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Jesus, F., Oliveira, T., Bacao F., Zahir, I., (2016) "Assessing the pattern between economic and digital development of countries”, Information Systems Frontiers, DOI: 10.1007/s10796-016-9634-1

Jesus, F., Vicente, M., Bacao, F., Oliveira, T., (2016) “The education-related digital divide: An analysis for the EU-28”, Computers in Human Behavior, (Final version published online: 30-NOV-2015) pp. 72-82

Aparicio, M., Bacao, F. & Oliveira, T. (2015) “An e-Learning Theoretical Framework”, Educational Technology & Society, ISSN 1436-4522 (online) and 1176-3647 (print). Available at: http://www.ifets.info/ets_journal/preprint.php 

E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e- learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here we propose an e- learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems’ services. This theoretical approach integrates learning strategies, technologies and stakeholders.

Hugo Costa, Hugo Carrão, Fernando Bação, Mario Caetano (2014) "Combining per-pixel and object-based classifications for mapping land cover over large areas", International Journal of Remote Sensing, Volume 35, Issue 2.

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.

Frederico Cruz-Jesus, Tiago Oliveira, Fernando Bacao (2012) "Digital Divide across the European Union", Information & Management 49 (6), 278-291.

Our research analyses the digital divide within the European Union 27 between the years of 2008 and 2010. To accomplish this we use multivariate statistical methods, more specifically factor and cluster analysis, to address the European digital disparities. Our results lead to an identification of two latent dimensions and five groups of countries. We conclude that a digital gap does, in fact, exist within the European Union. The process of European integration and the economic wealth emerge as explanatory factors for this divide. On the other hand, the educational attendance is not proven to be significant, as one would expect.

Henriques, R., F. Bacao and V. Lobo (2012). "Exploratory geospatial data analysis using the GeoSOM suite." Computers, Environment and Urban Systems 36(3): 218-232.

Clustering constitutes one of the most popular and important tasks in data analysis. This is true for any type of data, and geographic data is no exception. In fact, in geographic knowledge discovery the aim is, more often than not, to explore and let spatial patterns surface rather than develop predictive models. The size and dimensionality of the existing and future databases stress the need for efficient and robust clustering algorithms. This need has been successfully addressed in the context of general-purpose knowledge discovery. Geographic knowledge discovery, nonetheless can still benefit from better tools, especially if these tools are able to integrate geographic information and aspatial variables in order to assist the geographic analyst’s objectives and needs. Typically, the objectives are related with finding spatial patterns based on the interaction between location and aspatial variables. When performing clusterbased analysis of geographic data, user interaction is essential to understand and explore the emerging patterns, and the lack of appropriate tools for this task hinders a lot of otherwise very good work. In this paper, we present the GeoSOM suite as a tool designed to bridge the gap between clustering and the typical geographic information science objectives and needs. The GeoSOM suite implements the GeoSOM algorithm, which changes the traditional Self-Organizing Map algorithm to explicitly take into account geographic information. We present a case study, based on census data from Lisbon, exploring the GeoSOM suite features and exemplifying its use in the context of exploratory data analysis.