Hamada Rizk, H. Yamaguchi, T. Higashino, M. Youssef
Location-based services have undergone massive improvements over the last decade. Despite intense efforts in industry and academia, a pervasive infrastructure-free localization is still elusive. Towards making this possible, cellular-based systems have recently been proposed due to the wide-spread availability of the cellular networks and their support by commodity cellphones. However, these systems only consider locating the user in a 2D single floor environment, which reduces their value when used in multi-story buildings. In this paper, we propose CellRise, a deep learning-based system for floor identification in multi-story buildings using ubiquitous cellular signals. Due to the inherent challenges of leveraging the large propagation range and the overlap in the signal space between horizontal and vertical user movements, CellRise provides a novel module to generate floor-discriminative representations. These representations are then fed to a recurrent neural network that learns the sequential changes in signals to estimate the user floor level. Additionally, CellRise incorporates different modules that improve the deep model's generalization against avoiding overtraining and noise. These modules also permit CellRise to generalize to floors completely unseen during training. We have implemented and evaluated CellRise using two different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that CellRise can accurately estimate the exact user's floor 97.7% of the time and within one floor error 100% of the time. This is better than the state-of-the-art systems by at least 17.9% in floor identification accuracy. In addition, we show that CellRise has robust performance in various challenging conditions.
{"title":"A Ubiquitous and Accurate Floor Estimation System Using Deep Representational Learning","authors":"Hamada Rizk, H. Yamaguchi, T. Higashino, M. Youssef","doi":"10.1145/3397536.3422202","DOIUrl":"https://doi.org/10.1145/3397536.3422202","url":null,"abstract":"Location-based services have undergone massive improvements over the last decade. Despite intense efforts in industry and academia, a pervasive infrastructure-free localization is still elusive. Towards making this possible, cellular-based systems have recently been proposed due to the wide-spread availability of the cellular networks and their support by commodity cellphones. However, these systems only consider locating the user in a 2D single floor environment, which reduces their value when used in multi-story buildings. In this paper, we propose CellRise, a deep learning-based system for floor identification in multi-story buildings using ubiquitous cellular signals. Due to the inherent challenges of leveraging the large propagation range and the overlap in the signal space between horizontal and vertical user movements, CellRise provides a novel module to generate floor-discriminative representations. These representations are then fed to a recurrent neural network that learns the sequential changes in signals to estimate the user floor level. Additionally, CellRise incorporates different modules that improve the deep model's generalization against avoiding overtraining and noise. These modules also permit CellRise to generalize to floors completely unseen during training. We have implemented and evaluated CellRise using two different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that CellRise can accurately estimate the exact user's floor 97.7% of the time and within one floor error 100% of the time. This is better than the state-of-the-art systems by at least 17.9% in floor identification accuracy. In addition, we show that CellRise has robust performance in various challenging conditions.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155496","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}
Victor Zakhary, Ishani Gupta, Rey Tang, A. E. Abbadi
Recent works in social network stream analysis have shown that a user's online persona attributes (e.g., location, gender, ethnicity, political interest, etc.) can be accurately inferred from the topics the user writes about or engages with. Revealing a user's sensitive attributes could represent a privacy threat to some individuals. Microtargeting (e.g., the Cambridge Analytica scandal), surveillance, and discriminating ads are examples of threats to user privacy caused by sensitive attribute inference. In this paper, we propose Multifaceted privacy, a novel privacy model that aims to obfuscate a user's sensitive attributes while publicly preserving the user's public persona. To achieve multifaceted privacy, we build Aegis, a prototype client-centric social network stream processing system that helps preserve multifaceted privacy, and thus allowing social network users to freely express their online personas without revealing their sensitive attributes of choice. Aegis continuously suggests topics and hashtags to social network users to write about in order to obfuscate their sensitive attributes and hence confuse content-based sensitive attribute inferences. Our experiments show that adding as few as 0 to 4 obfuscation posts (depending on how revealing the original post is) successfully hides a user sensitive attributes without changing the user's public persona attributes.
{"title":"Multifaceted Privacy: Express Your Online Persona without Revealing Your Sensitive Attribute","authors":"Victor Zakhary, Ishani Gupta, Rey Tang, A. E. Abbadi","doi":"10.1145/3397536.3422253","DOIUrl":"https://doi.org/10.1145/3397536.3422253","url":null,"abstract":"Recent works in social network stream analysis have shown that a user's online persona attributes (e.g., location, gender, ethnicity, political interest, etc.) can be accurately inferred from the topics the user writes about or engages with. Revealing a user's sensitive attributes could represent a privacy threat to some individuals. Microtargeting (e.g., the Cambridge Analytica scandal), surveillance, and discriminating ads are examples of threats to user privacy caused by sensitive attribute inference. In this paper, we propose Multifaceted privacy, a novel privacy model that aims to obfuscate a user's sensitive attributes while publicly preserving the user's public persona. To achieve multifaceted privacy, we build Aegis, a prototype client-centric social network stream processing system that helps preserve multifaceted privacy, and thus allowing social network users to freely express their online personas without revealing their sensitive attributes of choice. Aegis continuously suggests topics and hashtags to social network users to write about in order to obfuscate their sensitive attributes and hence confuse content-based sensitive attribute inferences. Our experiments show that adding as few as 0 to 4 obfuscation posts (depending on how revealing the original post is) successfully hides a user sensitive attributes without changing the user's public persona attributes.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128189148","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}
Given a collection of N geo-located point samples of k types, we aim to detect spatial mixture patterns of interest, which are sub-regions of the study area that have significantly high or low mixture of points of different types. Spatial mixture patterns have important applications in many societal domains, including resilience of smart cities and communities, biodiversity, equity, business intelligence, etc. The problem is challenging because ranking and selection of candidate patterns can be highly susceptible to the effect of natural randomness, and real-world data often consists of various mixture patterns. In related work, the multi-nomial scan statistic does not support identification of high or low mixture due to its "directionless" nature and high sensitivity to the composition of mixture patterns in data. While species richness indices in biodiversity research allow specification of directions, the measures are very sensitive to spatial randomness effects. To bridge the gap, we first propose a spatial mixture index to provide robust ranking among candidate patterns. Then, we present a dual-level Monte-Carlo estimation method with a baseline algorithm for spatial mixture pattern detection. Finally, we propose both an exact algorithm and a distribution-inspired sequence-reduction heuristic to accelerate the baseline approach. Experiment results with both synthetic and real-world data show that the proposed approaches can detect mixture patterns with high accuracy, and the acceleration methods can greatly reduce computational cost while maintaining high solution quality.
{"title":"Discovering Spatial Mixture Patterns of Interest","authors":"Yiqun Xie, Han Bao, Y. Li, S. Shekhar","doi":"10.1145/3397536.3422217","DOIUrl":"https://doi.org/10.1145/3397536.3422217","url":null,"abstract":"Given a collection of N geo-located point samples of k types, we aim to detect spatial mixture patterns of interest, which are sub-regions of the study area that have significantly high or low mixture of points of different types. Spatial mixture patterns have important applications in many societal domains, including resilience of smart cities and communities, biodiversity, equity, business intelligence, etc. The problem is challenging because ranking and selection of candidate patterns can be highly susceptible to the effect of natural randomness, and real-world data often consists of various mixture patterns. In related work, the multi-nomial scan statistic does not support identification of high or low mixture due to its \"directionless\" nature and high sensitivity to the composition of mixture patterns in data. While species richness indices in biodiversity research allow specification of directions, the measures are very sensitive to spatial randomness effects. To bridge the gap, we first propose a spatial mixture index to provide robust ranking among candidate patterns. Then, we present a dual-level Monte-Carlo estimation method with a baseline algorithm for spatial mixture pattern detection. Finally, we propose both an exact algorithm and a distribution-inspired sequence-reduction heuristic to accelerate the baseline approach. Experiment results with both synthetic and real-world data show that the proposed approaches can detect mixture patterns with high accuracy, and the acceleration methods can greatly reduce computational cost while maintaining high solution quality.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123756405","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}
Prabin Giri, H. Hashemi, Evan Gossling, Jason T. Guo, Koshal P. Shah, Goce Trajcevski
In this paper, we present the CET-LATS (Compressing Evolution of TINs from Location Aware Time Series) system, which enables testing the impacts of various compression approaches on evolving Triangulated Irregular Networks (TINs). Specifically, we consider the settings in which values measured in distinct locations and at different time instants, are represented as time series of the corresponding measurements, generating a sequence of TINs. Different compression techniques applied to location-specific time series may have different impacts on the representation of the global evolution of TINs - depending on the distance functions used to evaluate the distortion. CET-LATS users can view and analyze compression vs. (im)precision trade-offs over multiple compression methods and distance functions, and decide which method works best for their application. We also provide an option to investigate the impact of the choice of a compression method on the quality of prediction. Our prototype is a web-based system using Flask, a lightweight Python framework, relying on Apache Spark for data management and JSON files to communicate with the front-end, enabling extensibility in terms of adding new data sources as well as compression techniques, distance functions and prediction methods.
{"title":"CET-LATS: Compressing Evolution of TINs from Location Aware Time Series","authors":"Prabin Giri, H. Hashemi, Evan Gossling, Jason T. Guo, Koshal P. Shah, Goce Trajcevski","doi":"10.1145/3397536.3422352","DOIUrl":"https://doi.org/10.1145/3397536.3422352","url":null,"abstract":"In this paper, we present the CET-LATS (Compressing Evolution of TINs from Location Aware Time Series) system, which enables testing the impacts of various compression approaches on evolving Triangulated Irregular Networks (TINs). Specifically, we consider the settings in which values measured in distinct locations and at different time instants, are represented as time series of the corresponding measurements, generating a sequence of TINs. Different compression techniques applied to location-specific time series may have different impacts on the representation of the global evolution of TINs - depending on the distance functions used to evaluate the distortion. CET-LATS users can view and analyze compression vs. (im)precision trade-offs over multiple compression methods and distance functions, and decide which method works best for their application. We also provide an option to investigate the impact of the choice of a compression method on the quality of prediction. Our prototype is a web-based system using Flask, a lightweight Python framework, relying on Apache Spark for data management and JSON files to communicate with the front-end, enabling extensibility in terms of adding new data sources as well as compression techniques, distance functions and prediction methods.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123130425","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}
The GISCUP 2020 problem is to write a fleet manager to manage a fleet of mobile agents/taxicabs to service resources/customers introduced in a road network. This paper describes a solution that uses a dynamic weighting system to send agents to locations where more resources may show up. It also tries to send agents to waiting resources and assign a resource to the best agent. Results show that the solution is effective at minimizing resource wait time and reducing agent search time.
{"title":"A fleet manager that brings agents closer to resources: GIS Cup","authors":"Wenli Li","doi":"10.1145/3397536.3427186","DOIUrl":"https://doi.org/10.1145/3397536.3427186","url":null,"abstract":"The GISCUP 2020 problem is to write a fleet manager to manage a fleet of mobile agents/taxicabs to service resources/customers introduced in a road network. This paper describes a solution that uses a dynamic weighting system to send agents to locations where more resources may show up. It also tries to send agents to waiting resources and assign a resource to the best agent. Results show that the solution is effective at minimizing resource wait time and reducing agent search time.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123349434","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}
Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called "Predictive Collision Management Path Planning" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.
{"title":"Predictive Collision Management for Time and Risk Dependent Path Planning","authors":"Carsten Hahn, Sebastian Feld, Hannes Schroter","doi":"10.1145/3397536.3422252","DOIUrl":"https://doi.org/10.1145/3397536.3422252","url":null,"abstract":"Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict movements intuitively and to avoid obstacles in a forward-looking way. The task of collision avoidance can be divided into a global and a local level. Regarding the global level, we propose an approach called \"Predictive Collision Management Path Planning\" (PCMP). At the local level, solutions for collision avoidance are used that prevent an inevitable collision. Therefore, the aim of PCMP is to avoid unnecessary local collision scenarios using predictive collision management. PCMP is a graph-based algorithm with a focus on the time dimension consisting of three parts: (1) movement prediction, (2) integration of movement prediction into a time-dependent graph, and (3) time and risk-dependent path planning. The algorithm combines the search for a shortest path with the question: is the detour worth avoiding a possible collision scenario? We evaluate the evasion behavior and the results show that a risk-sensitive agent can avoid 47.3% of the collision scenarios while making a detour of 1.3%. A risk-averse agent avoids up to 97.3% of the collision scenarios with a detour of 39.1%. Thus, an agent's evasive behavior can be controlled actively and risk-dependent using PCMP.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129642650","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}
This paper studies a spatial group-by query over complex polygons. Groups are selected from a set of non-overlapping complex polygons, typically in the order of thousands, while the input is a large-scale dataset that contains hundreds of millions or even billions of spatial points. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within boundaries of each polygon. This problem is challenging because real polygons (like counties, cities, postal codes, voting regions, etc.) are described by very complex boundaries. We propose a highly-parallelized query processing framework to efficiently compute the spatial group-by query. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques.
{"title":"Scalable Spatial GroupBy Aggregations Over Complex Polygons","authors":"Laila Abdelhafeez, A. Magdy, V. Tsotras","doi":"10.1145/3397536.3422222","DOIUrl":"https://doi.org/10.1145/3397536.3422222","url":null,"abstract":"This paper studies a spatial group-by query over complex polygons. Groups are selected from a set of non-overlapping complex polygons, typically in the order of thousands, while the input is a large-scale dataset that contains hundreds of millions or even billions of spatial points. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within boundaries of each polygon. This problem is challenging because real polygons (like counties, cities, postal codes, voting regions, etc.) are described by very complex boundaries. We propose a highly-parallelized query processing framework to efficiently compute the spatial group-by query. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"57 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129679135","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}
Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly easy to build object-detection models for your imagery datasets. In this paper, we use the Detectron2 framework to detect 18 different common traffic signs from 351.469 images. The purpose is to automate the asset management of traffic signs in large road networks. A task that today often is done in a manual and labor-intensive manner. To improve the accuracy of determining the locations of traffic signs, we develop a new, general method that uses the size of the object detected (in pixels) and the camera's GPS position and heading. To further enhance the accuracy, multiple detections of the same physical traffic sign are clustered. The traffic-sign type and computed location are stored in a spatial data warehouse. The clustered locations are presented on a digital road network in a web app. This app allows visual inspection of the overall approach. We demonstrate that the accuracy of the computed locations is good, e.g., signs are placed on the correct side of the road or in/out of a roundabout.
{"title":"Geolocating Traffic Signs using Crowd-Sourced Imagery","authors":"Kasper F. Pedersen, K. Torp","doi":"10.1145/3397536.3422340","DOIUrl":"https://doi.org/10.1145/3397536.3422340","url":null,"abstract":"Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly easy to build object-detection models for your imagery datasets. In this paper, we use the Detectron2 framework to detect 18 different common traffic signs from 351.469 images. The purpose is to automate the asset management of traffic signs in large road networks. A task that today often is done in a manual and labor-intensive manner. To improve the accuracy of determining the locations of traffic signs, we develop a new, general method that uses the size of the object detected (in pixels) and the camera's GPS position and heading. To further enhance the accuracy, multiple detections of the same physical traffic sign are clustered. The traffic-sign type and computed location are stored in a spatial data warehouse. The clustered locations are presented on a digital road network in a web app. This app allows visual inspection of the overall approach. We demonstrate that the accuracy of the computed locations is good, e.g., signs are placed on the correct side of the road or in/out of a roundabout.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132602107","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}
We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform the original STConvS2S and other baseline models on tasks related to predicting future time-steps. In tests related to predicting a missing time-step, some of the proposed extensions also lead to improvements over the original STConvS2S architecture, although simpler models seem to be beneficial in this scenario.
{"title":"Spatio-Temporal Forecasting With Gridded Remote Sensing Data Using Feed-Backward Decoding","authors":"Mário Cardoso, J. Estima, Bruno Martins","doi":"10.1145/3397536.3422247","DOIUrl":"https://doi.org/10.1145/3397536.3422247","url":null,"abstract":"We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform the original STConvS2S and other baseline models on tasks related to predicting future time-steps. In tests related to predicting a missing time-step, some of the proposed extensions also lead to improvements over the original STConvS2S architecture, although simpler models seem to be beneficial in this scenario.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114518565","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}
To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this paper, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics (MAE ≤ 3, RMSE ≤ 7) on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.
{"title":"Application of Kalman Filter to Large-Scale Geospatial Data: Modeling Population Dynamics","authors":"Hiroto Akatsuka, Masayuki Terada","doi":"10.1145/3397536.3422223","DOIUrl":"https://doi.org/10.1145/3397536.3422223","url":null,"abstract":"To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this paper, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics (MAE ≤ 3, RMSE ≤ 7) on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132216412","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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