2,579 research outputs found
Exploratory data analysis using self-organising maps defined in up to three dimensions
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
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 EstatĂstica). 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 SOMâs can be used to produce an informational framework for the study of cities
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Evaluating the utility of multispectral information in delineating the areal extent of precipitation
Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 Όm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 Όm) and 4 (10.7 Όm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society
A framework for exploratory analysis of extreme weather events using geostatistical procedures and 3D self-organizing maps
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Unsupervised classification of remote sensing images combining Self Organizing Maps and segmentation techniques
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 Äasopisa
From the field of cartography and geoinformation, there are journalâs 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 journalâs headline.Dan je izbor Älanaka iz podruÄja kartografije i geoinformacija iz Äasopisa, koji nisu u prvom redu kartografski, a kojima su cjeloviti tekstovi dostupni na internetu Älanovima hrvatske akademske i istraĆŸivaÄke zajednice. VeÄina Äasopisa dostupna je preko pretraĆŸivaÄa PERO (http:// knjiznica.irb.hr/pero/index.php). Za Äasopise koji nisu dostupni preko tog pretraĆŸivaÄa cjeloviti tekstovi navedenih Älanaka slobodno su pristupaÄni na upisanoj web-adresi. Uz svaki je Äasopis u zagradi naznaÄeno 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 Äasopise, dostupne preko pretraĆŸivaÄa PERO, postoji odgoda pristupa najnovijim brojevima od 6, 12, a ponekad i 18 mjeseci. Taj broj je naveden u zagradi uz naslov Äasopisa
Using the Global Positioning System (GPS) in household surveys for better economics and better policy
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
Environmental risk assessment in the mediterranean region using artificial neural networks
Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificaciĂłn y visualizaciĂłn 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 subterrĂĄneas, aire y salud humana). Los SOMs tambiĂ©n se utilizan para generar mapas de concentraciones de contaminantes en aguas subterrĂĄneas simulando las tĂ©cnicas geostadĂsticas de interpolaciĂłn como kriging y cokriging. Para evaluar la confiabilidad de las metodologĂas desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparaciĂłn: la metodologĂa DRASTIC para el estudio de vulnerabilidad en aguas subterrĂĄneas y el mĂ©todo de interpolaciĂłn espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el anĂĄlisis de calidad del aire.
Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologĂas y modelos que explĂcitamente 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.
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 scientistâs 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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