Gerard Pons Recasens, Besim Bilalli, Alberto Abelló, Santiago Blanco Sánchez
The Automatic Identification System (AIS) allows vessels to emit their position, speed and course while sailing. By international law, all larges vessels (e.g., bigger than 15m in Europe) are required to provide such data. The abundance and free availability of AIS data has created a huge interest in analyzing them (e.g., to look for patterns of how ships move, detailed knowledge about sailing routes, etc.). In this paper, we use AIS data to classify areas (i.e., spatial cells) of the South Atlantic Ocean as productive or unproductive in terms of the quantity of squid that can be caught. Next, together with daily satellite data about the area, we create a training dataset where a model is learned to predict whether an area of the Ocean is productive or not. Finally, real fishing data are used to evaluate the model. As a result, for blind movements (i.e., with no information about real catches in the previous days), our model trained on data generated from AIS obtains a precision that is 18% higher than the model trained on actual fishing data - this is due to AIS data being larger in volume than fishing data, and 36% higher than the precision of the actual decisions of the ships studied. The results show that despite their simplicity, AIS data have potential value in building training datasets in this domain.
{"title":"Learning fishing information from AIS data","authors":"Gerard Pons Recasens, Besim Bilalli, Alberto Abelló, Santiago Blanco Sánchez","doi":"10.1145/3557921.3565543","DOIUrl":"https://doi.org/10.1145/3557921.3565543","url":null,"abstract":"The Automatic Identification System (AIS) allows vessels to emit their position, speed and course while sailing. By international law, all larges vessels (e.g., bigger than 15m in Europe) are required to provide such data. The abundance and free availability of AIS data has created a huge interest in analyzing them (e.g., to look for patterns of how ships move, detailed knowledge about sailing routes, etc.). In this paper, we use AIS data to classify areas (i.e., spatial cells) of the South Atlantic Ocean as productive or unproductive in terms of the quantity of squid that can be caught. Next, together with daily satellite data about the area, we create a training dataset where a model is learned to predict whether an area of the Ocean is productive or not. Finally, real fishing data are used to evaluate the model. As a result, for blind movements (i.e., with no information about real catches in the previous days), our model trained on data generated from AIS obtains a precision that is 18% higher than the model trained on actual fishing data - this is due to AIS data being larger in volume than fishing data, and 36% higher than the precision of the actual decisions of the ships studied. The results show that despite their simplicity, AIS data have potential value in building training datasets in this domain.","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332740","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}
Ketevan Gallagher, Srihan Kotnana, Sachin Satishkumar, Kheya Siripurapu, Justin Elarde, T. Anderson, Andreas Züfle, H. Kavak
Location-Based Social Networks (LBSNs) combine location information with social networks and have been studied vividly in the last decade. The main research gap is the lack of available and authoritative social network datasets. Publicly available social network datasets are small and sparse, as only a small fraction of the population is captured in the dataset. For this reason, network generators are often employed to generate social networks to study LBSNs synthetically. In this work, we propose an evolving social network implemented in an agent-based simulation to generate realistic social networks. In the simulation, as agents move to different places of interest have the chance to make social connections with other agents as they visit the same place. A large-scale real-world mobility dataset informs the choice of places that agents visit in our simulation. We show qualitatively that our simulated social networks are more realistic than traditional social network generators, including the Erdos-Renyi, Watts-Strogatz, and Barabasi-Albert.
{"title":"Human mobility-based synthetic social network generation","authors":"Ketevan Gallagher, Srihan Kotnana, Sachin Satishkumar, Kheya Siripurapu, Justin Elarde, T. Anderson, Andreas Züfle, H. Kavak","doi":"10.1145/3557921.3565540","DOIUrl":"https://doi.org/10.1145/3557921.3565540","url":null,"abstract":"Location-Based Social Networks (LBSNs) combine location information with social networks and have been studied vividly in the last decade. The main research gap is the lack of available and authoritative social network datasets. Publicly available social network datasets are small and sparse, as only a small fraction of the population is captured in the dataset. For this reason, network generators are often employed to generate social networks to study LBSNs synthetically. In this work, we propose an evolving social network implemented in an agent-based simulation to generate realistic social networks. In the simulation, as agents move to different places of interest have the chance to make social connections with other agents as they visit the same place. A large-scale real-world mobility dataset informs the choice of places that agents visit in our simulation. We show qualitatively that our simulated social networks are more realistic than traditional social network generators, including the Erdos-Renyi, Watts-Strogatz, and Barabasi-Albert.","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825047","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}
Walking tourism, in which regional resources are organized with interesting themes, can provide visitors with original local walking experiences. Our project aims to collect user data through a mobile application and explore potential geographic resources such as appealing spots and streets for improving city-scale tourism. A density map with GPS trajectory data is one of the easiest ways of visualizing them without any modeling costs. However, both user and technical factors make it difficult to interpret the heatmap in a detailed and concise way. Specifically, analysts have difficulty in deciphering the areas of real interest based on the heat map using the data as areas associated with high density of GPS locations may not be solely due to their attractiveness, e.g., rest areas. In addition, the heat map that does not retain the topography of the streets cannot achieve hot street visualization. In our research, built-in smartphone sensors are employed to distinguish multiple user contexts (e.g., stopping / walking and indoors / outdoors) during their walking tours, which equalize the degree of inherent density biases in each GPS trajectory and add attributes to each location point. Our analysis software accumulates the processed trajectories and generates a density map by applying different weight rules (e.g., a street-oriented rule and an indoor-oriented rule) based on semantic attributes and analytical requests. Our mobile cooperative approach realizes adaptive heatmap generation to the analyzer's expectations, that is, concise hot spots visualization and hot streets visualization.
{"title":"Adaptive visualization of tourists' preferred spots and streets using trajectory articulation","authors":"Iori Sasaki, M. Arikawa, Min Lu","doi":"10.1145/3557921.3565539","DOIUrl":"https://doi.org/10.1145/3557921.3565539","url":null,"abstract":"Walking tourism, in which regional resources are organized with interesting themes, can provide visitors with original local walking experiences. Our project aims to collect user data through a mobile application and explore potential geographic resources such as appealing spots and streets for improving city-scale tourism. A density map with GPS trajectory data is one of the easiest ways of visualizing them without any modeling costs. However, both user and technical factors make it difficult to interpret the heatmap in a detailed and concise way. Specifically, analysts have difficulty in deciphering the areas of real interest based on the heat map using the data as areas associated with high density of GPS locations may not be solely due to their attractiveness, e.g., rest areas. In addition, the heat map that does not retain the topography of the streets cannot achieve hot street visualization. In our research, built-in smartphone sensors are employed to distinguish multiple user contexts (e.g., stopping / walking and indoors / outdoors) during their walking tours, which equalize the degree of inherent density biases in each GPS trajectory and add attributes to each location point. Our analysis software accumulates the processed trajectories and generates a density map by applying different weight rules (e.g., a street-oriented rule and an indoor-oriented rule) based on semantic attributes and analytical requests. Our mobile cooperative approach realizes adaptive heatmap generation to the analyzer's expectations, that is, concise hot spots visualization and hot streets visualization.","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966422","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}
Data collected about routine human activity and mobility is used in diverse applications to improve our society. Robust models are needed to address the challenges of our increasingly interconnected world. Methods capable of portraying the dynamic properties of complex human systems, such as simulation modeling, must comply to rigorous data requirements. Modern data sources, like SafeGraph, provide aggregate data collected from location aware technologies. Opportunities and challenges arise to incorporate the new data into existing analysis and modeling methods. Our research employs a multiscale spatial similarity index to compare diverse origin-destination mobility datasets. Established distance ranges accommodate spatial variability in the model's datasets. This paper explores how similarity scores change with different aggregations to address discrepancies in the source data's temporal granularity. We suggest possible explanations for variations in the similarity scores and extract characteristics of human mobility for the study area. The multiscale spatial similarity index may be integrated into a vast array of analysis and modeling workflows, either during preliminary analysis or later evaluation phases as a method of data validation (e.g., agent-based models). We propose that the demonstrated tool has potential to enhance mobility modeling methods in the context of complex human systems.
{"title":"Spatially weighted structural similarity index: a multiscale comparison tool for diverse sources of mobility data","authors":"Jessica Embury, A. Nara, Chanwoo Jin","doi":"10.1145/3557921.3565542","DOIUrl":"https://doi.org/10.1145/3557921.3565542","url":null,"abstract":"Data collected about routine human activity and mobility is used in diverse applications to improve our society. Robust models are needed to address the challenges of our increasingly interconnected world. Methods capable of portraying the dynamic properties of complex human systems, such as simulation modeling, must comply to rigorous data requirements. Modern data sources, like SafeGraph, provide aggregate data collected from location aware technologies. Opportunities and challenges arise to incorporate the new data into existing analysis and modeling methods. Our research employs a multiscale spatial similarity index to compare diverse origin-destination mobility datasets. Established distance ranges accommodate spatial variability in the model's datasets. This paper explores how similarity scores change with different aggregations to address discrepancies in the source data's temporal granularity. We suggest possible explanations for variations in the similarity scores and extract characteristics of human mobility for the study area. The multiscale spatial similarity index may be integrated into a vast array of analysis and modeling workflows, either during preliminary analysis or later evaluation phases as a method of data validation (e.g., agent-based models). We propose that the demonstrated tool has potential to enhance mobility modeling methods in the context of complex human systems.","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129556973","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}
Benjamin Robira, Andrea Corradini, F. Ossi, F. Cagnacci
In the Anthropocene, findings on animal behavioral flexibility in response to anthropogenic changes are accumulating: human presence and activity affect the distribution, movement, activity rhythm, physiology, and diet of animal species. However, conclusions are limited by the lack of simultaneous quantitative data on both the animal and human side. Hence, the dynamic link between animal behavior and human activity and mobility is often poorly estimated. Based on long-term monitoring of a wild bear population in the Trentino region (10 bears monitored from 2006 to 2019; 20 bear-years) combined with human mobility data (Cumulative Outdoor activity Index, derived from the Strava Global Heatmap) and tourist count records, we investigated how spatial behavior and activity rhythms of bears change with variations in experienced human disturbance. We found that bears were mainly nocturnal and that, on an annual scale, nocturnality was associated with movement behavior, but both were independent of experienced human disturbance. Furthermore, nocturnality tended to increase in periods of more intense exploitation of outdoor areas by humans. Overall, these preliminary findings show that bears exhibit a notable behavioral flexibility to minimize their exposure to human presence. Through the application of different sources of human activity data, this work showcases that the integration of high resolution animal movement data with dynamic data on human mobility is crucial to meaningfully catch wildlife responses to anthropisation.
{"title":"Bridging human mobility to animal activity: when humans are away, bears will play","authors":"Benjamin Robira, Andrea Corradini, F. Ossi, F. Cagnacci","doi":"10.1145/3557921.3565538","DOIUrl":"https://doi.org/10.1145/3557921.3565538","url":null,"abstract":"In the Anthropocene, findings on animal behavioral flexibility in response to anthropogenic changes are accumulating: human presence and activity affect the distribution, movement, activity rhythm, physiology, and diet of animal species. However, conclusions are limited by the lack of simultaneous quantitative data on both the animal and human side. Hence, the dynamic link between animal behavior and human activity and mobility is often poorly estimated. Based on long-term monitoring of a wild bear population in the Trentino region (10 bears monitored from 2006 to 2019; 20 bear-years) combined with human mobility data (Cumulative Outdoor activity Index, derived from the Strava Global Heatmap) and tourist count records, we investigated how spatial behavior and activity rhythms of bears change with variations in experienced human disturbance. We found that bears were mainly nocturnal and that, on an annual scale, nocturnality was associated with movement behavior, but both were independent of experienced human disturbance. Furthermore, nocturnality tended to increase in periods of more intense exploitation of outdoor areas by humans. Overall, these preliminary findings show that bears exhibit a notable behavioral flexibility to minimize their exposure to human presence. Through the application of different sources of human activity data, this work showcases that the integration of high resolution animal movement data with dynamic data on human mobility is crucial to meaningfully catch wildlife responses to anthropisation.","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134113463","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}
{"title":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","authors":"","doi":"10.1145/3557921","DOIUrl":"https://doi.org/10.1145/3557921","url":null,"abstract":"","PeriodicalId":387861,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128339567","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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