approach for updating geographic authoritative datasets from crowdsourced GPS traces." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1068/document.
Full textNowadays, the need for very up to date authoritative spatial data has significantly increased. Thus, to fulfill this need, a continuous update of authoritative spatial datasets is a necessity. This task has become highly demanding in both its technical and financial aspects. In terms of road network, there are three types of roads in particular which are particularly challenging for continuous update: footpath, tractor and bicycle road. They are challenging due to their intermittent nature (e.g. they appear and disappear very often) and various landscapes (e.g. forest, high mountains, seashore, etc.).Simultaneously, GPS data voluntarily collected by the crowd is widely available in a large quantity. The number of people recording GPS data, such as GPS traces, has been steadily increasing, especially during sport and spare time activities. The traces are made openly available and popularized on social networks, blogs, sport and touristic associations' websites. However, their current use is limited to very basic metric analysis like total time of a trace, average speed, average elevation, etc. The main reasons for that are a high variation of spatial quality from a point to a point composing a trace as well as lack of protocols and metadata (e.g. precision of GPS device used).The global context of our work is the use of GPS hiking and mountain bike traces collected by volunteers (VGI traces), to detect potential updates of footpaths, tractor and bicycle roads in authoritative datasets. Particular attention is paid on roads that exist in reality but are not represented in authoritative datasets (missing roads). The approach we propose consists of three phases. The first phase consists of evaluation and improvement of VGI traces quality. The quality of traces was improved by filtering outlying points (machine learning based approach) and points that are a result of secondary human behaviour (activities out of main itinerary). Remained points are then evaluated in terms of their accuracy by classifying into low or high accurate (accuracy) points using rule based machine learning classification. The second phase deals with detection of potential updates. For that purpose, a growing buffer data matching solution is proposed. The size of buffers is adapted to the results of GPS point’s accuracy classification in order to handle the huge variations in VGI traces accuracy. As a result, parts of traces unmatched to authoritative road network are obtained and considered as candidates for missing roads. Finally, in the third phase we propose a decision method where the “missing road” candidates should be accepted as updates or not. This decision method was made in multi-criteria process where potential missing roads are qualified according to their degree of confidence. The approach was tested on multi-sourced VGI GPS traces from Vosges area. Missing roads in IGN authoritative database BDTopo® were successfully detected and proposed as potential updates
Hammal, Mohamed Ali. "Contribution à la découverte de sous-groupes corrélés : Application à l’analyse des systèmes territoriaux et des réseaux alimentaires." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI024.
Full textBetter feeding cities in quantity and quality, especially large cities, is a major challenge, whose resolution requires a better understanding of the relationships between urban populations and their food. On the scale of urban food systems, we need to understand the availability of food resources crossed with the socio-economic profiles of the territories. But we lack tools and methods to systematically understand the relationships between consumption basins, supply and eating habits. The objective of this thesis is to contribute to the development of new IT tools to process temporal, heterogeneous and multi-sources data in order to identify and characterize behaviors specific to a geographic area. For this, we rely on the joint exploration of gradual patterns, to discover rank correlations, and subgroups in order to find contexts for which the correlations described by the gradual patterns are exceptionally strong compared to the remaining of the data. We propose an enumeration algorithm based on pruning properties with upper bounds, as well as another algorithm which samples the patterns according to the quality measure. These approaches are validated not only on benchmark datasets, but also through an empirical study of the formation of food deserts in the Lyon urban area
Katerattanakul, Nitsawan. "A pilot study in an application of text mining to learning system evaluation." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Katerattanakul_09007dcc807b614f.pdf.
Full textVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 19, 2010) Includes bibliographical references (p. 72-75).
Liew, Jie Shi. "Using Social Media Data Mining To Understand The Public Perception of Coal In The United States." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2744.
Full textPivato, Marina Abichabki. "Mineração de regras de associação em dados georreferenciados." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18092006-104657/.
Full textGeographic information systems are used to store, manipulate, and analyze spatial data and its descriptive aspects. Spatial data analysis can be done by searching association rules that describe relationships between the data. However, georeferenced data present topological relations unknown to traditional mining association rule algorithms. To solve this problem, Koperski and Han (1995) and Malerba et al. (2001) proposed a topological relation extraction process integrated to a mining association rule algorithm. This process requires all data to be translated as logical predicates. As an alternative to this solution, this work proposes to break down this process by pre-processing the georeferenced data to find topological relations, then executing traditional mining association rule algorithms. The resulting rules must present descriptive characteristics of the data and topological relations. To reach this objective, a process of rule extraction in georeferenced data was specified, in addition to a pre-processing module implementation. This module was evaluated by using a case study that uses a geographic information system of the city of Jaboticabal, in the context of urban planning. The generated rules were analyzed by a specialist using the measures of support and confidence. In addition, an analysis regarding the processing time and memory consumption was provided to find the topological relations, which shows that it is possible to extract the patterns with the proposed process and module.
Salap, Seda. "Development Of A Gis-based Monitoring And Management System For Underground Mining Safety." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609815/index.pdf.
Full text(ii) surveillance and maintenance
and (iii) emergency. The implementation has to be carried out in a Web-Based Geographic Information System. The process comprises first the safety concept as the application domain model and then a conceptual model was generated in terms of Entity- Relationship Diagrams. After the implementation of the logical model a user interface was developed and GIS was tested. Finally, one should deal with the question if it is possible to extend the method of resolution used to a national GIS infrastructure.
Braga, Reinaldo. "LIDU : Location-based approach to IDentify similar interests between Users in social networks." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENM055/document.
Full textSharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this thesis, we focus on investigating ways of efficiently analyzing user trajectories, extracting user preferences from them and identifying similar interests between users. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The final results provided interesting avenues for exploring Location-based Social Network (LBSN) applications
Prananto, Agnes Kristina. "The use of remotely sensed data to analyse spatial and temporal trends in vegetation patchiness within rehabilitated bauxite mines in the Darling Range, W.A. /." Connect to this title, 2005. http://theses.library.uwa.edu.au/adt-WU2006.0012.
Full textShakeel, Mohammad Danish. "Land Cover Classification Using Linear Support Vector Machines." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1231812653.
Full textLu, Hongwei Marketing Australian School of Business UNSW. "Small area market demand prediction in the automobile industry." Publisher:University of New South Wales. Marketing, 2008. http://handle.unsw.edu.au/1959.4/43027.
Full textMarques, Delano Brandes. "SISTEMA INTEGRADO DE MONITORAMENTO E CONTROLE DA QUALIDADE DE COMBUSTÍVEL." Universidade Federal do Maranhão, 2004. http://tedebc.ufma.br:8080/jspui/handle/tede/348.
Full textThis work aims the implantation of an Integrated System that, besides allowing a better, more efficient and more practical monitoring, makes possible the control and optimization of problems related to the oil industry. In order to guarantee fuel s quality and normalization, the development of efficient tools that allow it s monitoring of any point (anywhere) and for any type of fuel is indispensable. Considering the variety of criteria, a decision making should be based on the evaluation of the most varied types of space data and not space data. In this sense, Knowledge Discovery in Databases process is used, where the Data Warehouse and Data Mining steps allied to a Geographic Information System are emphasized. This system presents as objective including several fuel monitoring regions. From different information obtained in the ANP databases, an analysis was carried out and a Data Warehouse model proposed. In the sequel, Data Mining techniques (Principal Component Analysis, Clustering Analysis and Multiple Regression) were applied to the results in order to obtain knowledge (patterns).
O presente trabalho apresenta estudos que visam a implantação de um Sistema Integrado que, além de permitir um melhor monitoramento, praticidade e eficiência, possibilite o controle e otimização de problemas relacionados à indústria de petróleo. Para garantir qualidade e normalização do combustível, é indispensável o desenvolvimento de ferramentas eficientes que permitam o seu monitoramento de qualquer ponto e para qualquer tipo de combustível. Considerando a variedade dos critérios, uma tomada de decisão deve ser baseada na avaliação dos mais variados tipos de dados espaciais e não espaciais. Para isto, é utilizado o Processo de Descoberta de Conhecimento, onde são enfatizadas as etapas de Data Warehouse e Data Mining aliadas ao conceito de um Sistema de Informação Geográfica. O sistema tem por objetivo abranger várias regiões de monitoramento de combustíveis. A partir do levantamento e análise das diferentes informações usadas nos bancos de dados da ANP foi proposto um modelo de data warehouse. Na seqüência foram aplicadas técnicas de mineração de dados (Análise de Componentes Principais, Análise de Agrupamento e Regressão) visando à obtenção de conhecimento (padrões).
Duarte, Mariana de Luna Freire. "Mineração de dados usando programação genética." Universidade Federal da Paraíba, 2012. http://tede.biblioteca.ufpb.br:8080/handle/tede/6094.
Full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
Data mining has become an important activity for decision-making in large and small companies since it allows the extraction of relevant and non-trivial information so that corrections and adjustment in administrative and economic strategies could be selected. Consequently, an increase in the geographical data storage is seen in such a way that conventional data mining cannot carry out the extraction of knowledge from a high dimension database. According to the current literature, there are few tools capable of extracting knowledge from geographical data, mainly if the database is made of conventional (numeral and textual) and geographical (point, line and polygon) data. The aim of this study is to present a new algorithm for spatial data mining DMGP using the two types of data to carry out the information extraction from a determined base. This algorithm is based on the DMGeo algorithm which also seeks to extract knowledge from the two types of data. These algorithms are based on Genetic Programming and were developed to obtain classification rules of patterns existing in the numeral and geographical attributes. To obtain a better performance for the DMGeo, the use of meta-heuristic GRASP and ILS in the performance of DMGP algorithm was proposed to improve the individuals from the generated population . GRASP and ILS were used to generate the initial population and disturb some individuals aiming at finding better solutions.
A mineração de dados tornou-se uma importante atividade para o processo de tomada de decisão para grandes ou pequenas corporações, pois a partir dela é possível extrair informações relevantes e não triviais de forma que correções e ajustes em estratégias econômicas e administrativas possam ser selecionadas. Assim, vê-se um aumento no armazenamento de dados geográficos, de tal maneira que a mineração de dados convencionais não suporta realizar a extração de conhecimento em um banco de dados de elevada dimensão. De acordo com a literatura atual, poucas ferramentas capazes de extrair conhecimento a partir de dados geográficos são encontradas, principalmente, quando a base de dados é composta por dados convencionais (numéricos e textuais) e geográficos (ponto, linha e polígono). Este trabalho tem como objetivo principal apresentar um novo algoritmo, chamado DMGP, para a atividade de mineração de dados espaciais utilizando os dois tipos de dados para realizar a extração de informações de uma determinada base. O algoritmo em questão tem como base o algoritmo DMGeo que, por sua vez, também visa extrair conhecimento a partir dos dois tipos de dados. Estes algoritmos são baseados na Programação Genética e foram desenvolvidos a fim de obter regras de classificação de padrões existentes nos atributos numéricos e geográficos. Visando obter um melhor desempenho para o DMGeo, foi proposto a utilização das meta-heuríticas GRASP e ILS no funcionamento do algoritmo DMGP para aperfeiçoar os indivíduos das populações geradas. Tais meta-heurísticas foram usadas para gerar a população incial e para realizar uma perturbação de alguns indivíduos, com o intuito de encontrar soluções melhores.
Prananto, Agnes Kristina. "The use of remotely sensed data to analyse spatial and temporal trends in vegetation patchiness within rehabilitated bauxite mines in the Darling Range, W.A." University of Western Australia. School of Earth and Geographical Sciences, 2006. http://theses.library.uwa.edu.au/adt-WU2006.0012.
Full textAlatrista-Salas, Hugo. "Extraction de relations spatio-temporelles à partir des données environnementales et de la santé." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2013. http://tel.archives-ouvertes.fr/tel-00997539.
Full textGomes, Eduardo Luis. "Arquitetura RF-Miner: uma solução para localização em ambientes internos." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2898.
Full textThe use of passive UHF RFID tags for indoor location has been widely studied due to its low cost. However, there is still a great difficulty to reach good results, mainly due the radio frequency variation in environments that have materials with reflective surfaces, such as metal and glass. This research proposes a localization architecture for indoor environments using passive UHF RFID tags and data mining techniques. With the application of the architecture in real environment, it was possible to identify the exact position of objects with the precision of approximately five centimeters and in real time. The architecture has demonstrated an efficient alternative for the implantation of indoor localization systems, besides presenting a derivation technique of direct attributes that contributes effectively to the final results.
Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining." Thesis, 2002. http://hdl.handle.net/1959.13/25012.
Full textPhD Doctorate
Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining." 2002. http://hdl.handle.net/1959.13/25012.
Full textPhD Doctorate
Ribeiro, Vítor. "Mining Geographic Data for Fuel Consumption Estimation." Dissertação, 2013. http://hdl.handle.net/10216/75540.
Full textRibeiro, Vítor Daniel Ferreira da Cunha. "Mining Geographic Data for Fuel Consumption Estimation." Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/66841.
Full textRibeiro, Vítor Daniel Ferreira da Cunha. "Mining Geographic Data for Fuel Consumption Estimation." Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/66841.
Full textAbdelkareem, Nourhan Khalifa. "Analysis and visualization of energy use for university campus." Master's thesis, 2015. http://hdl.handle.net/10362/14567.
Full textGuo, Yunyong. "A Cloud Computing Based Platform for Geographically Distributed Health Data Mining." Thesis, 2013. http://hdl.handle.net/1828/4890.
Full textGraduate
0573
"spatiotemporal data mining, analysis, and visualization of human activity data." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.15915.
Full textDissertation/Thesis
Ph.D. Geography 2012
Chung, Chi Wei, and 鍾志偉. "Post office location analysis using geographic information system and data mining techniques." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58620853081761708899.
Full text國立政治大學
資訊科學學系
99
The amount of postal mail declines in recent years due to the efforts of paper-reduce policies implemented by the government, the industries, and the general publics. It becomes one of the important issues of the Chunghwa Post Company, to compete with other companies in domestic freight and mail services and to achieve the desired profits. Traditionally, the location of post offices were decided according to the government policies as well as the company regulations. The issues involved in the site selection analysis were seldom considered. Hence, developing an effective and fair mechanism to find the new post office locations that could improve the company’s surplus becomes an important problem to be solved. The purpose of this thesis is to provide recommendations to the post office site selection which will yield high profit to the company. We proposed a method to evaluate the effective profits that could be produced by a particular post office through the data mining techniques and the related GIS information. We first collect various data, such as neighborhood population, traffic flow, postal mail received at particular post office, competitor’s information, etc., and analyze these data using data mining techniques in order to establish prediction models. The most appropriate model was chosen to find the new post office sites. The Metropolitan Taipei area was chosen to illustrate our idea. The best sites for new post offices were selected through the buffering analysis as well as the data mining techniques. The experimental results show that our method can successfully find eleven locations which could generate most profit to Chunghwa Post Company if the new post offices were located in these places.
Wu, Pei-Hua, and 吳珮華. "ATM Location Set-up Analysis by Using Geographic Information System and Data Mining Method." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/44458260609632619451.
Full text國立政治大學
資訊科學學系
98
The convenience of the ATM banking facilities caused rapidly increasing in ATM demands during the past decades. The expenses for installation and maintenance of the ATMs are considerably high. However, there are no effective methods to evaluate the economic benefits on ATMs’ locations. Traditionally, the decision for ATM installation is based on policymaker’s experiences and subjective demands. The cost-effective issues and the spatial factors involved in location finding were seldom considered. Hence, develop a reasonable and effective mechanism to find the ATM locations that could improve economic efficiency become an important problem to be solved. The purpose of this thesis is to provide suggestion on the cost-effective ATM installation locations to help the policymaker in making decisions. We combine the techniques in geographical information system (GIS) as well as data mining for the cost-effective ATM installation location analysis. Using the ATM utilization factors for various ATMs, we can associate the attribute data with the spatial provided by GIS. Then, we use data mining techniques to analyze the factors that could influence the cost-effective installation location of ATMs. From this information, we can summarize the association rules that have the most impacts to localize the ATM installation locations. Finally, using these association rules, we can reach conclusion on ATMs’ installation locations. We use our local bank data to illustrate our idea. Experimental results show that we can successfully find the key factors that influence the cost-effective ATM installation locations. The range and the quantities of these events can be identified clearly, hence, making it possible to suggest whether an ATM should be removed or be relocated. Furthermore, we can suggest installing a new ATM at a particular location for potential customers or not.
Veiga, Pedro Guilherme Ribeiro. "Determine the potential and the extent to which geographic socio-demographic data impacts retail performance revenue and consumer behavior and determine how much discounts impact revenue." Master's thesis, 2021. http://hdl.handle.net/10362/129685.
Full textThe objective of this thesis is to describe and analyze the sales and revenue of a food distribution company, Tasty and Sweet, by product or product type and relate it to geographic socio-demographic data provided by National Bureau of Statistics (INE). Tasty and Sweet operates and covers the entire Portuguese national territory, distributing and selling their products to any retailer company who is willing to resell them. The goal of this work is to develop an analytical model that allows the tracking of all the sales and revenue, by group item or by item, relating them, eventually, to the social and demographic characterization of a specific geography so that patterns may be (or not) identified. Another goal of the thesis is to determine the extent to which sales promotions have an impact on retailer sales. In order to achieve these objectives several methods of data analysis will be developed, supported and backed up by software from SAS Institute (Sas Guide and Sas Miner). It was possible to come to the following conclusions: firstly there are relevant socio-demographic variables that impact, or are more related, with de retailer’s revenue like: Indicators of enterprises by municipality N. º/km2 - enterprise density, 2016; Territorial structure by municipality - weight of resident population, 2011; Distribution of declared gross income less individual tax income paid of tax households by municipality (less 5k), 2016 are in fact more impacting; Secondly revenue volume is in fact impacted by discounts and promotions.
Cowan, Terri. "A Framework for Investigating Volunteered Geographic Information Relevance in Planning." Thesis, 2013. http://hdl.handle.net/10012/7475.
Full textYu, Chih-ting, and 余致廷. "Where to open a clinic? Analysis of optimal clinic location using geographic information system and data mining techniques." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/bzu6rw.
Full text國立中央大學
資訊管理學系
102
In the past 10 years, the number of clinics in Taiwan has increased gradually, and the scale of hospitals has become larger, making the market of medical care in Taiwan more competitive. Therefore, to find an optimum location effectively and quickly is critical for new entrants to develop the business. This issue is not well addressed in the literature. To fill this knowledge gap, this study proposes a model to help select the most suitable site for a medical clinic, combining Geographic Information System (GIS) and data mining. Using data from a primary survey of clinics and various secondary sources in Taipei, we analyzed the critical determinants of clinic location choice. We expect to provide information showing where to open a clinic, which would help clinic owners reduce operation costs.
Antunes, Jorge Manuel Alves. "Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon." Master's thesis, 2016. http://hdl.handle.net/10362/17446.
Full textThe interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.
Penedos, Pedro Pais. "Precision Agriculture Using Unmanned Aerial Systems: Mapping Vigor’s Spatial Variability On Low Density Agricultures Using a Canopy Pixel Classification And Interpolation Model." Master's thesis, 2018. http://hdl.handle.net/10362/33277.
Full textIt is becoming more present in agriculture’s practices the use of Unmanned Aerial Systems with sensors capable of capturing light, in the visible and in longer wavelengths of the electromagnetic spectrum once reflected on the field. These sensors have been used to perform Remote Sensing also in other knowledge fields, describing phenomenon without the risk, cost and the time consuming processes associated with in site samples collection and analysis by a technician or satellite imagery acquisition. The Vegetation Indexes developed can explain the vigor of the cultivation and its data collection processes are more cost and time efficient, allowing farmers to monitor plant grow in every critical stage. These Vegetation Indexes started by being calculated from satellite and airborne imagery, one of the main source for crop management tools, however UAS is becoming more present in Precision Agriculture, achieving better spatial and temporal resolution. This gap in spatial resolution when studying low density cultivations like olive groves and vineyards, creates Vegetation Index’s maps polluted with noise caused by the soil and therefore difficult to interpret and analyse. Hence, when the agriculture has spaced and low density vegetation becomes challenging to understand and extract information from these vegetation index’s maps regarding different spatial variability patterns of the tree canopy vigor. In these cases, where vegetation is spaced it is important to filter this noise. A Classification Model was developed with the objective of extracting just the vegetation’s canopy data. The soil was filtered and the canopy data interpolated using spatial analysis tools. The final interpolated maps produced can provide meaningful information regarding the spatial variability and be used to support decision making, identifying critical areas to be intervened and managed, or be used as an input for Variable Rate Technology applications.
Šećerov, Milan. "Analysis of panoramio photo tags in order to extract land use information." Master's thesis, 2015. http://hdl.handle.net/10362/14549.
Full textDlamini, Wisdom Mdumiseni Dabulizwe. "Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques." Thesis, 2016. http://hdl.handle.net/10500/20692.
Full textEnvironmental Sciences
D. Phil. (Environmental Science)
"Developing a Cohesive Space-Time Information Framework for Analyzing Movement Trajectories in Real and Simulated Environments." Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.9514.
Full textDissertation/Thesis
Ph.D. Geography 2011
João, Paulo Abel de Almeida. "Modelo preditivo da criminalidade – georeferenciação ao concelho de Lisboa." Master's thesis, 2010. http://hdl.handle.net/10362/3424.
Full textPretende-se elaborar um modelo preditivo ou processo analítico e sistemático de descoberta do conhecimento, orientado segundo os princípios da pertinência e da oportunidade, que detecte os hot spots da criminalidade, que faça uma previsão e propensão de ocorrência e ainda, que faça uma previsão da sua evolução, estagnação ou redução, sendo realizado a partir do estabelecimento de correlações entre conjuntos de dados criminais ocorridos no primeiro semestre do ano de 2007 no concelho de Lisboa. Este modelo poderá posteriormente ser aplicado a outras regiões do país.
Pileththuwasan, Gallege Lahiru Sandakith. "Design, development and experimentation of a discovery service with multi-level matching." Thesis, 2013. http://hdl.handle.net/1805/3695.
Full textThe contribution of this thesis focuses on addressing the challenges of improving and integrating the UniFrame Discovery Service (URDS) and Multi-level Matching (MLM) concepts. The objective was to find enhancements for both URDS and MLM and address the need of a comprehensive discovery service which goes beyond simple attribute based matching. It presents a detailed discussion on developing an enhanced version of URDS with MLM (proURDS). After implementing proURDS, the thesis includes details of experiments with different deployments of URDS components and different configurations of MLM. The experiments and analysis were carried out using proURDS produced MLM contracts. The proURDS referred to a public dataset called QWS dataset. This dataset includes actual information of software components (i.e., web services), which were harvested from the Internet. The proURDS implements the different matching operations as independent operators at each level of matching (i.e., General, Syntactic, Semantic, Synchronization, and QoS). Finally, a case study was carried out with the deployed proURDS. The case study addresses real world component discovery requirements from the earth science domain. It uses the contracts collected from public portals which provide geographical and weather related data.