Hongtai Zhang, Jian Dai, Kuien Liu, Zhiming Ding, Huidan Liu
Map Generalization is one of the most fundamental technologies for modern digital maps. It can effectively reduce the storage space and fit to different applications according to their scale requirement. This paper presents an efficient solution for this problem that won the ACM SIGSPATTAL CUP 2014. Given the original geometries which are represented by sampling points sequence, this method divides the boundaries into many small segments based on their topological characteristics and constriants. It attempts to minimize the number of sampling points by simplifying the given map and constraining points. In addition, the method also employs many optimization techniques to reduce the total latency, like memory pool, parallel computing and string parsing. Experimental results on real datasets demonstrate the effectiveness and efficiency of the proposed method.
地图综合是现代数字地图最基本的技术之一。它可以有效地减少存储空间，并根据不同应用的规模要求适应不同的应用。本文提出了一种有效的解决方案，并赢得了ACM SIGSPATTAL CUP 2014。该方法以采样点序列表示的原始几何形状为基础，根据边界的拓扑特征和约束条件将边界划分为许多小段。它试图通过简化给定的地图和约束点来最小化采样点的数量。此外，该方法还采用了许多优化技术来减少总延迟，如内存池、并行计算和字符串解析。在实际数据集上的实验结果证明了该方法的有效性和高效性。
{"title":"An efficient method of map generalization using topology partitioning and constraints recognition","authors":"Hongtai Zhang, Jian Dai, Kuien Liu, Zhiming Ding, Huidan Liu","doi":"10.1145/2666310.2666423","DOIUrl":"https://doi.org/10.1145/2666310.2666423","url":null,"abstract":"Map Generalization is one of the most fundamental technologies for modern digital maps. It can effectively reduce the storage space and fit to different applications according to their scale requirement. This paper presents an efficient solution for this problem that won the ACM SIGSPATTAL CUP 2014. Given the original geometries which are represented by sampling points sequence, this method divides the boundaries into many small segments based on their topological characteristics and constriants. It attempts to minimize the number of sampling points by simplifying the given map and constraining points. In addition, the method also employs many optimization techniques to reduce the total latency, like memory pool, parallel computing and string parsing. Experimental results on real datasets demonstrate the effectiveness and efficiency of the proposed method.","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":"122944179","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}
Y. Huang, Markus Schneider, Michael Gertz, John Krumm, Jagan Sankaranarayanan
This is the proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS (2014), held in Dallas, TX USA, November 4--7, 2014. This conference is the twenty-second edition in a series of symposia and workshops that began in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. The conference provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage, query processing and indexing. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).
{"title":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","authors":"Y. Huang, Markus Schneider, Michael Gertz, John Krumm, Jagan Sankaranarayanan","doi":"10.1145/2666310","DOIUrl":"https://doi.org/10.1145/2666310","url":null,"abstract":"This is the proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS (2014), held in Dallas, TX USA, November 4--7, 2014. This conference is the twenty-second edition in a series of symposia and workshops that began in 1993 with the aim of promoting interdisciplinary discussions among researchers, developers, users, and practitioners and fostering research in all aspects of geographic information systems, especially in relation to novel systems based on geospatial data and knowledge. The conference provides a forum for original research contributions covering all conceptual, design, and implementation aspects of geospatial data ranging from applications, user interfaces, and visualization to data storage, query processing and indexing. The conference is the premier annual event of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL).","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"37 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":"114585670","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}
Randall T. Whitman, Michael B. Park, Sarah M. Ambrose, E. Hoel
Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Hadoop is one such open-source framework that is enjoying widespread adoption. In this paper, we detail an approach to indexing and performing key analytics on spatial data that is persisted in HDFS. Our technique differs from other approaches in that it combines spatial indexing, data load balancing, and data clustering in order to optimize performance across the cluster. In addition, our index supports efficient, random-access queries without requiring a MapReduce job; neither a full table scan, nor any MapReduce overhead is incurred when searching. This facilitates large numbers of concurrent query executions. We will also demonstrate how indexing and clustering positively impacts the performance of range and k-NN queries on large real-world datasets. The performance analysis will enable a number of interesting observations to be made on the behavior of spatial indexes and spatial queries in this distributed processing environment.
{"title":"Spatial indexing and analytics on Hadoop","authors":"Randall T. Whitman, Michael B. Park, Sarah M. Ambrose, E. Hoel","doi":"10.1145/2666310.2666387","DOIUrl":"https://doi.org/10.1145/2666310.2666387","url":null,"abstract":"Effective processing of extremely large volumes of spatial data has led to many organizations employing distributed processing frameworks. Hadoop is one such open-source framework that is enjoying widespread adoption. In this paper, we detail an approach to indexing and performing key analytics on spatial data that is persisted in HDFS. Our technique differs from other approaches in that it combines spatial indexing, data load balancing, and data clustering in order to optimize performance across the cluster. In addition, our index supports efficient, random-access queries without requiring a MapReduce job; neither a full table scan, nor any MapReduce overhead is incurred when searching. This facilitates large numbers of concurrent query executions. We will also demonstrate how indexing and clustering positively impacts the performance of range and k-NN queries on large real-world datasets. The performance analysis will enable a number of interesting observations to be made on the behavior of spatial indexes and spatial queries in this distributed processing environment.","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":"126380816","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}
Benjamin B. Krogh, O. Andersen, Edwin Lewis-Kelham, K. Torp
Traffic researchers, planners, and analysts want a simple way to query the large quantities of GPS trajectories collected from vehicles. In addition, users expect the results to be presented immediately even when querying very large transportation networks with huge trajectory data sets. This paper presents a novel query type called sheaf, where users can browse trajectory data sets using a single mouse click. Sheaves are very versatile and can be used for location-based advertising, travel-time analysis, intersection analysis, and reachability analysis (isochrones). A novel in-memory trajectory index compresses the data by a factor of 12.4 and enables execution of sheaf queries in 40 ms. This is up to 2 orders of magnitude faster than existing work. We demonstrate the simplicity, versatility, and efficiency of sheaf queries using a real-world trajectory set consisting of 2.7 million trajectories (1.36 billion GPS records) and a network with 1.5 million edges.
{"title":"Efficient one-click browsing of large trajectory sets","authors":"Benjamin B. Krogh, O. Andersen, Edwin Lewis-Kelham, K. Torp","doi":"10.1145/2666310.2666371","DOIUrl":"https://doi.org/10.1145/2666310.2666371","url":null,"abstract":"Traffic researchers, planners, and analysts want a simple way to query the large quantities of GPS trajectories collected from vehicles. In addition, users expect the results to be presented immediately even when querying very large transportation networks with huge trajectory data sets. This paper presents a novel query type called sheaf, where users can browse trajectory data sets using a single mouse click. Sheaves are very versatile and can be used for location-based advertising, travel-time analysis, intersection analysis, and reachability analysis (isochrones). A novel in-memory trajectory index compresses the data by a factor of 12.4 and enables execution of sheaf queries in 40 ms. This is up to 2 orders of magnitude faster than existing work. We demonstrate the simplicity, versatility, and efficiency of sheaf queries using a real-world trajectory set consisting of 2.7 million trajectories (1.36 billion GPS records) and a network with 1.5 million edges.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"15 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":"126465734","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}
Ran Wang, Chi-Yin Chow, Sarana Nutanong, Yan Lyu, Yanhua Li, Mingxuan Yuan, V. Lee
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.
{"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}
Surrounds is a topological relation that can exist between two regions or between collections of regions in R2. This paper provides an algebraic construction for surrounds within a partition and provides a complementary graph-theoretic approach for the detection of the surrounds conditions created by the operations within the algebra. These two approaches are contrasted to one another. Constraints are placed upon surrounds to maintain certain algebraic benefits and the consequences of their relaxations are assessed.
{"title":"Surrounds in partitions","authors":"Matthew P. Dube, M. Egenhofer","doi":"10.1145/2666310.2666380","DOIUrl":"https://doi.org/10.1145/2666310.2666380","url":null,"abstract":"Surrounds is a topological relation that can exist between two regions or between collections of regions in R2. This paper provides an algebraic construction for surrounds within a partition and provides a complementary graph-theoretic approach for the detection of the surrounds conditions created by the operations within the algebra. These two approaches are contrasted to one another. Constraints are placed upon surrounds to maintain certain algebraic benefits and the consequences of their relaxations are assessed.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"26 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":"124764539","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}
S. V. G. Magalhães, W. Randolph Franklin, Wenli Li, M. Andrade
We present Grid-Gen, an efficient heuristic for map simplification. Grid-Gen deals with a variation of the generalization problem where the idea is to simplify the polylines of a map without changing the topological relationships between these polylines or between the lines and control points. Grid-Gen uses a uniform grid to accelerate the simplification process and can handle a map with more than 3 million polyline points and 10 million control points in 9 seconds in a Lenovo T430s laptop.
{"title":"Fast map generalization heuristic with a uniform grid","authors":"S. V. G. Magalhães, W. Randolph Franklin, Wenli Li, M. Andrade","doi":"10.1145/2666310.2666421","DOIUrl":"https://doi.org/10.1145/2666310.2666421","url":null,"abstract":"We present Grid-Gen, an efficient heuristic for map simplification. Grid-Gen deals with a variation of the generalization problem where the idea is to simplify the polylines of a map without changing the topological relationships between these polylines or between the lines and control points. Grid-Gen uses a uniform grid to accelerate the simplification process and can handle a map with more than 3 million polyline points and 10 million control points in 9 seconds in a Lenovo T430s laptop.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"96 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":"129525262","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}
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.
{"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}
We present TRAVIC, a thin browser-based client that is able to display smooth vehicle movements on a map. The focus is on visualizing world-wide public transit vehicle movements in an interactive way. But we also investigate other use cases, for example, traffic simulation. We describe in detail which server requests are fired and how the received data is handled. We also provide a performance evaluation conducted on several browsers. We show that, in combination with an efficient back-end, TRAVIC is able to display many thousands of vehicle movements in real-time. Our prototype implementation can be accessed under http://tracker.geops.ch.
{"title":"TRAVIC: a visualization client for public transit data","authors":"H. Bast, P. Brosi, Sabine Storandt","doi":"10.1145/2666310.2666369","DOIUrl":"https://doi.org/10.1145/2666310.2666369","url":null,"abstract":"We present TRAVIC, a thin browser-based client that is able to display smooth vehicle movements on a map. The focus is on visualizing world-wide public transit vehicle movements in an interactive way. But we also investigate other use cases, for example, traffic simulation. We describe in detail which server requests are fired and how the received data is handled. We also provide a performance evaluation conducted on several browsers. We show that, in combination with an efficient back-end, TRAVIC is able to display many thousands of vehicle movements in real-time. Our prototype implementation can be accessed under http://tracker.geops.ch.","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":"130916374","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}
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.
{"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}
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