Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第9页

Eyewitness: identifying local events via space-time signals in twitter feeds 目击者:通过twitter feed中的时空信号识别当地事件

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820801

John Krumm, E. Horvitz

引用次数: 80

Fast computation of categorical richness on raster data sets and related problems 栅格数据集分类丰富度的快速计算及相关问题

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820825

M. D. Berg, Constantinos Tsirogiannis, Bryan T. Wilkinson

{"title":"Fast computation of categorical richness on raster data sets and related problems","authors":"M. D. Berg, Constantinos Tsirogiannis, Bryan T. Wilkinson","doi":"10.1145/2820783.2820825","DOIUrl":"https://doi.org/10.1145/2820783.2820825","url":null,"abstract":"In many scientific fields, it is common to encounter raster data sets consisting of categorical data, such as soil type or land usage of a terrain. A problem that arises in the presence of such data is the following: given a raster G of n cells storing categorical data, compute for every cell c in G the number of different categories appearing within a window centered at c. The window can either be a (2r+1)x(2r+1) square or a disk of radius r for a positive integer parameter r. We call this the categorical richness problem, and we present two algorithms for it: one for square windows that runs in O(n) time and one for circular windows that runs in O((1+K/r)n) time, where K is the number of distinct categories in G. The algorithms are not only efficient in theory, but also in practice: our experiments show that our algorithms can handle raster data of hundreds of millions of cells. The categorical richness problem is related to colored range counting, where the goal is to preprocess a colored point set such that we can efficiently count the number of colors appearing inside a query range. We present a data structure for colored range counting in R2 for the case where query ranges are squares. Our structure uses O(n polylog n) storage and has O(polylog n) query time, which is significantly better than what is known for arbitrary rectangular ranges.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"54 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116784374","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: 1

Learning functional compositions of urban spaces with crowd-augmented travel survey data 利用人群增强出行调查数据学习城市空间的功能组成

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820832

Zack Z. Zhu, J. Yang, Chen Zhong, J. Seiter, G. Tröster

{"title":"Learning functional compositions of urban spaces with crowd-augmented travel survey data","authors":"Zack Z. Zhu, J. Yang, Chen Zhong, J. Seiter, G. Tröster","doi":"10.1145/2820783.2820832","DOIUrl":"https://doi.org/10.1145/2820783.2820832","url":null,"abstract":"Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-source features. In this work, we form a comprehensive view of urban regions by fusing features depicting their temporal, spatial, and demographic aspects. Aggregating 47K explicitly stated trip purposes into their respective destination regions, we obtain multi-dimensional ground-truths on the functionalities of urban spaces. Given fused features and training labels, we can perform supervised learning, via multi-output regression, to estimate the functional composition of urban spaces. With 14 functional dimensions, our approach using crowd-augmented travel survey predictors delivers a mean absolute error of 3.9, approximately half of the error resulting from a mean-based straw man approach (mean absolute error of 7.9). Clustering estimated regional functionalities, we find highly coherent cluster assignments (adjusted Rand Index of 0.81) compared to clustering directly on regional functionality labels. Finally, we provide an illustrative case-study where clustering of estimated region functionalities can be used to intuitively differentiate prototypical spatial neighbourhoods of a large metropolitan.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122173809","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

Combining range-based and range-free methods: a unified approach for localization 结合基于距离和无距离的方法:一种统一的定位方法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820839

Anthony Quattrone, L. Kulik, E. Tanin

引用次数: 4

There and back again: using fréchet-distance diagrams to find trajectory turning points 再来一遍:使用区间距离图来找到轨迹转折点

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820878

L. Beckmann, B. Budig, Thomas C. van Dijk, J. Schamel

引用次数: 2

Vision paper: the future of scientific terrain visualization 远景论文:未来科学地形可视化

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820884

Matthias Thöny, M. Billeter, R. Pajarola

引用次数: 4

Sphinx: distributed execution of interactive SQL queries on big spatial data Sphinx:在大空间数据上分布式执行交互式SQL查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820869

A. Eldawy, Mostafa Elganainy, Ammar Bakeer, A. Abdelmotaleb, M. Mokbel

引用次数: 13

Stochastic location optimization in a dynamic environment 动态环境下的随机定位优化

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820787

Hongliang Guo, Yubo Dong, S. Ho

引用次数: 0

Traffic prediction in a bike-sharing system 自行车共享系统中的交通预测

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820837

Yexin Li, Yu Zheng, Huichu Zhang, Lei Chen

{"title":"Traffic prediction in a bike-sharing system","authors":"Yexin Li, Yu Zheng, Huichu Zhang, Lei Chen","doi":"10.1145/2820783.2820837","DOIUrl":"https://doi.org/10.1145/2820783.2820837","url":null,"abstract":"Bike-sharing systems are widely deployed in many major cities, providing a convenient transportation mode for citizens' commutes. As the rents/returns of bikes at different stations in different periods are unbalanced, the bikes in a system need to be rebalanced frequently. Real-time monitoring cannot tackle this problem well as it takes too much time to reallocate the bikes after an imbalance has occurred. In this paper, we propose a hierarchical prediction model to predict the number of bikes that will be rent from/returned to each station cluster in a future period so that reallocation can be executed in advance. We first propose a bipartite clustering algorithm to cluster bike stations into groups, formulating a two-level hierarchy of stations. The total number of bikes that will be rent in a city is predicted by a Gradient Boosting Regression Tree (GBRT). Then a multi-similarity-based inference model is proposed to predict the rent proportion across clusters and the inter-cluster transition, based on which the number of bikes rent from/ returned to each cluster can be easily inferred. We evaluate our model on two bike-sharing systems in New York City (NYC) and Washington D.C. (D.C.) respectively, confirming our model's advantage beyond baseline approaches (0.03 reduction of error rate), especially for anomalous periods (0.18/0.23 reduction of error rate).","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131348933","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: 317

Future connected vehicles: challenges and opportunities for spatio-temporal computing 未来互联汽车:时空计算的挑战与机遇

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820885

Reem Y. Ali, Venkata M. V. Gunturi, S. Shekhar, A. Eldawy, M. Mokbel, Andrew J. Kotz, W. Northrop

引用次数: 15