Centre of Geographical Studies

Funder

Organizational Unit

Publications

Biodeteção Móvel Participativa para o Desenho Urbano

Publication . Paiva, Daniel; Cachinho, Herculano; Estevens, Ana; Gonçalves, Ana; Ferreira, Daniela; Brito-Henriques, Eduardo; Boavida-Portugal, Inês; Rodrigues, Nuno; Pedro, Tomás; Equipa UrBio

Este guia foi especialmente desenvolvido para urbanistas interessados em aplicar metodologias de desenho urbano participativas para tornar as cidades em que trabalham mais sensíveis às emoções dos seus habitantes. A metodologia que se apresenta consiste na introdução de técnicas de biodeteção móvel, que nos oferecem informação sobre o estado fisiológico e emocional dos habitantes, em métodos participativos reconhecidos em análise e design urbano. Esta metodologia é recomendada para implementação em projetos de regeneração de escala local, nomeadamente ao nível de praças e ruas, em que a dimensão experiencial é importante, como é o caso de espaços de consumo ou turismo.

2023Other

Open access

Predicting dengue importation into Europe, using machine learning and model-agnostic methods

Publication . Salami, Donald; Sousa, Carla Alexandra; Martins, Maria do Rosário Oliveira; Capinha, César

The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model's predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country's dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

2020Journal article

Open access

From cyberspace to cyberspatialities?

Publication . Ferreira, Daniela; Vale, Mário

This paper is a short reflection on the evolution of the meaning of the term cyberspace for geographers. We argue that the concept of cyberspace has become a rhizomatic one as spatial thinkers have unveiled its complex inner and outer networkings. While cyberspace was initially understood as a new open space ripe for exploration, its intricate connections with real space through the technological infrastructures that make cyberspace possible have led geographers to consider the multiple points of access and types of cyberspace. More recently, there has been renewed attention to the inner geographies of cyberspace and its cyberdivides have been exposed. We briefly retrace this evolution to argue that the way forward is to shift from an idea of cyberspace as a predefined space to a notion of cyberspatialities as ongoing spatial digital formations.

2021Journal article

Open access

Improving the Walkability of High Streets: A Participatory Approach Using Biosensing and Scenario Co-Creation

Publication . Pedro, Tomás; Paiva, Daniel

In the 21st century, there has been a concerted effort to undo the automobilecentric urban planning of the 20th century, which has resulted in degraded public spaces that deter citizen permanence. However, the perpetuation of quantitative-based methodologies, along with low public participation, has led to underused public spaces. To create more appealing spaces, the methods need to feature more public involvement. This article addresses this gap by implementing the Participatory Mobile Biosensing methodology. Participants were asked to walk along two high streets in Lisbon using biosensors and, in a later workshop, to interpret their biodata and co-create scenarios to improve their walking experience. The participants were able to identify the intangible and physical factors that affected their walk, as well as devise scenarios to address them. When the participants formulated their scenarios, they were also able to demonstrate several ideals that influenced their vision for the streets. The subsequent discussion focused on the relevance of this methodology to high streets and how participatory methods could further the study of walkability by incorporating subjective experiences in the creation of public spaces.

2025Journal article

Open access

When do citizen scientists record biodiversity? Non‐random temporal patterns of recording effort and associated factors

Publication . Rosário, Inês T.; Tiago, Patrícia; Chozas, Sergio; Capinha, César

1. Citizen science data are increasingly used for ecological research, biodiversity conservation and monitoring. However, these data often present significant analytical challenges due to uneven recording efforts by citizen scientists. Biases caused by intra-annual differences in levels of recording activity can be particularly severe, hindering the use of citizen science data in research areas such as population dynamics and phenology. Therefore, understanding the temporal patterns and drivers of recording activity by citizen scientists is essential. 2. In this study, we provide a detailed assessment of how weather and calendarrelated factors relate to levels of biodiversity recording activity by citizen scientists at a daily resolution. To perform this, we analyse the recording patterns for six tree species in the Iberian Peninsula, which maintain a consistent appearance throughout the year. Observation data were collected from iNaturalist, a leading platform for citizen science data. We used boosted regression trees (BRT) to compare observed recording activity patterns with those expected by chance. Our analysis included a comprehensive set of explanatory variables, such as the day of the week, the month, holidays, temperature, accumulated precipitation, wind intensity and snow depth. 3. The BRT models demonstrated good predictive performance, with the correlation between predicted and observed patterns of recording activity (left out of model training) ranging from 0.55 to 0.91, depending on the species. The day of the week, month of the year, and daily temperature consistently emerged as the most important predictors. Recording activity was higher on weekends, to some extent on Fridays and during the spring months. Extreme low and high temperatures were generally associated with lower recording activity, although there were exceptions. Precipitation and wind speed had relatively lower importance but remained relevant, with increased precipitation and wind intensity typically associated with reduced recording activity. In contrast, public holidays and accumulated snow demonstrated minimal to negligible importance. 4. Our findings show that citizen scientists record more frequently on weekends, during mild weather and in spring. By addressing these non-random patterns in recording activity, we can maximise the utility of citizen-collected data for research and applied purposes.

2025Journal article

Open access