Time is a fundamental concept in geography and many other disciplines. This article introduces time at three levels. At the philosophical level, the article reviews various notions on the nature of time from early mythology to modern science and reveals the dual nature of reality: external (absolute, physical) and internal (perceived, cognitive). At the analytical level, it introduces the measurement of time, the two frames of temporal reference: calendar time and clock time, and the standard time for use globally. The article continues to discuss time in GIS at the practical level. The GISystem was first created as a “static” computer-based system that stores the present status of a dynamic system. Now, GISystems can track and model the dynamics in geographical phenomena and human-environment interactions. Representations of time in dynamic GISystems adopt three perspectives: discrete time, continuous time and Minkowski’s spacetime, and three representations: ordinal, interval, and cyclical. The appropriate perspective and representation depend on the observed temporal patterns, which can be static, oscillating, chaotic, or stochastic. Recent progress in digital technology brings us opportunities and challenges to collect, manage and analyze spatio-temporal data to advance our understanding of dynamical phenomena.

The selection of appropriate type on maps, far from an arbitrary design decision, is an integral part of establishing the content and tone of the map. Typefaces have personalities, which contribute to the rhetorical message of the map. It is important to understand how to assess typefaces for their personalities, but also to understand which typefaces may be more or less legible in a labeling context. Beyond the choice of typeface, effective map labels will have a visual hierarchy and allow the user to easily associate labels to their features and feature types. The cartographer must understand and modify typographic visual variables to support both the hierarchy and label-feature associations.

Unmanned Aerial Systems (UAS) are revolutionizing how GIS&T researchers and practitioners model and analyze our world. Compared to traditional remote sensing approaches, UAS provide a largely inexpensive, flexible, and relatively easy-to-use platform to capture high spatial and temporal resolution geospatial data. Developments in computer vision, specifically Structure from Motion (SfM), enable processing of UAS-captured aerial images to produce three-dimensional point clouds and orthophotos. However, many challenges persist, including restrictive legal environments for UAS flight, extensive data processing times, and the need for further basic research. Despite its transformative potential, UAS adoption still faces some societal hesitance due to privacy concerns and liability issues.