Introduction to the second international symposium of platial information science

People ‘live’ and constitute places every day through recurrent practices and experience. Our everyday lives, however, are complex, and so are places. In contrast to abstract space, the way people experience places includes a range of aspects like physical setting, meaning, and emotional attachment. This inherent complexity requires researchers to investigate the concept of place from a variety of viewpoints. The formal representation of place – a major goal in GIScience related to place – is no exception and can only be successfully addressed if we consider geographical, psychological, anthropological, sociological, cognitive, and other perspectives. This year’s symposium brings together place-based researchers from different disciplines to discuss the current state of platial research. Therefore, this volume contains contributions from a range of fields including geography, psychology, cognitive science, linguistics, and cartography

Introduction to the Second International Symposium on Platial Information Science

People ‘live’ and constitute places every day through recurrent practices and experience. Our everyday lives, however, are complex, and so are places. In contrast to abstract space, the way people experience places includes a range of aspects like physical setting, meaning, and emotional attachment. This inherent complexity requires researchers to investigate the concept of place from a variety of viewpoints. The formal representation of place – a major goal in GIScience related to place – is no exception and can only be successfully addressed if we consider geographical, psychological, anthropological, sociological, cognitive, and other perspectives. This year’s symposium brings together place-based researchers from different disciplines to discuss the current state of platial research. Therefore, this volume contains contributions from a range of fields including geography, psychology, cognitive science, linguistics, and cartography

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

Location-based games have become popular in recent years, with Pok&eacute mon 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&rsquo 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&rsquo 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. Document type: Articl

The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events

Although it is acknowledged that urban inequalities can lead to biases in the production of social media data, there is a lack of studies which make an assessment of the effects of intra-urban movements in real-world urban analytics applications, based on social media. This study investigates the spatial heterogeneity of social media with regard to the regular intra-urban movements of residents by means of a case study of rainfall-related Twitter activity in São Paulo, Brazil. We apply a spatial autoregressive model that uses population and income as covariates and intra-urban mobility flows as spatial weights to explain the spatial distribution of the social response to rainfall events in Twitter vis-à-vis rainfall radar data. Results show high spatial heterogeneity in the response of social media to rainfall events, which is linked to intra-urban inequalities. Our model performance (R2=0.80) provides evidence that urban mobility flows and socio-economic indicators are significant factors to explain the spatial heterogeneity of thematic spatiotemporal patterns extracted from social media. Therefore, urban analytics research and practice should consider not only the influence of socio-economic profile of neighborhoods but also the spatial interaction introduced by intra-urban mobility flows to account for spatial heterogeneity when using social media data

Random_Samples_Overlapping_Patterns

These datasets resemble an overlap of two patterns. Thereby, a range of configurations with respect to geographic scale is contained. Regarding the attribute values attached to the geometric points, we resemble the situation of spatial heterogeneity (i.e., differing means, but constant variance across the patterns). Overall, the aim of these datasets is to resemble the overlap of different phenomena within social media data, especially Twitter and similar kinds of feeds where users contribute fully autonomously

Correction: Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.

[This corrects the article DOI: 10.1371/journal.pone.0162360.]

A statistical test on the local effects of spatially structured variance

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

Heat map of pairwise covariance terms and semivariogram of topic associations.

<p>The dashed semivariogram refers to the right-hand y-axis (same line-style). The left-hand y-axis refers to the color-coded bins. Figure bases on the entire Twitter dataset from London, see Section ‘Datasets.’</p

Correlogram of the serial correlation at different lags for the slopes of the red component.

<p>Dashed line indicates the 95% confidence interval.</p

Typical Moran scatterplot for positively spatially autocorrelated data.

<p>Blue line shows the trend. HL: High-Low, LH: Low-High, LL: Low-Low and HH: High-High interaction.</p