Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon

New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users’ demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.info:eu-repo/semantics/publishedVersio

Characterizing and modelling the spatial patterns of wildfire ignitions in Portugal: fire initiation and resulting burned area

Modelling, Monitoring and Management of Forest FiresAccording to the statistics Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict ignition occurrence constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and fire fighting resources allocation. In this study we used a database with information about 127 490 fire ignitions that occurred in Portugal during a five year period. We performed frequency analysis to characterize the occurrence of wildfire ignitions in relation to both human and environmental variables and compared the spatial patterns of ignitions which originated fires larger or smaller than 500 ha. We also used logistic regression models to predict the relative probability of ignition occurrence, as a function of the resulting fire size. Results show that fire ignitions are strongly related to human presence and activity, and that the spatial patterns of ignitions are different for larger or smaller wildfires. Larger wildfires started in areas with lower population density, more distant from the main roads and at higher elevations, when compared to smaller fires, and also started more frequently in shrublands and forested areas. The results obtained can be useful in decision making for fire danger managementinfo:eu-repo/semantics/publishedVersio

Exploring spatial data through computational intelligence: a joint perspective

The dramatic increase in geospatial data occasioned by developments in digital mapping, remote sensing, IT, and widespread generalization of Geographic Information Systems (GIS), emphasises the importance of exploring new approaches to spatial analysis and modelling. This favours the creation of new knowledge and eventually helps the process of scientific discovery. In this context the special nature of spatial data is particularly relevant and should be taken into account (e.g. observations are not independent and data uncertainty and errors are often spatially structured). The tolerance of imprecision and uncertainty makes soft computing a potentially very useful tool in the GIS environment. Computational Intelligence (or Soft computing) fits particularly well with GIS applications in those cases where computationally hard problems cannot be solved by classical algorithmic approaches