Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques

Mocnik, Franz-Benjamin; Ludwig, Christina; Grinberger, A. Yair; Jacobs, Clemens; Klonner, Carolin; Raifer, Martin

doi:10.3390/ijgi8050242

Cited by 11 publications

(8 citation statements)

References 35 publications

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“…The empirical demonstrations have shown that even the application to traditional data sets can reveal interesting structures that might otherwise have gone unnoticed. Furthermore, there are other crowdsourcing and user-generated data sets, some of which share certain characteristics with geosocial media data (Mocnik et al., 2019; See et al., 2016). It would be instructive to see how spatial amplifier filtering works with these closely related types of data sets, not only to possibly gain clearer insights into their spatial structuring but also to learn more about the behaviour of the proposed methodology in different contexts.…”

Section: Discussionmentioning

confidence: 99%

“…Over the last decade and a half, the spatial, social and planning sciences have been confronted with a steadily growing number of georeferenced data sets, many of which are user-generated (Goodchild, 2007; Mocnik et al., 2019). There are many ways in which we now routinely leave our digital traces, for instance, when we use everyday public transportation, shop online and use cellular networks.…”

Section: Introductionmentioning

confidence: 99%

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Emphasising spatial structure in geosocial media data using spatial amplifier filtering

Westerholt

2021

Environment and Planning B: Urban Analytics and City Science

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In this article, a new method called spatial amplifier filtering is proposed. The presented method is related to Moran eigenvector filtering and allows the accentuation of spatial structures in heterogeneous data sets. The spatial amplifier filtering technique is based on the inclusion of certain eigenvectors of a spatial weights matrix into a regression model. The application of this method can be seen as a pre-processing step prior to subsequent analyses, and to separate different types of spatially correlated components in a data set. For this purpose, three different types of the so-called spatial amplifiers are proposed, each consisting of different subsets of eigenvectors of the weights matrix. These amplifiers can either emphasise the positive or negative spatial autocorrelation, or spatial structuring in general. In this way, it is possible to make desired spatial structures more visible, especially in spatially highly mixed data sets, whereby the focus here is on geosocial media data. In the empirical part of the article, it is first shown why georeferenced social media data are difficult to handle from a spatial analysis perspective, motivating the need for the method proposed. Subsequently, the technique of amplifier filtering is applied to two data sets: a census data set from Brazil and Twitter data from London. The results obtained show that the method is capable of strengthening existing spatial structures and mitigating potentially disturbing spatial randomness patterns and other nuisances. This facilitates the interpretation especially of the Twitter data used. While the analysis of the unfiltered Twitter data with established methods reveals little information about possible spatial structures in the tweets, the filtered data offer a much clearer picture with distinguishable clusters. In addition, the method also provides insights into the internal irregularity of spatial clusters and thus complements the toolbox for investigating spatial heterogeneity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

Westerholt

2021

Environment and Planning B: Urban Analytics and City Science

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“…Some of the insights gained in this study may be applicable to games in contexts other than OSM. The so-called triangle of shared data sources presented in [103] shows that user-generated datasets like Wikipedia or the Citizen Science Project eBird share similar principles as the OpenStreetMap project. In the light of the present study, this is promising, as it could allow other researchers to apply our findings to the motivational aspects of other related types of datasets.…”

Section: Discussionmentioning

confidence: 99%

Behavioural Effects of Spatially Structured Scoring Systems in Location-Based Serious Games—A Case Study in the Context of OpenStreetMap

Westerholt

Lorei

Höfle

2020

IJGI

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Location-based games have become popular in recent years, with Pokémon Go and Ingress being two very prominent examples. Some location-based games, known as Serious Games, go beyond entertainment and serve additional purposes such as data collection. Such games are also found in the OpenStreetMap context and playfully enrich the project’s geodatabase. Examples include Kort and StreetComplete. This article examines the role of spatially structured scoring systems as a motivational element. It is analysed how spatial structure in scoring systems is correlated with changes observed in the game behaviour. For this purpose, our study included two groups of subjects who played a modified game based on StreetComplete in a real urban environment. One group played the game with a spatially structured scoring system and the other with a spatially random scoring system. We evaluated different indicators and analysed the players’ GPS trajectories. In addition, the players filled out questionnaires to investigate whether they had become aware of the scoring system they were playing. The results obtained show that players who are confronted with a spatially structured scoring system are more likely to be in areas with high scores, have a longer playing time, walk longer distances and are more willing to take detours. Furthermore, discrepancies between the perception of a possible system in the scoring system and corresponding actions were revealed. The results are informative for game design, but also for a better understanding of how players interact with their geographical context during location-based games.

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“…Thus, social media platforms such as Twitter or Facebook are not defined as mass collaboration because users do not intentionally work collaboratively towards achieving a common goal or generating a common product (Mocnik et al, 2019).…”

Section: Openstreetmap: Online Mass Collaboration Of Individualsmentioning

confidence: 99%

Using OpenStreetMap as a Data Source in Psychology and the Social Sciences

Mayer¹,

Heck²,

Mocnik³

2022

Preprint

Self Cite

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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.

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Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques

Cited by 11 publications

References 35 publications

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

Emphasising spatial structure in geosocial media data using spatial amplifier filtering

Behavioural Effects of Spatially Structured Scoring Systems in Location-Based Serious Games—A Case Study in the Context of OpenStreetMap

Using OpenStreetMap as a Data Source in Psychology and the Social Sciences

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