Los sistemas de información geográfica en turismo

[Resumo] A internet converteuse nun dos lugares máis populares para publicar e buscar case calquera tipo de información. En particular, a información turística gañou moita atención na rede durante os últimos anos, e non só a información sobre viaxes, recursos, lugares, museos ou monumentos, senón tamén sobre turismo cultural. Neste artigo presentamos as posibilidades que ofrecen os sistemas de información xeográfica (SIX) para a publicación de información turística e o acceso a ela, a través de interfaces coa capacidade de xerar mapas interactivos que presenten información asociada a cada elemento de interese que apareza neles. Ademais, describimos como caso de estudo a viaxe virtual que se nos propón na Biblioteca Virtual Galega (http://bvg.udc.es), un sistema accesible a través da web que, por medio de tecnoloxías SIX, permite acceder a calquera información turística ou cultural de Galicia de xeito sinxelo.[Resumen] Internet se ha convertido en uno de los lugares más populares para publicar y buscar casi cualquier tipo de información. En particular, la información turística ha ganado mucha atención en la red durante los últimos años, no sólo información sobre viajes, recursos, lugares, museos o monumentos, sino también sobre turismo cultural. En este artículo presentamos las posibilidades que ofrecen los Sistemas de Información Geográfica (SIG) en la publicación y acceso a información turística, a través de interfaces con capacidades de generación de mapas interactivos con información asociada a cada elemento de interés presentado en los mapas. Además, describimos como caso de estudio el Viaje Virtual de la Biblioteca Virtual Gallega (http://bvg.udc.es), un sistema accesible a través de la Web que, utilizando tecnologías SIG, permite acceder a cualquier información turística o cultural de Galicia de manera sencilla.[Abstract] The Internet has become one of the most popular places to publish and search for almost any type of information. In particular, tourist information has received much attention in the Internet over the past few years, not only information about travel, resources, places, museums or monuments, but also about cultural tourism. In this article we discuss the potential offered by Geographic Information Systems (GIS) in the publication of and access to tourist information, through interfaces capable of generating interactive maps with information associated with each element of interest shown in the maps. In addition, as a case study, we describe the Virtual Trip of the Galician Virtual Library (http://bvg.udc.es), an Internet-accessible system which makes it possible, using GIS technologies, to easily access any tourist or cultural information about Galicia

Multilevel Modeling of Geographic Information Systems Based on International Standards

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Even though different applications based on Geographic Information Systems (GIS) provide different features and functions, they all share a set of common concepts (e.g., spatial data types, operations, services), a common architecture, and a common set of technologies. Furthermore, common structures appear repeatedly in different GIS, although they have to be specialized in specific application domains. Multilevel modeling is an approach to model-driven engineering (MDE) in which the number of metamodel levels is not fixed. This approach aims at solving the limitations of a two-level metamodeling approach, which forces the designer to include all the metamodel elements at the same level. In this paper, we address the application of multilevel modeling to the domain of GIS, and we evaluate its potential benefits. Although we do not present a complete set of models, we present four representative scenarios supported by example models. One of them is based on the standards defined by ISO TC/211 and the Open Geospatial Consortium. The other three are based on the EU INSPIRE Directive (territory administration, spatial networks, and facility management). These scenarios show that multilevel modeling can provide more benefits to GIS modeling than a two-level metamodeling approach.Xunta de Galicia; IN852A 2018/14Xunta de Galicia; ED431G 2019/01This work has been partially funded by grants: MICIU/FEDER-UE, MAGIST: PID2019-105221RB-C41; MICIU/FEDER-UEBIZDEVOPSGLOBAL: RTI-2018-098309-B-C32, Xunta de Galicia/FEDER-UE, ConectaPeme, GEMA: IN852A 2018/14; MINECOAEI/FEDER-UE Datos 4.0: TIN2016-78011-C4-1-R; MINECOAEI/FEDER-UE Velocity: TIN2016-77158-C4-3-R; CITIC research center funded by XUNTA and EU through the European Regional Development Fund- Galicia 2014-2020 Program, grant ED431G 2019/01. Funding for open access charge: Universidade da Coruña/CISUG

Using nondeterministic learners to alert on coffee rust disease

Motivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus,in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present 3 different implementations: one based on regression, and 2 more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning prediction

Optimizing different loss functions in multilabel classifications

Multilabel classification (ML) aims to assign a set of labels to an instance. This generalization of multiclass classification yields to the redefinition of loss functions and the learning tasks become harder. The objective of this paper is to gain insights into the relations of optimization aims and some of the most popular performance measures: subset (or 0/1), Hamming, and the example-based F-measure. To make a fair comparison, we implemented three ML learners for optimizing explicitly each one of these measures in a common framework. This can be done considering a subset of labels as a structured output. Then, we use structured output support vector machines tailored to optimize a given loss function. The paper includes an exhaustive experimental comparison. The conclusion is that in most cases, the optimization of the Hamming loss produces the best or competitive scores. This is a practical result since the Hamming loss can be minimized using a bunch of binary classifiers, one for each label separately, and therefore, it is a scalable and fast method to learn ML tasks. Additionally, we observe that in noise-free learning tasks optimizing the subset loss is the best option, but the differences are very small. We have also noticed that the biggest room for improvement can be found when the goal is to optimize an F-measure in noisy learning task

A simple and efficient method for variable ranking according to their usefulness for learning

The selection of a subset of input variables is often based on the previous construction of a ranking to order the variables according to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization, which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables. The experimental validation is made on a wide range of artificial data sets and the quality of the rankings is assessed using a ROC-inspired setting, to avoid biased estimations due to any particular learning algorith

A heuristic for learning decision trees and pruning them into classification rules

Let us consider a set of training examples described by continuous or symbolic attributes with categorical classes. In this paper we present a measure of the potential quality of a region of the attribute space to be represented as a rule condition to classify unseen cases. The aim is to take into account the distribution of the classes of the examples. The resulting measure, called impurity level, is inspired by a similar measure used in the instance-based algorithm IB3 for selecting suitable paradigmatic exemplars that will classify, in a nearest-neighbor context, future cases. The features of the impurity level are illustrated using a version of Quinlan's well-known C4.5 where the information-based heuristics are replaced by our measure. The experiments carried out to test the proposals indicate a very high accuracy reached with sets of classification rules as small as those found by RIPPE

Analysis of nutrition data by means of a matrix factorization method

We present a factorization framework to analyze the data of a regression learning task with two peculiarities. First, inputs can be split into two parts that represent semantically significant entities. Second, the performance of regressors is very low. The basic idea of the approach presented here is to try to learn the ordering relations of the target variable instead of its exact value. Each part of the input is mapped into a common Euclidean space in such a way that the distance in the common space is the representation of the interaction of both parts of the input. The factorization approach obtains reliable models from which it is possible to compute a ranking of the features according to their responsibility in the variation of the target variable. Additionally, the Euclidean representation of data provides a visualization where metric properties have a clear semantics. We illustrate the approach with a case study: the analysis of a dataset about the variations of Body Mass Index for Age of children after a Food Aid Program deployed in poor rural communities in Southern México. In this case, the two parts of inputs are the vectorial representation of children and their diets. In addition to discovering latent information, the mapping of inputs allows us to visualize children and diets in a common metric spac

Learning to assess from pair-wise comparisons

In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen’s Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approac