LAUSR

Geography and GIScience (geographic information science) are both concerned as disciplines with the infinite complexity of the surface and near-surface of the Earth, or what we might call the geographic domain. Many other disciplines also concern themselves with this domain, including most if not all of the social and environmental sciences, but none do so with the generality of geography and GIScience. Geography has a long tradition of concern with integration, with exploring the links that exist between disciplines and with problems whose solution requires knowledge that extends across many disciplines. It is not surprising, therefore, that an invitation to address the general principles and analytical frameworks in geography and GIScience has generated such a diversity of perspectives. There are clearly many questions one might ask about the geographic domain, and many routes to building representations that might be used to address those questions, especially when those representations must capture many distinct phenomena in the same framework. Geographers have long used maps as a framework with which to create, store and share representations of the geographic domain. But maps have obvious limitations: they are flat while the geographic domain is curved; they use two spatial dimensions to represent the three spatial dimensions of the domain; they must necessarily focus on static features; unlike numerical data, they are not readily submitted to quantitative analysis; and the scale of a map imposes a constraint on the representation’s level of detail. Today, the move to digital representations has in principle removed many of these limitations. Geographic information systems (GIS) and spatial databases now capture, represent and analyse the information that was previously shown in maps; they include the third spatial dimension; and it is now possible to represent and investigate time-dependent phenomena. Thus, tupu, the concept advanced by Chen Shupeng and the subject of Li’s paper (Li, this volume), is in many ways the guiding principle of today’s spatiotemporal databases. Although there have been very important advances in the capturing of greater detail, spatial and temporal resolution must always remain limited to some degree because of the limitations of our observing systems. Moreover, practice is often slow to adjust to new opportunities, and many of the decisions made in the early days of the digital transition, at a time when computational resources were extremely limited, still have their legacy effects today (Goodchild 2018). Clearly, any general principles that might apply to the geographic domain would be extremely valuable as a basis for digital representation and analytic frameworks, and several are identified in these papers. Many make reference to the principle of spatial dependence, nicely expressed by Tobler (1970) in what he suggested might qualify as a First Law of Geography: nearby things are more similar than distant things. The practice of mapping topography with contours would be impossible without it, as would the practice of dividing the world into areas of approximately uniform characteristics – the regions of regional geography or the polygons of GIS. Anselin (1989) argued that spatial heterogeneity was also a defining principle, a theme pursued by Fotheringham and Sachdeva (this volume) in their discussion of geographically weighted regression (GWR). Jiang (this volume) argues for scaling as a principle, based on the observation that small geographic phenomena tend to be much more abundant than large ones and that abundance is often almost precisely related to size by a power law. Central to all of these discussions is the concept of geographic context, or the tendency for geographical surroundings to influence outcomes. This is one possible basis for the similarity principle advanced by Zhu and Turner (this volume), and for the spatial heterogeneity discussed by Fotheringham and Sachdeva (this volume). Finally, scale and its related