Spatial Geographic Information Systems Research Articles

A Sample Enhancement Method Based on Simple Linear Iterative Clustering Superpixel Segmentation Applied to Multibeam Seabed Classification

Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

Association between PM2.5 Exposure and Cardiovascular and Respiratory Hospital Admissions Using Spatial GIS Analysis

Particulate Matter (PM) air pollution is a serious concern in the northern Moravia region of the Czech Republic. This study aimed to evaluate the association between the risk of acute hospital admissions for cardiovascular (CVD) and respiratory diseases and PM2.5 concentrations using a geographic information system (GIS). The data on acute hospital admissions for cardiovascular (I00-99 according to ICD-10) and respiratory (J00-99) diseases was assigned to 77 geographical units (population of 601,299) based on the residence. The annual concentrations of PM2.5 in the period from 2013–2019 were assigned to these units according to the respective concentration iso-shapes. The Incidence Rate Ratio (IRR) and 95% confidence interval (CI) were calculated for each concentration category and then compared with the reference category. Statistical analyses were performed using SW STATA v.15. In 2013, approx. half of the population (56%) belonged to the PM2.5 category 34–35 µg·m−3, and 4% lived in PM2.5 concentrations ≥ 38 µg·m−3. During the analysed period, the average concentrations decreased from 30.8 to 21.4 µg·m−3. A statistically significant risk of acute hospitalization for CVD causes was identified in categories ≥ 36 µg·m−3, and for respiratory causes from 34–35 µg·m−3. With increasing concentrations, the risk of both acute cardiovascular and respiratory hospitalizations increased.

Spatial distribution characteristics and accessibility analysis of beautiful leisure villages in China.

This study was designed to evaluate the spatial distribution characteristics of 1432 beautiful leisure villages in China using econometric geography and spatial geographic information system analysis methods, such as nearest distance index, K index, and nuclear density. We also used the grid cost weighted distance algorithm to determine the spatial accessibility of beautiful leisure villages and the overall accessibility of county units. In addition, our evaluations determined the spatial differences in county accessibility using exploratory spatial data analysis (ESDA). Our results showed that the spatial distribution of the beautiful leisure villages in China could be best described using the cohesion type classification and that there were large differences in their distribution between provinces and economic regions. The average accessibility time of beautiful leisure villages was 197.24 min with only 57.19% of these commutes being less than 2 h, and only 17.88% being less than 30 min. The area with the longest accessibility time was located on the Qinghai Tibet Plateau, at up to 1510.03 min. The spatial distribution of accessibility showed obvious traffic directivity producing a positive Moran I value for most counties. There was also a significant positive correlation between the accessibility of beautiful leisure villages and their adjacent areas, and clear patterns of hot spots–sub-hot spots–sub-cold spots—cold spots from east to west. The overall service scope of beautiful leisure villages was characterized by west > east > middle, with topography, population, economy, and location acting as the major factors in the spatial distribution of these beautiful leisure villages in China.

Spatial thinking and GIS: developing and assessing student competencies

Spatial thinking is an integral skill for geography students to develop. Whilst many spatial competencies have been identified by researchers, and the merits of GIS seemingly ubiquitous in the published literature, little work has been done into how students’ spatial thinking skills can be assessed. Therefore, further investigation into the relationship between spatial thinking and performance and attainment is needed. This research investigates the impact using a geographic information system (GIS) has on students’ spatial thinking skills and attempts to assess the extent using a framework. This was done through the design and implementation of two GIS-based interventions. This small-scale evaluation used qualitative methods to investigate students’ and teachers’ views. Student work was also analysed using the framework developed for the assessment of spatial thinking skills. The findings suggest that the use of a GIS does enhance, and in most cases improves students’ spatial thinking skills, but, that spatial thinking is hard to quantify and difficult to measure progress in. Another benefit that using a GIS affords is the creation of engaging, contemporary and interactive lessons, using real data, from which students derive a lot of geographical value.

Identification of spaces with cluster infection risks in small cities in China based on spatial syntax and GIS

Among modern cities developing in a large-scale, extensive and unbalanced manner, smaller cities are relatively lagged behind due to relatively underdeveloped infrastructure, inadequate capital and technology talents, and insufficient attention from the national government, and thus they are more vulnerable when hit by unexpected disasters. The rampant pandemic of coronavirus disease 2019 (COVID-19) has made it even clearer that small cities must be equipped with stronger abilities to timely identify and prevent potential disease outbreaks. This paper takes Zhaodong City as an example to study how to better locate spaces with cluster infection risks in small cities. It combines spatial syntax, points of interest (POI), and geographical information system (GIS), and adopts hotspot analysis, average nearest neighbour analysis, kernel density estimation and other methods, to identify and locate potentially vulnerable spaces in neighbourhoods with relatively frequent people-to-people contact and thus higher disease transmission risks. Results show that there are three point-space, four line-space, and one plane-space with high risk of outbreaks in Zhaodong City, verifying the efficacy of the identification method for small cities.

Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques

Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide. Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity. Reliable estimation of complex hydrochemical properties of GW is crucial for sustainable development. Real field and experimental studies in an agricultural area from the significant sandstone aquifers (Wajid Aquifer) were conducted. For the modelling purpose, three types of computational models, including the emerging Hammerstein–Wiener (HW), back propagation neural network (BPNN), and statistical multi-variate regression (MVR), were developed for the multi-station estimation of total dissolved solids (TDS) (mg/L) and total hardness (TH) (mg/L). A geographic information system (GIS) was used for the spatial variability assessment of 32 hydrochemical and physical properties of the GW aquifer. A comprehensive visualized literature review spanning several decades was conducted in order to gain an understanding of the existing research and debates relevant to a particular GW and artificial intelligence (AI) study. The experimental data, pre-processing, and feature selection were conducted to determine the most dominant variables for AI-based modelling. The estimation results were evaluated using determination coefficient (DC), mean bias error (MBE), mean square error (MSE), and root mean square error (RMSE). The outcomes proved that TDS (mg/L) and TH (mg/L) correlated more than 90% and 70–85% with Ca2+, Cl−, Br−, NO3−, and Fe, and Na+, SO42−, Mg2+, and F− combinations, respectively. HW-M1 justified promising among all the models with MBE = 1.41 × 10−11, 1.14 × 10−14, and MSE = 7.52 × 10−2, 3.88 × 10−11 for TDS (mg/L), TH (mg/L), respectively. The accuracy proved merit for the overall development of and practical estimation of hydrochemical variables (TDS, TH) (mg/L) and decision-making benchmarks.

Development of an anthropogenic atmospheric mercury emissions inventory in Thailand in 2018

Mercury (Hg) is a highly toxic pollutant characterized by its long-range transport in the atmosphere. In the present study, the atmospheric Hg emission inventory in Thailand was developed using a bottom-up approach. To reflect the emitting behaviors of various Hg sources in Thailand in 2018, activity data specific to individual sources were collected, and emission factors were obtained from local sources or adapted from the literature with proper adjustments. It was found that about 4,337.8 kg of anthropogenic Hg was emitted: 52 % from biomass open burning and 40 % from combustion sources. Mercury speciation was also investigated. Hg0 was found to be dominant at 73 %, followed by Hg2+ at 18 % and HgP at 9 %. Maps were produced using the Spatial Geographic Information System, which showed that the emissions were relatively dense in the Central region of Thailand where most industrial activities take place and in the Northern and Northwestern regions as a result of biomass open burning. Hg emissions were also observed to mainly take place during the early months of the year, particularly in March and April. These two months were determined to account for 47 % of the total annual emissions of Hg, mostly due to increased combustion activities and biomass open burning. The output of the present work is a Hg emission database that can serve as model to investigate the status of Hg and the risks it poses to human health and ecosystems as well as support policy makers in developing emission control strategies.

Introducing an Open-Source Regional Water Quality Data Viewer Tool to Support Research Data Access

Water quality data collection, storage, and access is a difficult task and significant work has gone into methods to store and disseminate these data. We present a tool to disseminate research in a simple method that does not replace but extends and leverages these tools. The tool is not geo-graphically limited and works with any spatially-referenced data. In most regions, government agencies maintain central repositories for water quality data. In the United States, the federal government maintains two systems to fill that role for hydrological data: the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the U.S. Environmental Protection Agency (EPA) Storage and Retrieval System (STORET), since superseded by the Water Quality Portal (WQP). The Consortium of the Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) has developed the Hydrologic Information System (HIS) to standardize the search and discovery of these data as well as other observational time series datasets. Additionally, CUAHSI developed and maintains HydroShare.org (5 May 2021) as a web portal for researchers to store and share hydrology data in a variety of formats including spatial geographic information system data. We present the Tethys Platform based Water Quality Data Viewer (WQDV) web application that uses these systems to provide researchers and local monitoring organizations with a simple method to archive, view, analyze, and distribute water quality data. WQDV provides an archive for non-official or preliminary research data and access to those data that have been collected but need to be distributed prior to review or inclusion in the state database. WQDV can also accept subsets of data downloaded from other sources, such as the EPA WQP. WQDV helps users understand what local data are available and how they relate to the data in larger databases. WQDV presents data in spatial (maps) and temporal (time series graphs) forms to help the users analyze and potentially screen the data sources before export for additional analysis. WQDV provides a convenient method for interim data to be widely disseminated and easily accessible in the context of a subset of official data. We present WQDV using a case study of data from Utah Lake, Utah, United States of America.

Spatial Autoregressive Model and Spatial Patterns of Poverty In Lampung Province

Many research in economics only focus on the independence of a region while neglecting the effects of space and the interaction that occurs between mutually adjacent areas. The purpose of this study is to measure the multidimensional poverty concept in 15 districts/cities in the province of Lampung in 2015-2019. Spatial analysis such as moran i statistics, LISA clustered map, and lisa signification are used to analyze spatial patterns and spatial autocorrelation. Spatial modeling with spatial autoregressive model, geoda and geographical information systems are used as explanatory spatial data and spatial modeling. The results show that the percentage of poor people between districts/cities in Lampung Province have positive Moran's I values, there is a clustered pattern in 2015-2019, Moran scatter plot depicts 4 quadrants, LISA Cluster map indicates high-high and low-low areas, and LISA map has 4 significant areas. Spatial regression results show that per capita expenditure for nonfood has a negative effect, per capita expenditure for food has a positive effect, population growth rate has a positive effect, household clean water has a positive effect, life expectancy has a negative effect, mean years of schooling has a negative effect, and simultaneously the independent variables have a significant influence on the percentage of poor people. Poverty in Lampung Province is spatially related to each other between regions, the findings suggest that the variables used affect spatially. The implication of this result is one of the basis for inter-regional policies in the interests of multi-dimensional poverty alleviation between regions.Keywords: Poverty, Spatial analysis, Spatial Autoregressive Model (SAR)

Spatial Analysis and Geographic Information Systems as Tools for Sustainability Research

The multidisciplinary fields of study on sustainability, which relate to ecological, geophysical, societal and environmental research, demand for the availability and processing of data that is capable to represent spatial phenomena [...]

Visualizing the geographical network of inter-provincial electricity in China

ABSTRACT This paper empirically visualizes the spatial patterns of the inter-provincial electricity flows in relation to the distribution of gross domestic product (GDP) per capita using spatial geographical information system (GIS) and cartograms. It highlights how developed provinces in eastern China are net importers of electricity from western and central China, which creates a west–east power transmission pattern and helps deepen the regional economy disparity.

Geographic Information Systems in Spatial Epidemiology: Unveiling New Horizons in Dental Public Health.

ABSTRACTObjectives:Research on the role of environment and place in various aspects of dental public health using geographic information systems (GIS) is escalating rapidly. Yet, the understanding of GIS and the analytical tools that it offers are still vaguely understood. This narrative review therefore draws from the utilization of GIS in the dental public health research.Materials and Methods:Electronic databases such as Google Scholar, PUBMED, and Scopus were searched using terms “spatial epidemiology,” “GIS,” “geographic information systems,” “health geography,” “environment public health tracking,” “spatial distribution,” “disease mapping,” “geographic correlation studies,” “cartography,” “big data,” and “disease clustering” through December 2019.Results:This review builds upon the prospects of GIS application in various aspects of dental public health. Studies were classified as: (1) GIS for mapping of disease, population at risk, and risk factors; (2) GIS in geographic correlation studies; (3) GIS for gauging healthcare accessibility and spatial distribution of healthcare providers. We also identified the commonly used GIS analytical techniques in oral epidemiology.Conclusions:We anticipate that this review will spur advancement in the utilization of spatial analytical techniques and GIS in the dental public health research.

Spatial site selection for a centralized treatment center of livestock excreta: Taking Nantong Town as an example

At present, processing substantial livestock excreta produced by numerous concentrated small–middle-type livestock farms (SMLFs) has been a major problem of livestock farm owners and government departments. To solve this problem, this study established a suitability evaluation system for the spatial layout of a centralized treatment center of livestock excreta (CTCLE). On this basis, combined with the collection points of livestock excreta (CPLEs) determined by spatial clustering algorithm and median center and matched farmlands, the spatial site selection of CTCLE was analyzed using the geographic information system (GIS) spatial analysis method and GIS component development technology. Practical application research was conducted in Nantong Town, and pigs were used as examples. Results showed that most regions of Nantong Town belong to forbidden and unsuitable regions, accounting for 68.63% of the total region of the town. A small part of a highly suitable region (6.428 ha) was determined as potential CTCLE. A total of 839 SMLFs were divided into 61 CPLEs, and the spatial relation between potential CTCLEs and farmlands was established. The spatial sites of 11 CTCLEs in Nantong Town, Minhou County were selected, and the total nitrogen and phosphorus of livestock excreta to be reduced were estimated. The results will provide a scientific basis for the utilization of livestock excreta and the spatial layout planning of livestock and poultry breeding.

Variation in Clinical Trial Results for Asthma by Environmental and Socioeconomic Exposures

Randomized clinical trials (RCTs) are generally considered the gold standard in medical research; by randomizing participants to treatment and control arms, they maximize internal validity and reduce between-group biases. RCTs, however, do not routinely examine variation by environmental and social exposures (e.g., socioeconomic status), which may impact clinical outcomes, treatment response, and study generalizability. To assess whether variation in socioeconomic positon (SEP) and environmental exposures modify treatment response, we have developed and applied Geographic Information Systems (GIS)-based methods to examine three RCTs performed by the NIH AsthmaNet network, which recruited and implemented RCTs using the same protocols across 17 U.S. cities. In preliminary analyses, we have found that: (1) compared to race-specific U.S. averages, AsthmaNet participants disproportionately live in impoverished census tracts. (2) At baseline, traffic density and SEP predicted variation in lung function, in the hypothesized directions. (3) In one longitudinal trial, we found that near-residence roadway density explained greater variation in asthma symptoms than did corticosteroid use, and that children in higher-poverty areas had significantly shorter times to first corticosteroid use. For this trial, we examined effect modification by near-residence roadway traffic and noise exposures, neighborhood violence, and health care access. Preliminary results suggest - only among higher-SES children - significantly greater negative impacts (shorter times to prednisone use) among participants receiving higher inhaled corticosteroid (ICS) doses (220 ug), relative to those receiving lower ICS doses (44 ug).Results suggest that participants in clinical trials may not be representative of U.S. asthmatics, and participants from different SES backgrounds may differentially respond to intensive corticosteroid interventions. Using spatial analysis and GIS to understand the neighborhoods of RCT participants – better accounting for socioeconomic and environmental conditions – may improve the interpretability and applicability of RCT results, by more clearly identifying subpopulations for whom a given intervention may be most effective.

Quantitative assessment of environmental exposure of delivery men in Wuhan

The take out industry is rapidly developing in China, but the environmental exposure of delivery men is neglected. “Ele.Me,” an online food ordering platform, is one of the two largest catering online-to-offline platforms in China. From January 2018 to April 2018, data from 16,528 take out shops in Wuhan were crawled by using Python. The data included store name, average delivery time, delivery price, region, and store type. With the use of Baidu map’s API batch reverse address resolution, the map locations of the take out shops were identified. Network service area was utilized to obtain the activity space of delivery men along the Wuhan traffic road network. Combined with the data on sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), PM2.5, PM10, and noise provided by the Wuhan Environmental Protection Bureau, the spatial interpolation, regional analysis, superposition analysis, and other geographic information system spatial analysis methods were used to study the air and noise pollution within the activity space of delivery men in various take out shops. Results revealed the unbalanced environment faced by Wuhan delivery men. The exposure to NO2, CO, and PM2.5 was mainly concentrated in the central city, whereas the exposure to SO2, O3, and PM10 was mainly noted far from the city. Noise pollution was mainly observed in the districts of Hannan, Caidian, and Dongxihu; and the most serious pollution was recorded in Shamao Street. The weighted comprehensive pollution index model was used to calculate the contribution weight of each evaluation factor to obtain the results of environmental exposure to air pollution. The influence of noise was further considered to obtain the consequences of comprehensive environmental exposure to pollution. The highly polluted regions were those in the central part of the city. Pollution was mainly concentrated in the third loop, whereas Zhucheng Street in the Xinzhou District provided the best environment. The results of this study can serve as a reference for the physical health risk assessment of delivery men.

Using social media data to map the areas most affected by ISIS in Syria

This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

Geoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review.

ABSTRACTLeptospirosis is a reemerging zoonosis caused by bacteria of the genus Leptospira sp. with global importance in the medical and veterinary fields, being responsible for about 59 thousand deaths each year in the world. The use of Geographic Information Systems (GIS) in the health sector is propitious and has been adopted by human and animal health professionals as an important tool in spatial analyses of health. The objective of this study was to conduct a systematic review on the geoprocessing and spatial analysis techniques adopted for mapping risk areas of human and animal leptospirosis. The articles were collected on scientific platforms by entering the following terms: SIG/GIS, leptospirose/leptospirosis, area de risco/risk area and distribuicao espacial/spatial distribution, and included in the study if they met the following criteria: a) publication in the period from 1998 to 2017; b) identification of risk areas and/or spatial distribution of leptospirosis as one of the research topics; and c) application of GIS in the methodology. As a result, we found 40 articles, published by 15 different countries, which adopted GIS for the spatial analysis and identification of risk areas of leptospirosis. Among these, only 45% (18) conducted an spatial statistical analysis. Brazil and USA had the highest numbers of publications, 16 and 7 articles, respectively. From 2007, the use of GIS and spatial analysis techniques, applied to the theme of this study, have been intensified and diversified, and 93% of the articles elected for this review were published from 2007 to 2017. The results point to a progressive interest of health professionals in applying these techniques for monitoring and conducting epidemiological analyses of leptospirosis, besides indicating a greater need for intersectoral integration between health professionals and others, in the use of spatial analysis and GIS techniques.

Variation in Asthma Clinical Trial Results by Environmental and Socioeconomic Exposures

PDS 64: Health impact assessment and environmental justice, Johan Friso Foyer, Floor 1, August 26, 2019, 1:30 PM - 3:00 PM Randomized clinical trials (RCTs) are generally considered the gold standard in medical research; by randomizing participants to treatment and control arms, they maximize internal validity and reduce between-group biases. RCTs, however, do not routinely examine variation by environmental and social exposures, which may impact clinical outcomes, treatment response, and generalizability. To assess whether variation in socioeconomic positon (SEP) and environmental exposures modify treatment response, we have developed and applied Geographic Information Systems (GIS)-based methods to three RCTs performed by the NIH AsthmaNet network, which is recruiting and implementing RCTs using the same protocols across 17 U.S. cities. In preliminary analyses, we have found that: (1) compared to race-specific U.S. averages, blacks recruited into AsthmaNet disproportionately live in impoverished census tracts; recruited whites disproportionately live in wealthier tracts. (2) At baseline, traffic density and SEP predicted variation in lung function, in the hypothesized directions. (3) In one trial, we found that near-residence roadway density explained greater variation in asthma symptoms than did corticosteroid use, and that children in higher-poverty areas had significantly shorter times to first corticosteroid use. We are now refining analyses of effect modification by near-residence roadway traffic and noise exposures, neighborhood violent crime, and health care access. Using spatial analysis and GIS to understand the lived context of RCT participants – better accounting for socioeconomic and environmental factors – may improve the interpretability and applicability of RCT results, by more clearly identifying subpopulations for whom a given intervention may be most effective.

SignA, el geoportal del IGN de España: explotando lo mejor de las IDE y los SIG

SignA, the National Geographic Information System of Spain, has been a strategic project of the National Geographic Information Centre (CNIG) and the National Geographic Institute (IGN) of Spain, which aims the IGN data and services integration within a Geographic Information System (GIS), for its management, analyse and query through the Internet. This implies the development of an own versatile, interoperable and efficient geoportal. SignA geoportal was opened to public on December 2010 and it is about to open the fourth version, combining the best features of the Spatial Data Infrastructures (SDI) and GIS technologies, allowing users to access data and services in an easy and transparent way, without special technical knowledge. After 7 years, the geo- portal is growing every day its importance and usage onto the geomatics community. Currently, receives an average of 19,000 visits per month, being a tool of daily use for many of our users and for many applications. This article will be a brief review of the main features of the geoportal and the changes made to improve usability over the versions.

SignA, el geoportal del IGN de España: explotando lo mejor de las IDE y los SIG

El Sistema de Información Geográfica Nacional (SignA) es un proyecto estratégico del Instituto Geográfico Nacional de España (IGN) que tiene como finalidad, la integración de los datos y servicios del IGN en un Sistema de Información Geográfi- ca (SIG), para su gestión, análisis y consulta, a través de Internet, lo que a su vez implica el desarrollo de un geoportal propio versátil, interoperable y eficiente. 
 El geoportal del SignA, se abrió al público en diciembre de 2010 y próxima- mente sacará la tercera versión, integrando lo mejor de los mundos SIG e IDE (In- fraestructura de Datos Espaciales) en una única herramienta, permitiendo a los usuarios acceder a datos y servicios de manera fácil y transparente, sin requerir de conocimientos técnicos concretos. Tras siete años, ha ido aumentando la demanda del geoportal, actualmente recibe una media de 19,000 visitas al mes, y es una he- rramienta de uso de diario de muchos de nuestros usuarios para multitud de aplica- ciones. 
 En este artículo se da un breve repaso a las características del portal, resaltando las mejoras de usabilidad realizadas a lo largo de las diferentes versiones.