Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献

An efficient method of map generalization using topology partitioning and constraints recognition 一种基于拓扑划分和约束识别的高效地图泛化方法

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666423

Hongtai Zhang, Jian Dai, Kuien Liu, Zhiming Ding, Huidan Liu

引用次数: 0

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 第22届ACM SIGSPATIAL国际地理信息系统进展会议论文集

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310

Y. Huang, Markus Schneider, Michael Gertz, John Krumm, Jagan Sankaranarayanan

引用次数: 4

Spatial indexing and analytics on Hadoop Hadoop的空间索引和分析

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666387

Randall T. Whitman, Michael B. Park, Sarah M. Ambrose, E. Hoel

引用次数: 60

Efficient one-click browsing of large trajectory sets 高效的一键浏览大型轨迹集

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666371

Benjamin B. Krogh, O. Andersen, Edwin Lewis-Kelham, K. Torp

引用次数: 3

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction 探索基于监督学习的兴趣点预测的蜂窝塔数据转储

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666478

Ran Wang, Chi-Yin Chow, Sarana Nutanong, Yan Lyu, Yanhua Li, Mingxuan Yuan, V. Lee

{"title":"Exploring cell tower data dumps for supervised learning-based point-of-interest prediction","authors":"Ran Wang, Chi-Yin Chow, Sarana Nutanong, Yan Lyu, Yanhua Li, Mingxuan Yuan, V. Lee","doi":"10.1145/2666310.2666478","DOIUrl":"https://doi.org/10.1145/2666310.2666478","url":null,"abstract":"Exploring massive mobile data for location-based services (LBS) becomes one of the key challenges in mobile data mining. In this paper, we propose a framework that uses large-scale cell tower data dumps and extracts points-of-interest (POIs) from a social network web site called Weibo, and provides new LBS based on these two data sets, i.e., predicting the existence of POIs and the number of POIs in a certain area. We use Voronoi diagram to divide a city area into non-overlapping regions, and a k-means clustering algorithm to aggregate neighboring cell towers into region groups. A supervised learning algorithm is adopted to build up a model between the number of connections of cell towers and the POIs in different region groups, where a classification or regression model is used to predict the POI existence or the number of POIs, respectively. We studied 12 state-of-the-art classification and regression algorithms, and the experimental results demonstrate the feasibility and effectiveness of the proposed framework.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115978158","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: 6

Surrounds in partitions 环绕在分区中

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666380

Matthew P. Dube, M. Egenhofer

引用次数: 20

Fast map generalization heuristic with a uniform grid 具有均匀网格的快速地图泛化启发式算法

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666421

S. V. G. Magalhães, W. Randolph Franklin, Wenli Li, M. Andrade

引用次数: 5

LORE: exploiting sequential influence for location recommendations LORE:利用位置推荐的顺序影响

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666400

Jiadong Zhang, Chi-Yin Chow, Yanhua Li

{"title":"LORE: exploiting sequential influence for location recommendations","authors":"Jiadong Zhang, Chi-Yin Chow, Yanhua Li","doi":"10.1145/2666310.2666400","DOIUrl":"https://doi.org/10.1145/2666310.2666400","url":null,"abstract":"Providing location recommendations becomes an important feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical influence and social influence have been intensively used in location recommendations based on the facts that geographical proximity of locations significantly affects users' check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any sequential influence of locations on users' check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L2TG). LORE then predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L2TG. Finally, LORE fuses sequential influence with geographical influence and social influence into a unified recommendation framework; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets collected from Foursquare and Gowalla. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131034841","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: 246

TRAVIC: a visualization client for public transit data TRAVIC:公共交通数据可视化客户端

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666369

H. Bast, P. Brosi, Sabine Storandt

引用次数: 5

How to eat a graph: computing selection sequences for the continuous generalization of road networks 如何吃图:计算道路网络连续泛化的选择序列

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2014-11-04 DOI: 10.1145/2666310.2666414

Markus Chimani, Thomas C. van Dijk, J. Haunert

{"title":"How to eat a graph: computing selection sequences for the continuous generalization of road networks","authors":"Markus Chimani, Thomas C. van Dijk, J. Haunert","doi":"10.1145/2666310.2666414","DOIUrl":"https://doi.org/10.1145/2666310.2666414","url":null,"abstract":"In a connected weighted graph, consider deleting the edges one at a time, in some order, such that after every deletion the remaining edges are still connected. We study the problem of finding such a deletion sequence that maximizes the sum of the weights of the edges in all the distinct graphs generated: the weight of an edge is counted in every graph that it is in. This effectively asks for the high-weight edges to remain in the graph as long as possible, subject to connectivity. We apply this to road network generalization in order to generate a sequence of successively more generalized maps of a road network so that these maps go well together, instead of considering each level of generalization independently. In particular, we look at the problem of making a road segment selection that is consistent across zoom levels. We show that the problem is NP-hard and give an integer linear program (ILP) that solves it optimally. Solving this ILP is only feasible for small instances. Next we develop constant-factor approximation algorithms and heuristics. We experimentally demonstrate that these heuristics perform well on real-world instances.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130423942","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: 20