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Projeto de investigação
Joint Doctorate in Geoinformatics - Enabling Open Cities
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Publicações
Design for geospatially enabled climate modeling and alert system (CLIMSYS)
Publication . Bhattacharya, Devanjan; Painho, Marco; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
The paper brings the focus on to multi-disciplinary approach of presenting climate analysis studies, taking help of interdisciplinary fields to structure the information. The system CLIMSYS provides the crucial element of spatially enabling climate data processing. Even though climate change is a matter of great scientific relevance and of broad general interest, there are some problems related to its communication. Its a fact that finding practical, workable and cost-efficient solutions to the problems posed by climate change is now a world priority and one which links government and non-government organizations in a way not seen before. An approach that should suffice is to create an accessible intelligent system that houses prior knowledge and curates the incoming data to deliver meaningful results. The objective of the proposed research is to develop a generalized system for climate data analysis that facilitates open sharing, central implementation, integrated components, knowledge creation, data format understanding, inferencing and ultimately optimal solution delivery, by the way of geospatial enablement.
E-participation adoption models research in the last 17 years
Publication . Naranjo-Zolotov, Mijail; Oliveira, Tiago; Casteleyn, Sven; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Elsevier Science B.V., Amsterdam.
This article explores the main factors that drive the adoption of e-participation. A weight and meta-analysis was carried out from previous quantitative research studies related to individual e-participation adoption published in journals and conferences over the last 17 years. A total of 60 studies were used for the weight and meta-analysis. We identify the ‘best’ and ‘promising’ predictors used in research models to study e-participation. The best predictors are: trust, effort expectancy, perceived usefulness, attitude, trust in government and social influence on intention to use, perceived ease of use on perceived usefulness, perceived usefulness on attitude, and intention to use on use. General public in urban areas account for the 69.78% of the respondents across all articles. Two thirds of all respondents belong to Asia and the Middle East. The countries with highest number of articles found are United States and Jordan. The article provides a wide view of the performance of the 483 relationships used in research models to study e-participation, which may allow researchers to identify trends, and highlights issues in the future use of some constructs. Implications for theory and practice, limitations and directions for future research are discussed.
Understanding the sharing economy and its implication on sustainability in smart cities
Publication . Akande, Adeoluwa; Cabral, Pedro; Casteleyn, Sven; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Elsevier
The purpose of this article is to evaluate the main drivers of the sharing economy through an exhaustive weighting and meta-analysis of previous relevant quantitative research articles, obtained using a systematic literature review methodology. The authors analysed 22 quantitative studies from 2008 through. Out of the 249 extracted relationships (independent – dependent variable), the paper identifies the “best” predictors used in theoretical models to study the sharing economy. These include: attitude on intention to share, perceived behavioural control on intention to share, subjective norm on intention to share, economic benefit on attitude, and perceived risk on attitude. Geographically, Germany and the United States of America were found to be the nations with the highest number of respondents. Temporally, an increasing trend in the number of articles on the sharing economy and respondents was observed. The consolidation of the drivers of the sharing economy provides a solid theoretical foundation for the research community to explore existing hypotheses further and test new hypotheses in emerging contexts of the sharing economy. Given the different conceptual theories that have been used to study the sharing economy and their application to different contexts, this study presents the first attempt at advancing knowledge by quantitatively synthesizing findings presented in previous literature.
Assessing the Gap between Technology and the Environmental Sustainability of European Cities
Publication . Akande, Adeoluwa; Cabral, Pedro; Casteleyn, Sven; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Springer Science Business Media
The growth of cities’ population increased the interest in the opportunities and challenges that Information and Communication Technology (ICT) have on carbon footprint reduction, which fosters their environmental sustainability. Using Principal Component Analysis (PCA), six ICT-related variables from European Union (EU) cities were combined into a single two-dimensional ICT index. Then, through cluster analysis, cities were clustered into four groups based on the ICT index and Carbon dioxide (CO 2 ) emissions. Using ICT as an indicator of smartness and CO 2 emissions as an indicator of sustainability, we show that it is possible for a city to be smart but not sustainable and vice versa. Results also indicate that there is a gap between cities in northern Europe, which are the top performers in both categories, and cities in south-eastern Europe, which do not perform as well. The need for a common strategy for achieving integrated smart, sustainable and inclusive growth at a European level is demonstrated.
Spatial and spatio-temporal point patterns on linear networks
Publication . Moradi, Mohammad Mehdi; Mateu Mahiques, Jorge; Costa, Ana Cristina Marinho da; Pebesma, Edzer
The last decade witnessed an extraordinary increase in interest in the analysis
of network related data and trajectories. This pervasive interest is partly caused
by a strongly expanded availability of such datasets. In the spatial statistics field,
there are numerous real examples such as the locations of traffic accidents and
geo-coded locations of crimes in the streets of cities that need to restrict the
support of the underlying process over such linear networks to set and define a
more realistic scenario. Examples of trajectories are the path taken by moving
objects such as taxis, human beings, animals, etc.
Intensity estimation on a network of lines, such as a road network, seems to
be a surprisingly complicated task. Several techniques published in the literature,
in geography and computer science, have turned out to be erroneous. We
propose several adaptive and non-adaptive intensity estimators, based on kernel
smoothing and Voronoi tessellation. Theoretical properties such as bias, variance,
asymptotics, bandwidth selection, variance estimation, relative risk estimation,
and adaptive smoothing are discussed. Moreover, their statistical performance is
studied through simulation studies and is compared with existing methods.
Adding the temporal component, we also consider spatio-temporal point patterns
with spatial locations restricted to a linear network. We present a nonparametric
kernel-based intensity estimator and develop second-order characteristics
of spatio-temporal point processes on linear networks such as K-function
and pair correlation function to analyse the type of interaction between points.
In terms of trajectories, we introduce the R package trajectories that contains
different classes and methods to handle, summarise and analyse trajectory data.
Simulation and model fitting, intensity estimation, distance analysis, movement
smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago
(US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın
(Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents
in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic
accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
642332