Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming最新文献

ST-CRF: A Novel Map Matching Approach for Low-frequency Floating Car Data ST-CRF:一种低频浮车数据地图匹配新方法

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833169

Xiliang Liu, F. Lu

{"title":"ST-CRF: A Novel Map Matching Approach for Low-frequency Floating Car Data","authors":"Xiliang Liu, F. Lu","doi":"10.1145/2833165.2833169","DOIUrl":"https://doi.org/10.1145/2833165.2833169","url":null,"abstract":"Integrating a raw GPS trajectory with spatial road networks is often referred to as the Map Matching problem. It's a fundamental component to support further analysis of intelligent transport systems. However, currently the occurrence of low-frequency trajectories (e.g. one point every 1-2 minutes) has brought lots of challenges to existing map matching algorithms. In this paper, we propose a novel global map-matching algorithm called ST-CRF based on the following insights: 1) the spatial positioning accuracy of GPS points as well as the topological information of the underlying road networks; 2) the spatial-temporal accessibility of a floating car; 3) the spatial distribution of the middle point between two consecutive GPS points; 4) the directional consistency of a GPS trajectory. Based on the spatial-temporal analysis, we construct a conditional random field (CRF) model and identify the best matching path sequence from all the candidate points. ST-CRF algorithm not only overcomes the long-existing \"label-bias\" problem of HMM-based models (e.g. ST-Matching, IVMM), but also performs more effective and robust based on a real trajectory dataset from Beijing. As a result, the ST-CRF algorithm outperforms the related models (Point-Line, ST-Matching, and IVMM).","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"252 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":"114898648","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: 3

MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833175

Dunstan Matekenya, Masaki Ito, Y. Tobe, R. Shibasaki, K. Sezaki

{"title":"MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data","authors":"Dunstan Matekenya, Masaki Ito, Y. Tobe, R. Shibasaki, K. Sezaki","doi":"10.1145/2833165.2833175","DOIUrl":"https://doi.org/10.1145/2833165.2833175","url":null,"abstract":"The ability to detect when a person change their place of residence in a city or country is vitally important not just for urban planning but also for business intelligence. Although there are traditional approaches such as population census to collect this type of data, they have serious drawbacks. Thanks to the ubiquity of mobile phones, researchers have demonstrated that data generated from cellular network such as Call Detailed Records(CDR) can provide similar information at a relatively lower cost and higher temporal resolution. In this paper, we investigate two research questions: first, whether we can reliably discover a person's residence change from unlabeled CDR data. Second, if we can develop an algorithm that can autamatically carry out this task. To this end, we first formulate the residence change discovery problem by learning from population census approach and then propose a sequential spatio-temporal clustering technique-MoveSense to solve this problem. We use a large scale CDR dataset with over 3.5 billion call records and 16 million unique users to conduct experiments to validate our technique. We find that across the three categories of test datasets, the technique performed well with average detection rate of 71 percent, 68 percent and 72 percent.","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"12 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":"121979729","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: 1

Spatio-temporal join technique for disaster estimation in large-scale natural disaster 大尺度自然灾害灾害估计的时空联接技术

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833171

H. Hayashi, A. Asahara, Natsuko Sugaya, Yuichi Ogawa, H. Tomita

引用次数: 2

REGULA: Utilizing the Regularity of Human Mobility for Location Recommendation REGULA:利用人类移动的规律性进行地点推荐

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833172

S. Mudda, S. Giordano

{"title":"REGULA: Utilizing the Regularity of Human Mobility for Location Recommendation","authors":"S. Mudda, S. Giordano","doi":"10.1145/2833165.2833172","DOIUrl":"https://doi.org/10.1145/2833165.2833172","url":null,"abstract":"In this paper, we address the problem of recommending new locations to the users of a Location Based Social Network (LBSN). LBSNs are social and physical information-rich networks that incorporate mobility patterns and social ties of humans. Most of the existing recommender systems are build on variants of graph-based techniques that utilize complete knowledge of location history and social ties of all users. Therefore, these recommender systems are computationally expensive for large scale LBSNs. Further, these systems do not take into account the mobility habits of humans. Recent studies on human mobility patterns have highlighted that people frequently visit a set of locations and go to places closer to them. In this paper, we validate the existence of these human mobility aspects in LBSN through the analysis of user check-in behavior and derive a set of observations. Further, we propose REGULA-- A location recommendation algorithm that exploits three behavior patterns of humans: 1) People regularly (or habitually) visit a set of locations 2) People go to places close to these regularly visited locations and 3) People are more likely to visit places that were recently visited by others like friends. Using these behavior patterns, REGULA minimizes the computational complexity by reducing the set of candidate locations to recommend. We evaluate the performance of REGULA by employing two large scale LBSN datasets: Gowalla and Brightkite. Based on our results, we show that REGULA outperforms existing state of the art recommendation algorithms for LBSNs while reducing the complexity.","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"68 6 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":"114563034","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

Vector Map Constrained Path Bundling in 3D Environments 三维环境中的矢量地图约束路径绑定

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833168

Matthias Thöny, R. Pajarola

{"title":"Vector Map Constrained Path Bundling in 3D Environments","authors":"Matthias Thöny, R. Pajarola","doi":"10.1145/2833165.2833168","DOIUrl":"https://doi.org/10.1145/2833165.2833168","url":null,"abstract":"Dense line graphs and polyline maps are challenging for interactive visualization in geographic information systems (GIS). Bundling techniques are a common approach to reduce clutter and have successfully been demonstrated for the display of complex planar graphs. Previous techniques typically employed some form of attraction or repulsion forces to bundle edges in two dimensions, and while in principle extensible to 3D they do not directly support hard intersection constraints in a 3D environment. In geographic visualization systems, e.g. such as interactive virtual globes or 3D GIS viewers, it is often necessary to take the 3D environment into account and to: (1) bundle lines and paths in 3D, (2) constrain path bundles to follow some reference network vector map, as well as (3) avoid intersections with the digital elevation model (DEM). In this paper we introduce a novel method which uses geographic vector map reference information to route, visualize and simplify path bundles along their network paths in a constrained 3D environment using adaptive B-splines. Moreover, we describe an efficient rendering architecture to flexibly display bundled paths within a 3D rendering pipeline at varying level of detail (LOD).","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"74 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":"128216041","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: 8

Component Moving Region Operations: Implementing Set Operations on Region Streams 组件移动区域操作:实现对区域流的集合操作

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833177

Mark McKenney, Rakeem Shelby, Sheetal Bagga

{"title":"Component Moving Region Operations: Implementing Set Operations on Region Streams","authors":"Mark McKenney, Rakeem Shelby, Sheetal Bagga","doi":"10.1145/2833165.2833177","DOIUrl":"https://doi.org/10.1145/2833165.2833177","url":null,"abstract":"Many natural phenomena are intuitively represented as spatiotemporal data objects, or moving objects. For example, vehicles, rivers, hurricanes, low pressure systems, areas of high density of foliage, etc align well with a geometric representation, and all change position or shape over time. Moving object models exist that represent real world objects as point, line, and region geometries that change continuously over time, leading to research into spatiotemporal analysis functionality over these objects. Models of moving objects are ideal for representing data streams that record the motion of spatial data over time. However, the implementation of operations to support spatiotemporal analysis over moving objects, particularly over moving regions, has proven difficult. In this paper, we develop a mechanism to support the implementation of the set operations of intersection, union, and difference between pairs of moving regions. The mechanism builds on the Component Model of Moving Regions and the semantic specifications of its operations. Specifically, we develop a generalized method of computing an intermediate data structure from which the results of various operations are then derived. The mechanism utilizes well-known 2D and 3D operational primitives and achieves O(n lg n) time complexity using appropriate data structures.","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"22 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":"123203004","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: 2

Semantics-Aware Warehousing of Symbolic Trajectories 符号轨迹的语义感知仓库

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833174

Goce Trajcevski, Ivana Donevska, A. Vaisman, Besim Avci, Tian Zhang, Di Tian

引用次数: 1

RendezView: Look at Meanings of an Encounter Region over Local Social Flocks renzview:在当地社会群体中观察相遇区域的意义

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833178

Kyoung-Sook Kim, Melissa Bica, I. Kojima, Hirotaka Ogawa

{"title":"RendezView: Look at Meanings of an Encounter Region over Local Social Flocks","authors":"Kyoung-Sook Kim, Melissa Bica, I. Kojima, Hirotaka Ogawa","doi":"10.1145/2833165.2833178","DOIUrl":"https://doi.org/10.1145/2833165.2833178","url":null,"abstract":"Social media data provide insight into people's opinions, thoughts, and reactions about real-world events such as hurricanes, infectious diseases, or urban crimes. In particular, the role of location-embedded social media is being emphasized to monitor surrounding situations and predict future effects by the geography of data shadows. However, it brings big challenges to find meaningful information about dynamic social phenomena from the mountains of fragmented, noisy data flooding. This paper proposes a data model to represent local flock phenomena as collective interests in geosocial streams and presents an interactive visual analysis process. In particular, we show a new visualization tool, called RendezView, composed of a three-dimensional map, word cloud, and Sankey flow diagram. RendezView allows a user to discern spatio-temporal and semantic contexts of local social flock phenomena and their co-occurrence relationships. An explanatory visual analysis of the proposed model is simulated by the experiments on a set of daily Twitter streams and shows the local patterns of social flocks with several visual results.","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","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":"128855839","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: 0

A Classification Schema for Fast Disambiguation of Spatial Prepositions 空间介词快速消歧的分类模式

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833167

André Dittrich, M. Vasardani, S. Winter, Timothy Baldwin, Fei Liu

{"title":"A Classification Schema for Fast Disambiguation of Spatial Prepositions","authors":"André Dittrich, M. Vasardani, S. Winter, Timothy Baldwin, Fei Liu","doi":"10.1145/2833165.2833167","DOIUrl":"https://doi.org/10.1145/2833165.2833167","url":null,"abstract":"In the field of Artificial Intelligence the task of spatial language understanding is a particularly complex one. Textual spatial information is frequently represented by so-called locative expressions, incorporating spatial prepositions. However, apart from the spatial domain, these prepositions can occur in a wide range of senses (e.g., temporal, manner, cause, instrument) as well as in semantically transformed senses (e.g., metaphors and metonymies). Existing practical approaches usually disregard semantic transformations or falsely classify them as spatial, although they represent the majority of cases. For the efficient extraction of locative expressions from data streams (e.g. from social media sources), a fast filter mechanism for this non-spatial information is needed. Hence, we present a classification schema to quickly and robustly disambiguate spatial from non-spatial uses of prepositions. We conduct an inter-annotator agreement test to highlight the feasibility and comprehensibility of our schema based on examples sourced from a large social media corpus. We further identify the most promising existing natural language processing tools in order to combine machine learning features with fixed rules.","PeriodicalId":264874,"journal":{"name":"Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming","volume":"28 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":"121372546","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: 10

A New Trajectory Similarity Measure for GPS Data 一种新的GPS数据轨迹相似度度量方法

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming Pub Date : 2015-11-03 DOI: 10.1145/2833165.2833173

Anas Ismail, A. Vigneron

引用次数: 15