José Luís Borges

Abstract
This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT. © 2020

Abstract
Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract

Abstract
With the increase in availability of online national archives and software to manage genealogical records, genealogy studies are growing in popularity. While conducting research, genealogists communicate their findings either in written narratives or in genealogical charts. In that context, visualization methods can be very effective for promoting the understanding of the intricacies of a family tree and the relations among its individuals. Most of the software designed for genealogy provides a collection of standard charts to plot family trees, despite having limited analysis capabilities in general. In addition, most of the research in family tree visualization designs have been focused on methods to represent very large trees in a restricted space. Herein, we propose the contextual family tree, a new visualization design for family trees that represents individuals and their spouses with enhanced details about their families' context. The design was developed through an iterative prototype-evaluation design cycle. For illustrating the potential of our new visualization design, we used contextual family trees created from publicly available genealogical data communication files, showing that the design can be useful to provide a better understanding of the data and also for validating the consistency of the genealogical data.

Abstract
The personal characteristics of entrepreneurs can be importantly related to entrepreneurial startup intentions and behaviors. A country-moderated hypothesis including the relationship between an individual's risk-taking propensity and entrepreneurship (behaviors or intentions of the person) was conceptually developed and empirically tested in this study. The data collection was performed through a structured questionnaire. Multinominal logistic regression was used for analyzing data obtained from 1,414 students in six countries. The crucial contribution of this research is the clarification of the character of risk-taking propensity in entrepreneurship and the indication that the risk-taking propensity-entrepreneurship relationship can be moderated contingent on power distance.