Citation

Lieske SN, Leao SZ, Conrow L, Pettit C. Environ. Plan. B Urban Anal. City Sci. 2021; 48(4): 775-792.

Copyright

(Copyright © 2021, SAGE Publishing)

DOI

10.1177/2399808319894334

PMID

unavailable

Abstract

In an era of data-driven smart cities, the possibility of using crowdsourced big data to support evidence-based planning and decision-making remains a challenge. Along with the increased availability and potential utility of crowdsourced data, there is a clear need to assess the validity of these data in order to determine their appropriate use for planning and management. Moreover, with growth and rapid urbanization in many cities, there are increasing challenges associated with urban mobility. The goal of this research is to develop an understanding of the geographical representativeness of crowdsourced data in the context of urban mobility through investigation of bicycling in Australian cities. In order to leverage both the geographic distribution and high volume of crowdsourced data for validity assessment, we present a two-stage statistical approach. First, we evaluate flow data through correlation between spatial interaction matrices in the presence of spatial autocorrelation. The second stage evaluates the quantity of information available within the interaction matrices. The approach is demonstrated with crowdsourced bicycling commuting routes recorded by the RiderLog app from 2010 to 2014 that are then correlated with census bicycling journey to work data. Data are from four of Australia's state capital cities: Adelaide, Brisbane, Melbourne and Perth. These methods assess the representativeness of individual bicycle routes that address the full pattern of flows within multiorigin multidestination systems and incorporate spatial autocorrelation.

RESULTS indicate that these crowdsourced data are geographically representative of regional travel where there are higher data volumes, generally in central business districts and occasionally in outlying areas. This research provides insights into both methods for statistical comparison of flow data and the use of crowdsourced bicycling routes for urban planning and management.

Language: en

Keywords

big data; crowdsourcing; Human movement; urban analytics; urban mobility