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Integration of folksonomies into the process of map generalization
Abstract
The growth of user-generated content in quantity and quality has changed the way people use digital services, including geo-services. The process of map generalization is not an exception to this phenomenon. Earlier research has considered user-generated content as data sources for the generalization process. However, little work has been accomplished to date considering the knowledge that may be extracted from those sources, in particular from special place-related semantics captured in user-contributed feature tags. This study considers doing so from the perspective of folksonomies, presenting some first steps in that direction. In particular, this short paper shows, on the example of OpenStreetMap, how different similarity measures can be used to exploit folksonomy-based semantics in map generalization. And it shows how these semantics can be used to introduce behaviour changes in generalization operators, in particular in the selection and aggregation operators, respectively.
... There are the outcomes of the mapping process by numerous volunteers, but also contributions by governments and NGOs. OSM has besides data on transportation infrastructure also information on points of interest (POIs), land use and buildings, always specified by user-contributed feature tags (Aliakbarian and Weibel 2016). ...
... The subsequent steps are influenced by inspecting the outcomes, which are in our case point clusters or aggregated polygons. This includes reasoning on place-related semantics, which is possible by inspecting user-contributed feature tags in the way of defining folksonomies (Aliakbarian and Weibel 2016). More specifically, we aim to adjust urban vitality definition on a city scale by testing different road segment density, together with respecting different types of geotagged photos. ...
... Therefore, ideally, the influence on mapping and on reading the map balances out, and existing issues can be systematically addressed. Also, the concepts for semantic description seem to be chosen in such a way in case of OSM that they hide the differences in conceptualization arising from individual differences in perception: they are chosen sufficiently coarse and compatible with our perception by being the result of an intensive coordination process (Aliakbarian & Weibel, 2016;Mocnik et al., 2017;Mocnik, 2020). ...
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.
... Mooney and Corcoran (2012a) have examined how the tags associated to an element change over time, and how the lack of control mechanisms can affect data quality. Finally, Aliakbarian and Weibel (2016) have shown how to make use of the OSM folksonomy when generalizing information for maps. ...
The comprehension of folksonomies is of high importance when making sense of Volunteered Geographic Information (VGI), in particular in the case of OpenStreetMap (OSM). So far, only little research has been conducted to understand the role and the evolution of folksonomies in VGI and OSM, which is despite the fact that without a comprehension of the folksonomies the thematic dimension of data can hardly be used. This article examines the history of the OSM folksonomy, with the aim to predict its future evolution. In particular, we explore how the documentation of the OSM folksonomy relates to its actual use in the data, and we investigate the historical and future scope and granularity of the folksonomy. Finally, a visualization technique is proposed to examine the folksonomy in more detail.
... There are a number of well-established as well as newly proposed approaches, methods, and algorithms for cartographic generalization (e.g. Aliakbarian & Weibel, 2016;Brassel & Weibel, 1988;Cecconi, 2003;Guilbert, Gaffuri, & Jenny, 2014;Harrie & Weibel, 2007;Jiang, Liu, & Jia, 2013;Mackaness & Gould, 2014;Stanislawski, Buttenfield, Bereuter, Savino, & Brewer, 2014;Stoter, Post, Van Altena, Nijhuis, & Bruns, 2014). However, reducing visual complexity while retaining sufficient detail is an ongoing challenge to this day. ...
We present a study on human perception of map complexity, with the objective of better understanding design decisions that may lead to undesirable levels of complexity in web maps. We compare three complexity metrics to human ratings of complexity obtained through a user survey. Specifically, we use two algorithmic approaches published by others, which measure feature congestion (FC) and subband entropy (SE), as well as our own approach of counting object types rather than individual objects. We compare these metrics with each other as well as with human complexity ratings for three maps of the same area from map providers Google Maps, Bing Maps, and OpenStreetMap. Each map design is assessed at three different scales (levels of detail). We find that (1) the FC and SE metrics appear to be adequate predictors of what humans consider complex; (2) object-type counts are slightly less successful at predicting human-rated complexity, implying that clutter is more important in perceived complexity than diversity of symbology; and (3) generalization choices do impact human complexity ratings. These findings contribute to our understanding of what makes a map complex, with implications for designing maps that are easy to use.
... User-generated data is known to commonly contain more semantic attributes, and hence might bear even better potential for semantic enrichment than topographic data from official sources. Also, user-generated data represent the vernacular, rather than the official, view of geography; this could be of interest when vernacular maps should be produced (Aliakbarian and Weibel 2016). On the other hand, such data sources, though available for the entire world, may introduce new challenges. ...
Acquiring and formalizing cartographic knowledge still is a challenge, especially when the generalization process concerns small-scale maps. We concentrate on the settlement selection process for small-scale maps, with the aim of rendering it more holistic, and making methodological contributions in four areas. First, we show how written specifications and rules can be validated against the actual published map products, thus pointing to gaps and potential improvements. Second, we use data enrichment based on supplementing information extracted from point-of-interest data in order to assign functional importance to particular settlements. Third, we use machine learning (ML) algorithms to infer additional rules from existing maps, thus making explicit the deep knowledge of cartographers and allowing to extend the cartographic rule set. And fourth, we show how the results of ML can be transformed into human-readable form for potential use in the guidelines of national mapping agencies. We use the case of settlement selection in the small-scale maps published by the Polish national mapping agency (GUGiK). However, we believe that the methods and findings of this paper can be adapted to other environments with minor modifications.
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