Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第6页

Finding non-dominated paths in uncertain road networks 在不确定路网中寻找非支配路径

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996964

Saad Aljubayrin, B. Yang, Christian S. Jensen, Rui Zhang

{"title":"Finding non-dominated paths in uncertain road networks","authors":"Saad Aljubayrin, B. Yang, Christian S. Jensen, Rui Zhang","doi":"10.1145/2996913.2996964","DOIUrl":"https://doi.org/10.1145/2996913.2996964","url":null,"abstract":"With the rapidly growing availability of vehicle trajectory data, travel costs such as travel time and fuel consumption can be captured accurately as distributions (e.g., travel time distributions) instead of deterministic values (e.g., average travel times). We study a new path finding problem in uncertain road networks, where paths have travel cost distributions. Given a source and a destination, we find optimal, non-dominated paths connecting the source and the destination, where the optimality is defined in terms of the stochastic dominance among cost distributions of paths. We first design an A based framework that utilizes the uncertain graph to obtain the most accurate cost distributions while finding the candidate paths. Next, we propose a three-stage dominance examination method that employs extreme values in each candidate path's cost distribution for early detection of dominated paths, thus reducing the need for expensive distributions convolutions. We conduct extensive experiments using real world road network and trajectory data. The results show that our algorithm outperforms baseline algorithms by up to two orders of magnitude in terms of query response time while achieving the most accurate results.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"301 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89036965","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: 21

Knowing without telling: integrating sensing and mapping for creating an artificial companion 不言而知:集成传感和绘图，创造一个人造伴侣

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996961

Marjan Alirezaie, Franziska Klügl-Frohnmeyer, A. Loutfi

引用次数: 1

Cleansing indoor RFID data using regular expressions 使用正则表达式清洗室内RFID数据

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996979

A. Baba, Hua Lu, Wei-Shinn Ku, T. Pedersen

引用次数: 2

Unsupervised annotated city traffic map generation 无监督标注城市交通地图生成

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996942

Rohit Verma, Surjya Ghosh, Aviral Shrivastava, Niloy Ganguly, Bivas Mitra, Sandip Chakraborty

引用次数: 8

Multi-user routing to single destination with confluence 多用户路由到具有合流的单个目的地

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2997018

Kazuki Takise, Yasuhito Asano, Masatoshi Yoshikawa

引用次数: 3

Scalable user assignment in power grids: a data driven approach 电网中可扩展的用户分配:数据驱动的方法

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996970

Bo Lyu, Shijian Li, Yanhua Li, Jie Fu, Andrew C. Trapp, Haiyong Xie, Yong Liao

{"title":"Scalable user assignment in power grids: a data driven approach","authors":"Bo Lyu, Shijian Li, Yanhua Li, Jie Fu, Andrew C. Trapp, Haiyong Xie, Yong Liao","doi":"10.1145/2996913.2996970","DOIUrl":"https://doi.org/10.1145/2996913.2996970","url":null,"abstract":"The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand << capacity) or under-supplied (demand ≈ capacity). In this paper, we make the first attempt to investigate the problem of power substation-user assignment by analyzing large-scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of our SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from a province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%--65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74627072","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

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":"191 1","pages":""},"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

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

Spatiotemporal topic association detection on tweets 推文的时空主题关联检测

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2016-10-31 DOI: 10.1145/2996913.2996933

Zhi Liu, Yan Huang, Joshua R. Trampier

引用次数: 4

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":"37 1","pages":""},"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