Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

Spatial transcriptomics are a collection of genomic technologies that have enabled
transcriptomic profiling on tissues with spatial localization information. Analyzing spatial …

This study develops an augmented geographically weighted regression (GWR) model to
analyze the spatial distribution of PM 2.5 concentrations through the incorporation of …

Feature extraction becomes increasingly important as data grows high dimensional.
Autoencoder as a neural network based feature extraction method achieves great success …

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation
of global dengue epidemics remains challenging. Here, we investigate climate indicators …

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global
burden of infectious diseases due to their epidemic potential, which can result in significant …

In the industrialization that started in the 1830s in countries such as England and America,
transportation networks, geographical locations and spatial size were taken as the basis in …

The majority of national governments now dedicate themselves to sustainable development
as it aims to produce a long-term, positive relationship between civilization and life …

Understanding the effects of scale is essential to the understanding of natural ecosystems,
particularly in marine environments where sampling is more limited and sporadic than in …