Detecting clusters over intercity transportation networks using K-shortest paths and hierarchical clustering: a case study of mainland China

TL;DR: Wang et al. as mentioned in this paper proposed a graph embedding algorithm that extends the traditional complex network approach of one-dimensional cognition of cities to twodimensional cognition, which can project city information into a high-dimensional mathematical space by learning the population flow relationships and computing a unique vector representation.

TL;DR: Two topological characterisations of the European International Railway Network are performed using Complex Networks Analysis (CNA), and the direct connection among cities in the international railway service is analyzed using the most commonly used metrics.

TL;DR: This research studies the process of the formation of links among nodes in different real-world PTNs and finds that this process may be appropriately explained by a generalized linear model (GLM) using local, global, and quasilocal similarity indexes as explanatory variables.

TL;DR: Wang et al. as mentioned in this paper explored China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model, and showed that the probability of poverty in all regions of China has shown an overall decreasing trend (PRE = 0.264), which indicates that the progress of poverty reduction during this period is significant.

TL;DR: Zhang et al. as discussed by the authors investigated the redistribution of economic activities resulting from high speed railway (HSR) by developing a locally weighted regression model, geographically network weighted regression (GNWR), which can reflect the net inflow or outflow of different cities in terms of their residents' investment and consumption.

TL;DR: In this article, the authors conducted surveys in the case study region Hamburg, Germany, to assess the impact of different logistics land use development scenarios on traffic and traffic impacts, and the results show that strategic location of logistics areas can reduce traffic.

TL;DR: Li et al. as mentioned in this paper proposed a new megalopolitan landscape dynamic model (MLDM) that is better suited for simulating the urban landscapes in an MCA by combining a gravitational field model (GFM) with a cellular automata model.

TL;DR: This article proposes a geo-distance-based method of detecting communities in spatially constrained networks to identify communities that are both highly topologically connected and spatially clustered, based on the fast modularity maximisation (CNM) algorithm.