Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-26-2023
L. Gianquintieri, D. Oxoli, E. Caiani, M. Brovelli
Abstract. Air pollution is identified as the primary environmental risk to health worldwide. Although most of the anthropic emissions are due to combustion processes, intensive farming activities may also contribute significantly, especially as a source of particulate matter 2.5 and ammonia. Investigations on particulate matter and precursors dynamics, identifying the most relevant environmental factors influencing their emissions, are critical to improving local and regional air quality policies. This work presents an analysis of the correlation between particulate matter 2.5 and ammonia concentrations, obtained from the Copernicus Atmosphere Monitoring Service, and local land use characteristics, to investigate the influence of agricultural activities on the space-time pollutant concentration patterns. The selected study area is the Lombardy region, northern Italy. Correlation is evaluated through Spearman’s coefficient. Agricultural areas resulted in a significant factor for high ammonia concentrations, while particulate matter 2.5 was strongly correlated with built-up areas. Natural areas resulted instead a protective factor for both pollutants. Results provide data-driven evidence of the land use effect on air quality, also quantifying such effects in terms of correlation coefficients magnitude.
{"title":"Land use influence on ambient PM2.5 and ammonia concentrations: Correlation analyses in the Lombardy region, Italy","authors":"L. Gianquintieri, D. Oxoli, E. Caiani, M. Brovelli","doi":"10.5194/agile-giss-4-26-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-26-2023","url":null,"abstract":"Abstract. Air pollution is identified as the primary environmental risk to health worldwide. Although most of the anthropic emissions are due to combustion processes, intensive farming activities may also contribute significantly, especially as a source of particulate matter 2.5 and ammonia. Investigations on particulate matter and precursors dynamics, identifying the most relevant environmental factors influencing their emissions, are critical to improving local and regional air quality policies. This work presents an analysis of the correlation between particulate matter 2.5 and ammonia concentrations, obtained from the Copernicus Atmosphere Monitoring Service, and local land use characteristics, to investigate the influence of agricultural activities on the space-time pollutant concentration patterns. The selected study area is the Lombardy region, northern Italy. Correlation is evaluated through Spearman’s coefficient. Agricultural areas resulted in a significant factor for high ammonia concentrations, while particulate matter 2.5 was strongly correlated with built-up areas. Natural areas resulted instead a protective factor for both pollutants. Results provide data-driven evidence of the land use effect on air quality, also quantifying such effects in terms of correlation coefficients magnitude.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131862093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-37-2023
Niloufar Pourshir Sefidi, Amin Shoari Nejad, P. Mooney
Abstract. The COVID-19 pandemic resulted in many deaths and much upheaval worldwide. Public health responses to the pandemic differed greatly between countries. In 2023, as we emerge from the aftermath of the pandemic, it is now timely to assess the impact of specific public health response measures such as lockdowns and vaccinations. This assessment can help inform the development of evidence-based strategies for future public health responses in pandemic scenarios. We describe the implementation of a Bayesian Hierarchical Poisson Regression (BHPR) model to estimate the impact of pandemic response measures and vaccination on all-cause deaths, including COVID-19, in Ireland. We find that the implementation of lockdown measures and an appropriate vaccination timeline were effective in reducing the number of deaths in Ireland by, most likely, reducing the COVID- 19 mortality rate. We believe our approach could be used to assess the impact of pandemic response measures and vaccination in other countries as well where similar data is available.
{"title":"An investigation of the effects of lockdowns and COVID-19 vaccinations in Ireland","authors":"Niloufar Pourshir Sefidi, Amin Shoari Nejad, P. Mooney","doi":"10.5194/agile-giss-4-37-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-37-2023","url":null,"abstract":"Abstract. The COVID-19 pandemic resulted in many deaths and much upheaval worldwide. Public health responses to the pandemic differed greatly between countries. In 2023, as we emerge from the aftermath of the pandemic, it is now timely to assess the impact of specific public health response measures such as lockdowns and vaccinations. This assessment can help inform the development of evidence-based strategies for future public health responses in pandemic scenarios. We describe the implementation of a Bayesian Hierarchical Poisson Regression (BHPR) model to estimate the impact of pandemic response measures and vaccination on all-cause deaths, including COVID-19, in Ireland. We find that the implementation of lockdown measures and an appropriate vaccination timeline were effective in reducing the number of deaths in Ireland by, most likely, reducing the COVID- 19 mortality rate. We believe our approach could be used to assess the impact of pandemic response measures and vaccination in other countries as well where similar data is available.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-34-2023
Nikita Murin, A. Kmoch, E. Uuemaa
Abstract. Landscape elements located on agricultural fields or on their edges play a crucial role in the biodiversity of agricultural land. The landscape elements’ database in Estonia is updated in accordance with the applications of the field owners, and usually it does not represent a real situation of the landscape elements on the field. Hence, the analysis and control over landscape elements are limited. The main aim of this study is to create a methodology to map landscape elements in Estonia with remote sensing data. The first method was created considering the importance of computational efficiency and therefore fast and non-complex map algebra solution was developed. The second, more precise but more computationally expensive way to map landscape elements, was the object-based image analysis method utilizing machine learning classification. Both methods displayed high overall accuracies, but users’ and producers’ accuracies were lower. Taking into account the computational time and accuracy, it was concluded that the map algebra method is better suitable for fast landscape elements’ detection. However, the object-based image analysis method is more suitable for identifying more exact classes of landscape elements.
{"title":"Detection of small-scale landscape elements with remote sensing","authors":"Nikita Murin, A. Kmoch, E. Uuemaa","doi":"10.5194/agile-giss-4-34-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-34-2023","url":null,"abstract":"Abstract. Landscape elements located on agricultural fields or on their edges play a crucial role in the biodiversity of agricultural land. The landscape elements’ database in Estonia is updated in accordance with the applications of the field owners, and usually it does not represent a real situation of the landscape elements on the field. Hence, the analysis and control over landscape elements are limited. The main aim of this study is to create a methodology to map landscape elements in Estonia with remote sensing data. The first method was created considering the importance of computational efficiency and therefore fast and non-complex map algebra solution was developed. The second, more precise but more computationally expensive way to map landscape elements, was the object-based image analysis method utilizing machine learning classification. Both methods displayed high overall accuracies, but users’ and producers’ accuracies were lower. Taking into account the computational time and accuracy, it was concluded that the map algebra method is better suitable for fast landscape elements’ detection. However, the object-based image analysis method is more suitable for identifying more exact classes of landscape elements.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-5-2023
Simon Groß, B. Herfort, S. Marx, A. Zipf
Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed.
{"title":"Exploring MapSwipe as a Crowdsourcing Tool for (Rapid) Damage Assessment: The Case of the 2021 Haiti Earthquake","authors":"Simon Groß, B. Herfort, S. Marx, A. Zipf","doi":"10.5194/agile-giss-4-5-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-5-2023","url":null,"abstract":"Abstract. Fast and reliable geographic information is vital in disaster management. In the late 2000s, crowdsourcing emerged as a powerful method to provide this information. Base mapping through crowdsourcing is already well-established in relief workflows. However, crowdsourced post-disaster damage assessment is researched but not yet institutionalized. Based on MapSwipe, an established mobile application for crowdsourced base mapping, a damage assessment approach was developed and tested for a case study after the 2021 Haiti earthquake. First, MapSwipe’s damage mapping results are assessed for quality by using a reference dataset in regard to different aggregation methods. Then, the MapSwipe data was compared to an already established rapid damage assessment method by the Copernicus Emergency Management Service (CEMS). Crowdsourced building damage mapping achieved a maximum F1-score of 0.63 in comparison to the reference data set. MapSwipe and CEMS data showed only slight agreement with Cohen’s Kappa values reaching a maximum of 0.16. The results highlight the potential of crowdsourcing damage assessment as well as the importance for a scientific evaluation of the quality of CEMS data. Next steps for further integrating the presented workflow into MapSwipe are discussed. \u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133746921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-18-2023
Molly Asher, Y. Oswald, N. Malleson
Abstract. The study of urban population dynamics has long been an important area of research. In particular, the ability to accurately predict the number of pedestrians in a place and time is critical for urban management, population health, crime, and for quantifying the impacts of public events. However, it can be extremely difficult to analyse the size and characteristics of the ambient population due to limited data availability and difficulties in capturing non-linear relationships between pedestrian counts and external factors. This paper reports on an ongoing project that is using machine learning techniques to: (i) better understand the impact that the built environment and other contextual factors, such as weather conditions, will have on the size of the pedestrian population during the day and; (ii) predict the number of pedestrians under different conditions. The case study area is the city of Melbourne, Australia, where abundant pedestrian count data exist. Early results demonstrate that, broadly, the model appears to perform sufficiently well to be useful, and that modelling errors are not consistent across space or time (some times/places are easier to predict than others).
{"title":"Predicting Pedestrian Counts using Machine Learning","authors":"Molly Asher, Y. Oswald, N. Malleson","doi":"10.5194/agile-giss-4-18-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-18-2023","url":null,"abstract":"Abstract. The study of urban population dynamics has long been an important area of research. In particular, the ability to accurately predict the number of pedestrians in a place and time is critical for urban management, population health, crime, and for quantifying the impacts of public events. However, it can be extremely difficult to analyse the size and characteristics of the ambient population due to limited data availability and difficulties in capturing non-linear relationships between pedestrian counts and external factors. This paper reports on an ongoing project that is using machine learning techniques to: (i) better understand the impact that the built environment and other contextual factors, such as weather conditions, will have on the size of the pedestrian population during the day and; (ii) predict the number of pedestrians under different conditions. The case study area is the city of Melbourne, Australia, where abundant pedestrian count data exist. Early results demonstrate that, broadly, the model appears to perform sufficiently well to be useful, and that modelling errors are not consistent across space or time (some times/places are easier to predict than others).\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-20-2023
N. Bearman, Rongbo Xu, P. Roddy, James D. Gaboardi, Qunshan Zhao, Huanfa Chen, L. Wolf
Abstract. Location-allocation is a key tool within the GIS and network analysis toolbox. In this paper we discuss the real world application of a location-allocation case study (approx 800 students, 500 schools) from UCL using public transport. The use of public transportation is key for this case study, as many location-allocation approaches only make use of drive-time or walking-time distances, but the location of UCL in Greater London, UK makes the inclusion of public transport vital for this case study. The location-allocation is implemented as a capacitated p-median location-allocation model, using the spopt library, part of the Python Spatial Analysis Library (PySAL). The capacitated variation of the p-median location-allocation problem is a new addition to the spopt library, which this work will present. The results from the initial version of the capacitated p-median location-allocation problem has shown a marked improvement on public transport travel time, with public transport travel time reduced by 891 minutes overall for an initial sample of 93 students (9.58 minutes per student). Results will be presented below and plans for further improvement shared.
{"title":"Developing capacitated p-median location-allocation model in the spopt library to allow UCL student teacher placements using public transport","authors":"N. Bearman, Rongbo Xu, P. Roddy, James D. Gaboardi, Qunshan Zhao, Huanfa Chen, L. Wolf","doi":"10.5194/agile-giss-4-20-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-20-2023","url":null,"abstract":"Abstract. Location-allocation is a key tool within the GIS and network analysis toolbox. In this paper we discuss the real world application of a location-allocation case study (approx 800 students, 500 schools) from UCL using public transport. The use of public transportation is key for this case study, as many location-allocation approaches only make use of drive-time or walking-time distances, but the location of UCL in Greater London, UK makes the inclusion of public transport vital for this case study. The location-allocation is implemented as a capacitated p-median location-allocation model, using the spopt library, part of the Python Spatial Analysis Library (PySAL). The capacitated variation of the p-median location-allocation problem is a new addition to the spopt library, which this work will present. The results from the initial version of the capacitated p-median location-allocation problem has shown a marked improvement on public transport travel time, with public transport travel time reduced by 891 minutes overall for an initial sample of 93 students (9.58 minutes per student). Results will be presented below and plans for further improvement shared.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132607535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-42-2023
Meilin Shi, Kitty Currier, Zilong Liu, Krzysztof Janowicz, Nina Wiedemann, J. Verstegen, Grant McKenzie, A. Graser, Rui Zhu, Gengchen Mai
Abstract. Driven by foundation models, recent progress in AI and machine learning has reached unprecedented complexity. For instance, the GPT-3 language model consists of 175 billion parameters and a training-data size of 570 GB. While it has achieved remarkable performance in generating text that is difficult to distinguish from human-authored content, a single training of the model is estimated to produce over 550 metric tons of CO2 emissions. Likewise, we see advances in GeoAI research improving large-scale prediction tasks like satellite image classification and global climate modeling, to name but a couple. While these models have not yet reached comparable complexity and emissions levels, spatio-temporal models differ from language and image-generation models in several ways that make it necessary to (re)train them more often, with potentially large implications for sustainability. While recent work in the machine learning community has started calling for greener and more energy-efficient AI alongside improvements in model accuracy, this trend has not yet reached the GeoAI community at large. In this work, we bring this issue to not only the attention of the GeoAI community but also present ethical considerations from a geographic perspective that are missing from the broader, ongoing AI-sustainability discussion. To start this discussion, we propose a framework to evaluate models from several sustainability-related angles, including energy efficiency, carbon intensity, transparency, and social implications. We encourage future AI/GeoAI work to acknowledge its environmental impact as a step towards a more resource-conscious society. Similar to the current push for reproducibility, future publications should also report the energy/carbon costs of improvements over prior work.
{"title":"Thinking Geographically about AI Sustainability","authors":"Meilin Shi, Kitty Currier, Zilong Liu, Krzysztof Janowicz, Nina Wiedemann, J. Verstegen, Grant McKenzie, A. Graser, Rui Zhu, Gengchen Mai","doi":"10.5194/agile-giss-4-42-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-42-2023","url":null,"abstract":"Abstract. Driven by foundation models, recent progress in AI and machine learning has reached unprecedented complexity. For instance, the GPT-3 language model consists of 175 billion parameters and a training-data size of 570 GB. While it has achieved remarkable performance in generating text that is difficult to distinguish from human-authored content, a single training of the model is estimated to produce over 550 metric tons of CO2 emissions. Likewise, we see advances in GeoAI research improving large-scale prediction tasks like satellite image classification and global climate modeling, to name but a couple. While these models have not yet reached comparable complexity and emissions levels, spatio-temporal models differ from language and image-generation models in several ways that make it necessary to (re)train them more often, with potentially large implications for sustainability. While recent work in the machine learning community has started calling for greener and more energy-efficient AI alongside improvements in model accuracy, this trend has not yet reached the GeoAI community at large. In this work, we bring this issue to not only the attention of the GeoAI community but also present ethical considerations from a geographic perspective that are missing from the broader, ongoing AI-sustainability discussion. To start this discussion, we propose a framework to evaluate models from several sustainability-related angles, including energy efficiency, carbon intensity, transparency, and social implications. We encourage future AI/GeoAI work to acknowledge its environmental impact as a step towards a more resource-conscious society. Similar to the current push for reproducibility, future publications should also report the energy/carbon costs of improvements over prior work.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124253637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-4-2023
Ioannis Dardavesis, E. Verbree, A. Rafiee
Abstract. Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility.
{"title":"Indoor localisation and location tracking in indoor facilities based on LiDAR point clouds and images of the ceilings","authors":"Ioannis Dardavesis, E. Verbree, A. Rafiee","doi":"10.5194/agile-giss-4-4-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-4-2023","url":null,"abstract":"Abstract. Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility. \u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129979602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-14-2023
B. Van Loenen, H. Ploeger, Noor van Everdingen, Kristian Cuervo, Jessica L. Monahan, Julia Pille, Carmel Verhaeghe
Abstract. A new active teaching and learning approach has been implemented in the BSc course Open Urban Data Governance.. This course is part of the minor Spatial Computing for Digital Twinning in the Bachelor of Architecture, Urbanism and Building Sciences at the Faculty of Architecture and The Built Environment, TU Delft, and offered TU wide as an elective. At the start of the course the students were tasked to collect and analyse noise level data, and to reflect on this process. In the second (main) assignment they had to develop an urban challenge to be answered with geo-data. Also here a reflection was required including an assessment of the FAIRness of the data. Both students and teachers highly appreciated the new approach. Through applying active teaching and learning we created an example of the theory on students’ activities. This approach also links students’ real world experiences to the content of the course, which allows them to put theory into context. Teachers especially appreciated the interaction with the students, the depth of the discussions, and were impressed by the steep learning curve of the students. Students enjoyed the freedom to operate, the frequent feedback sessions and the application of theory into practice. For the next year the lectures will take into account that ‘minor’ students do prepare well for their classes.
{"title":"Active teaching and learning in GI sciences: lessons learned from the BSc. Course Open Urban Data Governance","authors":"B. Van Loenen, H. Ploeger, Noor van Everdingen, Kristian Cuervo, Jessica L. Monahan, Julia Pille, Carmel Verhaeghe","doi":"10.5194/agile-giss-4-14-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-14-2023","url":null,"abstract":"Abstract. A new active teaching and learning approach has been implemented in the BSc course Open Urban Data Governance.. This course is part of the minor Spatial Computing for Digital Twinning in the Bachelor of Architecture, Urbanism and Building Sciences at the Faculty of Architecture and The Built Environment, TU Delft, and offered TU wide as an elective. At the start of the course the students were tasked to collect and analyse noise level data, and to reflect on this process. In the second (main) assignment they had to develop an urban challenge to be answered with geo-data. Also here a reflection was required including an assessment of the FAIRness of the data. Both students and teachers highly appreciated the new approach. Through applying active teaching and learning we created an example of the theory on students’ activities. This approach also links students’ real world experiences to the content of the course, which allows them to put theory into context. Teachers especially appreciated the interaction with the students, the depth of the discussions, and were impressed by the steep learning curve of the students. Students enjoyed the freedom to operate, the frequent feedback sessions and the application of theory into practice. For the next year the lectures will take into account that ‘minor’ students do prepare well for their classes.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"120 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133286955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-19-2023
E. Augustijn, Rosa Aguilar Bolivar, S. Abdulkareem
Abstract. Disease transmission and governmental interventions influence the spread of Covid-19. Models can be essential tools to optimise these governmental interventions. This requires the exploration of various ways to implement government agent behaviour. In Agent-Based Models (ABMs), government agent behaviour can be rule-based or data-driven, and the agent can be an isolated learner (using only its own data) or a social learner. We explore the creation of a data-driven social approach in which behaviour is based on a Machine Learning (ML) algorithm, and the government considers data from other European countries as input for their decision-making. Governmental actions start with risk perception, based on several parameters, e.g. the number of disease cases, deaths, and hospitalisation rate. The interventions are measured via the stringency index, measuring the simultaneous number of interventions (working from home, wearing a facemask, closing schools, etc.) taken. We test four machine learning algorithms (Bayesian Network (BN), c4.5, Naïve Bayes (NB) and Random Forest (RF)), using a 5-class and a 3-class classification of the stringency level. The algorithms are trained on disease data from many European countries. The best-performing algorithms were c4.5 and RF. The next step is to implement these algorithms into the ABM and evaluate the outcomes compared to the original model.
{"title":"Using Machine Learning to drive social learning in a Covid-19 Agent-Based Model","authors":"E. Augustijn, Rosa Aguilar Bolivar, S. Abdulkareem","doi":"10.5194/agile-giss-4-19-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-19-2023","url":null,"abstract":"Abstract. Disease transmission and governmental interventions influence the spread of Covid-19. Models can be essential tools to optimise these governmental interventions. This requires the exploration of various ways to implement government agent behaviour. In Agent-Based Models (ABMs), government agent behaviour can be rule-based or data-driven, and the agent can be an isolated learner (using only its own data) or a social learner. We explore the creation of a data-driven social approach in which behaviour is based on a Machine Learning (ML) algorithm, and the government considers data from other European countries as input for their decision-making. Governmental actions start with risk perception, based on several parameters, e.g. the number of disease cases, deaths, and hospitalisation rate. The interventions are measured via the stringency index, measuring the simultaneous number of interventions (working from home, wearing a facemask, closing schools, etc.) taken. We test four machine learning algorithms (Bayesian Network (BN), c4.5, Naïve Bayes (NB) and Random Forest (RF)), using a 5-class and a 3-class classification of the stringency level. The algorithms are trained on disease data from many European countries. The best-performing algorithms were c4.5 and RF. The next step is to implement these algorithms into the ABM and evaluate the outcomes compared to the original model.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128115896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：