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Maps, like other types of extensive data collections, are usually created and maintained by a larger number of individuals. The number of individuals using the map is even larger in most cases. Considering the complex interaction of these people, the question arises as to why maps can be used meaningfully. Ultimately, the represented geographical reality can rarely be perfectly reconstructed from the map, and misunderstandings are inevitable when using the map. This article sets factors into context that facilitate the readability of a map as well as factors that can lead to misunderstandings and non-interpretability. The creation of a map is thereby considered a complex system the stability, coherence, and heterogeneity of which can be explained by its attractors and, in the temporal context, by means of disruptive behavior and autopoiesis. To this end, a coherence theory of map making and reading is proposed. This allows for a broader perspective on the map-making process and a deeper understanding of a map’s affordances. In particular, the considerations made can serve as a starting point to develop better measures of data quality and fitness for purpose. Finally, a more reflective behavior and active influence on the map-making process is made possible.

The reasons for representing places are manifold, and so are the ways in which they are represented. Travel guides, music, and paintings give an impression of often distant places, and scientific texts seek to represent places and their context objectively. By going beyond the existing semantic discourse on representations and focussing on places more specifically, this article seeks a better understanding of the representations of places. First, naturally occurring representations are discussed as the result of the patterns that are formed by place-making and the very being of a place, such as archaeological finds and traditions. Subsequently, I argue that many of the natural as well as non-natural representations can be understood as ‘representation-as’ in Goodman’s and Elgin’s sense, because they represent places as part of a genre, for example, as a tourist attraction or a retreat. This understanding facilitates the conceptualization of individual representations and their placement in the overall context, which in turn helps laying the foundation for a conceptual framework for ‘platial’ information.

The genius loci of many places is beyond description. Beyond description in the sense that we are not able to linguistically express what it feels like to be in this place, or whatit is that ultimately makes this place. For example, how could we describe the familiarityand the sense of belonging that we usually feel when we are at home, a place of origin.Home as a place can develop a very special identity and give us love, security, and comfort,but for some may evoke anxiety and fear instead, such as in cases of domestic violence.Only when we develop personal bounds to the flat, the building, the neighbourhood, orthe city in which we live, does this feeling of home become accessible to us. Without thedistinctive everyday routine, the habitus, and the familiarity, and perhaps also withoutthe will to engage with the place, it never becomes a home. And this being elsewhere isthe feeling Dorothy Gale has when she, in the film The Wizard of Oz, is whisked away toMunchkinland in the Land of Oz. She longs for home and wants to leave Munchkinland,but as we all know, this turns out not to be an easy task. Dorothy’s famous and recitativelyrepeated saying “There’s no place like home” embodies the previously described specialaura of home to which she longs to return. Even if it remains unclear throughout the filmwhether Dorothy was only dreaming or really in the Land of Oz, this suggests again thatin many senses home is beyond compare

Place is a concept that can hardly be formally captured at the moment, as it is unclear how instances of places can formally be represented and how conclusions about places can practically be drawn by technological means. Geographical Information Science scholars hence tend to use the term ‘Place’ even when, in fact, they presume a paradigm similar to the one assumed for Geometrical Space. As a result, Space and thematic information is mostly treated separately, and the richness and variety of Place descriptions in terms of identities, affective states, affordances, and further aspects that have been discussed in Geography since a long time are not (yet) reflected well in corresponding discussions in Geographical Information Science. This article reviews the ongoing debate and outlines directions of how to extend it much beyond the currently assumed spatial paradigm towards platial information. Thereby, possible approaches and future prospects as well as limitations of Theories of Platial Information and Platial Information Systems are explored. The agenda laid out and discussed in this article aims to set a frame of reference for a re-focussing of the ongoing discourse on platial information and stimulate future developments towards a Platial Information Science.

Big data are not yet commonly used in psychological research as they are often difficultto access and process. One source of behavioral data containing both spatial andthematic information is OpenStreetMap, a collaborative online project aiming to developa comprehensive world map. Besides spatial and thematic information about buildings,streets, and other geographical features, the collected data also contains informationabout the contribution process itself. Even though such data can be potentially useful forstudying individual judgments and group processes within a natural context, behavioraldata generated in OpenStreetMap have not yet been easily accessible for scholars inpsychology and the social sciences. To overcome this obstacle, we developed a softwarepackage which makes OpenSteetMap data more accessible and allows researchers toextract data sets from the OpenStreetMap database as CSV or JSON files. Furthermore,we show how to select relevant map sections in which contributor activity is high and howto model and predict the behavior of contributors in OpenStreetMap. Moreover, wediscuss opportunities and possible limitations of using behavioral data fromOpenStreetMap as a data source.

The naïve algorithm for generating nearest-neighbour models determines the distance between every pair of nodes, resulting in quadratic running time. Such time complexity is common among spatial problems and impedes the generation of larger spatial models. In this article, an improved algorithm for the Mocnik model, an example of nearest-neighbour models, is introduced. Instead of solving k nearest-neighbour problems for each node (k dynamic in the sense that it varies among the nodes), the improved algorithm presented exploits the notion of locality through introducing a corresponding spatial index, resulting in a linear average-case time complexity. This makes possible to generate very large prototypical spatial networks, which can serve as testbeds to evaluate and improve spatial algorithms, in particular, with respect to the optimization of algorithms towards big geospatial data.

This editorial presents a special collection of papers addressing the concept of place and its use in geographical information science (GIScience). The concept of place is a topic of increasing interest among GIScience scholars. First attempts to formalise platial information have been made and it is increasingly held that user‐generated data sets in particular are often more platial than spatial in nature. At the same time, and especially when compared to geometric spatial concepts, the concept of place is ambiguous, complex and difficult to capture in formal and analytical terms, suggesting the need for interdisciplinary approaches. This collection presents articles covering a wide range of place‐related aspects, including both conceptual and more applied contributions. In the present editorial we summarise these and comment on their individual contributions, and hope that the readership of Transactions in GIS will find the special collection inspiring and informative.

Many complex networks expose global hub structures: for some nodes, the number of incident edges far exceeds the average, leading to a small average shortest path length. Such ‘small-world properties’ are often guided by a scale-free power-law distribution of the node degrees, and self-organization inside the network has been identified as a reason driving the emergence of this structure. Small-world networks have recently raised lots of interest, because they capture the global topology of the World-Wide Web, metabolic, and social networks. While small-world networks reflect global structures, little attention is paid to the local structure of complex networks. In this article neighbourhoods are demonstrated to share a common local structure in many real complex networks, manifested by a polynomial volume law. This law can, in case of networks that are embedded in space, be explained in terms of the embedding and the properties of Euclidean space. A model of hierarchical spatial networks is introduced to examine the effect of global structures, in particular of hierarchies, on the polynomial volume law. It turns out that the law is robust against the coexistence of such global structures. The local structure of space and global optimization can both be found in transport, brain, and communication networks, which suggests the polynomial volume law, often in combination with hierarchies or other global optimization principles, to be a generic property inherent to many networks.

Geospatial data is often spatially aggregated by the use of Discrete Global Grid Systems. References to grid cells are needed for the communication of such data, and different identifier schemes ha...

Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic information (VGI), while also applying to other geographical data and data in general. An evaluation of the ontology in the context of data quality measures for OpenStreetMap (OSM) data, a well-known example of VGI, provides insights about which types of quality measures for OSM data have and which have not yet been considered in literature.

Spatial variance is an important characteristic of spatial random variables. It describes local deviations from average global conditions and is thus a proxy for spatial heterogeneity. Investigating instability in spatial variance is a useful way of detecting spatial boundaries, analysing the internal structure of spatial clusters and revealing simultaneously acting geographic phenomena. Recently, a corresponding test statistic called ‘Local Spatial Heteroscedasticity’ (LOSH) has been proposed. This test allows locally heterogeneous regions to be mapped and investigated by comparing them with the global average mean deviation in a data set. While this test is useful in stationary conditions, its value is limited in a global heterogeneous state. There is a risk that local structures might be overlooked and wrong inferences drawn. In this paper, we introduce a test that takes account of global spatial heterogeneity in assessing local spatial effects. The proposed measure, which we call ‘Local Spatial Dispersion’ (LSD), adapts LOSH to local conditions by omitting global information beyond the range of the local neighbourhood and by keeping the related inferential procedure at a local level. Thereby, the local neighbourhoods might be small and cause small-sample issues. In the view of this, we recommend an empirical Bayesian technique to increase the data that is available for resampling by employing empirical prior knowledge. The usefulness of this approach is demonstrated by applying it to a Light Detection and Ranging-derived data set with height differences and by making a comparison with LOSH. Our results show that LSD is uncorrelated with non-spatial variance as well as local spatial autocorrelation. It thus discloses patterns that would be missed by LOSH or indicators of spatial autocorrelation. Furthermore, the empirical outcomes suggest that interpreting LOSH and LSD together is of greater value than interpreting each of the measures individually. In the given example, local interactions can be statistically detected between variance and spatial patterns in the presence of global structuring, and thus reveal details that might otherwise be overlooked.\ud \ud

Maps are good at representing geographic space, but texts have a stronger affordance of telling a story than maps. Telling stories is, however, important to make information more personal and to ar...