Distance decay is an essential concept in geography. At its core, distance decay describes how the relationship between two entities generally gets weaker as the separation between them increases. Inspired by long-standing ideas in physics, the concept of distance decay is used by geographers to analyze two kinds of relationships. First, the term expresses how measured interactions (such as trade volume or migration flow) generally decrease as the separation between entities increases, as is analyzed by spatial interaction models. Second, the term is used to describe how the implicit similarity between observations changes with separation, as measured by variograms. For either type of relationship, we discuss how "separation" must be clearly articulated according to the mechanism of the relationship under study. In doing this, we suggest that separation need not refer to positions in space or time, but can involve social or behavioral perceptions of separation, too. To close, we present how the "death of distance" is transforming distance decay in uneven ways.

Relationships between space and time evoke fundamental questions in the sciences and humanities. Many disciplines, including GIScience, consider that space and time extend in separate dimensions, are interchangeable, and form co-equal parts of a larger thing called space-time.  Our perception of how time operates in relation to space or vice verso influences how we represent space, time, and their relationships in GIS. The chosen representation, furthermore, predisposes what questions we can ask and what approaches we can take for analysis and modeling. There are many ways to think about space, time, and their relationships in GIScience. This article synthesizes five broad categories: (1) Time is independent of space but relates to space by movement and change; (2) Time collaborates with space to probe relationships, explanations, and predictions; (3) Time is spatially constructed and constrained; (4) Time and space are mutually inferable; and (5) Time and space are integrated and co-equal in the formation of flows, events, and processes. Concepts, constructs, or law-like statements arise in each of the categories as examples of how space, time, and their relationships help frame scientific inquiries in GIScience and beyond.

The past few decades have been characterized by an exponential growth of digital information resources. A considerable amount of this information is semi-structured, such as XML files and metadata records and unstructured, such as scientific reports, news articles, and historical archives. These resources include a wealth of latent knowledge in a form mainly intended for human use. Semantic information elicitation refers to a set of related processes: semantic information extraction, linking, and annotation that aim to make this knowledge explicit to help computer systems make sense of the content and support ontology construction, information organization, and knowledge discovery.

In the context of GIScience research, semantic information extraction aims at processing unstructured and semi-structured resources and identifying specific types of information: places, events, topics, geospatial concepts, and relations. These may be further linked to ontologies and knowledge bases to enrich the original unstructured content with well-defined meaning, provide access to information not explicit in the original sources, and support semantic annotation and search. Semantic analysis and visualization techniques are further employed to explore aspects latent in these sources such as the historical evolution of cities, the progression of phenomena and events and people’s perception of places and landscapes.

Shape is important in GI Science because the shape of a geographical entity can have far-reaching effects on significant characteristics of that entity. In geography we are mainly concerned with two-dimensional shapes such as the outlines of islands, lakes, and administrative areas, but three-dimensional shapes may become important, for example in the treatment of landforms. Since the attribute of shape has infinitely many degrees of freedom, there can be no single numerical measure such that closely similar shapes are assigned close numerical values. Therefore different shape descriptors have been proposed for different purposes. Although it is generally desirable for a shape descriptor to be scale invariant and rotation invariant, not all proposed descriptors satisfy both these requirements. Some methods by which a shape is described using a single number are described, followed by a discussion of moment-based approaches. It is often useful to represent a complex shape by means of a surrogate shape of simpler form which facilitates storage, manipulation, and comparison between shapes; some examples of commonly used shape surrogates are presented. Another important task is to compare different shapes to determine how similar they are. The article concludes with a discussion of a number of such measures of similarity.