当前位置: X-MOL 学术Int. J. Geograph. Inform. Sci. › 论文详情
Road and travel time cross-validation for urban modelling
International Journal of Geographical Information Science ( IF 3.545 ) Pub Date : 2019-08-29 , DOI: 10.1080/13658816.2019.1658876
Henry Crosby; Theodoros Damoulas; Stephen A. Jarvis

The physical and social processes in urban systems are inherently spatial and hence data describing them contain spatial autocorrelation (a proximity-based interdependency on a variable) that need to be accounted for. Standard k-fold cross-validation (KCV) techniques that attempt to measure the generalisation performance of machine learning and statistical algorithms are inappropriate in this setting due to their inherent i.i.d assumption, which is violated by spatial dependency. As such, more appropriate validation methods have been considered, notably blocking and spatial k-fold cross-validation (SKCV). However, the physical barriers and complex network structures which make up a city’s landscape mean that these methods are also inappropriate, largely because the travel patterns (and hence Spatial Autocorrelation (SAC)) in most urban spaces are rarely Euclidean in nature. To overcome this problem, we propose a new road distance and travel time k-fold cross-validation method, RT-KCV. We show how this outperforms the prior art in providing better estimates of the true generalisation performance to unseen data.
更新日期:2020-01-04

 

全部期刊列表>>
化学/材料学中国作者研究精选
Springer Nature 2019高下载量文章和章节
ACS材料视界
南京大学
自然科研论文编辑服务
剑桥大学-
中国科学院大学化学科学学院
南开大学化学院周其林
课题组网站
X-MOL
北京大学分子工程苏南研究院
华东师范大学分子机器及功能材料
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug