Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第2页

Contour trees of uncertain terrains 地形不确定的等高线树木

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820823

Wuzhou Zhang, P. Agarwal, S. Mukherjee

{"title":"Contour trees of uncertain terrains","authors":"Wuzhou Zhang, P. Agarwal, S. Mukherjee","doi":"10.1145/2820783.2820823","DOIUrl":"https://doi.org/10.1145/2820783.2820823","url":null,"abstract":"We study contour trees of terrains, which encode the topological changes of the level set of the height value ℓ as we raise ℓ from -∞ to +∞ on the terrains, in the presence of uncertainty in data. We assume that the terrain is represented by a piecewise-linear height function over a planar triangulation M, by specifying the height of each vertex. We study the case when M is fixed and the uncertainty lies in the height of each vertex in the triangulation, which is described by a probability distribution. We present efficient sampling-based Monte Carlo methods for estimating, with high probability, (i) the probability that two points lie on the same edge of the contour tree, within additive error; (ii) the expected distance of two points p, q and the probability that the distance of p, q is at least ℓ on the contour tree, within additive error, where the distance of p, q on a contour tree is defined to be the difference between the maximum height and the minimum height on the unique path from p to q on the contour tree. The main technical contribution of the paper is to prove that a small number of samples are sufficient to estimate these quantities. We present two applications of these algorithms, and also some experimental results to demonstrate the effectiveness of our approach.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"72 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":"121733023","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

Location familiarity based flickr photographer classification for POI mining 基于位置熟悉度的flickr拍照者POI挖掘分类

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820875

Chenyi Zhuang, Qiang Ma, Masatoshi Yoshikawa

引用次数: 1

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820802

M. Werner

引用次数: 7

Traffic matrix estimation from road sensor data: a case study 基于道路传感器数据的交通矩阵估计:一个案例研究

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820855

K. R. Moghadam, Quynh Nguyen, B. Krishnamachari, Ugur Demiryurek

引用次数: 6

Activity recognition of railway passengers by fusion of low-power sensors in mobile phones 基于手机低功耗传感器融合的铁路乘客活动识别

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820847

Moustafa Elhamshary, M. Youssef, A. Uchiyama, H. Yamaguchi, T. Higashino

引用次数: 11

Guide me through somewhere important: decision-point salience and collaborative navigation 引导我了解一些重要的地方:决策点突出性和协作导航

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820861

Makoto Takemiya, T. Ishikawa, Gengen He

{"title":"Guide me through somewhere important: decision-point salience and collaborative navigation","authors":"Makoto Takemiya, T. Ishikawa, Gengen He","doi":"10.1145/2820783.2820861","DOIUrl":"https://doi.org/10.1145/2820783.2820861","url":null,"abstract":"People often work together to collaboratively navigate environments. Unfortunately, people make mistakes when giving directions to others, which can be especially troublesome when giving directions via a cell phone to a partner that cannot be physically seen. To facilitate collaborative navigation between spatially separated partners, contemporary navigational aids provide information about important landmarks and decision points in an environment. To learn more about what kinds of mistakes people make in navigating and the problems they have in giving and receiving route directions, this article presents an empirical study on dynamic, collaborative wayfinding, which looked at decision points where wayfinders made navigational errors. We found metrics rating the salience of decision points in an environment that correlate with where people tended to make mistakes in navigation, and argue that navigational aids could highlight these points to wayfinders so that they can pay special attention when giving directions traversing these points. We finish by outlining how the lessons learned in this study can be applied to real-time navigational aids.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"60 3-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132330968","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: 0

Detecting collective anomalies from multiple spatio-temporal datasets across different domains 从不同领域的多个时空数据集中检测集体异常

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820813

Yu Zheng, Huichu Zhang, Yong Yu

{"title":"Detecting collective anomalies from multiple spatio-temporal datasets across different domains","authors":"Yu Zheng, Huichu Zhang, Yong Yu","doi":"10.1145/2820783.2820813","DOIUrl":"https://doi.org/10.1145/2820783.2820813","url":null,"abstract":"The collective anomaly denotes a collection of nearby locations that are anomalous during a few consecutive time intervals in terms of phenomena collectively witnessed by multiple datasets. The collective anomalies suggest there are underlying problems that may not be identified based on a single data source or in a single location. It also associates individual locations and time intervals, formulating a panoramic view of an event. To detect a collective anomaly is very challenging, however, as different datasets have different densities, distributions, and scales. Additionally, to find the spatio-temporal scope of a collective anomaly is time consuming as there are many ways to combine regions and time slots. Our method consists of three components: Multiple-Source Latent-Topic (MSLT) model, Spatio-Temporal Likelihood Ratio Test (ST_LRT) model, and a candidate generation algorithm. MSLT combines multiple datasets to infer the latent functions of a geographic region in the framework of a topic model. In turn, a region's latent functions help estimate the underlying distribution of a sparse dataset generated in the region. ST_LRT learns a proper underlying distribution for different datasets, and calculates an anomalous degree for each dataset based on a likelihood ratio test (LRT). It then aggregates the anomalous degrees of different datasets, using a skyline detection algorithm. We evaluate our method using five datasets related to New York City (NYC): 311 complaints, taxicab data, bike rental data, points of interest, and road network data, finding the anomalies that cannot be identified (or earlier than those detected) by a single dataset. Results show the advantages beyond six baseline methods.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"6 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":"122401893","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: 95

Fine-grained population estimation 细粒度总体估计

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820828

H. Bast, Sabine Storandt, Simon Weidner

{"title":"Fine-grained population estimation","authors":"H. Bast, Sabine Storandt, Simon Weidner","doi":"10.1145/2820783.2820828","DOIUrl":"https://doi.org/10.1145/2820783.2820828","url":null,"abstract":"We show how to estimate population numbers for arbitrary user-defined regions, down to the level of individual buildings. This is important for various applications like evacuation planning, facility placement, or traffic estimation. However, census data with precise population numbers is typically only available at the level of cities, villages, or districts, if at all. Previous approaches either rely on available census data for already small areas or on sophisticated input data like high resolution aerial images. Our framework uses only freely available data, in particular, OpenStreetMap data. In the OpenStreetMap project, crowd-sourced data is collected about street networks, buildings, places of interest as well as all kind of regions and natural structures world-wide. We use this data to learn three classifiers that are relevant for the population distribution inside an area: residential vs. industrial vs. commercial landuse, inhabited vs. uninhabited buildings, and single-family vs. multi-family houses. Once learned, we can use these classifiers for population estimation even in areas without any census data at all. Our experiments show good average accuracy (measured as the deviation from actual census data) for rural areas (25%), metropolitan areas (10%), and cities in countries other than that containing the training data (12%).","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"21 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":"134035863","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: 15

Spatio-temporal keyword queries for moving objects 移动对象的时空关键字查询

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820845

Paras Mehta, Dimitrios Skoutas, A. Voisard

引用次数: 12

Personalized route planning in road networks 道路网络中的个性化路线规划

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820830

S. Funke, Sabine Storandt

引用次数: 37