Geo-C Researcher Mentors USAID YouthMappers Research Fellow

Adeoluwa Akande, a PhD researcher at NOVA IMS has been selected to serve as a mentor in the 2018 YouthMappers Research Fellowship.

The 9-month program is designed to enlist, enable, and showcase the contributions of open geospatial data for research on the resilience of vulnerable populations around the world.

Adeoluwa will be mentoring Dennis Irorere, a student of the Federal University of Technology Akure, Nigeria on his researched titled “Leveraging open data and geospatial technology for food security”. His study seeks to develop a platform that can be a repository for farmer’s and agricultural produce data. Furthermore, a crop cultivation map will be developed for Ondo State, one of the most influential states in Nigeria, to foster positive decision making.

All new data created during the course of this research will be open and accessible to the public using the OpenStreetMap platform and tools to ensure it is freely available for the greater public good, particularly local populations planning for the welfare and vitality of their own communities.

NOVA IMS and GEO-C team are pleased to welcome the visit of Dr. Viswanath Venkatesh

Dr. Viswanath Venkatesh is a worldwide renowned academic researcher and authority in the field of Information Systems with nearly 80000 citations in Google scholar. He was invited by the GEO-C team to visit NOVA IMS school on 03 and 04 of July 2018 to deliver a seminar and provide feedback to improve the research of Mijail Naranjo-Zolotov (esr05).

Title of seminar: “Road to success: A guide for doctoral students and junior faculty members in the behavioral and social sciences”

Seminar with Prof. Viswanath Venkatesh: "The road to success"

 

 

Biography: Viswanath Venkatesh, who completed his PhD at the University of Minnesota in 1997, is a Distinguished Professor and Billingsley Chair in Information Systems at the Walton College of Business, University of Arkansas. He is widely regarded as one of the most influential scholars in business and economics, both in terms of premier journal publications and citations. His research focuses on understanding the diffusion of technologies in organizations and society.

Reproducible Research and Industrial Property Rights Protection: A Success Experience in Indoor Positioning

You all are cordially invited to attend a lecture talk “Reproducible Research and Industrial Property Rights Protection: A Success Experience in Indoor Positioning” by Dr. Joaquín Torres, Postdoc at Geotec and Visiting Researcher at CCG.

Abstract: Reproducible resarch and Open Science are concepts that should applear in researchers CV. Donating collected datasets and experimental data boost the disemination of the projetcs and research. Providing the methods implementation as supplementary materials enables the research community to perform fair comparisons and doing important advances in the research topics. However this enters in conclict to one main objective of researchers, transferring technological and scientific findings to society. This seminar is intended to show a success case where open science and transfer have been successfully balances, following the premise of the EC “as open as possible, as closed as necessary”.

 

Where: Sala multiusos (UB1206SM), Espaitec2, UJI
When: July 5th and 6th 2018, from 11:00 to 14:00

NOVA IMS and GEO-C team are pleased to welcome the visit of Dr. Christy M K Cheung

Dr. Christy M K Cheung is a worldwide renowned researcher in the field of Information Systems with more than 12400 citation in Google scholar. She was invited by the GEO-C team to visit NOVA IMS school from 21 to 25 of May 2018 to deliver a seminar and provide feedback to improve the research of esr#05 Mijail Naranjo-Zolotov. We are very pleased by her visit.

Title of seminar: Societal Impacts of ICT Use: Understanding Bystanders’ Proactive Reporting Responses to Online Harassment

Biography:
Christy M.K. Cheung is an Associate Professor of Information Systems and e-Business Management at Hong Kong Baptist University. She earned a Ph.D. in Information Systems from the College of Business at City University of Hong Kong. Her research interests include Technology Use and Well-Being, IT Adoption and Use, Societal Implications of IT Use, and Social Media. She has published over one hundred refereed articles in international journals, and conference proceedings, including Decision Support Systems, Information & Management, Journal of Information Technology, Journal of Management Information Systems, Journal of the Association for Information Science and Technology, MIS Quarterly and among others. She is currently serving as the Editor-in-Chief for Internet Research and President for AIS-HK Chapter.

Paper published in Sustainability Journal about “Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models”

The paper Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models (by Shivam Gupta, Edzer Pebesma, Jorge Mateu, Auriol Degbelo) has been published in the special issue Spatial and Spatio-Temporal Planning for Urban Health and Sustainability of Sustainability 2018, 10(5)

Abstract: A very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term exposure analysis. This paper introduces a systematic approach for identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing the approach over the study area has shown that it leads to a significant drop of the mean prediction error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of this work can guide the selection of sites while expanding or creating air quality monitoring networks for robust LUR estimations with minimal prediction errors.

According to United Nations estimates, 66% of the total world population is expected to be living in the urban spaces by 2050. At the same time, the Organisation for Economic Co-operation and Development (OECD) projects that by 2050 air pollution will be the top environmental cause of mortality worldwide. GIS and spatial analysis have increasingly become an essential tool for air pollution monitoring. Interpolation of pollution data collected by regulatory air quality monitoring stations can help in regional patterns, but the air quality monitoring networks are very sparsely arranged to collect informed data at a city level. Land Use Regression (LUR) models are helpful to take into account air pollution variability within the cities. LUR models are a promising alternative to these conventional approaches as they establish the relationship between easily accessible land use characteristics and pollutant measurement. Our knowledge of air pollution monitoring is mostly based on limited data. The published paper takes a new look at Monitoring Network Design (MND) using a new optimisation method. The proposed method identifies the combination of locations which minimise the spatial mean prediction error over the entire study area for two contexts: (1) without using any weighted function; and (2) with a spatial population weighted function for high population density areas. The optimisation method does not rely on monitoring station data for monitoring site placement, thus giving independence for planning and readjustments of the optimal air quality MND for the cities with no or insignificant amount of air quality data. Hence, the proposed method can be a helpful tool in air quality MND that enables LUR estimations with fewer errors for preventing air pollution exposure and advancing urban health sustainability.

For more detail information, please access the article from here.

The article is Open Access and is funded by European Commission within the Marie Skłodowska-Curie Actions, International Training Networks (ITN), European Joint Doctorates (EJD). The funding period is January 1, 2015 – December 31, 2018, Grant Agreement number 642332 — GEO-C — H2020-MSCA-ITN-2014.

Paper published in Statistics & Probability Letters Journal concerning “Quality of life, big data and the power of statistics”

The paper Quality of life, big data and the power of statistics (by Shivam Gupta, Jorge Mateu, Auriol Degbelo, Edzer Pebesma) has been published in Special issue dedicated to Statistics and Big Data of journal  Statistics & Probability Letters Volume 136 – May 2018

Abstract: The digital era has opened up new possibilities for data-driven research. This paper discusses big data challenges in environmental monitoring and reflects on the use of statistical methods in tackling these challenges for improving the quality of life in cities.

With an increasing number of people moving in (and to) urban areas, there is an urgent need of examining what this rising number means for the environment and QoL in cities. Air quality has an effect on the population’s QoL (Darçın, 2014), which is also the major environmental risk factor for health. Data for environmental and meteorological analysis are not only of a significant volume but are also complex in space and time. Formats and types of data are also very diverse (e.g., netCDF, GDB, CSV, GeoTIFF, shapefile, JSON, etc.), and many interconnections prevail within data, which make it complicated for traditional data analysis procedures. As Scott (2017) said, statistics remains highly relevant irrespective of ‘bigness’ of data. It provides the basis to make data speak while taking into account the inherent uncertainties. Statistical analysis involves developing data collection procedures to further handle different data sources and to propose formal models for analysis and predictions.

In the published paper we focused on the role of statistics in handling the five Vs (Volume, Velocity Variety, Veracity and Value) of big data, and the challenges posed.  We proposed to combine two well-established statistical methods to optimise the selection of variables and locations for spatial and temporal analysis of environmental data sources (with more focus on air quality monitoring). The combined use of both methods; Land Use Regression (LUR) and Spatial Simulated Annealing (SSA), proposed in the paper will help in designing data acquisition processes so that the maximum information can be extracted given a specific number of possible measurement sites. Limiting the data sources can increase the speed of the analysis. Hence, making big data analysis more effective regardless of the “bigness”.

For more detail information, please access the article from : https://www.sciencedirect.com/science/article/pii/S0167715218300750

The article is Open Access and is funded by European Commission within the Marie Skłodowska-Curie Actions, International Training Networks (ITN), European Joint Doctorates (EJD). The funding period is January 1, 2015 – December 31, 2018, Grant Agreement number 642332 — GEO-C — H2020-MSCA-ITN-2014.

Best Research Paper Award ICEGOV 2018 Galway, Ireland !!

11th International Conference on Theory and Practice of Electronic Governance ICEGOV 2018

Continued intention to use online participatory budgeting: The effect of empowerment and habit
Mijail Naranjo Zolotov | NOVA University of Lisbon, Portugal
Tiago Oliveira | NOVA University of Lisbon, Portugal
Sven Casteleyn | Jaume I University, Spain

Video of the closing session and awards here

 

 

Paper presented and published at WorldCIST 2018 Naples, Italy

WorldCist’18 – 6th World Conference on Information Systems and Technologies

Zolotov, M. N., Oliveira, T., Cruz-Jesus, F., & Martins, J. (2018, March). Satisfaction with e-participation: A Model from the Citizen’s Perspective, Expectations, and Affective Ties to the Place. In World Conference on Information Systems and Technologies (pp. 1049-1059). Springer, Cham.

Abstract

The diffusion and adoption of e-participation contributes to better democracy and more participative societies. Nevertheless, despite the potential benefits of e-participation, the level of citizen satisfaction regarding the use of e-participation and its effects on the continued intention to use have not been widely assessed yet in the literature. This article proposes a conceptual model that integrates the DeLone & McLean success model, that assesses the citizen satisfaction regarding the perception of the e-participation system quality; the expectation-confirmation model for the continued intention to use, which evaluates satisfaction based on the confirmation of ex-post experience on e-participation use and the perceived usefulness; and the dimensions of sense of place, which play a moderator role between the citizen satisfaction and the e-participation use.