Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. The existing spatiotemporal correlation as well as the diversity of influential factors, pose important challenges in accurately modeling these trends.

Ranking places in attributed temporal urban mobility networks.

Drawing on the recent advances in complex network theory, urban mobility flow patterns, typically encoded as origin-destination (OD) matrices, can be represented as weighted directed graphs, with nodes denoting city locations and weighted edges the number of trips between them. Such a graph can further be augmented by node attributes denoting the various socio-economic characteristics at a particular location in the city. In this paper, we study the spatio-temporal characteristics of "hotspots" of different types of socio-economic activities as characterized by recently developed attribute-augmented network centrality measures within the urban OD network.

Analysing successful public spaces in an urban street network using data from the social networks Foursquare and Twitter.

This paper analyzes success public spaces (specifically ) in the urban fabric of the city of Murcia, Spain. Two approaches were adopted. Firstly, the city was visualized as a complex network whose nodes represent .