Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第7页

Spatio-temporal modeling of the topology of swarm behavior with persistence landscapes 基于持续性景观的蜂群行为拓扑的时空建模

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996949

P. Corcoran, Christopher B. Jones

引用次数: 10

A traffic flow approach to early detection of gathering events 一种用于早期检测聚集事件的交通流方法

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996998

Xun Zhou, Amin Vahedian Khezerlou, A. Liu, M. Shafiq, Fan Zhang

{"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}

引用次数: 31

Predicting irregular individual movement following frequent mid-level disasters using location data from smartphones 利用智能手机的位置数据预测频繁的中级灾害后的不规则个人活动

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996929

T. Yabe, K. Tsubouchi, Akihito Sudo, Y. Sekimoto

引用次数: 11

Scalable spatial scan statistics through sampling 可扩展的空间扫描统计通过采样

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996939

Michael Matheny, Raghvendra Singh, L. Zhang, Kaiqiang Wang, J. M. Phillips

{"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}

引用次数: 13

Quantitative evaluation of public spaces using crowd replication 使用人群复制对公共空间进行定量评价

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996946

Samuli Hemminki, Keisuke Kuribayashi, S. Konomi, P. Nurmi, S. Tarkoma

{"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}

引用次数: 5

GeoTrend: spatial trending queries on real-time microblogs GeoTrend:实时微博上的空间趋势查询

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996986

A. Magdy, Ahmed M. Aly, M. Mokbel, S. Elnikety, Yuxiong He, Suman Nath, Walid G. Aref

{"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}

引用次数: 26

An online localization method for a subway train utilizing the barometer on a smartphone 利用智能手机上的气压计进行地铁列车在线定位的方法

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996999

S. Hyuga, Masaki Ito, M. Iwai, K. Sezaki

引用次数: 4

Pyspatiotemporalgeom: a python library for spatiotemporal types and operations Pyspatiotemporalgeom:一个用于时空类型和操作的python库

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996973

Mark McKenney, Niharika Nyalakonda, Jarrod McEvers, Mitchell Shipton

引用次数: 2

Automatic detection and matching of geospatial properties in transportation data sources (demo paper) 交通数据源中地理空间属性的自动检测和匹配(演示文件)

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996959

A. Masri, K. Zeitouni, Zoubida Kedad, Bertrand Leroy

引用次数: 0

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 第24届ACM SIGSPATIAL国际地理信息系统进展会议论文集

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913

Mohamed H. Ali, S. Newsam, S. Ravada, M. Renz, Goce Trajcevski

引用次数: 7