GI_Forum 2023, Volume 11, Issue 1
Nummer:
11
Jahrgang:
2023
Heft:
1
“GI_Forum” publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain. “GI_Forum” implements the policy of open access publication (CC-BY-ND-License) after a double-blind peer review process through a highly international team of established scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions.
The 2023-1 Issue comprises work of researchers from different disciplines, most of whom presented their work at the GI_Salzburg 2023 conference (https://gi-salzburg.org/en/). The articles address diverse collections of spatiotemporal data such as multispectral LIDAR, SAR or biology-related datasets, and advanced concepts, methods and tools applied for their analysis like OBIA or Google Earth Engine. Applications range from natural resources management, natural phenomena and hazards to green spaces, urban place perception, mobility, and infrastructure. The impact of ChatGPT on teaching and learning GIScience based on an anecdotal approach contributes to the overall discourse in the scientific and educational community respectively.
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Inhaltsverzeichnis
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Autor:innen / Herausgeber:innen
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Schlagworte
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weiterführende Links, Online Edition
The BioWhere Project: Unlocking the Potential of Biological Collections Data
Vast numbers of biological specimens (e.g. flora, fauna, soils) are stored in collections globally. Many of these have only a natural-language location description, such as ‘200ft above and south of main highway, 1.1 miles west of Porters Pass’, and numerical coordinates are unknown. The BioWhere project is pioneering methods to automatically determine the geographic coordinates (georeferences) of complex location descriptions. Particular challenges are posed by the variable accuracy of recent and historical data that might be used to train models to predict geographic coordinates from the natural-language descriptions; by the presence of historical place names in the descriptions that are not stored in existing gazetteers; and by the vague and context-sensitive nature (e.g. above, on, south of) of the descriptions. We are addressing these challenges by extending the latest transformer-based deep learning models to parse locality descriptions, and to build models for specific spatial terms that incorporate geographic context and data quality to more accurately predict georeferences. We also describe a gazetteer that contains enriched cultural content to support georeferencing of historical records, and to serve as a store of New Zealand Māori cultural knowledge for future generations.
Schlagworte: georeferencing, biological collections, machine learning, gazetteers
Kristin Stock - Christopher B. Jones - Hone Morris - Pragyan Das - David Medyckyj-Scott - Brandon Whitehead - Kalana Wijegunarathna
Seite 3 - 21 | doi: https://doi.org/10.1553/giscience2023_01_s3
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Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest
Above-ground biomass and carbon stock are fundamental components of the global carbon cycle, essential for climate change mitigation. Remote sensing data can provide timely and accurate estimates of various forest attributes, especially over large and remote forested areas. The objective of this research was to investigate the potential of multispectral LiDAR data for estimating the stem biomass (SB) and total biomass (TB) in a multi-layered fir forest using an Edge-tree corrected Area Based Approach (EABA). Subsequently, a Random Forest (RF) regression analysis was performed to develop SB and TB predictive models using LiDAR-derived height metrics. Two RF models were produced and evaluated in terms of their predictive performance. Overall, our work demonstrates the capability of multispectral LiDAR data to provide reliable SB and TB estimates in a complex structured forest, contributing significantly to sustainable forest management.
Schlagworte: forest biomass, multispectral LiDAR, remote sensing
Nikos Georgopoulos - Konstantinos Antoniadis - Michail Sismanis - Ioannis Gitas
Seite 22 - 33 | doi: https://doi.org/10.1553/giscience2023_01_s22
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Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows
Mass movement mapping is essential for susceptibility, vulnerability and risk assessments. Various mapping approaches based on Earth observation (EO) data have been used to identify different types of hazards. Object-based image analysis (OBIA) has been employed for EO-based landslide mapping worldwide. The development and application of efficient methods for recognition and mapping are essential to create standards for landslide inventory mapping, notably in Brazil where landslides are a frequent natural hazard. This study aims to detect landslide features and differentiate them into shallow landslides and debris flows using a semi-automated OBIA approach. RapidEye satellite images (5 m) were analysed and the Normalized Difference Vegetation Index (NDVI) was calculated. A Digital Elevation Model (DEM) (12.5 m) and its derived products were integrated into the analysis to support the OBIA landslide mapping. The results show that the method is suitable for the recognition of this type of hazard and are potentially of use for local stakeholders and decision-makers in disaster management.
Schlagworte: mass movement, landslide inventory, object-based image analysis, semi-automated mapping
Helen Christina Dias - Daniel Hölbling - Vivian Cristina Dias - Carlos Henrique Grohmann
Seite 34 - 44 | doi: https://doi.org/10.1553/giscience2023_01_s34
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SAR Change Detection to Evaluate Flood Event Characteristics, Pre- and Post-Restoration of a Floodplain
A method using Sentinel-1 synthetic aperture radar (SAR) data to evidence flood changes after floodplain restoration is proposed. A Natural Flood Management (NFM) project on the Ouse river in southern England, undertaken by the Sussex Flow Initiative, was analysed to ascertain any reduction of previous flood risk. GIS operations were conducted on the results of the change detection analysis to identify how flood area, form and compactness were affected after the NFM installation, and how these changes relate to the project aims. Flood records based on internet-published drone footage verified the change detection methodology. A scorecard was developed to evaluate the benefits and disadvantages of spatial changes seen in post-restoration floods in comparison to inundation before the measures were installed. Evaluation results were used in the annual report of the SFI project to demonstrate the attenuation of floodwaters in accordance with the aims of the project.
Schlagworte: natural flood management, Sentinel-1, change detection, synthetic aperture radar (SAR)
Sean Jarrett - Daniel Hölbling
Seite 45 - 68 | doi: https://doi.org/10.1553/giscience2023_01_s45
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Mapping Spatiotemporal Changes of Evergreen Forest Patches that are Heritage Sites in Southern Mozambique using Google Earth Engine
Using forests as burial and ceremonial places is a long-standing cultural practice in Mozambique. However, this information is still not translated into land-cover and land-use maps. Thus the locations of these forests and their descriptions remain unknown. To address this gap in the knowledge, this paper presents the results of mapping 52 local heritage sites in Inhambane, and analysing land-cover changes of two locally protected forest patches. Results from spatiotemporal change analysis show that these patches experienced fewer disturbances in comparison to other areas of vegetation.
Schlagworte: Landsat, remote sensing, land cover, cultural heritage
Pascoal Gota
Seite 69 - 82 | doi: https://doi.org/10.1553/giscience2023_01_s69
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Spatial and Temporal Analysis of Location and Usage of Public Electric Vehicle Charging Infrastructure in the United States
Switching to electric vehicles (EVs) has increased rapidly over recent years. This paradigm change provides an important pillar in the United States transport sector to reach sustainability goals. EVs rely on a network of charging locations to operate. This study analyses the spatial distribution, accessibility and usage patterns of the public EV infrastructure in the US. First, using a negative binomial regression model, the influence of socio-economic and other factors on the abundance of EV charging locations in a state is investigated. Second, analysis of the network’s use and of service areas generated around charging locations provides insight into the accessibility of these stations to populations living in urban and rural areas. Third, the study compares publicly available datasets on the EV charging infrastructure provided by different companies in the Miami urbanized area, and lastly, it analyses real-time data from the SemaConnect charging network. Results indicate increased access of residents to the EV charging infrastructure over the years. Economic activity, highway density and political preference were statistically associated with the number of charging stations. Charging behaviour was found to follow the patterns of a regular workday, indicating that EV owners rely primarily on the public infrastructure as opposed to charging their vehicles only at home.
Schlagworte: mobility, electric vehicle, EV, charger, network analysis, sustainability
Levente Juhász - Hartwig H. Hochmair
Seite 83 - 100 | doi: https://doi.org/10.1553/giscience2023_01_s83
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Promoting Active and Sustainable Commuting: A Tool for Analysing Location-specific Conditions and Potentials for Walking, Cycling and Public Transport
Active and sustainable commuting has a positive impact not only on the environment but also on individuals’ health. A shift from using unsustainable motorized transport modes to active and sustainable alternatives (cycling, walking and public transport) is desirable. To enable such a shift, it is important to raise public awareness and to call for joint efforts by individuals, employers, planning practitioners and decision-makers. In the ActNow research project, a tool was developed which provides location-specific information vital for promoting active and sustainable commuting. Applying GIS methods, heterogeneous data were analysed and integrated into a 500-metre raster. This raster is embedded in a web application, which provides users with a holistic view of commuter traffic, the accessibility of infrastructure, as well as the potentials, strengths and weaknesses at locations of interest for active and sustainable forms of commuting. The tool provides planners, traffic associations and mobility consultants with evidence that can support them to achieve improvements in traffic, the environment and public health.
Schlagworte: active and sustainable mobility, commuting, web application
Yingwen Deng - Dagmar Lahnsteiner - Thomas Prinz
Seite 101 - 109 | doi: https://doi.org/10.1553/giscience2023_01_s101
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A Comparative Study of Geocoder Performance on Unstructured Tweet Locations
Geocoding is a process of converting human-readable addresses into latitude and longitude points. Whilst most geocoders tend to perform well on structured addresses, their performance drops significantly in the presence of unstructured addresses, such as locations written in informal language. In this paper, we make an extensive comparison of geocoder performance on unstructured location mentions within tweets. Using nine geocoders and a worldwide English-language Twitter dataset, we compare the geocoders’ recall, precision, consensus and bias values. As in previous similar studies, Google Maps showed the highest overall performance. However, with the exception of Google Maps, we found that geocoders which use open data have higher performance than those which do not. The open-data geocoders showed the least per-continent bias and the highest consensus with Google Maps. These results suggest the possibility of improving geocoder performance on unstructured locations by extending or enhancing the quality of openly available datasets.
Schlagworte: commercial geocoders, natural language, Twitter, open data, spaCy
Helen Ngonidzashe Serere - Umut Nefta Kanilmaz - Sruthi Ketineni - Bernd Resch
Seite 110 - 117 | doi: https://doi.org/10.1553/giscience2023_01_s110
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Urban Green for Child- and Youth-Friendly Cities
Urban green plays a central role in discussions about quality of life in urban areas. Young people have particular requirements of urban green, understanding of which is key in planning infrastructure and design aspects in cities. In order to support the spatial planning of child- and youth-friendly cities, including urban green, some questions must first be answered: (i) What kind of urban green is important? (ii) What kind of infrastructure elements are needed? (iii) What are the central characteristics and design aspects of urban green and its elements? (iv) How are they rated? The u3Green project aims to answer these questions by developing a survey app in cooperation with schools. The data gathered via the app will be analysed in order for recommendations to be made. Preliminary research activities were conducted. (1) A literature review investigated children’s and young people’s requirements of urban green. (2) An online questionnaire asked about urban outdoor activities, liked places, and the personal meaning of favourite places. The survey confirmed the needs and requirements identified in the literature review, but also revealed a new and surprising category of activity: walking. (3) A number of workshops in schools revealed what young people consider as negative factors in urban areas, especially traffic.
Schlagworte: young people, liveable cities, cultural ecosystem services, participation, infrastructure and design of urban green
Robert Vogler - Sabine Hennig - Florian Albrecht
Seite 118 - 139 | doi: https://doi.org/10.1553/giscience2023_01_s118
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Ch(e)atGPT? An Anecdotal Approach Addressing the Impact of ChatGPT on Teaching and Learning GIScience
Natural language processing systems like ChatGPT have recently attracted enormous attention in the field of higher education. We aim to contribute to this discussion by scrutinizing the suitability of current testing methods and potentially necessary shifts in learning objectives in GIScience. This paper presents an anecdotal approach to the impact of ChatGPT on teaching and learning based on a real-world use case. It focuses on the results of a fictional student who used ChatGPT for the completion of application-development assignments, including coding. The solutions were submitted to the instructor, who assessed the results in a single-blind experiment. The instructor’s feedback and grading as well as the AI-plagiarism results were part of our evaluation of the testing methods applied. This triggered a discussion on the adequacy of current learning objectives in the development of GIS applications and the integration of AI into the learning process.
Schlagworte: GIScience education, coding skills, NLP, chatGPT, learning objectives
Petra Stutz - Maximilian Elixhauser - Judith Grubinger-Preiner - Vivienne Linner - Eva Reibersdorfer-Adelsberger - Christoph Traun - Gudrun Wallentin - Katharina Wöhs - Thomas Zuberbühler
Seite 140 - 147 | doi: https://doi.org/10.1553/giscience2023_01_s140
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Ausgabe:
978-3-7001-9443-9, E-Journal, digital, 27.06.2023
Seitenzahl:
147 Seiten
Sprache:
Englisch
DOI (Link zur Online Edition):