Urban policy transfer has the potential to enhance urban systems by applying successful strategies from one city to another. However, existing models for predicting policy impacts are often context-specific and lack generalisability across different urban settings. This study introduces TransGM, a novel framework that enables the transfer of spatial interaction models between cities via adaptive transfer learning. The framework employs: (1) spatial Kullback-Leibler divergence to quantify structural differences across urban contexts; (2) productionconstrained gravity models for flow prediction, accounting for residues in urban features; and (3) featurespecific regularisation weights that adapt parameters based on the degree of spatial pattern similarity between source and target cities. TransGM is demonstrated through a case study of workplace attractiveness and its impact on commuting flows transferred from Birmingham to Coventry. The adapted transfer model replicates Birmingham's urban configuration in Coventry rather than learning Coventry's own priority structure. Transfer process is jointly governed by spatial divergence between the two cities and the data sufficiency: where spatial patterns align, local data dominate parameter estimation, while in data-sparse contexts, source-city regularisation guides model behaviour. This balance between place-specific urban structure and cross-city mobility and amenity transferability positions adaptive transfer learning as an effective tool for evaluating the feasibility of replicating one city's development model in another, offering simulation-based evidence to inform urban planning and policy decisions.

Large-scale transport infrastructure can influence national patterns of population and employment, and induce agglomeration effects and long-term economic development. However, conventional appraisal methods often do not capture these indirect impacts. This study investigates how transport investments may affect the spatial distribution of population and employment and the resulting agglomeration effects and productivity changes, using East West Rail in England as a case study. We develop an extended land use-transport interaction (LUTI) model with industry-level disaggregation. It integrates a recursive gravity framework with a multimodal transport network and iteratively updates residential and employment distributions in response to accessibility changes. Results reveal that the rail line stimulates significant growth within the region, particularly along its route, but attracts limited inflows from more distant areas. Employment relocation (particularly in retail and service sectors) responds more sensitively to accessibility gains than residential patterns. When we apply our model to the national level (England and Wales), we capture the net benefits of agglomeration and disagglomeration. We find significant productivity gains in the corridor, with an estimated uplift of about 0.7%, and a modest but positive impact at the national scale around 0.02%. The results suggest that part of the corridor-level gain reflects redistribution through displacement from elsewhere rather than wholly additional national output. The analysis can thus contribute to the understanding of granular spatial patterns related to agglomeration and disagglomeration that arise from transport investments.

The current COVID-19 pandemic has profoundly impacted people's lifestyles and travel behaviours, which may persist post-pandemic. An effective monitoring tool that allows us to track the level of change is vital for controlling viral transmission, predicting travel and activity demand and, in the long term, for economic recovery. In this paper, we propose a set of Twitter mobility indices to explore and visualise changes in people's travel and activity patterns, demonstrated through a case study of London. We collected over 2.3 million geotagged tweets in the Great London Area (GLA) from Jan 2019 -Feb 2021. From these, we extracted daily trips, origin-destination matrices, and spatial networks. Mobility indices were computed based on these, with the year 2019 as a pre-Covid baseline. We found that in London, (1) People are making fewer but longer trips since March 2020. (2) In 2020, travellers showed comparatively reduced interest in central and sub-central activity locations compared to those in outer areas, whereas, in 2021, there is a sign of a return to the old norm. (3) Contrary to some relevant literature on mobility and virus transmission, we found a poor spatial relationship at the Middle Layer Super Output Area (MSOA) level between reported COVID-19 cases and Twitter mobility. It indicated that daily trips detected from geotweets and their most likely associated social, exercise and commercial activities are not critical causes for disease transmission in London. Aware of the data limitations, we also discuss the representativeness of Twitter mobility by comparing our proposed measures to more established mobility indices. Overall, we conclude that mobility patterns obtained from geo-tweets are valuable for continuously monitoring urban changes at a fine spatiotemporal scale.

In this article, we compare short‐term rental (STR) and long‐term rental (LTR) price patterns in London using one of the most popular STR platforms, Airbnb, and the LTR platform, Zoopla property website. This research aims to enhance our understanding of both LTR and STR price patterns; as well as STR dynamics specifically, using predictive modeling to analyze how the patterns might evolve. We used the coefficient of variation and correlation analysis to examine the rental price patterns of both short‐ and long‐term markets. Then we developed a rent‐based gravity model to predict STR price pattern that is sensitive to the changes in visits to tourist destinations. Based on our analysis, we concluded that: (1) STR prices tend to be higher overall with an indication of higher volatility (less stability) compared to LTR; (2) there is statistical evidence supporting the arguments that STR and LTR markets are indeed in competition; and (3) the proposed gravity model provides a robust prediction of the STR pattern with a characteristic that higher‐priced short‐term properties are found to be geographically concentrated in the core city areas and those surrounding residential areas with easy access to popular tourist attractions.

Housing is a major source of inequality in England, but most house price variation studies are conducted at national or regional scale or, conversely, in a specific city. Detailed research at sub-regional level is missing, especially for the period after the global financial crisis. This research addresses this gap with an analysis of variation at local authority level across England between 2009 and 2016. A novel house price per square meter (HPM) dataset is used to control for property size effects in transaction price variation. The effects of two spatial levels (local authority (LA)-and Middle Layer Super Output) together with three time categorizations (quarterly, half-yearly, and yearly) is systematically explored using multilevel models. Results show that the time categorization effects are essentially identical and extremely small, in comparison with the LA effects. As annual effects provide the best model fit, LA annual house price trajectories are explored further. Overall higher HPM LAs grew faster over the 80-year period than lower HPM LAs. More locally the spatial pattern shows some variation in the overall pattern, with some LAs near London or Bristol exhibiting higher relative percentage HPM increases with a relatively lower initial HPM compared with their neighbors.

In this paper we created a novel framework for understanding housing affordability in England using a linked house price dataset. Regional house price studies revealed that after the global economic crisis, there was an unprecedented regional house price divergence driven by faster price increases in London from 2009 onwards. To ease England’s resulting housing affordability issues, we consider the scenario of a typical London homeowner to offer a new insight into local housing affordability by different property type in England and explore the best property search areas for homeowners moving out of London.

Exploring the nature of spatial and temporal variation in house prices is important because it can help better understand such issues as affordability and equity of access to housing. In the UK, research on house price variation has been hindered by a lack of extensive data linking the prices of properties at different places and times to their physical attributes. This paper addresses this gap through using a new dataset linking Land Registry Price Paid Data to attribute data from Ordnance Survey and Energy Performance Certificates datasets. The new data are used to investigate spatial disparities in England’s house prices at four geographical scales (from local authority to individual address) between 2009 and 2016 – a period of sustained price rises after the global financial crisis of 2008. We selected two housing price measures for comparison, namely transaction price and the house price per square metre. Multilevel variance components models are used to estimate variation in the two house price measures at four different spatial scales and we compare spatial disparities in the two measures at these different scales. Our results suggest that accounting for the size of properties by using house price per square metre offers a more accurate picture of house price variation than does the use of transaction prices at the same geographic scale. Spatial disparities in house price per square metre are more apparent and are seen to be clustered at local authority level and highly clustered at Middle Layer Super Output Area level, with imbalances increasing during this eight-year period and highlighting the strong and growing influence of London on the national housing market.

Current research on residential house price variation in the UK is limited by the lack of an open and comprehensive house price database that contains both transaction price alongside dwelling attributes such as size. This research outlines one approach which addresses this deficiency in England and Wales through combining transaction information from the official open Land Registry Price Paid Data (LR-PPD) and property size information from the official open Domestic Energy Performance Certificates (EPCs). A four-stage data linkage is created to generate a new linked dataset, representing 79% of the full market sales in the LR-PPD. This new linked dataset offers greater flexibility for the exploration of house price (/m 2 ) variation in England and Wales at different scales over postcode units between 2011 and 2019. Open access linkage codes will allow for future updates beyond 2019.

This paper explores a decentralisation initiative in the United Kingdom - the Northern Powerhouse strategy (NPS) - in terms of its main goal: strengthening connectivity between Northern cities of England. It focuses on economic interactions of these cities, defined by ownership linkages between firms, since the NPS's launch in 2010. The analysis reveals a relatively weak increase in the intensity of economic regional patterns in the North, in spite of a shift away from NPS cities' traditional manufacturing base. These results suggest potential directions for policy-makers in terms of the future implementation of the NPS.

The spatial and temporal diffusion of house prices has been investigated at regional level in England, with London and the South East playing a leading role in terms of spillovers to other regions. High house prices in London not only increase neighbouring house prices but also force workers to live outside London and commuting in. To better understand this London effect, this research aims to explore the effect of travel time to London on house price variation across England. We conducted this research at local authority level rather than region level to offer a clearer insight into the relationship between house price variation and travel time to London, concentrating especially on the period post the 2008 financial crisis. Results show that local authorities with shorter travel tomes to London generally have greater house prices increases, but with some exceptions. The majority of local authorities within 75 minutes travel time to London had a high house price increase between 2009 and 2016. This underlies the London ripple effect and is reinforced by the high proportion of workers commuting to London.

Most spatio-temporal studies of house price in the UK are carried out at national or regional scale, but house prices differences could be better understood at finer spatial scales. Since England’s house prices, standardised by the size of the property (£/m), have been shown to be somewhat clustered at local authority level and highly clustered at Middle Layer Super Output (MSOA) level, in the period 2009 to 2016, this research aims to further explore the nature of spatial and temporal variation in house prices at local authority level in England. Growth curve modelling offers a model-based description of the spatio-temporal patterns of local authority house price variation. This research explores local authority effects and three different time effects (quarter, half-year and year) on house price spatio-temporal variation. Results show that these three time effects are essentially identical and are extremely small, in comparison with local authority effects. Since annual effects provide the best fit, local authority annual house price trajectories between 2009 and 2016 are further explored. Local authorities with higher house prices in 2009 are found to have faster growing prices over the eight-year period than local authorities with lower house prices. Moreover, two clear geographic hubs of house price change over the period are observed, one centred on London, the other on Bristol.

The competition in space between rail and sea transport is of great significance to the integration of Eurasia. This paper proposes a land and sea transport spatial balance model for container transport, which can extract a partition line on which transport costs by rail and sea are equal given a destination. Four scenarios are discussed to analyse the effects of different factors on the model. Then the model is empirically tested on current rail and sea transport networks to identify the transport competition pattern in Eurasia. The location of destinations, the freight costs, and time costs are the three main factors affecting the model. Among them, time costs are determined by the value of a container and its contents, the interest rate, and by time differences between land and sea transport. The case study shows that Eurasia forms a transport competition pattern with a land area to sea area ratio of about 1:2; this ratio, however, changes to 1:1 when time costs are considered. Further, the land and sea transport balance lines are consistent with the theories of geopolitics, which indicate that the same processes may exist in the spatial pattern of geo-economics and geopolitics in Eurasia. According to the balance lines, we get a spatial partition, dividing Eurasia into the land transport preferred area, the land–sea transport indifference area, and the sea transport preferred area. The paper brings a new perspective to the exploration of geopolitical economic spatial patterns of Eurasia and provides a practical geographic theory as an analytic basis for the implementation of the Belt and Road Initiative.

The morphology of urban agglomeration is studied here in the context of information exchange between different spatio-temporal scales. Urban migration to and from cities is characterised as non-random and following non-random pathways. Cities are multidimensional non-linear phenomena, so understanding the relationships and connectivity between scales is important in determining how the interplay of local/regional urban policies may affect the distribution of urban settlements. In order to quantify these relationships, we follow an information theoretic approach using the concept of Transfer Entropy. Our analysis is based on a stochastic urban fractal model, which mimics urban growing settlements and migration waves. The results indicate how different policies could affect urban morphology in terms of the information generated across geographical scales.

We pose the central problem of defining a measure of complexity, specifically for spatial systems in general, city systems in particular. The measures we adopt are based on Shannon's (in Bell Syst Tech J 27:379-423, 623-656, 1948) definition of information. We introduce this measure and argue that increasing information is equivalent to increasing complexity, and we show that for spatial distributions, this involves a trade-off between the density of the distribution and the number of events that characterize it; as cities get bigger and are characterized by more events-more places or locations, information increases, all other things being equal. But sometimes the distribution changes at a faster rate than the number of events and thus information can decrease even if a city grows. We develop these ideas using various information measures. We first demonstrate their applicability to various distributions of population in London over the last 100 years, then to a wider region of London which is divided into bands of zones at increasing distances from the core, and finally to the evolution of the street system that characterizes the built-up area of London from 1786 to the present day. We conclude by arguing that we need to relate these measures to other measures of complexity, to choose a wider array of examples, and to extend the analysis to two-dimensional spatial systems.

“Thermodynamics of the City” (Wilson 2008) poses the question, in relation to the doubly constrained trip distribution model –What is Z?- where Z is the partition function. To answer this question the entropy maximising procedure of Jaynes(1957) is employed, the partition function derived and expressions given for Helmholtz free energy, for more general free energies and for specific heat. Phase changes are identified using these measures. The implications of these results are discussed and the possibility of a spatially based exergy analysis is suggested .

The use of growth factor models for trip distribution has given way in the past to the use of more complex synthetic models. Nevertheless growth factor models are still used, for example in modelling external trips, in small area studies, in input-output analysis, and in category analysis. In this article a particular growth factor model, the Furness, is examined. Its application and functional form are described together with the method of iteration used in its operation. The “expected information” statistic is described and interpreted and it is shown that the Furness model predicts a trip distribution which, when compared with observed trips, has the minimum expected information subject to origin and destination constraints. An equivalent entropy maximising derivation is described and the two methods compared to show how the Furness iteration can be used in gravity models with specified deterrence functions. A trip distribution model explicitly incorporating information from observed trips, is then derived.