Recent advancements in data mining coupled with the ubiquity of mobile devices has led to the possibility of mining for events in real-time. We introduce the problem of mining for an individual's encounters. As people travel, they may have encounters with one another. We are interested in detecting the encounters of traveling individuals at the exact moment in which each of them occur. A simple solution is to use a nearest neighbor search to return potential encounters, this results in slow query response times. To mine for encounters in real-time, we introduce a new algorithm that is efficient in capturing encounters by exploiting the observation that just the neighbors in a defined proximity needs to be maintained. Our evaluation demonstrates that our proposed method mines for encounters for millions of individuals in a city area within milliseconds.
{"title":"Mining city-wide encounters in real-time","authors":"Anthony Quattrone, L. Kulik, E. Tanin","doi":"10.1145/2996913.2996995","DOIUrl":"https://doi.org/10.1145/2996913.2996995","url":null,"abstract":"Recent advancements in data mining coupled with the ubiquity of mobile devices has led to the possibility of mining for events in real-time. We introduce the problem of mining for an individual's encounters. As people travel, they may have encounters with one another. We are interested in detecting the encounters of traveling individuals at the exact moment in which each of them occur. A simple solution is to use a nearest neighbor search to return potential encounters, this results in slow query response times. To mine for encounters in real-time, we introduce a new algorithm that is efficient in capturing encounters by exploiting the observation that just the neighbors in a defined proximity needs to be maintained. Our evaluation demonstrates that our proposed method mines for encounters for millions of individuals in a city area within milliseconds.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88586673","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}
Payam Tabrizian, A. Petrasova, B. Harmon, V. Petras, H. Mitásová, R. Meentemeyer
Tangible Landscape is a tangible interface for geographic information systems (GIS). It interactively couples physical and digital models of a landscape so that users can intuitively explore, model, and analyze geospatial data in a collaborative environment. Conceptually Tangible Landscape lets users hold a GIS in their hands so that they can feel the shape of the topography, naturally sculpt new landforms, and interact with simulations like water flow. Since it only affords a bird's-eye view of the landscape, we coupled it with an immersive virtual environment so that users can virtually walk around the modeled landscape and visualize it at a human-scale. Now as users shape topography, draw trees, define viewpoints, or route a walkthrough, they can see the results on the projection-augmented model, rendered on a display, or rendered on a head-mounted display. In this paper we present the Tangible Landscape Immersive Extension, describe its physical setup and software architecture, and demonstrate its features with a case study.
{"title":"Immersive tangible geospatial modeling","authors":"Payam Tabrizian, A. Petrasova, B. Harmon, V. Petras, H. Mitásová, R. Meentemeyer","doi":"10.1145/2996913.2996950","DOIUrl":"https://doi.org/10.1145/2996913.2996950","url":null,"abstract":"Tangible Landscape is a tangible interface for geographic information systems (GIS). It interactively couples physical and digital models of a landscape so that users can intuitively explore, model, and analyze geospatial data in a collaborative environment. Conceptually Tangible Landscape lets users hold a GIS in their hands so that they can feel the shape of the topography, naturally sculpt new landforms, and interact with simulations like water flow. Since it only affords a bird's-eye view of the landscape, we coupled it with an immersive virtual environment so that users can virtually walk around the modeled landscape and visualize it at a human-scale. Now as users shape topography, draw trees, define viewpoints, or route a walkthrough, they can see the results on the projection-augmented model, rendered on a display, or rendered on a head-mounted display. In this paper we present the Tangible Landscape Immersive Extension, describe its physical setup and software architecture, and demonstrate its features with a case study.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80664204","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}
A. Magdy, Ahmed M. Aly, M. Mokbel, S. Elnikety, Yuxiong He, Suman Nath, Walid G. Aref
This paper presents GeoTrend; a system for scalable support of spatial trend discovery on recent microblogs, e.g., tweets and online reviews, that come in real time. GeoTrend is distinguished from existing techniques in three aspects: (1) It discovers trends in arbitrary spatial regions, e.g., city blocks. (2) It supports trending measures that effectively capture trending items under a variety of definitions that suit different applications. (3) It promotes recent microblogs as first-class citizens and optimizes its system components to digest a continuous flow of fast data in main-memory while removing old data efficiently. GeoTrend queries are top-k queries that discover the most trending k keywords that are posted within an arbitrary spatial region and during the last T time units. To support its queries efficiently, GeoTrend employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In case of peak times, a main-memory optimization technique is employed to shed less important data, so that the system still sustains high query accuracy with limited memory resources. Experimental results based on real Twitter feed and Bing Mobile spatial search queries show the scalability of GeoTrend to support arrival rates of up to 50,000 microblog/second, average query latency of 3 milli-seconds, and at least 90+% query accuracy even under limited memory resources.
{"title":"GeoTrend: spatial trending queries on real-time microblogs","authors":"A. Magdy, Ahmed M. Aly, M. Mokbel, S. Elnikety, Yuxiong He, Suman Nath, Walid G. Aref","doi":"10.1145/2996913.2996986","DOIUrl":"https://doi.org/10.1145/2996913.2996986","url":null,"abstract":"This paper presents GeoTrend; a system for scalable support of spatial trend discovery on recent microblogs, e.g., tweets and online reviews, that come in real time. GeoTrend is distinguished from existing techniques in three aspects: (1) It discovers trends in arbitrary spatial regions, e.g., city blocks. (2) It supports trending measures that effectively capture trending items under a variety of definitions that suit different applications. (3) It promotes recent microblogs as first-class citizens and optimizes its system components to digest a continuous flow of fast data in main-memory while removing old data efficiently. GeoTrend queries are top-k queries that discover the most trending k keywords that are posted within an arbitrary spatial region and during the last T time units. To support its queries efficiently, GeoTrend employs an in-memory spatial index that is able to efficiently digest incoming data and expire data that is beyond the last T time units. The index also materializes top-k keywords in different spatial regions so that incoming queries can be processed with low latency. In case of peak times, a main-memory optimization technique is employed to shed less important data, so that the system still sustains high query accuracy with limited memory resources. Experimental results based on real Twitter feed and Bing Mobile spatial search queries show the scalability of GeoTrend to support arrival rates of up to 50,000 microblog/second, average query latency of 3 milli-seconds, and at least 90+% query accuracy even under limited memory resources.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80028767","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 analysis of Twitter data can help to predict or explain many real world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In this paper, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results. The algorithms are evaluated on a Twitter dataset with 27,956,257 tweets.
{"title":"Spatiotemporal topic association detection on tweets","authors":"Zhi Liu, Yan Huang, Joshua R. Trampier","doi":"10.1145/2996913.2996933","DOIUrl":"https://doi.org/10.1145/2996913.2996933","url":null,"abstract":"The analysis of Twitter data can help to predict or explain many real world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In this paper, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results. The algorithms are evaluated on a Twitter dataset with 27,956,257 tweets.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80303387","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 propose a method for modeling the topology of swarm behavior in a manner which facilitates the application of machine learning techniques such as clustering. This is achieved by modeling the persistence of topological features, such as connected components and holes, of the swarm with respect to time using zig-zag persistent homology. The output of this model is subsequently transformed into a representation known as a persistence landscape. This representation forms a vector space and therefore facilitates the application of machine learning techniques. The proposed model is validated using a real data set corresponding to a swarm of 300 fish. We demonstrate that it may be used to perform clustering of swarm behavior with respect to topological features.
{"title":"Spatio-temporal modeling of the topology of swarm behavior with persistence landscapes","authors":"P. Corcoran, Christopher B. Jones","doi":"10.1145/2996913.2996949","DOIUrl":"https://doi.org/10.1145/2996913.2996949","url":null,"abstract":"We propose a method for modeling the topology of swarm behavior in a manner which facilitates the application of machine learning techniques such as clustering. This is achieved by modeling the persistence of topological features, such as connected components and holes, of the swarm with respect to time using zig-zag persistent homology. The output of this model is subsequently transformed into a representation known as a persistence landscape. This representation forms a vector space and therefore facilitates the application of machine learning techniques. The proposed model is validated using a real data set corresponding to a swarm of 300 fish. We demonstrate that it may be used to perform clustering of swarm behavior with respect to topological features.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79057679","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}
Mid-level disasters that frequently occur, such as typhoons and earthquakes, heavily affect human activities in urban areas by causing severe congestion and economic loss. Predicting the irregular movement of individuals following such disasters is crucial for managing urban systems. Past survey results show that mid-level disasters do not force many individuals to evacuate away from their homes, but do cause irregular movement by significantly delaying the movement timings, resulting in severe congestion in urban transportation. We propose a novel method that predicts such irregularity of individuals' movements in several mid-level disasters using various types of features including the victims' usual movement patterns, disaster information, and geospatial information of victims' locations. Using real GPS data of 1 million people in Tokyo, we show that our method can predict mobility delay with high accuracy,
{"title":"Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones","authors":"T. Yabe, K. Tsubouchi, Akihito Sudo, Y. Sekimoto","doi":"10.1145/2996913.2996929","DOIUrl":"https://doi.org/10.1145/2996913.2996929","url":null,"abstract":"Mid-level disasters that frequently occur, such as typhoons and earthquakes, heavily affect human activities in urban areas by causing severe congestion and economic loss. Predicting the irregular movement of individuals following such disasters is crucial for managing urban systems. Past survey results show that mid-level disasters do not force many individuals to evacuate away from their homes, but do cause irregular movement by significantly delaying the movement timings, resulting in severe congestion in urban transportation. We propose a novel method that predicts such irregularity of individuals' movements in several mid-level disasters using various types of features including the victims' usual movement patterns, disaster information, and geospatial information of victims' locations. Using real GPS data of 1 million people in Tokyo, we show that our method can predict mobility delay with high accuracy,","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88055478","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}
Knowing the location of a train is necessary for the development of useful services for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate, especially on a subway. This paper proposes an online algorithm for localization on a subway using only a barometer. We estimate the motion state from the change of elevation, then estimate the last station stopped at using the similarity of a series of elevations recorded when the train stopped to the actual elevations of the stations. We evaluated the proposed method using data from the subway in Tokyo. We also developed a mobile application to demonstrate the proposed method.
{"title":"An online localization method for a subway train utilizing the barometer on a smartphone","authors":"S. Hyuga, Masaki Ito, M. Iwai, K. Sezaki","doi":"10.1145/2996913.2996999","DOIUrl":"https://doi.org/10.1145/2996913.2996999","url":null,"abstract":"Knowing the location of a train is necessary for the development of useful services for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate, especially on a subway. This paper proposes an online algorithm for localization on a subway using only a barometer. We estimate the motion state from the change of elevation, then estimate the last station stopped at using the similarity of a series of elevations recorded when the train stopped to the actual elevations of the stations. We evaluated the proposed method using data from the subway in Tokyo. We also developed a mobile application to demonstrate the proposed method.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89775322","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}
A. Masri, K. Zeitouni, Zoubida Kedad, Bertrand Leroy
Integrating transportation data is a key issue to provide passengers with optimized and more suitable trips that combines multiple transportation modes. Current integration solutions in the transportation domain mostly rely on experts knowledge and manual matching tasks. Besides, existing automatic matching solutions do not exploit the geospatial features of the data. This demo introduces an instance based system to identify geospatial properties and match transportation points of transfers using geocoding services as mediators.
{"title":"Automatic detection and matching of geospatial properties in transportation data sources (demo paper)","authors":"A. Masri, K. Zeitouni, Zoubida Kedad, Bertrand Leroy","doi":"10.1145/2996913.2996959","DOIUrl":"https://doi.org/10.1145/2996913.2996959","url":null,"abstract":"Integrating transportation data is a key issue to provide passengers with optimized and more suitable trips that combines multiple transportation modes. Current integration solutions in the transportation domain mostly rely on experts knowledge and manual matching tasks. Besides, existing automatic matching solutions do not exploit the geospatial features of the data. This demo introduces an instance based system to identify geospatial properties and match transportation points of transfers using geocoding services as mediators.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73762677","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}
Mark McKenney, Niharika Nyalakonda, Jarrod McEvers, Mitchell Shipton
The Pyspatiotemporalgeom library is a pure-python library implementing spatial data types, spatiotemporal data types for moving regions, and operations to create and analyze those types. The library is available on the Python Package Index (PyPI) and has been downloaded over 18,000 times since its release. In this paper, we demonstrate mechanisms to create random spatial data and perform operations over them. We then show how to create moving regions from existing data, and demonstrate aggregate operations over moving regions.
{"title":"Pyspatiotemporalgeom: a python library for spatiotemporal types and operations","authors":"Mark McKenney, Niharika Nyalakonda, Jarrod McEvers, Mitchell Shipton","doi":"10.1145/2996913.2996973","DOIUrl":"https://doi.org/10.1145/2996913.2996973","url":null,"abstract":"The Pyspatiotemporalgeom library is a pure-python library implementing spatial data types, spatiotemporal data types for moving regions, and operations to create and analyze those types. The library is available on the Python Package Index (PyPI) and has been downloaded over 18,000 times since its release. In this paper, we demonstrate mechanisms to create random spatial data and perform operations over them. We then show how to create moving regions from existing data, and demonstrate aggregate operations over moving regions.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73695617","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}
Some analytic queries on road networks, usually concentrating in a local area spanning several cities, need a high-throughput solution such as performing millions of shortest distance computations per second. However, most existing solutions achieve less than 5, 000 shortest distance computations per second per machine even with multi-threads. We demonstrate a solution, termed City Distance Oracles (CDO), using our previously developed ε-distance oracle to achieve as many as 7 million shortest distance computations per second per commodity machine on a city road network, i.e., 10K × 10K origin-distance (OD) matrix can be finished in 14 seconds.
{"title":"CDO: extremely high-throughput road distance computations on city road networks","authors":"Shangfu Peng, H. Samet","doi":"10.1145/2996913.2996921","DOIUrl":"https://doi.org/10.1145/2996913.2996921","url":null,"abstract":"Some analytic queries on road networks, usually concentrating in a local area spanning several cities, need a high-throughput solution such as performing millions of shortest distance computations per second. However, most existing solutions achieve less than 5, 000 shortest distance computations per second per machine even with multi-threads. We demonstrate a solution, termed City Distance Oracles (CDO), using our previously developed ε-distance oracle to achieve as many as 7 million shortest distance computations per second per commodity machine on a city road network, i.e., 10K × 10K origin-distance (OD) matrix can be finished in 14 seconds.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76111647","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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