Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-38-2022
I. Ismayilova, S. Timpf
Abstract. Environmental and human benefits of Urban Green Spaces (UGSs) have been known for a long time. However, the definition of a reasonable greening strategy still remains challenging due to the lack of sufficient baseline information as well as a lack of consensus what constitutes a UGS. Therefore, accurate identification of the existing green spaces in cities is crucial for developing UGS inventories for urban planning and resource management activities. In this paper we explore the potential of freely available highest resolution multi-spectral remote sensing imagery to identify large homogeneous as well small heterogeneous UGSs. The approach of using a Random Forest classification on Sentinel-2 imagery is shown to be useful to identify various UGSs with a 97 % accuracy. Freely available data and a relatively straightforward implementation of the proposed approach makes it a valuable tool for decision and policy makers.
{"title":"Classifying Urban Green Spaces using a combined Sentinel-2 and Random Forest approach","authors":"I. Ismayilova, S. Timpf","doi":"10.5194/agile-giss-3-38-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-38-2022","url":null,"abstract":"Abstract. Environmental and human benefits of Urban Green Spaces (UGSs) have been known for a long time. However, the definition of a reasonable greening strategy still remains challenging due to the lack of sufficient baseline information as well as a lack of consensus what constitutes a UGS. Therefore, accurate identification of the existing green spaces in cities is crucial for developing UGS inventories for urban planning and resource management activities. In this paper we explore the potential of freely available highest resolution multi-spectral remote sensing imagery to identify large homogeneous as well small heterogeneous UGSs. The approach of using a Random Forest classification on Sentinel-2 imagery is shown to be useful to identify various UGSs with a 97 % accuracy. Freely available data and a relatively straightforward implementation of the proposed approach makes it a valuable tool for decision and policy makers.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357416","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 : 2022-06-10DOI: 10.5194/agile-giss-3-15-2022
Hyesop Shin, Costanza Cagnina, A. Basiri
Abstract. Active travel provides significant public health benefits including improving physical and mental health and air quality. Given the geography of congested roads, availability of required infrastructure and cost of transportation in cities, promoting active travel, including cycling, can be a good solution for commuting within built environments. Having a better understanding of the key drivers that may influence bike ridership can help with designing cities that accommodate cyclists’ needs for healthier citizens. This paper examines the built environment features that may affect commuting cyclists. We respectively employ Ordinary Linear Square (OLS) regression and Geographically Weighted Regression (GWR) for 136 Intermediate Zones of the city of Glasgow, UK. The results of GWR show that the significant local variation in green areas suggests that even though the global regression showed a negative association between the greenness and commute cycling, over half of the IZ areas had a strong positive association with the green areas. Building height and Public Transport Availability Index show geographic patterns where the residuals are fairly stationary across the study area with some clusters of high residuals. Performance wise, the results from GWR provided an R2 of 0.73 which was higher than OLS at 0.3. Our results can provide insights into how to use crowdsourced cycling data when there are spatially and temporally limited resources available.
{"title":"The Impact of Built Environment on Bike Commuting: Utilising Strava Bike Data and Geographically Weighted Models","authors":"Hyesop Shin, Costanza Cagnina, A. Basiri","doi":"10.5194/agile-giss-3-15-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-15-2022","url":null,"abstract":"Abstract. Active travel provides significant public health benefits including improving physical and mental health and air quality. Given the geography of congested roads, availability of required infrastructure and cost of transportation in cities, promoting active travel, including cycling, can be a good solution for commuting within built environments. Having a better understanding of the key drivers that may influence bike ridership can help with designing cities that accommodate cyclists’ needs for healthier citizens. This paper examines the built environment features that may affect commuting cyclists. We respectively employ Ordinary Linear Square (OLS) regression and Geographically Weighted Regression (GWR) for 136 Intermediate Zones of the city of Glasgow, UK. The results of GWR show that the significant local variation in green areas suggests that even though the global regression showed a negative association between the greenness and commute cycling, over half of the IZ areas had a strong positive association with the green areas. Building height and Public Transport Availability Index show geographic patterns where the residuals are fairly stationary across the study area with some clusters of high residuals. Performance wise, the results from GWR provided an R2 of 0.73 which was higher than OLS at 0.3. Our results can provide insights into how to use crowdsourced cycling data when there are spatially and temporally limited resources available.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131031708","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 : 2022-06-10DOI: 10.5194/agile-giss-3-17-2022
Marie-Dominique Van Damme, Ana-Maria Olteanu-Raimond
Abstract. The increase of recreational activities in the mountains and a growing amount of websites proposing geographic data, offer new opportunities for societal needs such as mountain rescue, biodiversity monitoring, outdoor activities. However, the main issue with the websites data is the lack of metadata that minimizes its reuse outside the community that produced the data. The goal of this paper is to study and generate quality and descriptive metadata using ISO standards. To this end, we propose a method based on a common vocabulary such as an ontology and a data matching process. The first one allows to associate to each type of feature from an available geographic dataset an ontology class that will facilitate data matching, reproducibility of results and minimize semantic heterogeneity. The second one allows to define matching links between features representing the same entity in the real world and compute quality indicators based on the validated links. Finally, at the end of this process, we are able to generate descriptive and quality metadata. By following ISO standards and using the QualityML dictionary for measures, the metadata is serialized to XML and can finally be published as open source. Our approach was applied to five different landmark datasets in the French Alps region. New insights were acquired regarding positional accuracy and semantic granularity.
{"title":"A method to produce metadata describing and assessing the quality of spatial landmark datasets in mountain area","authors":"Marie-Dominique Van Damme, Ana-Maria Olteanu-Raimond","doi":"10.5194/agile-giss-3-17-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-17-2022","url":null,"abstract":"Abstract. The increase of recreational activities in the mountains and a growing amount of websites proposing geographic data, offer new opportunities for societal needs such as mountain rescue, biodiversity monitoring, outdoor activities. However, the main issue with the websites data is the lack of metadata that minimizes its reuse outside the community that produced the data. The goal of this paper is to study and generate quality and descriptive metadata using ISO standards. To this end, we propose a method based on a common vocabulary such as an ontology and a data matching process. The first one allows to associate to each type of feature from an available geographic dataset an ontology class that will facilitate data matching, reproducibility of results and minimize semantic heterogeneity. The second one allows to define matching links between features representing the same entity in the real world and compute quality indicators based on the validated links. Finally, at the end of this process, we are able to generate descriptive and quality metadata. By following ISO standards and using the QualityML dictionary for measures, the metadata is serialized to XML and can finally be published as open source. Our approach was applied to five different landmark datasets in the French Alps region. New insights were acquired regarding positional accuracy and semantic granularity.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122916626","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 : 2022-06-10DOI: 10.5194/agile-giss-3-10-2022
T. Niers, J. Stenkamp, N. Jakuschona, T. Bartoschek, S. Schade
Abstract. Recent developments in image recognition technology including artificial intelligence and machine learning led to an intensified research in computer vision models. This progress also allows advances for the collection of spatio-temporal data on Invasive Alien Species (IAS), in order to understand their geographical distribution and impact on the biodiversity loss. Citizen Science (CS) approaches already show successful solutions how the public can be involved in collecting spatio-temporal data on IAS, e.g. by using mobile applications for monitoring. Our work analyzes recently developed image-based species recognition models suitable for the monitoring of IAS in CS applications. We demonstrate how computer vision models can be benchmarked for such a use case and how their accuracy can be evaluated by testing them with IAS of European Union concern. We found out that available models have different strengths. Depending on which criteria (e.g. high species coverage, costs, maintenance, high accuracies) are considered as most important, it needs to be decided individually which model fits best. Using only one model alone may not necessarily be the best solution, thus combining multiple models or developing a new custom model can be desirable. Generally, cooperation with the model providers can be advantageous.
{"title":"Benchmarking Invasive Alien Species Image Recognition Models for a Citizen Science Based Spatial Distribution Monitoring","authors":"T. Niers, J. Stenkamp, N. Jakuschona, T. Bartoschek, S. Schade","doi":"10.5194/agile-giss-3-10-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-10-2022","url":null,"abstract":"Abstract. Recent developments in image recognition technology including artificial intelligence and machine learning led to an intensified research in computer vision models. This progress also allows advances for the collection of spatio-temporal data on Invasive Alien Species (IAS), in order to understand their geographical distribution and impact on the biodiversity loss. Citizen Science (CS) approaches already show successful solutions how the public can be involved in collecting spatio-temporal data on IAS, e.g. by using mobile applications for monitoring. Our work analyzes recently developed image-based species recognition models suitable for the monitoring of IAS in CS applications. We demonstrate how computer vision models can be benchmarked for such a use case and how their accuracy can be evaluated by testing them with IAS of European Union concern. We found out that available models have different strengths. Depending on which criteria (e.g. high species coverage, costs, maintenance, high accuracies) are considered as most important, it needs to be decided individually which model fits best. Using only one model alone may not necessarily be the best solution, thus combining multiple models or developing a new custom model can be desirable. Generally, cooperation with the model providers can be advantageous.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130413435","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 : 2022-06-10DOI: 10.5194/agile-giss-3-22-2022
S. Zourlidou, J. Golze, Monika Sester
Abstract. This article presents a method for traffic control recognition at junctions (traffic lights, stop, priority and right of way rule) using crowd-sensed GPS data (vehicle trajectories), as well as features extracted from OpenStreetMap. Traffic regulators are not mapped in most maps, although the way they regulate traffic at intersections affects the traffic flow and therefore the vehicle idle time at intersections, the fuel consumption, the CO2 emissions, and the arrival time at a destination. Because of the controlled interaction that road users have with each other at intersections, driving safety or assistance applications can be enabled if intersection regulators are mapped. In order to verify the proposed method two sets of trajectories were used, one of which is an open dataset, from two different cities, Hannover and Chicago. Two classification methods were tested, random forest and gradient boosting, using exclusively either dynamic features (trajectories), or static (only data from OSM) or a combination of the dynamic and static features (hybrid model). The results show that the gradient boosting classification with hybrid features can predict traffic regulations with high accuracy (93% in Chicago and 94% in Hannover), outperforming the other detection models (static and dynamic). At the end directions for further research on this topic are proposed.
{"title":"Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach","authors":"S. Zourlidou, J. Golze, Monika Sester","doi":"10.5194/agile-giss-3-22-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-22-2022","url":null,"abstract":"Abstract. This article presents a method for traffic control recognition at junctions (traffic lights, stop, priority and right of way rule) using crowd-sensed GPS data (vehicle trajectories), as well as features extracted from OpenStreetMap. Traffic regulators are not mapped in most maps, although the way they regulate traffic at intersections affects the traffic flow and therefore the vehicle idle time at intersections, the fuel consumption, the CO2 emissions, and the arrival time at a destination. Because of the controlled interaction that road users have with each other at intersections, driving safety or assistance applications can be enabled if intersection regulators are mapped. In order to verify the proposed method two sets of trajectories were used, one of which is an open dataset, from two different cities, Hannover and Chicago. Two classification methods were tested, random forest and gradient boosting, using exclusively either dynamic features (trajectories), or static (only data from OSM) or a combination of the dynamic and static features (hybrid model). The results show that the gradient boosting classification with hybrid features can predict traffic regulations with high accuracy (93% in Chicago and 94% in Hannover), outperforming the other detection models (static and dynamic). At the end directions for further research on this topic are proposed.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131043371","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 : 2022-06-10DOI: 10.5194/agile-giss-3-32-2022
A. Courtial, G. Touya, X. Zhang
Abstract. Recently, many researchers tried to generate (generalised) maps using deep learning, and most of the proposed methods deal with deep neural network architecture choices. Deep learning learns to reproduce examples, so we think that improving the training examples, and especially the representation of the initial geographic information, is the key issue for this problem. Our article extracts some representation issues from a literature review and proposes different ways to represent vector geographic information as a tensor.We propose two kinds of contributions: 1) the representation of information by layers; 2) the representation of additional information. Then, we demonstrate the interest of some of our propositions with experiments that show a visual improvement for the generation of generalised topographic maps in urban areas.
{"title":"Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation","authors":"A. Courtial, G. Touya, X. Zhang","doi":"10.5194/agile-giss-3-32-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-32-2022","url":null,"abstract":"Abstract. Recently, many researchers tried to generate (generalised) maps using deep learning, and most of the proposed methods deal with deep neural network architecture choices. Deep learning learns to reproduce examples, so we think that improving the training examples, and especially the representation of the initial geographic information, is the key issue for this problem. Our article extracts some representation issues from a literature review and proposes different ways to represent vector geographic information as a tensor.We propose two kinds of contributions: 1) the representation of information by layers; 2) the representation of additional information. Then, we demonstrate the interest of some of our propositions with experiments that show a visual improvement for the generation of generalised topographic maps in urban areas.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503151","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 : 2021-07-05DOI: 10.5194/AGILE-GISS-2-44-2021
P. Kyriakidis, Dimitris Kavroudakis, Philip Fayad, Stylianos Hadjipetrou, G. Leventis, A. Papakonstantinou
Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.
{"title":"Promoting the adoption of agent-based modelling for synergistic interventions and decision-making during pandemic outbreaks","authors":"P. Kyriakidis, Dimitris Kavroudakis, Philip Fayad, Stylianos Hadjipetrou, G. Leventis, A. Papakonstantinou","doi":"10.5194/AGILE-GISS-2-44-2021","DOIUrl":"https://doi.org/10.5194/AGILE-GISS-2-44-2021","url":null,"abstract":"Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128261029","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 : 2021-06-04DOI: 10.5194/AGILE-GISS-2-1-2021
G. Dax, M. Werner
Abstract. In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.
{"title":"Information-optimal Abstaining for Reliable Classification of Building Functions","authors":"G. Dax, M. Werner","doi":"10.5194/AGILE-GISS-2-1-2021","DOIUrl":"https://doi.org/10.5194/AGILE-GISS-2-1-2021","url":null,"abstract":"Abstract. In the past decade, major breakthroughs in sensor technology and algorithms have enabled the functional analysis of urban regions based on Earth observation data. It has, for example, become possible to assign functions to areas in cities on a regional scale. With this paper, we develop a novel method for extracting building functions from social media text alone. Therefore, a technique of abstaining is applied in order to overcome the fact that most tweets will not contain information related to a building function albeit they have been sent from a specific building as well as the problem that classification schemes for building functions are overlapping.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122211861","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 : 2021-06-04DOI: 10.5194/AGILE-GISS-2-9-2021
Alec Parise, Miguel-Ángel Manso-Callejo, Hung Cao, M. Wachowicz
Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.
{"title":"Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors","authors":"Alec Parise, Miguel-Ángel Manso-Callejo, Hung Cao, M. Wachowicz","doi":"10.5194/AGILE-GISS-2-9-2021","DOIUrl":"https://doi.org/10.5194/AGILE-GISS-2-9-2021","url":null,"abstract":"Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114757901","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 : 2021-06-04DOI: 10.5194/AGILE-GISS-2-25-2021
Heiko Figgemeier, Christin Henzen, Arne Rümmler
Abstract. In Earth System Sciences, a data-driven research domain, several communities discuss the importance, guidance and implementation of making research data findable, accessible, interoperable, and reusable. To foster these principles, in particular to support reusability, users need easy-to-use user interfaces with meaningful visualizations for detailed metainformation, e.g. on dataset’s origin and quality. However, visualization tools to facilitate the evaluation of fitness for use of ESS research data on domainspecific metainformation, do hardly exist.We provide a Geo-dashboard concept for user-friendly interactive and linked visualizations of provenance and quality information using standardized geospatial metadata. A provenance graph visualization serves as overview and entry point for further evaluations. Quality information is essential to evaluate the fitness for use of data. Therefore, we developed quality visualizations on several levels of detail to foster evaluation, e.g. by enabling users to choose and classify quality parameters based on their use-case-specific needs.
{"title":"A Geo-Dashboard Concept for the Interactively Linked Visualization of Provenance and Data Quality for Geospatial Datasets","authors":"Heiko Figgemeier, Christin Henzen, Arne Rümmler","doi":"10.5194/AGILE-GISS-2-25-2021","DOIUrl":"https://doi.org/10.5194/AGILE-GISS-2-25-2021","url":null,"abstract":"Abstract. In Earth System Sciences, a data-driven research domain, several communities discuss the importance, guidance and implementation of making research data findable, accessible, interoperable, and reusable. To foster these principles, in particular to support reusability, users need easy-to-use user interfaces with meaningful visualizations for detailed metainformation, e.g. on dataset’s origin and quality. However, visualization tools to facilitate the evaluation of fitness for use of ESS research data on domainspecific metainformation, do hardly exist.We provide a Geo-dashboard concept for user-friendly interactive and linked visualizations of provenance and quality information using standardized geospatial metadata. A provenance graph visualization serves as overview and entry point for further evaluations. Quality information is essential to evaluate the fitness for use of data. Therefore, we developed quality visualizations on several levels of detail to foster evaluation, e.g. by enabling users to choose and classify quality parameters based on their use-case-specific needs.","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122450841","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}
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