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":null,"pages":null},"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}
Xun Zhou, Amin Vahedian Khezerlou, A. Liu, M. Shafiq, Fan Zhang
Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events (edge) problem aims to discover and localize a set of most likely gathering events. It is important for city planners to identify emerging gathering events which might cause public safety or sustainability concerns. However, it is challenging to solve the edge problem due to numerous candidate gathering footprints in a spatial field and the non-trivial task to balance pattern quality and computational efficiency. Prior solutions to model the edge problem lack the ability to describe the dynamic flow of traffic and the potential gathering destinations because they rely on static or undirected footprints. In contrast, in this paper, we model the footprint of a gathering event as a Gathering directed acyclic Graph (G-Graph), where the root of the G-Graph is the potential destination and the directed edges represent the most likely paths traffic takes to move towards the destination. We also proposed an efficient algorithm called SmartEdge to discover the most likely non-overlapping G-Graphs in the given spatial field. Our analysis shows that the proposed G-Graph model and the SmartEdge algorithm have the ability to efficiently and effectively capture important gathering events from real-world human mobility data. Our experimental evaluations show that SmartEdge saves 50% computation time over the baseline algorithm.
{"title":"A traffic flow approach to early detection of gathering events","authors":"Xun Zhou, Amin Vahedian Khezerlou, A. Liu, M. Shafiq, Fan Zhang","doi":"10.1145/2996913.2996998","DOIUrl":"https://doi.org/10.1145/2996913.2996998","url":null,"abstract":"Given a spatial field and the traffic flow between neighboring locations, the early detection of gathering events (edge) problem aims to discover and localize a set of most likely gathering events. It is important for city planners to identify emerging gathering events which might cause public safety or sustainability concerns. However, it is challenging to solve the edge problem due to numerous candidate gathering footprints in a spatial field and the non-trivial task to balance pattern quality and computational efficiency. Prior solutions to model the edge problem lack the ability to describe the dynamic flow of traffic and the potential gathering destinations because they rely on static or undirected footprints. In contrast, in this paper, we model the footprint of a gathering event as a Gathering directed acyclic Graph (G-Graph), where the root of the G-Graph is the potential destination and the directed edges represent the most likely paths traffic takes to move towards the destination. We also proposed an efficient algorithm called SmartEdge to discover the most likely non-overlapping G-Graphs in the given spatial field. Our analysis shows that the proposed G-Graph model and the SmartEdge algorithm have the ability to efficiently and effectively capture important gathering events from real-world human mobility data. Our experimental evaluations show that SmartEdge saves 50% computation time over the baseline algorithm.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82618484","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":null,"pages":null},"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}
Michael Matheny, Raghvendra Singh, L. Zhang, Kaiqiang Wang, J. M. Phillips
Finding anomalous regions within spatial data sets is a central task for biosurveillance, homeland security, policy making, and many other important areas. These communities have mainly settled on spatial scan statistics as a rigorous way to discover regions where a measured quantity (e.g., crime) is statistically significant in its difference from a baseline population. However, most common approaches are inefficient and thus, can only be run with very modest data sizes (a few thousand data points) or make assumptions on the geographic distributions of the data. We address these challenges by designing, exploring, and analyzing sample-then-scan algorithms. These algorithms randomly sample data at two scales, one to define regions and the other to approximate the counts in these regions. Our experiments demonstrate that these algorithms are efficient and accurate independent of the size of the original data set, and our analysis explains why this is the case. For the first time, these sample-then-scan algorithms allow spatial scan statistics to run on a million or more data points without making assumptions on the spatial distribution of the data. Moreover, our experiments and analysis give insight into when it is appropriate to trust the various types of spatial anomalies when the data is modeled as a random sample from a larger but unknown data set.
{"title":"Scalable spatial scan statistics through sampling","authors":"Michael Matheny, Raghvendra Singh, L. Zhang, Kaiqiang Wang, J. M. Phillips","doi":"10.1145/2996913.2996939","DOIUrl":"https://doi.org/10.1145/2996913.2996939","url":null,"abstract":"Finding anomalous regions within spatial data sets is a central task for biosurveillance, homeland security, policy making, and many other important areas. These communities have mainly settled on spatial scan statistics as a rigorous way to discover regions where a measured quantity (e.g., crime) is statistically significant in its difference from a baseline population. However, most common approaches are inefficient and thus, can only be run with very modest data sizes (a few thousand data points) or make assumptions on the geographic distributions of the data. We address these challenges by designing, exploring, and analyzing sample-then-scan algorithms. These algorithms randomly sample data at two scales, one to define regions and the other to approximate the counts in these regions. Our experiments demonstrate that these algorithms are efficient and accurate independent of the size of the original data set, and our analysis explains why this is the case. For the first time, these sample-then-scan algorithms allow spatial scan statistics to run on a million or more data points without making assumptions on the spatial distribution of the data. Moreover, our experiments and analysis give insight into when it is appropriate to trust the various types of spatial anomalies when the data is modeled as a random sample from a larger but unknown data set.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75830233","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}
Samuli Hemminki, Keisuke Kuribayashi, S. Konomi, P. Nurmi, S. Tarkoma
We propose crowd replication as a low-effort, easy to implement and cost-effective mechanism for quantifying the uses, activities, and sociability of public spaces. Crowd replication combines mobile sensing, direct observation, and mathematical modeling to enable resource efficient and accurate quantification of public spaces. The core idea behind crowd replication is to instrument the researcher investigating a public space with sensors embedded on commodity devices and to engage him/her into imitation of people using the space. By combining the collected sensor data with a direct observations and population model, individual sensor traces can be generalized to capture the behavior of a larger population. We validate the use of crowd replication as a data collection mechanism through a field study conducted within an exemplary metropolitan urban space. Results of our evaluation show that crowd replication accurately captures real human dynamics (0.914 correlation between indicators estimated from crowd replication and visual surveillance) and captures data that is representative of the behavior of people within the public space.
{"title":"Quantitative evaluation of public spaces using crowd replication","authors":"Samuli Hemminki, Keisuke Kuribayashi, S. Konomi, P. Nurmi, S. Tarkoma","doi":"10.1145/2996913.2996946","DOIUrl":"https://doi.org/10.1145/2996913.2996946","url":null,"abstract":"We propose crowd replication as a low-effort, easy to implement and cost-effective mechanism for quantifying the uses, activities, and sociability of public spaces. Crowd replication combines mobile sensing, direct observation, and mathematical modeling to enable resource efficient and accurate quantification of public spaces. The core idea behind crowd replication is to instrument the researcher investigating a public space with sensors embedded on commodity devices and to engage him/her into imitation of people using the space. By combining the collected sensor data with a direct observations and population model, individual sensor traces can be generalized to capture the behavior of a larger population. We validate the use of crowd replication as a data collection mechanism through a field study conducted within an exemplary metropolitan urban space. Results of our evaluation show that crowd replication accurately captures real human dynamics (0.914 correlation between indicators estimated from crowd replication and visual surveillance) and captures data that is representative of the behavior of people within the public space.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79823991","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":null,"pages":null},"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}
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":null,"pages":null},"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}
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":null,"pages":null},"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}
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":null,"pages":null},"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}
Mohamed H. Ali, S. Newsam, S. Ravada, M. Renz, Goce Trajcevski
These proceedings contain the papers from the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), held in the San Francisco Bay Area, California, USA, October 31 through November 3, 2016. The conference started as a series of symposia and workshops back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces and visualization, to data storage, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).
{"title":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","authors":"Mohamed H. Ali, S. Newsam, S. Ravada, M. Renz, Goce Trajcevski","doi":"10.1145/2996913","DOIUrl":"https://doi.org/10.1145/2996913","url":null,"abstract":"These proceedings contain the papers from the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2016), held in the San Francisco Bay Area, California, USA, October 31 through November 3, 2016. The conference started as a series of symposia and workshops back in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. It provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces and visualization, to data storage, query processing, indexing and data mining. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80071580","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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