Learning functional compositions of urban spaces with crowd-augmented travel survey data

TY - CHAP

T1 - Learning functional compositions of urban spaces with crowd-augmented travel survey data

AU - Zhu, Zack

AU - Yang, Jing

AU - Zhong, Chen

AU - Seiter, Julia

AU - Tröster, Gerhard

PY - 2015/11/3

Y1 - 2015/11/3

N2 - Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-source features. In this work, we form a comprehensive view of urban regions by fusing features depicting their temporal, spatial, and demographic aspects. Aggregating 47K explicitly stated trip purposes into their respective destination regions, we obtain multi-dimensional ground-truths on the functionalities of urban spaces. Given fused features and training labels, we can perform supervised learning, via multi-output regression, to estimate the functional composition of urban spaces. With 14 functional dimensions, our approach using crowd-augmented travel survey predictors delivers a mean absolute error of 3.9, approximately half of the error resulting from a mean-based straw man approach (mean absolute error of 7.9). Clustering estimated regional functionalities, we find highly coherent cluster assignments (adjusted Rand Index of 0.81) compared to clustering directly on regional functionality labels. Finally, we provide an illustrative casestudy where clustering of estimated region functionalities can be used to intuitively differentiate prototypical spatial neighbourhoods of a large metropolitan.

AB - Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-source features. In this work, we form a comprehensive view of urban regions by fusing features depicting their temporal, spatial, and demographic aspects. Aggregating 47K explicitly stated trip purposes into their respective destination regions, we obtain multi-dimensional ground-truths on the functionalities of urban spaces. Given fused features and training labels, we can perform supervised learning, via multi-output regression, to estimate the functional composition of urban spaces. With 14 functional dimensions, our approach using crowd-augmented travel survey predictors delivers a mean absolute error of 3.9, approximately half of the error resulting from a mean-based straw man approach (mean absolute error of 7.9). Clustering estimated regional functionalities, we find highly coherent cluster assignments (adjusted Rand Index of 0.81) compared to clustering directly on regional functionality labels. Finally, we provide an illustrative casestudy where clustering of estimated region functionalities can be used to intuitively differentiate prototypical spatial neighbourhoods of a large metropolitan.

KW - Multi-output regression

KW - Travel survey

KW - Urban analytics

UR - https://www.scopus.com/pages/publications/84961216131

U2 - 10.1145/2820783.2820832

DO - 10.1145/2820783.2820832

M3 - Conference paper

AN - SCOPUS:84961216131

SN - 9781450339674

VL - 03-06-November-2015

BT - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

PB - Association for Computing Machinery

T2 - 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015

Y2 - 3 November 2015 through 6 November 2015

ER -