Pub Date : 2023-06-06DOI: 10.5194/agile-giss-4-44-2023
R. Stopic, E. Dias, Maurice de Kleijn, E. Koomen
Abstract. Parking management plays a critical role in keeping urban spaces accessible and urban managers strive for an optimal balance between not enough and too much parking. Deciding which parking space can be liberated or needs to be extended requires detailed data on parking occupancy trends. In person inspection and in-situ sensors can provide such data but are too costly for city wide deployment. High-resolution satellite imagery is becoming more affordable, has the advantage of instantaneously collecting information from the whole city, is continuously being updated, and available for several years now to allow building a time series. Yet, identifying cars in satellite imagery is not a trivial task. We propose a method for classifying parking spot occupancy based on thresholding the reflectance range. The method requires individual parking spot data to be available and analyses each parking zone individually. We tested the method on a 0.5 metre resolution image (Pleiades satellite) that was specifically ordered for this purpose during a clear spring day in a medium-size city. The method has the advantage of not requiring extensive training data and is non-parametric. To assess accuracy, we collected ground truth data for the exact same moment as the image was ordered. The colour bands (blue, green, and red) performed equally well, while NIR seriously underperformed. We achieved a F1 score of 0.82 for all parking spots in the ground truth. The method is sensitive to tree canopy. When removing the tree obscured spots, the F1 score increased to 0.85. Tree canopy spots were automatically determined and filtered using NDVI.
{"title":"Satellite parking: a new method for measuring parking occupancy","authors":"R. Stopic, E. Dias, Maurice de Kleijn, E. Koomen","doi":"10.5194/agile-giss-4-44-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-44-2023","url":null,"abstract":"Abstract. Parking management plays a critical role in keeping urban spaces accessible and urban managers strive for an optimal balance between not enough and too much parking. Deciding which parking space can be liberated or needs to be extended requires detailed data on parking occupancy trends. In person inspection and in-situ sensors can provide such data but are too costly for city wide deployment. High-resolution satellite imagery is becoming more affordable, has the advantage of instantaneously collecting information from the whole city, is continuously being updated, and available for several years now to allow building a time series. Yet, identifying cars in satellite imagery is not a trivial task. We propose a method for classifying parking spot occupancy based on thresholding the reflectance range. The method requires individual parking spot data to be available and analyses each parking zone individually. We tested the method on a 0.5 metre resolution image (Pleiades satellite) that was specifically ordered for this purpose during a clear spring day in a medium-size city. The method has the advantage of not requiring extensive training data and is non-parametric. To assess accuracy, we collected ground truth data for the exact same moment as the image was ordered. The colour bands (blue, green, and red) performed equally well, while NIR seriously underperformed. We achieved a F1 score of 0.82 for all parking spots in the ground truth. The method is sensitive to tree canopy. When removing the tree obscured spots, the F1 score increased to 0.85. Tree canopy spots were automatically determined and filtered using NDVI.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"26 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":"114250880","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-4-21-2023
Anna Brauer, V. Mäkinen, J. Oksanen
Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.
{"title":"Human mobility tracks as FAIR data: Designing a privacy-preserving repository for GNSS-based activity tracking data","authors":"Anna Brauer, V. Mäkinen, J. Oksanen","doi":"10.5194/agile-giss-4-21-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-21-2023","url":null,"abstract":"Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"37 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":"124584507","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-4-52-2023
R. Westerholt
Abstract. Spatial autocorrelation is a fundamental statistical property of geographical data. A number of estimators have been introduced, with Moran’s I being one of the most commonly used methods. The characterisation of spatial autocorrelation is useful for a number of applications, including finding clusters, testing model assumptions, investigating spatial outliers, and others. Most estimators of spatial autocorrelation are based on assessing the degree of correspondence between structures in an attribute and structures among spatial units, both of which are operationalised in matrix form. Associated inference procedures then rely on holding the spatial configuration fixed, but varying the attribute values over the geometries. Although fixing the geometries is useful in many scenarios, there are cases where it would be more appropriate to allow the geometries to vary as well, such as in the analysis of social media feeds or mobile sensor observations. In this short paper, the case is considered where geometries are the result of inhomogeneous spatial Poisson processes. Using diagonal and circular types of spatial structuring, it is investigated how random geometries affect critical values used to assess the significance of global Moran’s I scores. It is shown that the critical values resulting from an established inference framework often underestimate the bounds that would result if geometric randomness were taken into account. This leads to type-I errors and thus potential false positive patterns.
{"title":"Studying critical values for global Moran’s I under inhomogeneous Poisson point processes","authors":"R. Westerholt","doi":"10.5194/agile-giss-4-52-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-52-2023","url":null,"abstract":"Abstract. Spatial autocorrelation is a fundamental statistical property of geographical data. A number of estimators have been introduced, with Moran’s I being one of the most commonly used methods. The characterisation of spatial autocorrelation is useful for a number of applications, including finding clusters, testing model assumptions, investigating spatial outliers, and others. Most estimators of spatial autocorrelation are based on assessing the degree of correspondence between structures in an attribute and structures among spatial units, both of which are operationalised in matrix form. Associated inference procedures then rely on holding the spatial configuration fixed, but varying the attribute values over the geometries. Although fixing the geometries is useful in many scenarios, there are cases where it would be more appropriate to allow the geometries to vary as well, such as in the analysis of social media feeds or mobile sensor observations. In this short paper, the case is considered where geometries are the result of inhomogeneous spatial Poisson processes. Using diagonal and circular types of spatial structuring, it is investigated how random geometries affect critical values used to assess the significance of global Moran’s I scores. It is shown that the critical values resulting from an established inference framework often underestimate the bounds that would result if geometric randomness were taken into account. This leads to type-I errors and thus potential false positive patterns.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"45 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":"130966572","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-4-2-2023
R. Arabsheibani, E. Hamzei, Kimia Amoozandeh, S. Winter, M. Tomko
Abstract. Text-based games are environments in which defining the world, the representation of the world to the player (hereafter, agent) and agent interactions with the environment are all through text. Text-based games expose abstract, executable representations of indoor spaces through verbally referenced concepts. Yet, the ability of text-based games to represent indoor environments of real-world complexity is currently limited due to insufficient support for complex space decomposition and space interaction concepts. This paper suggests a procedure to automate the mapping of real-world geometric floorplan information into text-based game environment concepts, using the Microsoft TextWorld game platform as a case. To capture the complexities of indoor spaces, we enrich existing TextWorld concepts supported by theoretical navigation concepts.We first decompose indoor spaces using skeletonization, and then identify formal space concepts and their relationships. We further enhance the spectrum of supported agent interactions with an extended grammar, including egocentric navigation instructions. We demonstrate and discuss these new capabilities in an evacuation scenario. Our implementation extends the capabilities of TextWorld to provide a research testbed for spatial research, including symbolic spatial modelling, interaction with indoor spaces, and agent-based machine learning and language processing tasks.
{"title":"From Floorplan to Navigation Concepts: Automatic Generation of Text-based Games","authors":"R. Arabsheibani, E. Hamzei, Kimia Amoozandeh, S. Winter, M. Tomko","doi":"10.5194/agile-giss-4-2-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-2-2023","url":null,"abstract":"Abstract. Text-based games are environments in which defining the world, the representation of the world to the player (hereafter, agent) and agent interactions with the environment are all through text. Text-based games expose abstract, executable representations of indoor spaces through verbally referenced concepts. Yet, the ability of text-based games to represent indoor environments of real-world complexity is currently limited due to insufficient support for complex space decomposition and space interaction concepts. This paper suggests a procedure to automate the mapping of real-world geometric floorplan information into text-based game environment concepts, using the Microsoft TextWorld game platform as a case. To capture the complexities of indoor spaces, we enrich existing TextWorld concepts supported by theoretical navigation concepts.We first decompose indoor spaces using skeletonization, and then identify formal space concepts and their relationships. We further enhance the spectrum of supported agent interactions with an extended grammar, including egocentric navigation instructions. We demonstrate and discuss these new capabilities in an evacuation scenario. Our implementation extends the capabilities of TextWorld to provide a research testbed for spatial research, including symbolic spatial modelling, interaction with indoor spaces, and agent-based machine learning and language processing tasks.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"57 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":"127224621","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-4-45-2023
Apeksha Tare, M. Nefs, E. Koomen, E. Verhoef
Abstract. The increasing demand for logistics real estate calls for a better understanding of the location dynamics of logistics firms. Previous empirical studies have largely focused on describing the spatial patterns of logistics but not on explaining the factors that lead to them. To fill this void, we develop a unique dataset of logistics buildings in the Netherlands and employ it in a multinomial logistic regression model to study the impact of key spatial factors on logistics development in the Netherlands during the period 1990-2020. In general, we find a positive influence of highway accessibility on logistics development. Contrary to previous studies in the US, we find a positive influence of rail accessibility and a negative influence of accessibility to airports. The effect of port accessibility and other factors varies with the type of logistics development. Finally, we also present probability maps that illustrate the combined effect of these factors.
{"title":"Mapping Logistics Development in the Netherlands","authors":"Apeksha Tare, M. Nefs, E. Koomen, E. Verhoef","doi":"10.5194/agile-giss-4-45-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-45-2023","url":null,"abstract":"Abstract. The increasing demand for logistics real estate calls for a better understanding of the location dynamics of logistics firms. Previous empirical studies have largely focused on describing the spatial patterns of logistics but not on explaining the factors that lead to them. To fill this void, we develop a unique dataset of logistics buildings in the Netherlands and employ it in a multinomial logistic regression model to study the impact of key spatial factors on logistics development in the Netherlands during the period 1990-2020. In general, we find a positive influence of highway accessibility on logistics development. Contrary to previous studies in the US, we find a positive influence of rail accessibility and a negative influence of accessibility to airports. The effect of port accessibility and other factors varies with the type of logistics development. Finally, we also present probability maps that illustrate the combined effect of these factors.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"1 20 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":"127978126","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-4-10-2023
E. Nyamsuren, Haiqi Xu, Eric Top, S. Scheider, N. Steenbergen
Abstract. There is an increasing trend of applying AIbased automated methods to geoscience problems. An important example is a geographic question answering (geoQA) focused on answer generation via GIS workflows rather than retrieval of a factual answer. However, a representative question corpus is necessary for developing, testing, and validating such generative geoQA systems. We compare five manually constructed geographical question corpora, GeoAnQu, Giki, GeoCLEF, GeoQuestions201, and Geoquery, by applying a conceptual transformation parser. The parser infers geo-analytical concepts and their transformations from a geographical question, akin to an abstract GIS workflow. Transformations thus represent the complexity of geo-analytical operations necessary to answer a question. By estimating the variety of concepts and the number of transformations for each corpus, the five corpora can be compared on the level of geo-analytical complexity, which cannot be done with purely NLP-based methods. Results indicate that the questions in GeoAnQu, which were compiled from GIS literature, require a higher number as well as more diverse geo-analytical operations than questions from the four other corpora. Furthermore, constructing a corpus with a sufficient representation (including GIS) may require an approach targeting a uniquely qualified group of users as a source. In contrast, sampling questions from large-scale online repositories like Google, Microsoft, and Yahoo may not provide the quality necessary for testing generative geoQA systems.
{"title":"Semantic complexity of geographic questions - A comparison in terms of conceptual transformations of answers","authors":"E. Nyamsuren, Haiqi Xu, Eric Top, S. Scheider, N. Steenbergen","doi":"10.5194/agile-giss-4-10-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-10-2023","url":null,"abstract":"Abstract. There is an increasing trend of applying AIbased automated methods to geoscience problems. An important example is a geographic question answering (geoQA) focused on answer generation via GIS workflows rather than retrieval of a factual answer. However, a representative question corpus is necessary for developing, testing, and validating such generative geoQA systems. We compare five manually constructed geographical question corpora, GeoAnQu, Giki, GeoCLEF, GeoQuestions201, and Geoquery, by applying a conceptual transformation parser. The parser infers geo-analytical concepts and their transformations from a geographical question, akin to an abstract GIS workflow. Transformations thus represent the complexity of geo-analytical operations necessary to answer a question. By estimating the variety of concepts and the number of transformations for each corpus, the five corpora can be compared on the level of geo-analytical complexity, which cannot be done with purely NLP-based methods. Results indicate that the questions in GeoAnQu, which were compiled from GIS literature, require a higher number as well as more diverse geo-analytical operations than questions from the four other corpora. Furthermore, constructing a corpus with a sufficient representation (including GIS) may require an approach targeting a uniquely qualified group of users as a source. In contrast, sampling questions from large-scale online repositories like Google, Microsoft, and Yahoo may not provide the quality necessary for testing generative geoQA systems. \u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"2016 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":"114457585","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-4-23-2023
Néstor DelaPaz-Ruíz, E. Augustijn, M. Farnaghi, R. Zurita-Milla
Abstract. Population mobility can change pollutants variability in domestic wastewater (DW). However, the implications of mobility on DW variability in small localities are rarely analyzed and visualized in space and time. Often, only limited mobility data is available for these types of areas. In this study, we investigate the implications of population mobility on DW variability using an Agent-Based model (ABM). The ABM simulates the spatiotemporal DW variability of chemical oxygen demand (COD) across the sewage network. Two scenarios are tested, one where inhabitants commute daily to school and work and the other when the population remains at home. In each scenario, the spatial variability of COD loads is mapped and analyzed at the sewage maintenance holes. Apparent changes are observed between these spatial patterns. The obtained maps show that DW loads vary across space, where substantial COD load differences exist between the two mobility scenarios. Population mobility implicates higher COD loads at some maintenance holes compared to a scenario with inhabitants remaining home. The spatial DW variability also gets higher upstream and lower downstream, implicating that mobility does not substantially generates variability at the wastewater treatment plant inflow. The preliminary results suggest that population mobility impacts the spatial DW variability across the sewage network, which requires further analysis with wider temporal coverage.
{"title":"Spatiotemporal domestic wastewater variability: Assessing implications of population mobility in pollutants dynamics","authors":"Néstor DelaPaz-Ruíz, E. Augustijn, M. Farnaghi, R. Zurita-Milla","doi":"10.5194/agile-giss-4-23-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-23-2023","url":null,"abstract":"Abstract. Population mobility can change pollutants variability in domestic wastewater (DW). However, the implications of mobility on DW variability in small localities are rarely analyzed and visualized in space and time. Often, only limited mobility data is available for these types of areas. In this study, we investigate the implications of population mobility on DW variability using an Agent-Based model (ABM). The ABM simulates the spatiotemporal DW variability of chemical oxygen demand (COD) across the sewage network. Two scenarios are tested, one where inhabitants commute daily to school and work and the other when the population remains at home. In each scenario, the spatial variability of COD loads is mapped and analyzed at the sewage maintenance holes. Apparent changes are observed between these spatial patterns. The obtained maps show that DW loads vary across space, where substantial COD load differences exist between the two mobility scenarios. Population mobility implicates higher COD loads at some maintenance holes compared to a scenario with inhabitants remaining home. The spatial DW variability also gets higher upstream and lower downstream, implicating that mobility does not substantially generates variability at the wastewater treatment plant inflow. The preliminary results suggest that population mobility impacts the spatial DW variability across the sewage network, which requires further analysis with wider temporal coverage.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"434 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":"122873035","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-4-25-2023
N. Fulman, M. Marinov, I. Benenson
Abstract. Public transportation (PT) studies often overlook non-routine trips, focusing on commuting trips. However, recent research reveals that occasional trips comprise a significant portion of public transportation trips. Furthermore, traveler preferences for non-routine trips essentially differ from their preferences for regular commuting. We investigate non-routine trips based on a database of 63 million records of PT boardings made in Israel during June 2019. The behavioral patterns of PT users are revealed by clustering their boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority and constitute less than 15% of Israeli riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of a day, and land use composition around the trip origin.
{"title":"Exploring Non-Routine Trips Through Smartcard Transaction Analysis","authors":"N. Fulman, M. Marinov, I. Benenson","doi":"10.5194/agile-giss-4-25-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-25-2023","url":null,"abstract":"Abstract. Public transportation (PT) studies often overlook non-routine trips, focusing on commuting trips. However, recent research reveals that occasional trips comprise a significant portion of public transportation trips. Furthermore, traveler preferences for non-routine trips essentially differ from their preferences for regular commuting. We investigate non-routine trips based on a database of 63 million records of PT boardings made in Israel during June 2019. The behavioral patterns of PT users are revealed by clustering their boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority and constitute less than 15% of Israeli riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of a day, and land use composition around the trip origin.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"58 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":"133423817","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-4-39-2023
Qilin Ren, Pengxiang Zhao, A. Mansourian
Abstract. In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, especially with machine learning. In this paper, we explore the potential of the XGBoost model to model the three types of shared micro-mobility services, including docked bike, docked e-bike, and dockless e-scooter, in Zurich, Switzerland. The model achieves an accuracy of 72.6%. Moreover, the permutation feature importance is implemented to interpret the model prediction. It is found that trip duration, trip distance, and difference in elevation present higher feature importance in the prediction. The findings are beneficial for urban planners and operators to further improve the shared micro-mobility services toward sustainable urban mobility.
{"title":"Modeling the choice of shared micro-mobility services using XGBoost machine learning algorithm","authors":"Qilin Ren, Pengxiang Zhao, A. Mansourian","doi":"10.5194/agile-giss-4-39-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-39-2023","url":null,"abstract":"Abstract. In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, especially with machine learning. In this paper, we explore the potential of the XGBoost model to model the three types of shared micro-mobility services, including docked bike, docked e-bike, and dockless e-scooter, in Zurich, Switzerland. The model achieves an accuracy of 72.6%. Moreover, the permutation feature importance is implemented to interpret the model prediction. It is found that trip duration, trip distance, and difference in elevation present higher feature importance in the prediction. The findings are beneficial for urban planners and operators to further improve the shared micro-mobility services toward sustainable urban mobility.\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":"131310639","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-4-16-2023
Adel Al-Huraibi, S. Amer, J. Blanford
Abstract. Ensuring populations can easily access testing and vaccination centers is important during a pandemic to minimize future infection risks. Many factors can affect accessibility to such vital health services. Of these, physical accessibility and the ease in which people can get to a center are important. In this study, we examined accessibility to COVID-19 vaccination centers in the Netherlands using a common mode of transportation, the bicycle. Our study utilized data available in the public domain. Accessibility was determined using cumulative opportunities measure to identify variations in accessibility by bicycle at the height of the vaccination campaign (N=193 vaccination centers) and as centers were closed (N=99). Initially, 45% of the population had access to a vaccination facility by bicycle. However, after some centers closed, this number fell to 28%. The elderly (> 65 years) were the most affected age group. Our study shows how open data and a GIS-based approach can provide accurate and timely information to the general public and public health officials and aid in critically assessing infrastructure needs.
{"title":"Cycling to get my vaccination: how accessible are COVID-19 vaccination centers in the Netherlands?","authors":"Adel Al-Huraibi, S. Amer, J. Blanford","doi":"10.5194/agile-giss-4-16-2023","DOIUrl":"https://doi.org/10.5194/agile-giss-4-16-2023","url":null,"abstract":"Abstract. Ensuring populations can easily access testing and vaccination centers is important during a pandemic to minimize future infection risks. Many factors can affect accessibility to such vital health services. Of these, physical accessibility and the ease in which people can get to a center are important. In this study, we examined accessibility to COVID-19 vaccination centers in the Netherlands using a common mode of transportation, the bicycle. Our study utilized data available in the public domain. Accessibility was determined using cumulative opportunities measure to identify variations in accessibility by bicycle at the height of the vaccination campaign (N=193 vaccination centers) and as centers were closed (N=99). Initially, 45% of the population had access to a vaccination facility by bicycle. However, after some centers closed, this number fell to 28%. The elderly (> 65 years) were the most affected age group. Our study shows how open data and a GIS-based approach can provide accurate and timely information to the general public and public health officials and aid in critically assessing infrastructure needs.\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":"129112354","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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