Proceedings of the 28th International Conference on Advances in Geographic Information Systems最新文献_第2页

Addressing Under-Reporting to Enhance Fairness and Accuracy in Mobility-based Crime Prediction 解决低报问题，提高基于流动性的犯罪预测的公平性和准确性

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422205

Jiahui Wu, E. Frías-Martínez, V. Frías-Martínez

引用次数: 6

SpiderWeb: A Spatial Data Generator on the Web SpiderWeb:网络上的空间数据生成器

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422351

Puloma Katiyar, Tin Vu, A. Eldawy, S. Migliorini, A. Belussi

引用次数: 12

A Generator for 2D Moving Regions 用于2D移动区域的生成器

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422336

José Duarte, Mark McKenney

引用次数: 0

STARE-based Integrative Analysis of Diverse Data Using Dask Parallel Programming Demo Paper 基于stare的多任务并行编程综合分析演示论文

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422346

M. Rilee, Niklas Griessbaum, K. Kuo, J. Frew, R. Wolfe

{"title":"STARE-based Integrative Analysis of Diverse Data Using Dask Parallel Programming Demo Paper","authors":"M. Rilee, Niklas Griessbaum, K. Kuo, J. Frew, R. Wolfe","doi":"10.1145/3397536.3422346","DOIUrl":"https://doi.org/10.1145/3397536.3422346","url":null,"abstract":"Scaling up volume and variety in Big Earth Science Data is particularly difficult when combining low-level, ungridded data, such as swath observations obtained with, for example, Moderate Resolution Imaging Spectroradiometers (MODIS). A unified way to index and combine data with different geo-spatiotemporal layouts and incomparable native array formatting is required for scalable integrative analyses based on data at its full instrument resolution, that is, without extra interpolation (or extrapolation) onto a common grid. The SpatioTemporal Adaptive Resolution Encoding (STARE) uses the Hierarchical Triangular Mesh (HTM) and the Hierarchical Calendrical Partitioning (HCP), recursive partitionings of solid angle and time into tree data structures, to encode spatiotemporal neighborhoods as sets of integers. Regions sharing common paths through the STARE tree hierarchy have similar index values, which can then serve as keys in algorithms and data structures supporting scalable integrative analyses. Thus, STARE co-aligns data in both physical (spatiotemporal) and cyber (memory) spaces, providing a means for marshalling computing resources and conducting analysis with minimum data movement, addressing volume scalability while simultaneously unifying diverse data for variety scaling. In this paper, we demonstrate how easy it is to use the Python STARE API (PySTARE) and the parallel programming platform Dask to integrate MODIS and Geostationary Operational Environmental Satellite (GOES) data, datasets with very different geo-spatiotemporal characteristics.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126115290","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

Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases 在超大型时空数据库中发现频繁的空间模式

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422206

R. U. Kiran, Sourabh Shrivastava, Philippe Fournier-Viger, K. Zettsu, Masashi Toyoda, M. Kitsuregawa

{"title":"Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases","authors":"R. U. Kiran, Sourabh Shrivastava, Philippe Fournier-Viger, K. Zettsu, Masashi Toyoda, M. Kitsuregawa","doi":"10.1145/3397536.3422206","DOIUrl":"https://doi.org/10.1145/3397536.3422206","url":null,"abstract":"Frequent pattern mining is an important model in data mining. It involves finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that a pattern must cover in a transactional database. Since only minSup is used to evaluate a pattern's interestingness, the frequent pattern model implicitly assumes that spatial information of the items will not impact the interestingness of a pattern in the database. This assumption limits the applicability of the frequent pattern model in many real-world applications. It is because patterns whose items are close to each other are typically more attractive to the user than the patterns whose items are far from each other in a coordinate system. With this motivation, this paper proposes a novel model of frequent spatial pattern that may exist in a spatiotemporal database. An efficient pattern-growth algorithm, called Frequent Spatial Pattern-growth (FSP-growth), has also been presented to mine all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient. The usefulness of the proposed patterns has also been shown with a real-world application.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124625502","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

Predicting Human Mobility with Federated Learning 用联邦学习预测人类流动性

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422270

Anliang Li, Shuang Wang, Wenzhu Li, Shengnan Liu, Siyuan Zhang

引用次数: 18

Visualizing SpatioTemporal Keyword Trends in Online News Articles 可视化网络新闻文章的时空关键词趋势

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422339

J. Kastner, H. Samet

引用次数: 3

The impact of highly compact algorithmic redistricting on the rural-versus-urban balance 高度紧凑的算法重划对城乡平衡的影响

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422249

Archer Wheeler, P. Klein

引用次数: 4

A Visual Explorer for Geolocated Time Series 用于定位时间序列的可视化资源管理器

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422345

Georgios Chatzigeorgakidis, Kostas Patroumpas, Dimitrios Skoutas, Spiros Athanasiou

引用次数: 1

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub 基于位置感知的分布式空间关键字kNN监控

Proceedings of the 28th International Conference on Advances in Geographic Information Systems Pub Date : 2020-11-03 DOI: 10.1145/3397536.3422199

Shohei Tsuruoka, Daichi Amagata, Shunya Nishio, Takahiro Hara

{"title":"Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub","authors":"Shohei Tsuruoka, Daichi Amagata, Shunya Nishio, Takahiro Hara","doi":"10.1145/3397536.3422199","DOIUrl":"https://doi.org/10.1145/3397536.3422199","url":null,"abstract":"Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given m workers, we divide a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. We allow an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution.","PeriodicalId":233918,"journal":{"name":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114340409","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