Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Space–time analysis of disease data has historically involved the search for patterns in
aggregated data to identify how regions of high and low risk change through time. Space …

This study evaluates the role of black residential segregation and spatial access to health
care in explaining the variation in late-stage diagnosis of breast cancer in metropolitan …

We present the serendipitous discovery of a 'Giant Arc on the Sky'at∼ 0.8. The Giant Arc
(GA) spans∼ 1 Gpc (proper size, present epoch) and appears to be almost symmetrical on …

Importance There are stark disparities in cervical cancer burden in the United States, notably
by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate …

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to
communicate and disseminate ideas, innovations, best practices and challenges across …

Epidemiologists are gradually incorporating spatial analysis into health-related research as
geocoded cases of disease become widely available and health-focused geospatial …

Background Multiple composite indices of small-area socioeconomic characteristics have
been used to examine how neighborhood characteristics influence cancer care, but there is …

Cancer is a major concern among chronic diseases today. Spatial epidemiology plays a
relevant role in this matter and we present here a review of this subject, including a …

Objective: To examine systematically the literature on the effect of geographical location
variation on breast cancer stage at diagnosis, race/ethnicity, and socioeconomic status …