Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems最新文献

Spatial trajectories segmentation: trends and challenges 空间轨迹分割:趋势与挑战

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3007201

M. Damiani

{"title":"Spatial trajectories segmentation: trends and challenges","authors":"M. Damiani","doi":"10.1145/3004725.3007201","DOIUrl":"https://doi.org/10.1145/3004725.3007201","url":null,"abstract":"Given a sequence S of temporally ordered observations, non necessarily of spatial nature, the segmentation task partitions S in a set of disjoint sub-sequences si, .., sn - the segments - such that ∪i∈[1, n] si = S. Typically, segments represents sub-sequences that are somehow homogeneous with respect to some criteria. Depending on the context and the nature of observations, segments can be given an approximated representation, for example segments can be assigned a descriptive label or one of the data points is chosen as representative of the whole sub-sequence. The final result is a summarized representation of the sequence. This simple and intuitive mechanism has been extensively studied in literature, for example, for the summarization of time series. Interestingly, the notion of segment is also at the basis of the most recent trajectory data models. For example, segments are the informative units in the semantic trajectories, where they are called episodes. Episodes are spatial sub-trajectories that can be semantically annotated using application-dependent descriptions, e.g. place names [1]. Similarly the recent symbolic trajectory data model [2] describes the individual movement as a sequence of temporally annotated labeled states s1, ..sn, where each state si is associated with a time interval. Beyond data modeling, segmentation can be employed for the indexing of trajectories in moving object databases while another major role is to support data analysis, especially for the extraction of individual mobility patterns. The concept of trajectory segment is thus emerging as shared and perhaps unifying concept across data modeling, indexing and analysis.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"134 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114090894","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

A low-dimensional feature vector representation for alignment-free spatial trajectory analysis 无对齐空间轨迹分析的低维特征向量表示

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004733

M. Werner, Marie Kiermeier

{"title":"A low-dimensional feature vector representation for alignment-free spatial trajectory analysis","authors":"M. Werner, Marie Kiermeier","doi":"10.1145/3004725.3004733","DOIUrl":"https://doi.org/10.1145/3004725.3004733","url":null,"abstract":"Trajectory analysis is a central problem in the era of big data due to numerous interconnected mobile devices generating unprecedented amounts of spatio-temporal trajectories. Unfortunately, datasets of spatial trajectories are quite difficult to analyse because of the computational complexity of the various existing distance measures. A significant amount of work in comparing two trajectories stems from calculating temporal alignments of the involved spatial points. With this paper, we propose an alignment-free method of representing spatial trajectories using low-dimensional feature vectors by summarizing the combinatorics of shape-derived string sequences. Therefore, we propose to translate trajectories into strings describing the evolving shape of each trajectory, and then provide a sparse matrix representation of these strings using frequencies of adjacencies of characters (n-grams). The final feature vectors are constructed by approximating this matrix with low-dimensional column space using singular value decomposition. New trajectories can be projected into this geometry for comparison. We show that this construction leads to low-dimensional feature vectors with surprising expressive power. We illustrate the usefulness of this approach in various datasets.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124715859","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

Temporally enhanced network-constrained (TENC) R-tree 时间增强型网络约束r树

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004736

M. Fouladgar, R. Elmasri

{"title":"Temporally enhanced network-constrained (TENC) R-tree","authors":"M. Fouladgar, R. Elmasri","doi":"10.1145/3004725.3004736","DOIUrl":"https://doi.org/10.1145/3004725.3004736","url":null,"abstract":"This paper describes a new Network-constrained Moving objects indexing structure, which extends the state-of-the-art for this kind of data. The indexing structure we propose is called Temporally Enhanced Network-Constrained R-tree (TENC R-tree), which solves the shortcomings in other Network-Constrained access methods like the FNR-tree [7], MON-tree [1] and UTR-tree. These existing indexing methods are designed to store and retrieve the moving objects based on spatial features, followed by their temporal ones. They are generally not efficient when a query has only temporal constraints, or when a specific moving object id is also part of the query conditions. In such cases, existing methods have to scan the entire database to retrieve the result. Furthermore, the aforementioned methods are not efficient in processing Strict-path query, which is a query type that retrieves trajectories that follow all the edges in the queried path [10]. Our proposed TENC R-tree index allows good performance for almost all types of queries on moving objects in a constrained network, whether the constraints are spatial, temporal, or based on object id. Also, the TENC R-tree out-performs other access methods on the case of Path queries. Our experiments show the performance has been improved by 10 to 100 times for such queries.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132338919","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

Recommending most popular travel path within a region of interest from historical trajectory data 根据历史轨迹数据，在感兴趣的地区推荐最受欢迎的旅行路线

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004728

Samia Shafique, Mohammed Eunus Ali

{"title":"Recommending most popular travel path within a region of interest from historical trajectory data","authors":"Samia Shafique, Mohammed Eunus Ali","doi":"10.1145/3004725.3004728","DOIUrl":"https://doi.org/10.1145/3004725.3004728","url":null,"abstract":"Advancement in mobile and GPS technologies have enabled users to record and publish their route activities or trajectories through location based social networking sites. Existing research mainly focus on finding popular routes and recommending suitable routes for the users based on the historical movements of users between different Point of Interests (POIs). However, users often spend most of their time around different POIs (e.g., Colosseo) and less time traveling between POIs. Thus, existing methods fail to capture the detailed movement of users around a POI, which we call Region of Interest (ROI). A major challenge of identifying patterns of routes inside an ROI comes from the inaccurate and incomplete data of user trajectories. In this paper we propose a novel technique to find the most popular path within an ROI from historical trajectory data by rephrasing trajectories into smaller part and eliminating noisy points from trajectories. We then devise an algorithm to produce the most popular path inside each ROI. We perform experiments on a real dataset extracted from Flickr to show the effectiveness of our approach.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794562","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: 12

Taxi cab service optimization using spatio-temporal implementation to hot-spot analysis with taxi trajectories: a case study in Seoul, Korea 出租车服务优化的时空实现与出租车轨迹热点分析:以韩国首尔为例

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004732

S. Yun, Sanghyun Yoon, Sungha Ju, Won Seob Oh, J. Ma, J. Heo

{"title":"Taxi cab service optimization using spatio-temporal implementation to hot-spot analysis with taxi trajectories: a case study in Seoul, Korea","authors":"S. Yun, Sanghyun Yoon, Sungha Ju, Won Seob Oh, J. Ma, J. Heo","doi":"10.1145/3004725.3004732","DOIUrl":"https://doi.org/10.1145/3004725.3004732","url":null,"abstract":"Currently there are demands for maximization of taxi services and also for saving fuel usage within massive cities. Spatial big data extracted from taxi service records and GPS can be used to suggest optimal routing options to achieve these goals. The taxi cab ride data contains 7,000 unique taxies being serviced in Seoul, South Korea. In this study one week worth of data with the size of 3.13GB were used. Also road network data provided by Ministry of Land, Infrastructure and Transport (MOLIT), which contains 19,229 nodes and 22,192 links, and census map provided by Statistics Korea were used as base-map. Lastly floating population data of Seoul city area, gathered with mobile phones, has been used as an index of demand for taxi service. By using taxi cab ride data, which contains trajectory with time and 2D coordinates, and information about whether passenger is on the taxi or not, hot spots were analyzed for 1) taxies without passengers whom are available to pick-up passengers, 2) places where people are experiencing difficulty hailing a taxi due to high demand for taxi. Combination of these two types of hot spots can provide new insight for both public and commercial sectors to maximize the efficiency of taxi service and to reduce idle fuel usage. Afterwards the floating population data is used to provide indices for taxi usage in Seoul area, providing further insights. Utilizing the time stamp records on the taxi GPS data, hourly based hot spots for both 'demand' and 'supply' for taxi cab ride can be derived, and this outcome can be practically used to guide taxi drivers to high demanding places and avoid high supplying places.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131835035","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

System architecture of cloud-based web GIS for real-time macroeconomic loss estimation 基于云的web GIS实时宏观经济损失估算系统架构

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004731

R. Nourjou, Joel Thomas

{"title":"System architecture of cloud-based web GIS for real-time macroeconomic loss estimation","authors":"R. Nourjou, Joel Thomas","doi":"10.1145/3004725.3004731","DOIUrl":"https://doi.org/10.1145/3004725.3004731","url":null,"abstract":"This paper presents a system architecture of a web GIS that is used to develop a web mapping app for real-time macroeconomic impact decision support tool. It incorporates web GIS on the cloud with an autonomous software system for real-time situational awareness (outage statue and economic loss) from power & electric utilities. Our web GIS is a system of systems, and we deployed ESRI's ArcGIS platform, Amazon Web Services (AWS), enterprise spatial database, C#, RESTful API, and JSON format. The system implementation results in a web GIS that contains a GIS server with a set of REST APIs of GIS web services (map service, geodata service, etc) on the cloud that can be used by web mapping apps, mobile GIS apps, or desktop programs to share, display, analyze, and update a geodatabase, which is embedded in cloud. To evaluate our approach, we developed a web map application and an operations dashboard that used the created GIS web services and APIs. Our web GIS is applicable for the \"Internet of Things\" domain, public safety, cloud communication, crisis response, web map application, location-based services, and real-time GIS.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121388286","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

Pedestrian flow detection using Bluetooth for evacuation route finding 利用蓝牙检测行人流量，寻找疏散路线

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004726

Miku Hoshino, Masaki Ito, K. Sezaki

引用次数: 5

Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network 轻型道路管理器:基于智能手机的深度神经网络自动判断道路损伤状态

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004729

Hiroya Maeda, Y. Sekimoto, Toshikazu Seto

{"title":"Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network","authors":"Hiroya Maeda, Y. Sekimoto, Toshikazu Seto","doi":"10.1145/3004725.3004729","DOIUrl":"https://doi.org/10.1145/3004725.3004729","url":null,"abstract":"Citizens in various locations can report local infrastructure issues to the government by posting reports on certain websites, such as Chiba Report and FixMyStreet. Recently, these systems have begun operating worldwide. In these systems, a large volume of information is collected on infrastructure problems that are identified by citizens (e.g., broken paving slabs, fly tipping, graffiti, potholes). This information is expected to be utilized for infrastructure maintenance. However, local problems (especially road defection) identified by citizens are sometimes not deemed an urgent matter for road managers. This is because it is difficult for an average person to determine road damage status. Furthermore, non-critical reports may be a burden for local government because each report requires visual confirmation. We therefore propose a smartphone application based on a deep neural network that can determine road damage status using only photographs of the road. This application is based on a deep neural network model trained by citizen reports and road manager inspection results, which are gathered daily on a government server. The application updates the model parameters each time it launches and thereby becomes increasingly more intelligent and effective. The proposed system enables average citizens to easily determine road damage status using only a smartphone application. In addition, because not only expert road managers but also local government officials without expert knowledge can inspect the road, the proposed system can be useful for local governments that lack expert road managers.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121644191","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: 29

Deviation maps: enhancing robustness and predictability of indoor positioning systems 偏差图:增强室内定位系统的鲁棒性和可预测性

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004727

H. Blunck, Sylvie Temme, J. Vahrenhold

引用次数: 1

Capturing complex behaviour for predicting distant future trajectories 捕捉复杂的行为以预测遥远的未来轨迹

Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/3004725.3004730

B. Chapuis, A. Moro, Vaibhav Kulkarni, B. Garbinato

{"title":"Capturing complex behaviour for predicting distant future trajectories","authors":"B. Chapuis, A. Moro, Vaibhav Kulkarni, B. Garbinato","doi":"10.1145/3004725.3004730","DOIUrl":"https://doi.org/10.1145/3004725.3004730","url":null,"abstract":"We put forth a system, to predict distant-future positions of multiple moving entities and index the forecasted trajectories, in order to answer predictive queries involving long time horizons. Today, the proliferation of mobile devices with GPS functionality and internet connectivity has led to a rapid development of location-based services, accounting for user mobility prediction as a key paradigm. Mobility prediction is already playing a major role in traffic management, urban planning and location-based advertising, which demand accurate and long time horizon forecasting of user movements. Existing prediction methodologies either use motion patterns or techniques based on frequently visited places for predicting the next move. However, when it comes to distant-future, human mobility is too complex to be represented by such statistical functions. Therefore, the existing techniques are not well suited to answer distant-future queries with a satisfactory level of accuracy. To tackle this problem, we introduce a novel spatial object, 'Representative Trajectory', which embodies the movements of users amongst their zones of interest. We propose means to empirically evaluate the quality of this object and dynamically adapt its extraction method based on user mobility behaviour. We rely on an inverted index to store the predicted trajectories that scales well with the number of moving entities. Our evaluation results show that the technique achieves more than 70% accurate predictions with the best extraction technique. This shows that longer query time horizons do not necessarily demand complex spatial indexing schemes, which have to be rebalanced as they grow and which is a constantly experienced problem while answering predictive queries.","PeriodicalId":154980,"journal":{"name":"Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127385423","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