当前位置: X-MOL 学术J. Geogr. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Simplifying the interpretation of continuous time models for spatio-temporal networks
Journal of Geographical Systems ( IF 2.417 ) Pub Date : 2021-07-26 , DOI: 10.1007/s10109-020-00345-z
Sarah C. Gadd 1, 2 , Alexis Comber 1 , Keiran Suchak 1, 2 , Alison J. Heppenstall 1, 2, 3 , Mark S. Gilthorpe 2, 3, 4
Affiliation  

Autoregressive and moving average models for temporally dynamic networks treat time as a series of discrete steps which assumes even intervals between data measurements and can introduce bias if this assumption is not met. Using real and simulated data from the London Underground network, this paper illustrates the use of continuous time multilevel models to capture temporal trajectories of edge properties without the need for simultaneous measurements, along with two methods for producing interpretable summaries of model results. These including extracting ‘features’ of temporal patterns (e.g. maxima, time of maxima) which have utility in understanding the network properties of each connection and summarising whole-network properties as a continuous function of time which allows estimation of network properties at any time without temporal aggregation of non-simultaneous measurements. Results for temporal pattern features in the response variable were captured with reasonable accuracy. Variation in the temporal pattern features for the exposure variable was underestimated by the models. The models showed some lack of precision. Both model summaries provided clear ‘real-world’ interpretations and could be applied to data from a range of spatio-temporal network structures (e.g. rivers, social networks). These models should be tested more extensively in a range of scenarios, with potential improvements such as random effects in the exposure variable dimension.



中文翻译:

简化对时空网络连续时间模型的解释

时间动态网络的自回归和移动平均模型将时间视为一系列离散步骤,这些步骤假设数据测量之间的间隔是均匀的,如果不满足此假设,则会引入偏差。本文使用来自伦敦地铁网络的真实和模拟数据,说明了使用连续时间多级模型来捕获边缘属性的时间轨迹,而无需同时测量,以及两种生成模型结果可解释摘要的方法。这些包括提取时间模式的“特征”(例如最大值、最大值时间),它在理解每个连接的网络属性和总结整个网络属性作为时间的连续函数方面具有效用,这允许在任何时间估计网络属性,而无需非同时测量的时间聚合。以合理的准确度捕获了响应变量中的时间模式特征的结果。模型低估了暴露变量的时间模式特征的变化。这些模型显示出一些精度不足。两个模型摘要都提供了清晰的“现实世界”解释,并且可以应用于来自一系列时空网络结构(例如河流、社交网络)的数据。这些模型应该在一系列场景中进行更广泛的测试,

更新日期:2021-07-26
down
wechat
bug