This study investigates the quality (in terms of elevation accuracy and systematic errors) of three recent publicly available elevation model datasets over Australia: (i) the 9 arc second national ...GEODATA DEM-9S ver3 from Geoscience Australia and the Australian National University; (ii) the 3 arc second SRTM ver4.1 from CGIAR-CSI; and (iii) the 1 arc second ASTER-GDEM ver1 from NASA/METI. The main features of these datasets are reported from a geodetic point of view. Comparison at about 1 billion locations identifies artefacts (e.g. residual cloud patterns and stripe effects) in ASTER. For DEM-9S, the comparisons against the space-collected SRTM and ASTER models demonstrate that signal omission (due to the ∼270 m spacing) may cause errors of the order of 100-200 m in some rugged areas of Australia. Based on a set of geodetic ground control points over Western Australia, the vertical accuracy of DEM-9S is ∼9 m, SRTM ∼6 m and ASTER ∼15 m. However, these values vary as a function of the terrain type and shape. Thus, CGIAR-CSI SRTM ver4.1 may represent a viable alternative to DEM-9S for some applications. While ASTER GDEM has an unprecedented horizontal resolution of ∼30 m, systematic errors present in this research-grade version of the ASTER GDEM ver1 will impede its immediate use for some applications.
The application of the linked data principles provide a simple and effective way to publish and share data on the Web. The publication of geodata on the Web bas been studied and developed for a long ...time, leading to the creation of geo vocabularies and ontologies as well as geographic query languages. This is also true for 3D geodata, for which multiple RDF vocabularies and OWL ontologies exist (at the building, city, or territory level). In this paper we show that at least two issues remain to be addressed to reach a satisfactory publication framework for 3D linked geodata. First, the semantic model interconnection issue is not magically solved by the creation of ontologies, it requires the careful design of interconnection and extension schemes and their management. Second, the representation of additional dimensions, such as time, level of detail, confidence level, provenance, etc. cannot be directly handled in RDF. For these two issues we propose methodologies and design patterns and show how they can actually be used to create a semantically rich representation of 3D or nD linked geodata.
Emerging big datasets about human mobility provide new and powerful ways of studying cities and addressing various urban issues. However, human mobility has usually been defined narrowly in prior ...research that limits the understanding of its values for urban applications. The aim of this study is to reveal the complexity and multiplicity of human mobility concept for various urban application scenarios, and present a comprehensive review of mobility-driven urban studies through four re-conceptualized urban mobility perspectives. Using a systematic review approach, existing mobility-driven urban studies are classified based on whether they interpret urban mobility as spatial movements, a social phenomenon, an economic indicator or a policy tool. Then, the core values of knowledge about urban mobility for addressing contemporary urban challenges are analyzed, and the current trends and future directions of mobility-driven urban studies are also discussed. Moving forward, the application of urban mobility knowledge can be further advanced by the evolution of mobility concepts, the improvement of mobility data quality and the innovation of mobility analytical methods. This review can contribute to the understanding the state of the art of mobility-driven urban studies, and provide inspiration and guidelines for studies of this area in the future.
•This paper presents a literature review of mobility-driven urban studies through four reconceptualized mobility perspectives.•Over four hundred papers are clustered in twelve themes and systematically analyzed.•The evolution of the concept of mobility motivates applications of mobility knowledge in various urban contexts.•Applications of urban mobility knowledge can be further advanced by improvements in the data quality and analytical methods.•This review provides inspiration and suggestions for mobility-driven urban studies in the future.
Rural communities in Ghana, dependent on agriculture and lacking resources and infrastructure, are highly vulnerable to climate and environmental change. Internal migration is often considered as a ...strategy to mitigate local livelihood constraints. Understanding the challenges of rural communities requires knowledge of local conditions. As only few studies have mapped vulnerable areas in the context of migration in Ghana at a spatially explicit and nationwide level, this study provides a geodata-based examination of how rural areas in Ghana are vulnerable to multiple, co-occurring socio-economic and environmental factors influencing migration. A multifactorial and expert-based weighted overlay analysis was applied, integrating diverse data sources including climate, remote sensing, and recent census data from Ghana. Bivariate maps visualize vulnerable areas where a high impact of the factors coincides with a high rural population density. High levels of factor impact are observed in the northern regions and coastal areas of Ghana. Relatively low impact is found in more central parts of the country. The results align with current net migration rates, confirming the applicability of our method for assessing rural internal migration. This method enhances the understanding of migration dynamics in Ghana and emphasizes the role of spatial data in migration studies.
•Spatial data is used to map vulnerability and assess rural migration in Ghana.•The study integrates expert-weighted socio-economic and environmental factors.•The results of the weighted overlay analysis are combined with rural population density.•High migration likelihood is found in vulnerable areas in northern and coastal regions.•The results largely align with current net migration rates, confirming the method's applicability.
Abstract This article presents a discussion of the emerging ethical issue of geodata privacy in geographical research. The paper highlights the importance of considering challenges to privacy when ...working with geographically explicit data and explores explicit ways in which researchers and practitioners can be conscious of these issues. Through summarising the key problems in this area and presenting outstanding open research areas and questions from a seminar series on geodata privacy, we highlight important considerations for future research in this field. We focus on the specific topics of appropriate anonymization, responsible data dissemination, the balance between data sharing and privacy, and the challenges posed by working across international contexts. We conclude by recommending approaches to manage various legal and ethical frameworks, raise the importance of the international context, and inspire future research to address the challenges of safeguarding sensitive geodata while promoting openness and transparency.
Groundwater-dependent vegetation (GDV) is essential for maintaining ecosystem functions and services, providing critical habitat for species, and sustaining human livelihoods. However, climate and ...land-use change are threatening GDV, highlighting the need for harmonised, global mapping of the distribution and extent of GDV. This need is particularly crucial in vulnerable biodiversity hotspots such as the Mediterranean biome. This study presents a novel multicriteria index to identify areas in the Mediterranean biome that provide suitable environmental conditions to support potentially groundwater-dependent vegetation (pGDV) where vegetation behaviour is also indicative of groundwater use. Global datasets targeting 1) groundwater vegetation interaction; 2) soil water holding capacity; 3) topographical landscape wetness potential; 4) land use land cover and 5) hydraulic conductivity of rocks have been combined for the first time in an easy-to-use index. Layer weightings from Analytical Hierarchy Process and Random Forest showed limited applicability on biome scale, but an unweighted overlay of eleven thematic layers produced plausible results. The final pGDV map indicates that 31 % of the natural vegetation in the Mediterranean biome likely depend on groundwater. Moreover, moderate to good agreement was found compared to actual GDV locations in Campania, Italy (91 % with at least moderate potential) and California, USA (87 % with at least moderate potential). The results provide valuable information for identifying regions with a substantial presence of pGDV in the Mediterranean biome and can be used for decision making, e.g. to prioritise field surveys and high-resolution remote sensing for GDV mapping. It can therefore support effective groundwater resource management and the conservation of biodiversity hotspots.
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•Potentially groundwater-dependent vegetation (pGDV) was derived for the Mediterranean biome.•The novel pGDV index integrates globally-available remote sensing and geodata.•Vegetation surveys and regional pGDV maps show moderate to good agreement with the pGDV index.•31 % of the naturally vegetated areas in the Mediterranean likely depend on groundwater.•The pGDV index supports the local identification of actual GDV and biodiversity conservation.
Inferring the unknown properties of a place relies on both its observed attributes and the characteristics of the places to which it is connected. Because place characteristics are unstructured and ...the metrics for place connections can be diverse, it is challenging to incorporate them in a spatial prediction task where the results could be affected by how the neighborhoods are delineated and where the true relevance among places is hard to identify. To bridge the gap, we introduce graph convolutional neural networks (GCNNs) to model places as a graph, where each place is formalized as a node, place characteristics are encoded as node features, and place connections are represented as the edges. GCNNs capture the knowledge of the relevant geographic context by optimizing the weights among graph neural network layers. A case study was designed in the Beijing metropolitan area to predict the unobserved place characteristics based on the observed properties and specific place connections. A series of comparative experiments was conducted to highlight the influence of different place connection measures on the prediction accuracy and to evaluate the predictability across different characteristic dimensions. This research enlightens the promising future of GCNNs in formalizing places for geographic knowledge representation and reasoning.
•Visualizing perceptual responses to landscape through crowdsourced spatial content.•Description of the overall process, allowing project specific adaption.•Three novel types of visualizations ...presented.•Basis for establishing a counterbalance offsetting expert landscape assessments.
Assessing information on aspects of identification, perception, emotion, and social interaction with respect to the environment is of particular importance to the fields of natural resource management. Our ability to visualize this type of information has rapidly improved with the proliferation of social media sites throughout the Internet in recent years. While many methods to extract information on human behavior from crowdsourced geodata already exist, this paper focuses on visualizing landscape perception for application to the fields of landscape and urban planning. Visualization of peoples’ perceptual responses to landscape is demonstrated with crowdsourced photo geodata from Flickr, a popular photo sharing community. A basic, general method to map, visualize, and evaluate perception and perceptual values is proposed. The approach utilizes common tools for spatial knowledge discovery and builds on existing research, but is specifically designed for implementation within the context of landscape perception analysis and particularly suited as a base for further evaluation in multiple scenarios. To demonstrate the process in application, three novel types of visualizations are presented: the mapping of sightlines in Yosemite Valley, the assessment of landscape change in the area surrounding the High Line in Manhattan, and individual location analysis for Coit Tower in San Francisco. The results suggest that analyzing crowdsourced data may contribute to a more balanced assessment of the perceived landscape, which provides a basis for a better integration of public values into planning processes.
Replacing individual natural gas heating with district heating based to increasing shares of renewable energy sources may further reduce CO
2-emissions in the Danish Building mass, while increasing ...flexibility of the energy system to accommodate significantly larger amounts of variable renewable energy production. The present paper describes a geographical study of the potential to expand district heating into areas supplied with natural gas. The study uses a highly detailed spatial database of the built environment, its current and potential future energy demand, its supply technologies and its location relative to energy infrastructure. First, using a spatially explicit economic model, the study calculates the potentials and costs of connection to expanded district heating networks by supply technology. Then a comprehensive energy systems analysis is carried out to model how the new district heat can be supplied from an energy system with higher shares of renewable energy. It can be concluded on the basis of these analyses that the methods used proved highly useful to address issues of geographically dependent energy supply; however the spatio-economic model still is rather crude. The analyses suggest to expand district heating from present 46% to somewhere in between 50% and 70%. The most attractive potential is located around towns and cities. The study also suggests that CO
2-emissions, fuel consumption and socio-economic costs can be reduced by expanding district heating, while at the same time investing in energy savings in the building mass as well as increased district heating network efficiency.
Geodata science and geochemical mapping Zuo, Renguang; Xiong, Yihui
Journal of geochemical exploration,
February 2020, 2020-02-00, Letnik:
209
Journal Article
Recenzirano
Geodata science (GDS) is an interdisciplinary field in which geoscience data are mined for us to well understand the origin, evolution and future of our Earth and planet with prediction and ...assessment of its resources, environments, and natural hazards. The data chain of GDS involves collecting geosciences data, mining geoinformation, discovering geo-knowledge, and making spatial decisions. There are three groups of GDS methods for exploring and mining geoscience data including data statistics, data mining, and data insight and prediction. A case study on geochemical exploration data mapping was conducted to demonstrate the powerful use of GDS. The results show that GDS is a new research paradigm for exploring the spatial association of geochemical patterns, mining elemental association, and recognizing geochemical anomalies associated with mineralization via geo-computation and geo-visualization techniques in support of mineral exploration.
•GDS is the science to studying and mining geospatial patterns•GDS is a new research paradigm in geoscience and can be used for geochemical mapping in support of mineral exploration•GDC can reveal the spatial association, identify the elemental association, and recognize geochemical anomalies.
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