The purpose of this publication is to analyze air pollution on the example of the city of Krakow, as well as to consider the possibility of using geodata for environmental protection. In addition to ...case study analysis as the leading research method, the article also uses the observation, analysis, and statistical methods. The article presents the concept of using GIS spatial analyzes and spatial planning as an element of the Green New Deal in the process of ventilating the city of Krakow.When developing a project related to city ventilation, it is extremely important to have the most accurate data on the strength, direction of the wind, type of pollution, and the number of emitters. Spatial analyzes are also able to indicate the main ventilation corridors of the city. These include, above all, areas located on the Vistula River, but also the widest city streets. Such results make it possible to more consciously manage space.
The spatial arrangement of settlements constitutes a long-lasting legacy and shapes the prospects for transformations toward sustainability. Thus, understanding the drivers of changes in settlement ...patterns is essential. In this article, we present a spatially explicit, geostatistical analysis of settlement dynamics, and a qualitative investigation of its regulative, demographic, and economic drivers, using the example of Vienna, Austria between 1984 and 2018. Combining spatially explicit metrics of urban sprawl and cluster analysis, we analyzed high-resolution maps of buildings, population, and jobs to identify distinct settlement trajectories. Societal drivers of more or less sprawled settlement dynamics are analyzed with desk research and expert interviews. We distinguish five types of settlement dynamics: persistently dense areas with increasing use intensity, re-densification of dense areas, persistently sprawled areas, redensification of sprawled areas, and persistently isolated buildings. Urban renewal schemes have fostered the re-densification of dense areas in response to population growth and urban economic restructuring. The combination of urban renewal schemes and green space policies has successfully limited urban expansion. Challenges arise from the demand for single-family housing and corresponding zoning regulations. These factors solidify existing sprawled settlements, posing obstacles to the efficient re-densification of such areas crucial for sustainable urban development.
•Mixed-methods approach for linking settlement dynamics to potential drivers.•Geostatistical spatiotemporal clustering was combined with expert interviews.•Dense and sprawled areas in Vienna show high persistence between 1984 and 2018.•Urban renewal schemes and sustained green space protection halted new sprawl.•Urban renewal schemes can re-shape settlements towards increased density.
Mobility planning in rural areas with a high number of tourists is important for creating sustainable destinations. By identifying mobility gaps in the transportation system, measures to improve the ...situation can be implemented. In order to identify such mobility gaps, decision-makers need a spatial decision support system (SDSS). The aim of this paper is to identify vital aspects of creating such an SDSS and to build a prototype. Two important aspects were identified, data and system design. The result of the analysis of available data shows a lack of data portals with disaggregated socio-economic and intra-destination travel data. Further, it shows that data on points of interest (POI) and public transit data are primarily found in company databases. The system design analysis showed that most SDSS today are relying on public data and are not designed to integrate disparate data sources. They are primarily developed to be used by experts. Based on these findings an SDSS that automatically integrates both public and private data was developed. It comprises of a self-hosted web mapping system and several geospatial tools. Our main conclusion is that both data and system design are important aspects to consider when building an SDSS for mobility planning. By using the architecture proposed in this article, new data can easily be incorporated in an SDSS. Furthermore, the system design also facilitates the involvement of stakeholders in the planning process.
This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) ...and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable criteria, and combines multiple mappable criteria into a mineral potential map. A workflow of the GSMPM is proposed and compared with the traditional workflow of mineral prospectivity mapping. More specifically, each component in such a workflow is explained in detail to demonstrate how geodata science serves mineral prospectivity mapping by deriving geoinformation from geoscience data, generating geo-knowledge from geoinformation, and allowing spatial decision-making by integrating geoinformation and geo-knowledge on the formation of mineral deposits. This review also presents several research directions for GSMPM in the future.
•A new spatiotemporal model for giant linear features in the Ob loess plateau is presented.•Geomorphological evidence points to an erosive-aeolian genesis of these impressive features.•Comparison ...with other landforms reveals the largest system of mega-yardangs world wide.
The foreland of the Russian Altai is dominated by the vast Ob loess plateau. The flat landscape exhibits striking linear features, partially more than 100 km in length and tens of km wide. The bottoms of these features are covered by forested dunes, whereas the loess ridges in between are intensively cultivated. To the north, the land cover changes due to gradual transition from the steppe towards the Siberian taiga. The genesis of these prominent features was debated within the last decades. Possible explanations cover tectonic lineaments, fluvial erosion, and landforms caused by outbursts of catastrophic floods from the Altai Mountains. Here, we present geomorphological evidence for the aeolian origin of these features based on field observations and geodata. These large lineaments do not show characteristic features of fluvial valleys, since the shape of the lineaments is too straight and does not show braided river characteristics as, e.g., the Ob or the Irtysh valley. The sheer size of these features also does not support the hypothesis of tectonic activity or a catastrophic flood since events like this would be imprinted in other environmental archives of the region. We show that these linear landforms show remarkable similarities with Pleistocene mega yardang systems throughout the world. These systems can usually be found in arid to hyper-arid environments, but were also described in, e.g., mid-latitude regions. We hypothesis that the Pleistocene glaciations of the Altai Mountains enhanced the strength and the influence of the westerlies in the Altai forelands. Therefore, we propose an erosive-aeolian origin of these remarkable landforms.
Geotechnologies play an increasingly extensive and diverse role in our society. Their implementation in educational settings is growing at all levels, especially in higher education, and in a wide ...variety of disciplines. The analysis and management of cultural heritage are two of the areas where they are most used. The multiple, heterogeneous, and unspecific supply of geoinformation opens up considerable possibilities for learning and new opportunities for analysis by future specialists in cultural heritage, despite the difficulties in the management of, and approach to, the data. In a scenario in which open-source educational resources are increasingly important, this work presents an exploratory analysis of open sources of georeferenced information that facilitate access to geodata for teaching and learning on cultural heritage. As an example of the differences and shortcomings of the availability of georeferenced sources in Spain, this work presents a case study on the city of Toledo, recognised as a world heritage site.
Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government, solving societal problems, and increasing economic value. To ...describe and monitor the state of open data in countries and organisations, several open data assessment frameworks were developed. Despite high scores in these assessment frameworks, the actual (re)use of open government data (OGD) fails to live up to its expectations. Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem. We have developed a framework, which assesses open data supply, open data governance, and open data user characteristics holistically. This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention. Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data, such as healthcare data. Therefore, open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.
•Convolutional network for predicting daily maps of the probability of a wildfire burn.•Convolutional networks demonstrate higher predictive accuracy and map quality.•Exploratory feature statistical ...importance metrics improves model transparency.
Wildfire continues to be a major environmental problem in the world. To help land and fire management agencies manage and mitigate wildfire-related risks, we need to develop tools for mapping those risks. Big geodata—in the form of remotely sensed images, ground-based sensor observations, and topographical datasets—can help us characterize the dynamics of wildfire related events. In this study, we design a deep fully convolutional network, called AllConvNet, to produce daily maps of the probability of a wildfire burn over the next 7 days. We applied it to burns in Victoria, Australia for the period of 2006–2017. Fifteen factors that were extracted from six different datasets and resulted into 29 quantitative features, were selected as input to the network. We compared it with three baseline methods: SegNet, multilayer perceptron, and logistic regression. AllConvNet outperforms the other three baseline methods in four of the six quantitative metrics considered. AllConvNet and SegNet provide smoother and more regularized predicted maps, with SegNet providing greater sensitivity in dificriminating less wildfire-prone locations. Input feature statistical importance was measured for all the networks and compared against logistic regression coefficients. Total precipitation, lightning flash density, and land surface temperature occur to be consistently highly weighted by all models while terrain aspect components, wind direction components, certain land cover classes (such as crop field and woodland), and distance from power lines are ranked on the lower end. We conclude that wild-fire burn prediction methods based on deep learning present quantitative and qualitative gains.
The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location ...tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities.
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