
Journal of Convex Analysis 30 (2023), No. 1, 371400 Copyright Heldermann Verlag 2023 On Betweenness and Equidistance in Metric Spaces Paul Bankston Dept. of Mathematical and Statistical Sciences, Marquette University, Milwaukee, U.S.A. paul.bankston@marquette.edu Aisling McCluskey School of Mathematical and Statistical Sciences, University of Galway, Ireland aisling.mccluskey@universityofgalway.ie The classic notions of betweenness and equidistance in Euclidean geometry readily generalize to the context of metric spaces. We view these notions from an axiomatic perspective, then analyze the role various axioms play when interpreted in a metric space  especially one where the metric is induced by a vector space norm. The points lying between two given points constitute the betweenness interval bracketed by those points, and the points equidistant from two given points constitute the equiset with those points as cocenters. An equiset gives rise to a division of the underlying space into two comparative nearness regions; in the case of the Euclidean plane, each such region is a halfplane bounded by the line that is the equiset. Betweenness intervals naturally engender a notion of convexity, and one focus of this investigation is the issue of when equisets and their comparative nearness regions, as well as the betweenness intervals themselves, are convex. For normed vector spaces, betweenness intervals are always convex when the dimension is at most two, but this convexity property easily fails in higher dimensions. Equisets and comparative nearness regions in a normed vector space are convex precisely when the norm arises from an inner product. This is one of several characterizations we present of normed vector space properties purely in terms of abstract betweenness, equidistance and comparative nearness. Keywords: Betweenness, equidistance, comparative nearness, convexity, IRaxioms, metric spaces, normed vector spaces, taut arcconnectedness. MSC: 51F99; 46B20, 52A01, 52A10, 52A21, 52A30, 54A05, 54E35. [ Fulltextpdf (221 KB)] for subscribers only. 