2,579 research outputs found

    Exploratory data analysis using self-organising maps defined in up to three dimensions

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    The SOM is an artificial neural network based on an unsupervised learning process that performs a nonlinear mapping of high dimensional input data onto an ordered and structured array of nodes, designated as the SOM output space. Being simultaneously a quantization algorithm and a projection algorithm, the SOM is able to summarize and map the data, allowing its visualization. Because using the most common visualization methods it is very difficult or even impossible to visualize the SOM defined with more than two dimensions, the SOM output space is generally a regular two dimensional grid of nodes. However, there are no theoretical problems in generating SOMs with higher dimensional output spaces. In this thesis we present evidence that the SOM output space defined in up to three dimensions can be used successfully for the exploratory analysis of spatial data, two-way data and three-way data. Although the differences between the methods that are proposed to visualize each group of data, the approach adopted is commonly based in the projection of colour codes, which are obtained from the output space of 3D SOMs, in some specific bi-dimensional surface, where data can be represented according to its own characteristics. This approach is, in some cases, also complemented with the simultaneous use of SOMs defined in one and two dimensions, so that patterns in data can be properly revealed. The results obtained by using this visualization strategy indicates not only the benefits of using the SOM defined in up to three dimensions but also shows the relevance of the combined and simultaneous use of different models of the SOM in exploratory data analysis

    Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe 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

    Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques

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    Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This study aimed a procedure of unsupervised classification for remote sensing images based on a combination of Self-Organizing maps (SOM) and segmentation. The integration is conceived first obtaining clusters of the spectral behavior of the satellite image using Self-Organizing Maps. As visualization technique for the SOM is used the U-matrix. Subsequently is used seeded region growing segmentation technique to obtain a delimitation of the clusters in the data. Finally, from the regions of neurons in the U-matrix are deduced the clusters in the original pixels of the image. To evaluate the proposed methodology it was considered a subset of a satellite image as use case. The results were measured through accuracy assessment of the case and comparing definition of the obtained clusters against each technique separately. Cramers'V was used to evaluate the association between clustering obtained each method separately and reference data for the specific use case

    Iz časopisa

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    From the field of cartography and geoinformation, there are journal’s article extracts given which are not cartographic first and whose complete texts are on the Internet, accessible to the members of Croatian academic and research community. Most journals can be accessed through the PERO browser (http://knjiznica.irb.hr/pero/index.php). For the journals not found through this browser, the complete texts of the mentioned articles are available for free on the givenweb-address.Next to every journal headline, in the brackets, it is noted which prominent bibliographic and quotation bases it is placed in: CC (Current Contents), SCIE (Science Citation Index Expanded), and SSCI (Social Science Citation Index). It should be noted that, for some journals accessible throughPERObrowser, thereisadelayof6,12 and even 18 months in accessing the newestissues.Thisnumberisgiveninthe brackets next to the journal’s headline.Dan je izbor članaka iz područja kartografije i geoinformacija iz časopisa, koji nisu u prvom redu kartografski, a kojima su cjeloviti tekstovi dostupni na internetu članovima hrvatske akademske i istraĆŸivačke zajednice. Većina časopisa dostupna je preko pretraĆŸivača PERO (http:// knjiznica.irb.hr/pero/index.php). Za časopise koji nisu dostupni preko tog pretraĆŸivača cjeloviti tekstovi navedenih članaka slobodno su pristupačni na upisanoj web-adresi. Uz svaki je časopis u zagradi naznačeno u koje je ugledne bibliografske i citatne baze uvrĆĄten: CC (Current Contents), SCIE (Science Citation Index Expanded), SSCI (Social Science Citation Index). Treba naglasiti da za neke časopise, dostupne preko pretraĆŸivača PERO, postoji odgoda pristupa najnovijim brojevima od 6, 12, a ponekad i 18 mjeseci. Taj broj je naveden u zagradi uz naslov časopisa

    Using the Global Positioning System (GPS) in household surveys for better economics and better policy

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    Distance and location are important determinants of many choices that economists study. While these variables can sometimes be obtained from secondary data, economists often rely on information that is self-reported by respondents in surveys. These self-reports are used especially for the distance from households or community centers to various features such as roads, markets, schools, clinics and other public services. There is growing evidence that self-reported distance is measured with error and that these errors are correlated with outcomes of interest. In contrast to self-reports, the Global Positioning System (GPS) can determine almost exact location (typically within 15 meters). The falling cost of GPS receivers (typically below US$100) makes it increasingly feasible for field surveys to use GPS as a better method of measuring location and distance. In this paper we review four ways that GPS can lead to better economics and better policy: (i) through constructing instrumental variables that can be used to understand the causal impact of policies, (ii) by helping to understand policy externalities and spillovers, (iii) through better understanding of the access to services, and (iv) by improving the collection of household survey data. We also discuss several pitfalls and unresolved problems with using GPS in household surveys

    A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

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    Environmental risk assessment in the mediterranean region using artificial neural networks

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    Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterråneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterråneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterråneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el anålisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

    Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

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    A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data. Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GIS-minded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.(...
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