Adam Dennett

Build-to-Rent (BTR) developments have expanded rapidly in the UK since 2013, often advertised as providing better quality rented accommodation for university-educated Millennials than available elsewhere in the private rental sector. However, the implications of this type of housing development, and especially its affordability, are poorly understood at the city scale, partly due to a lack of evidence of where these developments cluster and what they add to the housing stock in terms of property type, amenities and cost. This article draws on data relating to 373 BTR developments in London (representing over 40,000 housing units) to show that developments are clustered where transport-related infrastructural investments have opened ‘rent gaps’ that can be exploited by developers. Exploring how these BTR schemes are marketed, the article shows that this accommodation is typically provided through new short-term ‘subscription services’ which allow developers to rent property at a premium. Questioning whether BTRs really add affordable ‘local’ homes to the city, the article concludes that BTR provides ‘quick-fix’ rental accommodation which is doing little to solve London’s housing crisis. We focus on the London BTR market and how the expansion of this housing type is reshaping the sociospatial geographies of the city.

Poor energy efficiency of homes is a major problem with urgent environmental and social implications. Housing in the UK relies heavily on fossil fuels for energy supply and has some of the lowest energy efficiency in Europe. We explore spatial variations in energy efficiency across England using data from Energy Performance Certificates (EPCs), which cover approximately half of the residential stock (14M homes between 2008-22). We examine variations between authorities after accounting for the composition of the housing stock in terms of its fixed characteristics of property type, building age and size. We explore variations in terms of geographical and social context (region, urban-rural and deprivation), which gives a picture of the scale of the challenge each faces. We also examine variations in relation to the more readily upgraded factors, such as glazing types, and in relation to local participation in improvement programmes which gives some insight into local actions or progress achieved.

Urban transit systems have differential impacts across population groups, including the perceptions of impacts. Nevertheless, the evaluation of difference in perceived benefits of transport investment is under researched, and few attempts have been made to quantify the extent to which transport provision meets users’ requirements. This paper explores how the impact of rail transit on development and regeneration differs across different income groups and migrants, assessing equity dimensions that arise through surveys on residents’ perceptions, using evidence from Chongqing, China. The analysis utilises both MANOVA and discriminant analysis. The result shows that the lowest income group perceives they benefit least from the rail transit impacts, while the highest income group perceives they benefit most. There is a significant unequal perceived benefit distribution between migrants and local residents within the low-income groups. Reflections are made on policies and planning interventions which might be introduced to achieve greater social equity in impacts.

Spatial interaction models (SIMs) are a core tool in spatial data modelling to predict spatial flows and understand their underpinning factors. SIMs have been applied to provide data insights and support decision making in multiple settings, notably in transport, human mobility, migration and epidemiology. While considerable progress has been made on advancing the theoretical and methodological underpinnings of SIMs, key challenges remain to facilitate the application of SIMs, extend existing modelling approaches, and leverage the opportunities afforded by Big Data. We identify three key challenges: reproducibility, calibration and Big Data modelling. We propose a blueprint to tackle these challenges by identifying four areas of development: (1) to enable essential infrastructure to facilitate the training, calibration and reproducibility of SIMs; (2) to embrace modelling frameworks to capture spatial, temporal and population heterogeneity; (3) to enhance statistical inference to accommodate Big Data analysis; and, (4) to integrate data science approaches to enhance SIM-generated predictions and statistical inference

While recent debates have widely acknowledged gentrification's varied manifestations, success in enumerating and disentangling the process and its defining features from other forms of neighbourhood change at‐scale and across entire cities, has remained largely elusive. This paper addresses this gap and employs a novel, open and reproducible urban analytics approach to systematically examine the past and future trajectories of neighbourhood change using London, England, as a case‐study example. Using suites of datasets relating to population, house prices, and built environment development, the nature of gentrification's mutations and its spatial patterns are extracted through a multi‐stage data dimensionality reduction and classification methodology. Machine learning is subsequently adopted to model gentrification's observed trends and predict its future frontiers with interactive visualisation methods offering new insights into gentrification's projected dynamics and geographies.