Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第8页

TOSCA: two-steps clustering algorithm for personal locations detection TOSCA:个人位置检测的两步聚类算法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820818

Riccardo Guidotti, R. Trasarti, M. Nanni

引用次数: 33

Trajectory inference for mobile devices using connected cell towers 使用连接的蜂窝塔的移动设备的轨迹推断

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820804

K. Perera, T. Bhattacharya, L. Kulik, J. Bailey

{"title":"Trajectory inference for mobile devices using connected cell towers","authors":"K. Perera, T. Bhattacharya, L. Kulik, J. Bailey","doi":"10.1145/2820783.2820804","DOIUrl":"https://doi.org/10.1145/2820783.2820804","url":null,"abstract":"Trajectory inference from raw location samples of a mobile device is an important task for many location based services, such as crowd sourced traffic monitoring, fleet management and personalized trip planning. This task becomes challenging when location samples are obtained only from the connected cell towers (GSM localization), instead of using other localization sensors such as GPS or Wi-Fi. Cell tower based localization consumes negligible energy compared to GPS or Wi-Fi and has high availability. However, it can have large inaccuracy, making the task of cellular trajectory mapping extremely challenging. In previous studies, cellular trajectory inference has been performed assuming the availability of knowledge of the cellular network or the signal strengths of the neighbouring cell towers. However, for a mobile application running on a user's device, this information may be hard to obtain and it may also require additional storage and computation costs. In this paper, we propose a novel cellular trajectory inference method which requires only the user's connected cell tower location, time and speed information. Exploiting the preciseness of the time dimension, we accurately compute the distance a user has travelled within a cell and use it to infer the straight line segments and turning points of a trajectory. We show that using the distance information of three consecutive cells, exact inference of the line segment is possible. Our method achieves high accuracy for trajectory inference in urban areas with high cell density and straight line road segments. It does not require any historical trajectory information or pre-training and incurs low storage and computation costs.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121386501","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}

引用次数: 16

Spatial queries with k-nearest-neighbor and relational predicates 具有k近邻和关系谓词的空间查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820815

Ahmed M. Aly, Walid G. Aref, M. Ouzzani

{"title":"Spatial queries with k-nearest-neighbor and relational predicates","authors":"Ahmed M. Aly, Walid G. Aref, M. Ouzzani","doi":"10.1145/2820783.2820815","DOIUrl":"https://doi.org/10.1145/2820783.2820815","url":null,"abstract":"The ubiquity of location-aware devices and smartphones has unleashed an unprecedented proliferation of location-based services that require processing queries with both spatial and relational predicates. Many algorithms and index structures already exist for processing k-Nearest-Neighbor (kNN, for short) predicates either solely or when combined with textual keyword search. Unfortunately, there has not been enough study on how to efficiently process queries where kNN predicates are combined with general relational predicates, i.e., ones that have selects, joins and group-by's. One major challenge is that because the kNN is a ranking operation, applying a relational predicate before or after a kNN predicate in a query evaluation pipeline (QEP, for short) can result in different outputs, and hence leads to different query semantics. In particular, this renders classical relational query optimization heuristics, e.g., pushing selects below joins, inapplicable. This paper presents various query optimization heuristics for queries that involve combinations of kNN select/join predicates and relational predicates. The proposed optimizations can significantly enhance the performance of these queries while preserving their semantics. Experimental results that are based on queries from the TPC-H benchmark and real spatial data from OpenStreetMap demonstrate that the proposed optimizations can achieve orders of magnitude enhancement in query performance.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788041","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}

引用次数: 7

Choosing thresholds for density-based map construction algorithms 选择基于密度的地图构建算法的阈值

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820810

M. Ahmed, Brittany Terese Fasy, Matt Gibson, C. Wenk

{"title":"Choosing thresholds for density-based map construction algorithms","authors":"M. Ahmed, Brittany Terese Fasy, Matt Gibson, C. Wenk","doi":"10.1145/2820783.2820810","DOIUrl":"https://doi.org/10.1145/2820783.2820810","url":null,"abstract":"Due to the ubiquitous use of various positioning technologies in smart phones and other devices, geospatial tracking data has become a routine data source. One of its uses that has gained recent popularity is the construction of street maps from vehicular tracking data. Due to the inherent noise in the data, many map construction algorithms are based on thresholding a density function. While kernel density estimation provides a firm theoretical foundation for computing the density from the measurements, the thresholds are generally picked in a heuristic, and often brute-force way, which results in slow algorithms with no guarantees on the map construction quality. In this paper, we formalize the selection of thresholds in a density-based street map construction algorithm. We propose a new thresholding technique that uses persistent homology combined with statistical analysis to determine a small set of thresholds that captures all relevant topological features. We formally prove that when the samples are drawn uniformly from the street map, a constant number of thresholds suffices to recover the street map. We also provide algorithms to compute the thresholds for different sampling assumptions. Finally, we show the effectiveness of our algorithms in several experiments on artificially generated data and on real GPS trajectory data.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129673100","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}

引用次数: 9

A reeb graph approach to tractography 牵引术的一种reeb图方法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820848

Jonathan Sun, M. Cieslak, Scott T. Grafton, S. Suri

引用次数: 2

An online marketplace for geosocial data 一个地理社交数据的在线市场

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820881

Y. Kanza, H. Samet

引用次数: 21

LIMO: learning programming using interactive map activities LIMO:使用交互式地图活动学习编程

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820796

Ruby Y. Tahboub, Jaewoo Shin, Aya Abdelsalam, Jalaleldeen W. Aref, Walid G. Aref, Sunil Prabhakar

引用次数: 1

Citywide traffic congestion estimation with social media 利用社交媒体估算全市交通拥堵

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820829

Senzhang Wang, Lifang He, Leon Stenneth, Philip S. Yu, Zhoujun Li

{"title":"Citywide traffic congestion estimation with social media","authors":"Senzhang Wang, Lifang He, Leon Stenneth, Philip S. Yu, Zhoujun Li","doi":"10.1145/2820783.2820829","DOIUrl":"https://doi.org/10.1145/2820783.2820829","url":null,"abstract":"Conventional traffic congestion estimation approaches require the deployment of traffic sensors or large-scale probe vehicles. The high cost of deploying and maintaining these equipments largely limits their spatial-temporal coverage. This paper proposes an alternative solution with lower cost and wider spatial coverage by exploring traffic related information from Twitter. By regarding each Twitter user as a traffic monitoring sensor, various real-time traffic information can be collected freely from each corner of the city. However, there are two major challenges for this problem. Firstly, the congestion related information extracted directly from real-time tweets are very sparse due both to the low resolution of geographic location mentioned in the tweets and the inherent sparsity nature of Twitter data. Secondly, the traffic event information coming from Twitter can be multi-typed including congestion, accident, road construction, etc. It is non-trivial to model the potential impacts of diverse traffic events on traffic congestion. We propose to enrich the sparse real-time tweets from two directions: 1) mining the spatial and temporal correlations of the road segments in congestion from historical data, and 2) applying auxiliary information including social events and road features for help. We finally propose a coupled matrix and tensor factorization model to effectively integrate rich information for Citywide Traffic Congestion Eestimation (CTCE). Extensive evaluations on Twitter data and 500 million public passenger buses GPS data on nearly 700 mile roads of Chicago demonstrate the efficiency and effectiveness of the proposed approach.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121935847","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}

引用次数: 68

Land use classification using convolutional neural networks applied to ground-level images 利用卷积神经网络对地面图像进行土地利用分类

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820851

Yi Zhu, S. Newsam

引用次数: 46

Semantic multimedia-enhanced spatio-temporal queries in a crowdsourced environment 众包环境下语义多媒体增强的时空查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820798

F. Rehman, A. Lbath, Imad Afyouni, Abdullah Murad, Mohamed Abdur Rahman, Bilal Sadiq, Akhlaq Ahmad, Saleh M. Basalamah

{"title":"Semantic multimedia-enhanced spatio-temporal queries in a crowdsourced environment","authors":"F. Rehman, A. Lbath, Imad Afyouni, Abdullah Murad, Mohamed Abdur Rahman, Bilal Sadiq, Akhlaq Ahmad, Saleh M. Basalamah","doi":"10.1145/2820783.2820798","DOIUrl":"https://doi.org/10.1145/2820783.2820798","url":null,"abstract":"Social networks, such as Facebook, Instagram, and Twitter, provide intuitive ways to share a variety of information including geotagged multimedia data within users' communities of interest (COI) or publicly in real-time. Real-time geotagged multimedia data can provide semantics to conventional spatial queries in order to enrich the user navigation experience. In this paper, we introduce a mechanism to process spatio-temporal queries by leveraging geotagged multimedia data such as images, audio, video, and text, in order to add semantics to the conventional queries. Our framework collects, stores, and spatially tags multimedia data shared by users through social networks or through our developed mobile application. The system then uses such data in order to enhance the conventional routing services by resolving existing usability issues and by providing semantics to the routes in terms of enriched points of interest while taking dynamic road conditions into account. A proof of concept of the system will be demonstrated with the following spatio-temporal queries on road networks: 1) multimedia-enhanced shortest path queries; 2) multimedia-enhanced k-nearest neighbor queries; and 3) multimedia-enhanced range queries. Finally, a novel technique for finding lost individuals using geotagged multimedia data is also introduced. The results are tailor-made to the users' smartphone bandwidth and resolution requirements","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121025189","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}

引用次数: 4