Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation最新文献

Integrating social networks into large-scale urban simulations for disaster responses 将社会网络整合到大规模的城市灾难响应模拟中

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428168

Na Jiang, Annetta Burger, A. Crooks, W. Kennedy

引用次数: 5

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation 第三届ACM SIGSPATIAL国际地理空间模拟研讨会论文集

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335

引用次数: 0

Generating contextual trajectories from user profiles 从用户配置文件生成上下文轨迹

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428166

Jian Yang, C. Poellabauer, Pramita Mitra, Abhishek Sharma, Cynthia Neubecker, Arpita Chand

{"title":"Generating contextual trajectories from user profiles","authors":"Jian Yang, C. Poellabauer, Pramita Mitra, Abhishek Sharma, Cynthia Neubecker, Arpita Chand","doi":"10.1145/3423335.3428166","DOIUrl":"https://doi.org/10.1145/3423335.3428166","url":null,"abstract":"The trajectory of traffic participants is an essential source for pattern mining and knowledge discovery in urban mobility. However, real-world trajectory data are often not publicly available due to privacy concerns or intellectual property constraints. Although some simulators or synthetic trajectory datasets have been proposed, many of them only consider the spatial-temporal aspects of the trajectory data, but ignore other contextual information that could impact trajectories. On one hand, trajectories are usually associated with and affected by user profiles (e.g., a person's daily routines and preferred modes of transportation). On the other hand, an individual's movements are also affected by environmental conditions and interactions with other traffic participants, particularly in urban scenarios (e.g., routing choices due to congestion or road conditions). Such contextual trajectories provide a more realistic representation of the mobility patterns of traffic participants. Due to the lack of such datasets or trace generators, this work presents ConTraSim (Contextual Trajectory Simulation), a novel approach for generating contextual trajectories based on the Simulation of Urban Mobility (SUMO) traffic simulator. More specifically, the proposed approach is designed to produce GPS traces annotated by contextual information that mimic the movements of multiple types of traffic participants in urban areas. As a case study, we also generate a sample dataset using the proposed method and compare it to real-world data to demonstrate how well the synthetic data reflects real-world data characteristics.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"17 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":"126920541","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

Towards the automatic selection of moving regions representation methods 探讨了自动选择运动区域的表示方法

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428170

R. L. C. Costa, Enrico S. Miranda, José Moreira

{"title":"Towards the automatic selection of moving regions representation methods","authors":"R. L. C. Costa, Enrico S. Miranda, José Moreira","doi":"10.1145/3423335.3428170","DOIUrl":"https://doi.org/10.1145/3423335.3428170","url":null,"abstract":"Moving region is an abstraction used to represent the spatio-temporal behavior of real-world phenomena in database systems. The most common approach to model moving regions uses geometries to represent their position and shape at different times (observations), and interpolation functions to generate the evolution of the geometries between observations. Several region interpolation methods have been proposed in the databases literature, but as there is no suitable method for all use cases, users must select the most adequate algorithm to represent each region by visual inspection. This can be infeasible when dealing with large datasets. This paper presents the first steps towards a system that suggests which methods (and configurations) can generate representations fitting the requirements of a particular application. It includes an abstract specification of user-defined rules on the spatio-temporal evolution of moving regions to assess the suitability of region interpolation functions, a discussion on optimization strategies for efficient implementation of the rules and illustrative examples using real-world data to show how to use this approach to select the best methods to represent a spatio-temporal phenomena.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"7 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":"127876805","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

A review of methods to model route choice behavior of bicyclists: inverse reinforcement learning in spatial context and recursive logit 自行车骑行者路径选择行为建模方法综述:空间背景下的逆强化学习和递归逻辑

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428165

Thomas Koch, E. Dugundji

引用次数: 5

An exploration of the effect of buyer preference and market composition on the rent gradient using the ALMA framework 利用ALMA框架探讨买家偏好和市场构成对租金梯度的影响

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428167

Alexander Michels, Jeon-Young Kang, Shaowen Wang

引用次数: 3

A sequential sampling model of pedestrian road crossing choice 行人过马路选择的序贯抽样模型

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428163

Obi Thompson Sargoni, E. Manley

引用次数: 5

Spatial analysis of border closure intervention scheme in conflict-induced displacement 冲突导致流离失所的边境关闭干预方案的空间分析

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428162

Zahra Jafari, Toby P Davies, Shane D. Johnson

引用次数: 1

Agent-based wildfire evacuation with spatial simulation: a case study 基于agent的野火疏散与空间模拟:一个案例研究

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2020-11-03 DOI: 10.1145/3423335.3428169

S. Grajdura, Sachraa G. Borjigin, Debbie A. Niemeier

引用次数: 3

STAND: a spatio-temporal algorithm for network diffusion simulation STAND:网络扩散模拟的时空算法

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation Pub Date : 2019-04-12 DOI: 10.1145/3423335.3428164

Fangcao Xu, B. Desmarais, D. Peuquet

{"title":"STAND: a spatio-temporal algorithm for network diffusion simulation","authors":"Fangcao Xu, B. Desmarais, D. Peuquet","doi":"10.1145/3423335.3428164","DOIUrl":"https://doi.org/10.1145/3423335.3428164","url":null,"abstract":"Information, ideas, and diseases, or more generally, contagions, spread over time and space through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a detailed network structure and individual diffusion pathways are obtained. Studying such diffusion networks provides valuable insights to understand important actors in carrying and spreading contagions and to help predict occurrences of new infections. Most prior research focuses on modeling diffusion process only in the temporal dimension. The advent of rich social, media and geo-tagged data now allows us to study and model this diffusion process in both temporal and spatial dimensions than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall spatiotemporal process is difficult to trace. This propagation is continuous over time and space, where individual transmissions occur at different rates via complex and latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion simulation (STAND) is developed based on the survival model in this research. Both time and geographic distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes denote geographic locations at a large scale.","PeriodicalId":126625,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977738","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: 17