Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems最新文献_第3页

Understanding hotspots: a topological visual analytics approach 理解热点:拓扑可视化分析方法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820817

Jonas Lukasczyk, Ross Maciejewski, C. Garth, H. Hagen

引用次数: 37

Fusion of color images and LiDAR data for lane classification 融合彩色图像和激光雷达数据进行车道分类

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820859

Xiaodong Gu, Andi Zang, Xinyu Huang, A. Tokuta, Xin Chen

引用次数: 9

An arc orienteering algorithm to find the most scenic path on a large-scale road network 在大规模道路网络中寻找最优美路径的圆弧定向算法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820835

Ying Lu, C. Shahabi

{"title":"An arc orienteering algorithm to find the most scenic path on a large-scale road network","authors":"Ying Lu, C. Shahabi","doi":"10.1145/2820783.2820835","DOIUrl":"https://doi.org/10.1145/2820783.2820835","url":null,"abstract":"Traditional route planning problems mainly focus on finding the shortest path considering the travel distance or time. In this paper, we aim to find the most scenic path that offers the most beautiful sceneries on the arcs of a path while the total travel cost (distance or time) is within a user-specified budget. This is a challenging problem as the optimization objective is to maximize the value of the path (i.e., its scenic value) instead of minimizing its cost (distance or time). The problem can be formulated as a variant of the Arc Orienteering Problem (AOP), which is a well-known NP-hard combinatorial optimization problem. Due to the fast response-time requirements of interactive mobile and online applications (e.g., within 300 milliseconds) and the large scale of real-world road networks, existing heuristic algorithms for AOP fail to solve the most scenic road problem. Therefore, unlike the existing approaches for AOP where they treat the road network as a traditional graph in which all-pair distances are pre-computed a priori, in this work, we treat the road network as a spatial network, utilizing the techniques from the field of spatial database: ellipse pruning and spatial indexing. Experiments on two real-world datasets demonstrate the efficiency and accuracy of our proposed algorithms, which can achieve over 95% accuracy within 300 milliseconds on large-scale datasets (over 100K network nodes).","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":"128214293","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: 28

A stacked gaussian process for predicting geographical incidence of aflatoxin with quantified uncertainties 具有量化不确定性预测黄曲霉毒素地理发病率的叠加高斯过程

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820849

Hui Li, Asif J. Chowdhury, G. Terejanu, Anindya Chanda, Sourav Banerjee

{"title":"A stacked gaussian process for predicting geographical incidence of aflatoxin with quantified uncertainties","authors":"Hui Li, Asif J. Chowdhury, G. Terejanu, Anindya Chanda, Sourav Banerjee","doi":"10.1145/2820783.2820849","DOIUrl":"https://doi.org/10.1145/2820783.2820849","url":null,"abstract":"The objective of this paper is to develop a methodology for generating probabilistic risk maps for unobserved quantities of interests such as aflatoxin. Aflatoxin is a naturally occurring carcinogenic and it is a serious global issue and an emerging risk for crop producers. The production of aflatoxin is highly dependent on environmental conditions such temperature and water activity, and it can contaminate grains before harvest or during storage. The focus of this paper is to develop a procedure to account for spatial dependencies and uncertainties in risk calculations, to provide various stakeholders with situational awareness to better understand, communicate, and mitigate the aflatoxin risk before harvest. The proposed probabilistic model is obtained in two stages: the production of aflatoxin with quantified uncertainties is modeled under various temperature and water activity conditions within a controlled environment (wet-lab), and then the predictive aflatoxin model is linked with environmental conditions obtained on a regular basis to generate regional probabilistic risk maps. Since both aflatoxin production and environmental data are modeled using Gaussian processes, the resulted probabilistic model is a stacked Gaussian process, where the environmental Gaussian process model governs the input space of the aflatoxin Gaussian process model. The regional prediction of aflatoxin is obtained by marginalizing over the latent space provided by the environmental variables. The methodology is applied to calculate the aflatoxin levels of corn lands in South Carolina in the drought year 2012, where few field measurements are available for an initial comparison with our aflatoxin predictions.","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":"130377436","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: 10

Evidence of long range dependence and self-similarity in urban traffic systems 城市交通系统中长距离依赖和自相似性的证据

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820819

Gautam Thakur, Pan Hui, A. Helmy

{"title":"Evidence of long range dependence and self-similarity in urban traffic systems","authors":"Gautam Thakur, Pan Hui, A. Helmy","doi":"10.1145/2820783.2820819","DOIUrl":"https://doi.org/10.1145/2820783.2820819","url":null,"abstract":"Transportation simulation technologies should accurately model traffic demand, distribution, and assignment parameters for urban environment simulation. These three parameters significantly impact transportation engineering benchmark process, are also critical in realizing realistic traffic modeling situations. In this paper, we model and characterize traffic density distribution of thousands of locations, intersection, and roadways around the world. The traffic densities are generated from millions of images collected over several years and processed using computer vision techniques. The resulting traffic density distribution time series are then analyzed. It is found using the goodness-of-fit test that the traffic density distributions follow heavy-tail models such as Weibull in over 90% of analyzed locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators strongly indicates that the traffic distribution patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding that will influence the design and development of the next generation traffic simulation techniques and also aid in accurately modeling traffic engineering of urban systems. In addition, it shall provide a much-needed input for the development of smart cities.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"37 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":"121812398","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: 2

Presents: probabilistic resource-search networks 介绍:概率资源搜索网络

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820866

Qing Guo, O. Wolfson

引用次数: 3

Moving beyond transportation: utility network management 超越交通:公用事业网络管理

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820879

E. Hoel, P. Bakalov, Sangho Kim, T. Brown

{"title":"Moving beyond transportation: utility network management","authors":"E. Hoel, P. Bakalov, Sangho Kim, T. Brown","doi":"10.1145/2820783.2820879","DOIUrl":"https://doi.org/10.1145/2820783.2820879","url":null,"abstract":"Transportation networks and associated analytic algorithms have been extensively studied in the research domain, with numerous systems in widespread production. An equally important domain that has not been well addressed by the research community are network models used to support the utility domain (e.g., water, wastewater, stormwater, gas, electric, pipeline, and telecommunications). The utility domain places a very different set of requirements on a network model and the associated analytic operations than are found with transportation networks. Existing models (e.g., transportation or social graph focused) suffer from a number of limitations and constraints that limit the ability of utility companies from most effectively modeling their network connected infrastructure. Although many utilities have succeeded in implementing systems on top of simple graph models, the solutions have often involved either having to author considerable amounts of custom application code to go with the model (a very expensive and cumbersome proposition), or modifying the workflows in order to compensate for the graph model limitations. This paper proposes the creation of a new utility-centric graph information model to directly support the modeling of utility infrastructures. It is focused on supporting additional requirements for improved performance and scalability (through optimized data models), efficiency and productivity (using a model that supports underground, inside-plant, etc.), data quality (rule-based system which prevents bad data from being created), real-time data acquisition (support for field-based telemetry), enhanced visualization capabilities (schematic visualizations of data), and integration across a services-centric platform model (e.g., RESTful services, desktop, mobile, and web-based clients).","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"2 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":"114912437","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: 5

Meet-and-go: finding optimal single connecting points considering companionship preferences 见面会:考虑伴侣偏好，寻找最佳的单一连接点

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820785

George Tsatsanifos, Alexandr Petcovici, M. Nascimento

引用次数: 3

FIFUS: a rule-based fuzzy inference model for fuzzy spatial objects in spatial databases and GIS FIFUS:一种基于规则的模糊推理模型，用于空间数据库和GIS中的模糊空间对象

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820874

A. Carniel, Markus Schneider, R. Ciferri

{"title":"FIFUS: a rule-based fuzzy inference model for fuzzy spatial objects in spatial databases and GIS","authors":"A. Carniel, Markus Schneider, R. Ciferri","doi":"10.1145/2820783.2820874","DOIUrl":"https://doi.org/10.1145/2820783.2820874","url":null,"abstract":"Decision support based on spatial (and not only alphanumerical) data has received increasing interest in geographical applications, such as geoscience, agriculture, and economics applications, and has led to Spatial Decision Support Systems (SDSS). SDSS use spatial database systems and Geographical Information Systems as their data management and analysis components in order to get and handle the needed spatial data and perform recommendations, estimations, or predictions. For instance, farmers want to know what the best areas of their farmland are to grow a specific crop. In most cases, the extent and the properties of the spatial phenomena of interest are vague and imprecise. They can be adequately represented by fuzzy spatial objects (e.g., fuzzy points, fuzzy lines, fuzzy regions). In this paper, we formally propose a model named Fuzzy Inference on Fuzzy Spatial Objects (FIFUS), which infers recommendations, estimations, and predictions based on fuzzy rules and knowledge of domain specialists. It incorporates fuzzy spatial objects into the components of the existing fuzzy inference methods in order to take into account the spatial imprecision found in the real world. As a main advantage, FIFUS is a general-purpose model and can thus be applied in many geoscience applications.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"7 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":"125894038","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: 5

Efficient map reconstruction and augmentation via topological methods 有效的地图重建和增强通过拓扑方法

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems Pub Date : 2015-11-03 DOI: 10.1145/2820783.2820833

Suyi Wang, Yusu Wang, Yanjie Li

{"title":"Efficient map reconstruction and augmentation via topological methods","authors":"Suyi Wang, Yusu Wang, Yanjie Li","doi":"10.1145/2820783.2820833","DOIUrl":"https://doi.org/10.1145/2820783.2820833","url":null,"abstract":"In recent years, with the rapid growth in the amount of publicly available Volunteered Geographic Information (VGI) data, automatic map generation from GPS trajectories has attracted great attention. Maps generated from these data can for example complement commercial maps in less developed areas. Two main challenges in the automatic generation of maps from volunteered GPS data are the handling of noise and of non-homogeneous sampling of road segments (for example, roads in downtown area can receive significantly more GPS traces than roads in residential areas). In this paper, we present a novel framework for map reconstruction based on a topological idea: the Morse theory. In particular, the use of Morse theory and topological simplification allows us to handle the issues of both noise and non-homogeneous sampling in an elegant unified framework. Our algorithm is significantly simpler than previous approaches, both conceptually and implementation speaking. Little pre- and post-processing is required, and yet the algorithm can reconstruct robust road-networks from challenging data sets (such as GPS traces for Berlin or Beijing cities) that are comparable or better than the output of previous state-of-the-art approaches. The new algorithm is also orders of magnitude faster than previous approaches on large data sets (for example, the entire processing of the Berlin city data with about 27189 trajectories takes less than one minute). Furthermore, our framework can be easily extended to handle the map integration problem, where one wishes to integrate multiple maps into a single one. Here, roads in different maps can have different confidence levels, and higher confident roads will have larger influence in the final integrated road. We also present an effective algorithm for a slightly different map augmentation problem, where one wishes to augment a map, say G2, using partial but more trust-worthy map G1, in the sense that in the final map, information in G1 needs to be completely preserved.","PeriodicalId":241302,"journal":{"name":"Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"3 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":"124356826","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: 55