Road and travel time cross-validation for urban modelling

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 gene...

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Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2020-01, Vol.34 (1), p.98-118
Main Authors: Crosby, Henry, Damoulas, Theodoros, Jarvis, Stephen A.
Format: Article
Language:English
Subjects:
Algorithms
Autocorrelation
Barriers
cross-validation
GIS
Learning algorithms
Machine learning
Road distance
Spatial data
Travel
Travel patterns
Travel time
Urban areas
urban science
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Summary: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.
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2019.1658876