Throughout most of human history, events and phenomena of interest have been
characterized using space and time as their major characteristic dimensions, in either …

About 30 years have passed since Michael F. Goodchild proposed the term geographical
information science (GIScience) in 1992. In the past 30 years, GIScience has made great …

As a typical form of geographical phenomena, spatial flow events have been widely studied
in contexts like migration, daily commuting, and information exchange through …

Spatial flow data represent meaningful spatial interaction (SI) phenomena between
geographic regions that are often highly dynamic. However, most existing flow analytical …

Geographical flow (hereafter flow) can be modeled as an orderly connected point pair
composed of an origin (O) and a destination (D). Aggregation is the most common form of …

Flows can reflect the spatiotemporal interactions or movements of geographical objects
between different locations. Measuring the spatiotemporal autocorrelation of flows can help …

Massive spatio-temporal big data about human mobility have become increasingly
available. Revealing underlying dynamic patterns from these data is essential for …

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial
dynamics and associations of movements. However, conventional point-based co-location …

Spatial flow data are often used to represent spatial interaction phenomena such as daily
commuting trips, human or animal migrations, and the exchanges of commodities, capital, or …

Spatial flow outlier (SFO) detection aims to discover spatial flows whose non-spatial attribute
values are significantly different from their neighborhoods. Different from spatial flow …