"Waiting on the train": The anticipatory (causal) effects of Crossrail in Ealing

This paper estimates the willingness-to-pay for anticipated journey-time savings introduced by the Crossrail intervention in the London Borough of Ealing. Given Crossrail remains under construction, we estimate how the anticipated benefit of Crossrail\u27s announcement enters the house price determination process. Anticipated journey-time savings should enter the home-buyer\u27s pricing equation because these benefits are speculatively internalised even before the service becomes operational. Using a experimental method that accounts for the possibility of a spatial autoregressive process in housing values, we test the hypotheses that the announcement of a new commuter rail service generated a location premium, and that house price appreciation reflected proximity to Crossrail terminals. Our evidence suggests home-buyers significantly valued proximity to planned Crossrail terminals following the post-announcement period

Buy online collect in-store: Exploring grocery click & collect using a national case study

21st century online retailing has reshaped the retail landscape. Grocery shopping is emerging as the next fastest growing category in online retailing in the UK, having implications for the channels we use to purchase goods. Using Sainsbury’s data, we create a bespoke set of grocery click&collect catchments. The resultant catchments allow an investigation of performance within the emerging channel of grocery click&collect. The spatial interaction method of ‘Huff gravity modeling’ is applied in a semi-automated approach, used to calculate grocery click&collect catchments for 95 Sainsbury’s stores in England. The catchments allow investigation of the spatial variation and particularly rural-urban differences. Store and catchment characteristics are extracted and explored using ordinary least squares regression applied to investigate ‘demand per day’ (a confidentiality transformed revenue value) as a function of competition, performance and geodemographic factors. Our findings show that rural stores exhibit a larger catchment extent for grocery click&collect when compared with urban stores. Linear regression finds store characteristics as having the greatest impact on demand per day, adhering to wider retail competition literature. Conclusions display a need for further investigation (e.g. quantifying loyalty). New insights are contributed at a national level for grocery click&collect, as well as e-commerce, multichannel shopping and retail geography. Areas for further investigation are identified, particularly quantitatively capturing brand loyalty. The research has commercial impact as the catchments are being applied by Sainsbury’s to decide the next 100 stores and plan for the next five years of their grocery click&collect offering

Sustainable urban development indicators in Great Britain from 2001 to 2016

Current planning strategies promoting suburbanisation, land use zoning and low built-up density areas tend to increase the environmental footprint of cities. In the last decades, international and local government plans are increasingly targeted at making urban areas more sustainable. Urban structure has been proved to be an important factor guiding urban smart growth policies that promote sustainable urban environments and improve neighbourhood social cohesion. This paper draws on a series of unique historical datasets obtained from Ordnance Survey, covering the largest British urban areas over the last 15 years (2001–2016) to develop a set of twelve indicators and a composite Sustainable Urban Development Index to quantitatively measure and assess key built environment features and their relative change compared to other areas at each point in time based on regular 1 km2 grids. The results show that there is a relative increase in urban structure sustainability of areas in and around city centres and identify that the primary built environment feature driving these improvements was an increase in walkable spaces

A course on Geographic Data Science

Material used in ENVS3/563 - Geographic Data Science at the University of Liverpool in the Fall'18, including paper for JOSE

darribas/gds16: predoi

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