Preprint: Testing the ability of multivariate hybrid spatial network analysis to predict the effect of a major urban redevelopment on pedestrian flows

Cooper, C.H.V., Harvey, I., Orford, S. and Chiaradia, A.J. 2018. Preprint: Testing the ability of multivariate hybrid spatial network analysis to predict the effect of a major urban redevelopment on pedestrian flows. https://doi.org/10.48550/arXiv.1803.10500

TitlePreprint: Testing the ability of multivariate hybrid spatial network analysis to predict the effect of a major urban redevelopment on pedestrian flows
AuthorsCooper, C.H.V., Harvey, I., Orford, S. and Chiaradia, A.J.
Description

Predicting how changes to the urban environment will affect town centre vitality, mediated as pedestrian flows, is important for environmental, social and economic sustainability. This study is a longitudinal investigation of before and after urban environmental change in a town centre and its association with vitality. The case study baseline is Cardiff town centre in 2007, prior and after major changes instigated by re-configuring Cardiff public and quasi-public street layout due to implementation of the St David’s Phase 2 retail development. We present a Multivariate Hybrid Spatial Network Analysis (MHSpNA) model, which bridges the gap between existing Spatial Network Analysis models and four stage modelling techniques. Multiple theoretical flows are computed based on retail floor area (everywhere to shops, shop to shop, stations to shops and parking to shops). The calibration process determines a suitable balance of these to best match observed pedestrian flows, using generalized cross-validation to prevent overfit. Validation shows that the 2007 model successfully predicts vitality as pedestrian flows measured in 2010 and 2011. This is the first time, to our knowledge, that a vitality-pedestrian flow model has been evaluated for its ability to forecast town centre vitality changes over time.

Year2018
Output mediaarXiv preprint
Publication dates
Published28 Mar 2018
ISSN2331-8422
Digital Object Identifier (DOI)https://doi.org/10.48550/arXiv.1803.10500
JournalARXIV

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