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Abstract There is an epistemological divide in the field of road network analysis, concerning the way network distance should be conceptualized. On one hand, the generality of studies in the field adopt metric distance as a self-evident choice. On the other, space syntax studies adopt a different distance concept, namely that of angular distance, which ignores actual physical lengths. Theoretically, these two distance concepts imply quite different assumptions. Analytically, they produce also different results. In this paper we assess the differences between these two network distance concepts, using a model of the UK's complete road network (2,031,971 nodes) and a very large dataset of vehicular movement counts (20,752 locations). We describe the statistical associations between observed vehicular flows and the betweenness centrality of the road-network nodes where such flows were measured, the latter calculated with metric and angular distance functions, across a number of increasing radii. Relations to road capacity are also discussed in principal roads where this is known. The geographical comprehensiveness of our model and the size of our movement sample allow us to state, with unprecedented statistical validity, the clear outperformance of angular distance over metric distance, in what concerns the strength of the studied correlations. This is true for all types of roads (urban and non-urban) and for all motorized vehicles, representing 99.05% of the total traffic; the remaining 0.95% correspond to pedal cycles, which yielded inconclusive results. We also demonstrate the existence of two distinct regimes of association between movement and centrality, conspicuous in cities but altogether absent in non-urban areas, which represent new evidence supporting space syntax's dual model of urban form. Highlights • There is a basic epistemological divide in the field of street and road network analysis, which concerns the way network distance should be conceptualized. • In this paper we address this divide, through a nationwide study assessing the differences between the correlations of vehicular movement flows and two types of network distance (metric and angular). • We use a nationwide network model (the UK mainland) and a very large dataset of vehicular movement count points (N = 20,752, distributed over the whole UK territory). • The geographical comprehensiveness of our model and the size of our movement sample allows us to state, with unprecedented statistical validity, the clear outperformance of angular distance over metric distance, in what concerns the strength of the studied correlations. • This result strongly supports the adoption of angular distance in the analysis of street and road networks and has important consequences for their modelling and theoretical understanding. [ABSTRACT FROM AUTHOR]

This paper details an innovative approach which enables the analysis of small area population change across four decades. Population surfaces are generated using small area data (enumeration districts or output areas) for each Census from 1971 to 2011 inclusive. The paper details the methods used in the creation of these surfaces, and discusses the rationale behind this approach, arguing that grids represent the most appropriate model for assessing population distributions. Methods for grid creation are tested using pre-existing population grids for Northern Ireland as a benchmark. The method developed is then applied to create population grids for the rest of the UK for 1971, 1981, 1991, 2001 and 2011. The changing population structures of small areas across these five time points are explored here to illustrate the value of this approach. The publically-available data resource – the final product of the ‘ PopChange ’ project – will facilitate exploration of long-term changes in populations over small areas. The paper argues that maximum advantage could be taken of the ‘big data revolution’ if such data were gridded in a similar way, allowing them to be placed in a longer-term historical context, using tools made available through the PopChange project. [ABSTRACT FROM AUTHOR]

Abstract In order to assess the potential future impacts of climate change on urban areas, tools to assist decision-makers to understand future patterns of risk are required. This paper presents a modelling framework to allow the downscaling of national- and regional-scale population and employment projections to local scale land-use changes, providing scenarios of future socio-economic change. A coupled spatial interaction population model and cellular automata land development model produces future urbanisation maps based on planning policy scenarios. The framework is demonstrated on Greater London, UK, with a set of future population and land-use scenarios being tested against flood risk under climate change. The framework is developed in Python using open-source databases and is designed to be transferable to other cities worldwide. Highlights • Urban climate impacts must consider processes of socio-economic change alongside climate change. • A multi-scale modelling framework for climate impact assessment is presented and applied to London, UK. • Flood risk will increase with future climate change and socio-economic change may increase risk and impacts. • Potential adaptation options have trade-offs and co-benefits that can be assessed with the framework. [ABSTRACT FROM AUTHOR]

Abstract The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points. Highlights • Spatial datasets of building age are needed for energy modelling studies, but are not often available. • We demonstrate a data-driven approach to predict a per-building age classification and refine predictions based on spatial reasoning. • Map data, including building footprints, digital surface modelling and neighbourhood metrics serve as input predictors. • Spatial and topological relationships of buildings, including nearest neighbour analysis and graph-cuts based label optimisation are used to improve predictions. • Quantitative assessments of the accuracy for a UK case study. [ABSTRACT FROM AUTHOR]

Fracking has proven to be a contentious issue in Great Britain, receiving wide press coverage from the initial sale of exploration and development licences, to the current moratorium. This research tracks the public activity online related to this 'fracking' journey by analysing over 317 million geolocated tweets from 2015 to 2020, mapping their location to compare the spatial distribution against the shale gas exploration sites. To spatially normalise the results for population density a χ-squared expectation surface was generated revealing higher than expected levels of interest near the previously active fracking site of Preston New Road and licenced extraction blocks in Lancashire. The data granularity allows for peaks of activity to be identified and topics analysed at higher temporal and spatial resolution than previously possible with more traditional surveys. The paper demonstrates the use of χ-squared expectation surfaces for normalising geotweets and the value of social media spatial-temporal analysis for monitoring local involvement in environmental issues, and for monitoring the changing level of interest across different regions in reaction to political decisions. • This research investigates the ways that fracking social media discourse has evolved over time and across space in Great Britain • Spatial-temporal analysis of 5 years of fracking related geo-located tweets from Great Britain (317 million tweets) • Chi-squared expectation surfaces were used to find regions of greater online level of activity than would be expected • Time-sliced chi-squared expectation surfaces were used to monitor the level of sustained high/low activity over the 5 years • Named Entity Recognition was used to identify place names mentioned in tweets and used to create heat maps [ABSTRACT FROM AUTHOR]

The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities' urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions. These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions. • This paper improved calibration process of urban model using optimization method from machine learning • The improved process allowed examination of wide range of port cities to counter the current trend of case-specific studies • Quantified interactions from wide range of port cities provide insights of the different land use behaviour in different types of port cities • Port-urban interactions in smaller urban areas with port are stronger thn in most other port cities • Interactions quantified in the study can be used in simulation to predict future port city urban form [ABSTRACT FROM AUTHOR]

3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. • This paper proposes and implements a novel approach to generate 3D maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available. • The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. • Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5m accuracy, which is comparable to the accuracy of the national mapping agency of Great Britain, Ordnance Survey GB. [ABSTRACT FROM AUTHOR]

Abstract: Traditionally, researchers have employed statistical methods to model crime. However, these approaches are limited by being unable to model individual actions and behaviour. Brantingham and Brantingham (1993) described that in their opinion a useful and productive model for simulating crime would have the ability to model the occurrence of crime and the motivations behind it both temporally and spatially. This paper presents the construction and application of an agent-based model (ABM) for simulating occurrences of residential burglary at an individual level. It presents a novel framework that allows both human and environmental factors to be simulated. Although other agent-based models of crime do exist, this research represents the first working example of integrating a behavioural framework into an ABM for the simulation of crime. An artificial city, loosely based on the real city of Leeds, UK, and an artificial population were constructed, and experiments were run to explore the potential of the model to realistically simulate the main processes and drivers within this system. The results are highly promising, demonstrating the potential of this approach for both understanding processes behind crime and improving policies and developing effective crime prevention strategies. [Copyright &y& Elsevier]

Abstract: In modern GIS applications, flow mapping and geodynamics are not particularly well represented. This is somewhat surprising given the potential of such approaches to shed light on the spatial characteristics associated with the movement of people and goods. Despite the attention of a select number of researchers whose contributions have been significant, however, the subject matter remains underdeveloped and the full potential of spatial interaction data remains largely unrealised. Therefore, this paper represents an attempt to popularise the use of flow mapping within a contemporary GIS, with a view to aiding and enhancing decision support at a range of different spatial scales. It discusses advances in flow mapping, proposes an approach to mapping a large migration matrix from the United Kingdom’s 2001 census, and considers the ways in which this can add value to the understanding of geographical patterns of residential mobility. Subsequently, it considers possible dissemination methods via new developments in web-based geospatial technologies before offering some general principles and identifying future challenges. The findings indicate that it is now entirely feasible to map large spatial interaction datasets with common GIS software, that this has the benefit of producing instant, query-led information, and that the knowledge derived from this could be used to inform all kinds of local, regional and national policy formulation and evaluation. [Copyright &y& Elsevier]

This paper discusses the opportunities afforded by novel population sensing technologies in the field of 'smart' urban management. In particular, it focuses on the application of these new sources of data in retail analysis. Our goal is to integrate data derived through novel pedestrian counting and point-of-sale systems to build a statistical model that captures the relationship between retail turnover and footfall in the UK. The point-of-sales data are provided by two UK-based food & beverage retailers. To accurately measure the pedestrian activity around retail units, we make use of the data generated by the SmartStreetSensor project: a deployment of a large network of sensors installed across 105 towns and cities in the UK that collect Wi-Fi probe requests generated by mobile devices. We propose and implement novel methods for processing these raw signals into accurate estimates of pedestrian activity without compromising participants' privacy. The resulting data is then integrated into seasonal ARIMA and dynamic regression models that can be used to predict future sales. Our results indicate that the dynamic regression model that accounts for fluctuations in footfall data outperforms seasonal ARIMA model that uses only past values and behaviours of transaction data to predict future sales. Thus, we conclude that footfall does have a strong impact on retail sales and therefore integrating footfall measures into sales forecasting can significantly improve the forecasting results. We also examine differences between the two retailers and observe a stronger correlation at the Fast Food Retailer locations compared to the correlation at Family Restaurant locations. [ABSTRACT FROM AUTHOR]

The roll-out of 5G infrastructure can provide enhanced high capacity, low latency communications enabling a range of new use cases. However, to deliver the improvements 5G promises, we need to understand how to enhance capacity and coverage, at reasonable cost, across space and over time. In this paper, we take a spatio-temporal simulation modeling approach, using industry-standard engineering models of 5G wireless networks, to test how different infrastructure strategies perform under scenarios of uncertain future demand. We use coupled open-source models to analyze a UK growth corridor, a system-of-cities comprising 7 urban areas, known as the Oxford-Cambridge Arc. We find that population growth has a marginal impact on total demand for 5G (up to 15%), as the main factor driving demand is the increase in per user data consumption resulting mainly from video. Additionally, the results suggest only limited justification for deploying 5G based purely on the need for more capacity. Strategies which reuse existing brownfield Macro Cell sites are enough to meet future demand for Enhanced Mobile Broadband, except in the densest urban areas. While spatio-temporal analysis of infrastructure is common in some sectors (e.g. transport, energy and water), there has been a lack of open analysis of digital infrastructure. This study makes a novel contribution by providing an open and reproducible spatio-temporal assessment of different 5G technologies at a time when 5G is starting to roll-out around the world. • Spatio-temporal assessment of infrastructure systems is essential. • We quantify how population change from a new growth corridor affects 5G demand and supply. • The performance of 5G infrastructure strategies is quantified using an open-source spatio-temporal model. • Population growth has a marginal impact of up to 15% on 5G demand. • Upgrades to existing brownfield Macro Cells satisfy demand for 5G enhanced mobile broadband. [ABSTRACT FROM AUTHOR]

Geographic variation in digital inequality manifests as a result of a range of demographic, attitudinal, behavioural and locational factors. To better understand this multidimensional geography, our paper develops a new geodemographic classification for the spatial extent of Great Britain. In this model, we integrate a range of new small area measures that are drawn from multiple new forms of data including consumer purchasing data, survey and open data sources. Our analytical approach innovatively provides an integration of machine learning into a small-area estimation technique to obtain Lower Super Output Area / Data Zone estimates of Internet use, alongside a range of online engagement and consumption measures. Following the collation of a range of input measures, we implemented a more standard geodemographic framework that utilises the unsupervised clustering algorithm k-means to produce a map of the multidimensional characteristics of digital inequality for Great Britain; creating the Internet User Classification (IUC). Our outputs provide a new and nuanced understanding of the contemporary salient characteristics of digital inequality in Great Britain, which we evaluate both internally and externally within the context of preparations for the 2021 UK Census of the Population, exploring the geodemographic patterns of Census test response rates and the prevalence to complete the survey online. Our innovative work illustrates the strength of a geodemographic approach in mapping spatial patterns of digital inequality, and through the presented application concerning Census response rates and characteristics we demonstrate how the IUC can be operationalised within such settings for local intervention or benchmarking. • Great Britain can be differentiated into ten groups of salient digital inclusion / exclusion. • Supervised machine learning can provide methodological enhancement to small area estimation. • Census response rates have a relationship to patterns of digital inclusion / exclusion. [ABSTRACT FROM AUTHOR]

• The availability of online content of local interest attracts individuals online. • We are linking individual online preferences with online content • We employ data from the largest archive of webpages, the Internet Archive. This paper illustrates whether the availability of online content of local interest affects the likelihood of individuals to connect to the internet and spend more time online. While the literature demonstrates a number of factors which push or enable individuals to spend more time online, we know little about the conditions that pull or attract individuals online. Although we know that individuals use the internet to access information, we do not know whether such attraction forces are relevant at the local scale too. Gaining a better understanding of how such mechanisms work at the local scale can assist our efforts to bridge digital divides, which tend to be geographically clustered. To explore this we utilise innovative data, which contain all the archived webpages under the UK top level domain name (.uk) and we calculate the volume of internet content of local interest at the neighbourhood level using the geolocation information included in the text of these webpages. Specifically, we calculate the radius of gyration for every archived website using the different postcodes included in the archived webpages and then we create an aggregated measure at the neighbourhood level discounting websites that have less of a local focus. We merge this measure of Local Internet Content (LIC) with a large population survey, which contains information about the frequency of internet usage in the UK and estimate the effect of LIC on the likelihood of an individual being a frequent internet user. Multilevel models are employed to utilise both individual and geographical level characteristics. Our results indicate that even after controlling for the individual and geographical characteristics, which according to previous studies affect internet usage, the availability of internet content of local interest still attracts individuals online. [ABSTRACT FROM AUTHOR]