A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

South East Europe electricity roadmap – modelling energy transition in the electricity sectors

One of the most important challenges for the South East Europe region will be replacing more than 30% of its presently installed fossil fuel generation capacity by the end of 2030, and more than 95% by 2050 if its age structure is considered. This requires a strong policy framework to incentivise new investments in a region currently lacking investors, but also presents an opportunity to shape the electricity sector over the long term according to the broader energy transition strategy of the EU and the Energy Community. The aim of this paper is to assess what type of long-term pathways exist for electricity sector development in the region if they follow the energy transition process of the EU. In this model-based scenario assessment, long term electricity sector futures are explored using a set of interlinked electricity models evaluating the level of renewable energy investment required in the region to reach a deep decarbonization target, assuming emission reduction above 94% by 2050 compared to 1990 in line with the long term market integration and climate policy goals of the EU. It also explores what are the most important system wide impacts of the high deployment of renewable energy concerning generation adequacy and security of supply.

Key policy insights

• Energy policies in the South East Europe (SEE) region, both at the national and regional level, should focus on enabling renewable energy integration, as this will be a key component of the future energy mix.

• EU and Energy Community policies should be incorporated into national energy planning to ensure that SEE countries embark on the energy transition process at an early stage.

• Stranded costs should be carefully considered in decision-making on new fossil-fuel generation and gas network investment in order to avoid lock-in to carbon intensive technologies.

• If consistent decarbonization policy prevails, with a significant and persistent CO2 price signal, the role of natural gas remains transitory in the region.

• The SEE region offers relatively cheap decarbonization options: the power sector can reduce GHG emissions above 94% by 2050 in the modelled scenarios.

     

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.     

Web-based 4D visualization of marine geo-data using WebGL

For marine logistics and maintenance of extensive and expensive marine engineering projects in the coastal zone, it is essential that institutions provide the public with relevant information in an easily understandable yet comprehensive manner over the web. A perennial challenge, however, has been the development of spatio-temporal (four-dimensional (4D)) geo-visualization algorithms to enable the integration of time-varying geo-information in map-based visualizations on the Internet. In this paper, we address the challenge of visualizing marine spatial data in web-based applications through a 4D visualization concept, focusing on usability criteria, performance parameters, the required implementation effort, and delivering a breath of spatial information that supports decision-making on multiple levels. We used Web Graphic Library (WebGL) to validate our concept through a prototypical implementation. In our technology evaluation, WebGL proved highly suitable for the development of interactive, responsive, efficient, and mobile web-based Geographic Information applications, including 2D, 3D, and 4D (spatiotemporal) content. During our research, we identified a number of open research questions, including mapping graphic variables to thematic expressivity, representation of the time dimension in 4D systems, generic temporal generalization, and integration of (pseudo-)photorealistic illustrations in web-based geo-visualization systems.     

Asynchronous Geospatial Processing: An Event‐Driven Push‐Based Architecture for the OGC Web Processing Service

Geospatial processing tasks like solar potential analyses or floodplain investigations within flood scenarios are often complex and deal with large amounts of data. If such analysis operations are performed in distributed web‐based systems, technical capabilities are mostly not sufficient. Major shortcomings comprise the potentially long execution times and the vast amount of messaging overhead that arise from common poll‐based approaches. To overcome these issues, an approach for an event‐driven architecture for web‐based geospatial processing is proposed within this article. First, this article presents a thorough qualitative discussion of different available technologies for push‐based notifications. The aim of this discussion is to find the most suitable push‐based messaging technologies for application with OGC Web Processing Services (WPS). Based on this, an event‐driven architecture for asynchronous geospatial processing with the WPS is presented, building on the Web Socket Protocol as the transport protocol and the OGC Event Service as the message‐oriented middleware. The proposed architecture allows pushing notifications to clients once a task has completed. This paradigm enables the efficient execution of web‐based geospatial processing tasks as well as the integration of geographical analyses into event‐driven real‐time workflows.