Dr Alex Hagen-Zanker

Spatial modelling approaches to aid land-use decisions which benefit both wildlife and humans are often limited to the comparison of pre-determined landscape scenarios, which may not reflect the true optimum landscape for any end-user. Furthermore, the needs of wildlife are often under-represented when considered alongside human financial interests in these approaches. We develop a method of addressing these gaps using a case-study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA-II with a process-based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we 'evolve' a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives. We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real-life landscapes promote or compromise objectives for different landscape end-users. Our investigation suggests that optimisation set-up (decision-unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human-centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape-level needs when using genetic algorithms to support biodiversity-inclusive decision-making in multi-functional landscapes.

Weather-related disruption is a pressing issue for transport infrastructure in the UK, which is expected to aggravate due to climate change. Infrastructure managers, such as Network Rail, need to adapt to these changes, tackling the challenges brought about by wide-ranging uncertainties from various sources. This paper explores the relationship between climate change and bridge scour, identifying barriers to sustainable adaptation. Scour is the removal of riverbed material at bridge foundations due to hydraulic action and is the foremost cause of bridge failure in the UK and worldwide. A model is developed that simulates the causal chain from climate change to scour risk. This is applied to four case study bridges in Wales and the south-west of England, quantifying the effects of climate change and tracing key uncertainties in the process. Results show that the current scour risk models in Network Rail may be insensitive to increases in risk due to climate change. One way to tackle this may be to introduce models to assess absolute risk; current scour risk models are used only for the prioritisation of vulnerable sites.

This paper presents an extension to the Constrained Cellular Automata (CCA) land use model of White et al. [White, R., Engelen, G., Uljee, L, 1997. The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design 24(3), 323-343] to make it better suited for modelling the dynamics of shifting cultivation. In the extended model the time passed since the last land use transition of a location is a factor of its land use potential. The model can now account for the gradual decrease in soil fertility after an area of forest has been cleared for cultivation and also capture the process of regeneration once the plot is fallowed. The model is applied for the Ruhunupura area of Sri Lanka where chena, a particular practice of shifting cultivation, is a common land use that dominates the landscape dynamics. The model is calibrated for the period 1985-2001 and the results are assessed in terms of location to location overlap as well as structural similarity at multiple scales. These results give confidence in the representation of land use dynamics for the main land use classes. On the basis of a long term scenario run for the period 2001-2030, it is verified that the model captures stylized facts related to chena dynamics, in particular shortening fallow periods and increasingly long cultivation periods of chena, as a result of increasing land use pressure. We conclude that the model extension is crucial for regions with substantial areas of shifting cultivation. The extension affects not only the land use class shifting cultivation, but also through spatio-temporal interactions that are already present in the original CCA model the whole land use system is better represented. (C) 2009 Elsevier B.V. All rights reserved.

Bridge owners worldwide manage large numbers of assets with limited budgets through risk assessments, using asset-specific data. However, when managing a large stock of aging assets, maintaining robust and up-to-date data records can be challenging. This issue comes to the fore when trying to understand asset vulnerability to current and future weather events in the context of a changing climate. By using a sample of data on railway bridges in the UK, this paper explores uncertainty associated with raw data used in bridge scour risk assessments for bridge stocks and its interaction with climate change uncertainty. Results indicate that our ability to foresee climate change impacts is not only limited by the aleatory uncertainty of climate change projections; avoidable uncertainty in basic asset data can outweigh aleatory uncertainty by an order of magnitude. Some parameters, such as floodplain width and the width of abutments, were found to be both subject to high uncertainty and also very influential for the estimation of scour risk, leading to reduction in the confidence in scour risk assessments. This finding contrasts with the unchallenged assumption in the field that dimensions of bridge elements are not associated with uncertainty. The nature of scour implies that a potential increase in the frequency and severity of extreme weather events will increase scour risk. This paper shows that in order to be able to understand and account for this increase, scour management processes must effectively address data uncertainty. Active measures to control data quality would be an effective step towards understanding and managing bridge resilience in the context of current and future climatic conditions.

Nature-based solutions (NbS) provide direct benefits to people who live in areas where these approaches are present. The degree of direct benefits (thermal comfort, reduced flood risk, and mental health) varies across temporal and spatial scales, and it can be modelled and quantified. Less clear are the indirect benefits related to opportunities to learn about the environment and its influence on personal behaviour and action. The present study, based on survey data from 1955 participants across 17 cities worldwide, addressed whether participation in NbS through two types of interactions (a passive learning experience about NbS and a more active experience based on Citizen Science) stimulates motivation and willingness to be more environmentally sustainable. Over 75% of participants improved their understanding of environmental sustainability and were highly motivated and more confident in their ability to improve sustainability in their local environment/nature. Similar percentage improvements arose from both types of activity across all cities. Those NbS that had elements of both blue and green infrastructure rated higher than those that had predominantly green NbS. Interestingly, a large percentage of the participants did not live near the NbS that were the focus of these activities. This indicated that expected spatial limitations between benefit and recipient may be overcome when dedicated programmes involve people in learning or monitoring NbS. Therefore, opportunities have arisen to expand inclusion from the immediately local to the larger community through participation and Citizen Science, with potential benefits to social cohesion and urban sustainability.

The Fuzzy Kappa statistic expresses the agreement between two categorical raster maps. The statistic goes beyond cell-by-cell comparison and gives partial credit to cells based on the categories found in the neighborhood. When matching categories are found at shorter distances the agreement is higher. Like the well-established Kappa statistic the Fuzzy Kappa statistic expresses the mean agreement relative to the expected agreement. The model underlying the expected agreement assumes absence of spatial autocorrelation in both compared maps. In reality however, spatial autocorrelation does lower the expected agreement as matching categories become less likely to be found close-by. Since most maps have some degree of spatial autocorrelation, the calculated expected agreement is generally higher than the true expected agreement. This leads to counterintuitive results when maps that appear to have considerable agreement obtain negative Fuzzy Kappa values. Furthermore, the Fuzzy Kappa may be biased, as it systematically attributes lower agreement to maps with stronger spatial autocorrelation. This paper proposes an improved Fuzzy Kappa statistic that is based on the same local agreement and has the same attractive properties as the original Fuzzy Kappa. The novelty is that the new statistic accounts for spatial autocorrelation, such that the expected Fuzzy Kappa for maps that are not cross-correlated is equal to zero. The improved statistic is applied on two cases to demonstrate its properties.

Job accessibility and environmental quality are rarely equally distributed in spatial and/or social dimensions within metropolitan regions. Availability of these affects the quality of residential locations, and can be expected to be capitalised into house prices. For prospective house owners, their options will be limited to sub housing markets within certain price bands depending on their available housing budgets. Availability and marginal prices of job accessibility and environmental quality, as well as trade-offs between them, might be different between these submarkets. Using Greater London as the case metropolitan region, this study explored such differences, to shed light on the role of housing market in equity and/or inequity in job accessibility, environmental quality and their interactions. Results of this study show that lower-price submarkets have advantages in job accessibility in terms of marginal price, but are disadvantaged in terms of availability. Differences are more mixed in marginal price and availability between the submarkets for environmental quality. When balancing job accessibility and environmental quality within constrained housing budgets, households in lower-price submarkets would find it relatively easier to gain job accessibility with less sacrifice on environmental quality as compared to those searching in higher-price submarkets, but hard to reach the higher levels of job accessibility that are mainly reserved for the higher-price submarkets. •Sub housing markets are defined by price band to reflect housing affordability.•Lower-price submarkets hardly reach most accessible places.•Lower-price submarkets only offer flats and no houses in moderately accessible places.•Trading off environmental quality for job accessibility is more rewarding in lower-price submarkets.

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

Landscape decisions are multi-faceted. Framing landscape decision-making as a governance process that requires a collective approach can encourage key stakeholders to come together to co-inform a discussion about their priorities and what constitutes good governance, leading to more holistic landscape decisions. In this paper, we recognise that a suite of complementary and multi-dimensional approaches are in practice used to inform and evaluate land use decisions. We have called these approaches ‘lenses’ because they each provide a different perspective on the same problem. The four lenses are: i) Power and Market Gain, ii) Ecosystem Services, iii), Place based Identity and iv) Ecocentric. Each brings a different set of evidence and viewpoints (narrative, qualitative and experiential, as well as quantitative metrics such as monetary) to the decision-making process and can potentially reveal problems and solutions that others do not. Considering all lenses together allows dialogue to take place which can reveal the true complexities of landscape decision-making and can facilitate more effective and more holistic decisions. Employing the lenses requires governance structures that give equal weight to all lenses, enable dialogue and coexistence between top down and bottom up approaches, and permit adaptation to local and granular place specifics rather than developing “one-size-fits-all” solutions. We propose that formalising the process of balancing all the lenses requires public participation, and that a lens approach should be used to support landscape decisions alongside a checklist that facilitates transparency in the conversation, showing how all evidence has been considered and critically assessed.

A need for multi-functional assessment tools evaluating trade-offs and co-benefits for various types of Nature-Based Solution (NBS) has been increasingly identified in recent years. Methodologically, concepts for a tool are presented which include quantifying the demand and potential for NBS to enhance ecosystem service (ES) provision, and linking ecosystem services to readily quantifiable and legislatively-relevant environmental quality indicators (EQIs). The objective of tool application is to identify optimal NBS placement across a diverse set of socio-environmental indicators, whilst also incorporating issues of relative location of areas of implementation and benefit accrual. Embedded within the tool is the importance of evaluating outcomes in terms of economic benefits and of sustainable development goals. The concepts are illustrated with simplified examples, relating to the case of implementing urban forestry as an exemplar NBS. By summarising the knowledge base it is demonstrated that benefits of NBS are substantially scale-dependent in two main respects; those of extent and proximity to receptors. Evaluation tools should be capable of quantifying scale-dependence. The substantive importance of these considerations and how their dynamics vary between indicators and services is illustrated graphically through schematic functions. When developed, the tool should be used as a focus for consultation and co-design to pinpoint the size of NBS necessary to achieve a sufficient level of benefit for a particular receptor. This could be measured against target levels of benefit for each indicator, distinguishing between primary intended outcomes and those co-benefits or trade-offs that are secondary or unintended.

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration. This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures. The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.

A crucial task in the calibration and validation of geosimulation models is to measure the agreement between model and reality. In recent years many map comparison methods have been developed for this purpose. This paper presents a framework to systematically assess different aspects of model performance and express the results relative to a common reference level. Application on a constrained cellular automata model of the Netherlands demonstrates that the framework gives an in-depth account of model performance. It also shows that any performance assessment that does not follow a multi-criteria approach or lacks a reference level results in an unbalanced account and ultimately false conclusions.

Chloride induced corrosion, caused primarily by salt spray in marine environments, airborne salts and de-icing salts, is one of the most common deterioration processes in reinforced concrete structures. At present, most of the models found in literature describe uniform corrosion and those that do address localized corrosion focus on a simplified definition of the reduced cross-sectional area of corroded rebars without due attention to physical characteristics and spatial variability. This may be attributed to the limitations of current manual methods used in evaluating the corrosion characteristics on the surface of reinforcement. In this paper, an automated procedure for the acquisition of corrosion depth data on rebars based on 3D laser scanning is investigated. Moreover, the first results of an analysis process based on image analysis using wavelet theory are presented. These results show a promising way of improving the classification of corrosion depths. This can be useful for the relation between spatial distribution of corrosion and mechanical properties of the corroded element. © 2013 Taylor & Francis Group, London.

Geosimulation is a form of microsimulation that seeks to understand geographical patterns and dynamics as the outcome of micro level geographical processes. Geosimulation has been applied to understand such diverse systems as lake ecology, traffic congestion and urban growth. A crucial task common to these applications is to express the agreement between model and reality and hence the confidence one can have in the model results. Such evaluation requires a geospatial perspective; it is not sufficient if the micro-level interactions are realistic. Importantly the interactions should be such that the meso and macro level patterns that emerge from the model are realistic. In recent years, a host of map comparison methods have been developed that address different aspects of the agreement between model and reality. This paper places such methods in a framework to systematically assess the breadth and width of model performance. The framework expresses agreement at the continuum of spatial scales ranging from local to the whole landscape and separately addresses agreement in structure and presence. A common reference level makes different performance metrics mutually comparable and guides the interpretation of results. The framework is applied for the evaluation of a constrained cellular automata model of the Netherlands. The case demonstrates that a performance assessment lacking either a multi-criteria and multi-scale perspective or a reference level would result in an unbalanced account and ultimately false conclusions.

Motorized traffic discourages cycling, but the relative influence of different aspects of traffic intensity on commuter cycling rates is under-examined. This paper investigates these influences. It employs census data that describes the origins, destinations and mode choices of commuters travelling 2-5km in Surrey, UK (n = 172,665) and derives the shortest cycling route available for each commuter. Observed and modelled transport data is used to characterize traffic on these routes. The relationship between route-level traffic characteristics and the probability a commuter chooses to cycle is then examined using logit models for all commuters, males (42%) and females (58%). Two models consider the following aspects of traffic intensity both along the cycling route and crossing (intersecting) the route at junctions: speed; volume; and the proportion of heavy goods vehicles. The models also consider the influence of separated cycle paths, hilliness and distance. Above-median traffic speeds (>29km/h) along a commuting route is shown to have the greatest negative influence on cycling propensity, followed by above-median traffic volumes (>273 veh/h) and above-median speeds (in combination) crossing the commuting route. Cycle paths have a positive influence on cycling propensity, whereas the proportion of heavy goods vehicles does not show a significant relationship. The results imply that careful management of traffic at the route scale or the provision of separated cycle paths could encourage cycling on key commuting corridors. The relative influence of different characteristics could also identify the optimal locations for intervention. Findings support 30km/h (20mph) speed limits as a mechanism for encouraging cycling.

Fuzzy set map comparison offers a novel approach to map comparison. The approach is specifically aimed at categorical raster maps and applies fuzzy set techniques, accounting for fuzziness of location and fuzziness of category, to create a similarity map as well as an overall similarity statistic: the Fuzzy Kappa. To date, the calculation of the Fuzzy Kappa (or K-fuzzy) has not been formally derived, and the documented procedure was only valid for cases without fuzziness of category. Furthermore, it required an infinitely large, edgeless map. This paper presents the full derivation of the Fuzzy Kappa; the method is now valid for comparisons considering fuzziness of both location and category and does not require further assumptions. This theoretical completion opens opportunities for use of the technique that surpass the original intentions. In particular, the categorical similarity matrix can be applied to highlight or disregard differences pertaining to selected categories or groups of categories and to distinguish between differences due to omission and commission.

Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K-Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K-Simulation are compared using the results of a land-use model. It is found that only K-Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time. (C) 2011 Elsevier BM. All rights reserved.

Characterisation of the urban expansion processes using time series of binary urban/non-urban land cover data is complex due to the need to account for the initial configuration and the rate of urban expansion over the analysed period. Failure to account for these factors makes the interpretation of landscape metrics for compactness, fragmentation, or clumpiness problematic and the comparison between geographical areas and time periods contentious. This paper presents an approach for characterisation using spatio-dynamic modelling which is data-centred using a process based model, Bayesian optimization, cluster identification, and maximum likelihood classification. An application of the approach across 652 functional urban areas in Europe (1975-2014) demonstrates the consistency of the approach and its ability to identify spatial and temporal trends in urban expansion processes.

Scenarios of future urban expansion are expected to be plausible: they must be diverse to reflect future uncertainty, yet realistic in their depiction of urban expansion processes. We investigated the plausibility of scenarios derived from a novel data-driven simulation approach. In a Turing-like test, experts completed a quiz in which they were asked to identify the map showing true urban expansion amidst three model-generated scenarios. Across diverse expansion patterns, ranging from compact to dispersed, the experts had no significant ability to identify the true pattern. The results support the hypothesis that the investigated scenarios are plausible and hence that cluster analysis of estimated dynamic models is a viable method for producing scenarios of future urban expansion.

After having set out the challenges connected with land ownership and real estate in the insular, micro context of Reunion Island, we plan to create a robot land-use model based on satellite images integrated with a cellular robot. Once the general framework of the modelling has been established, we will make the case for calibrating the cellular robot, and will discuss four scenarios in an approach to long-term territorial planning. La problématique de l'usage du sol à La Réunion.

To explore how people will live and work in Europe, what the landscape will look like and what the environmental consequences will be in some 35 years from now, the PRELUDE project (EEA 2007) of the European Environment Agency developed five different land-use scenarios for Europe. The project was carried out according to a Story And Simulation (SAS) approach in which, iteratively, storylines developed in participatory sessions are underpinned by land-use models. Storylines in this context are defined as narratives about future developments in Europe. They provide qualitative information on a broad range of issues in an integrated context.

The processes of urban growth vary in space and time. There is a lack of model transferability, which means that models estimated for a particular study area and period are not necessarily applicable for other periods and areas. This problem is often addressed through scenario analysis, where scenarios reflect different plausible model realisations based typically on expert consultation. This study proposes a novel framework for data-driven scenario development which, consists of three components - (i) multi-area, multi-period calibration, (ii) growth mode clustering, and (iii) cross-application. The framework finds clusters of parameters, referred to as growth modes: within the clusters, parameters represent similar spatial development trajectories; between the clusters, parameters represent substantially different spatial development trajectories. The framework is tested with a stochastic dynamic urban growth model across European functional urban areas over multiple time periods, estimated using a Bayesian method on an open global urban settlement dataset covering the period 1975–2014. The results confirm a lack of transferability, with reduced confidence in the model over the validation period, compared to the calibration period. Over the calibration period the probability that parameters estimated specifically for an area outperforms those for other areas is 96%. However, over an independent validation period, this probability drops to 72%. Four growth modes are identified along a gradient from compact to dispersed spatial developments. For most training areas, spatial development in the later period is better characterized by one of the four modes than their own historical parameters. The results provide strong support for using identified parameter clusters as a tool for data-driven and quantitative scenario development, to reflect part of the uncertainty of future spatial development trajectories. A promising further application is to use the growth modes to characterize past spatial development patterns. A trend of increasingly dispersed patterns could be identified over the studied functional urban areas which calls for more detailed explorations.

Women are under-represented in commuter cycling in England and Wales. Consequently, women miss out on the health benefits of active commuting over distances where walking is less practical. Similarly, where cycling could replace motorised forms of transport, society is missing out on the wider health benefits associated with reductions in air pollution, road noise and social severance. This paper uses aggregate (ecological) models to investigate the reasons behind the gender gap in cycling. The relative attractiveness of cycling in different areas is described using a set of 17 determinants of commuter cycling mode share: distance, population density, cycle paths, cycle lanes, traffic density, hilliness, temperature, sun, rain, wind, wealth, lower social status, children, green votes, bicycle performance, traffic risk and parking costs. The correlation between these determinants and census-recorded cycling mode share is examined in logit models for commuters who work 2-5 km from home. The models explain a large share of the variation in cycling levels. There are small but significant differences in the importance of individual determinants between men and women. However, the gender gap is largely explained by a differentiated response to the relative attractiveness of an area for cycling, the sum effect of all determinants. The ratio of male to female cycling rates is greatest in areas that are less attractive for cycling, whereas in the most attractive areas the ratio approaches parity. On average, women require a more conducive environment for cycling than men. Since the typical environment in England and Wales is not conducive for cycling, women are under-represented in commuter cycling rates and miss out on the health dividend. The results suggest improvements to the cycling environment may be moderated by the existing attractiveness of the environment for cycling, with improvements in less attractive areas having a smaller absolute effect on cycling rates.

This paper explores the relationships between commuting times, job accessibility, and commuting satisfaction based on a large-scale survey applied in the Greater London Area (GLA), the municipality of São Paulo (MSP) and the Dutch Randstad (NLR). Potential accessibility to jobs is estimated under 3 different scenarios: reported actual commuting times (ACT), ideal commuting times (ICT), and maximum willingness to commute (MCT). Additionally, Binary Logistic Regression models, estimated using generalized linear modeling (GLM), are performed to assess the impact of these temporal preferences on the likelihood of being satisfied with commuting. As expected, ideal and maximum commuting preferences strongly impact the volume and spatial distribution of the measured accessibility to jobs. In the selected case studies, estimated ICT-based job accessibility significantly decreases total measured accessibility (60 to 100 percent), with those living in the lowest accessibility zones impacted the most. Furthermore, although specific results varied between regions, the overall findings show an association between ACT and satisfaction. Additionally, commuting mode was found to be a strong predictor of travel satisfaction. Those actively traveling in all three metropolitan regions tend to be more satisfied with their commutes. Potential job accessibility is found to be only weakly associated with travel satisfaction.

People with low-income often experience higher exposures to air pollutants. We compared the exposure to particulate matter (PM1, PM2.5 and PM10), Black Carbon (BC) and ultrafine particles (PNC; 0.02-1 µm) for typical commutes by car, bus and underground from 4 London areas with different levels of income deprivation (G1 to G4, from most to least deprived). The highest BC and PM concentrations were found in G1 while the highest PNC in G3. Lowest concentrations for all pollutants were observed in G2. We found no systematic relationship between income deprivation and pollutant concentrations, suggesting that differences between transport modes are a stronger influence. The underground showed the highest PM concentrations, followed by buses and a much lower concentrations in cars. BC concentrations in the underground were overestimated due to Fe interference. BC concentrations were also higher in buses than cars because of a lower infiltration of outside pollutants into the car cabin. PNCs were highest in buses, closely followed by cars, but lowest in underground due to the absence of combustion sources. Concentration in the road modes (car and bus) were governed by the traffic conditions (such as traffic flow interruptions) at the specific road section. Exposures were reduced in trains with non-openable windows compared to those with openable windows. People from lower income deprivation areas have a predominant use of car, receiving the lowest doses (RDD

For the evaluation of results from remote sensing and high-resolution spatial models it is often necessary to assess the similarity of sets of maps. This paper describes a method to compare raster maps of categorical data. The method applies fuzzy set theory and involves both fuzziness of location and fuzziness of category. The fuzzy comparison yields a map, which specifies for each cell the degree of similarity on a scale of 0 to 1. Besides this spatial assessment of similarity also an overall value for similarity is derived. This statistic corrects the cell-average similarity value for the expected similarity. It can be considered the fuzzy equivalent of the Kappa statistic and is therefore called KFuzzy. A hypothetical case demonstrates how the comparison method distinguishes minor changes and fluctuations within patterns from major changes. Finally, a practical case illustrates how the method can be useful in a validation process.

Fuzzy set map comparison offers a novel approach to map comparison.The approach is specifically aimed at categorical raster maps and applies fuzzy set techniques, accounting for fuzziness of location and fuzziness of category, to create a similarity map as well as an overall similarity statistic: the Fuzzy Kappa. To date, the calculation of the Fuzzy Kappa (or K-fuzzy) has not been formally derived, and the documented procedure was only valid for cases without fuzziness of category. Furthermore, it required an infinitely large, edgeless map. This paper presents the full derivation of the Fuzzy Kappa; the method is now valid for comparisons considering fuzziness of both location and category and does not require further assumptions. This theoretical completion opens opportunities for use of the technique that surpass the original intentions. In particular, the categorical similarity matrix can be applied to highlight or disregard differences pertaining to selected categories or groups of categories and to distinguish between differences due to omission and commission.

Most metrics of urban spatial structure are snapshots, summarizing spatial structure at one particular moment in time. They are therefore not ideal for the analysis of urban change patterns. This paper presents a new spatio-temporal analytical method for raster maps that explicitly registers changesin patterns. The main contribution is a transition matrix which cross-tabulates the distance to the nearest urbanized location at the beginning and end of the analyzed period. The transition matrix by itself offers a powerful description of urban change patterns from which further metrics can be derived. In particular, a metric that is an indicator of the compactness of urban change is derived. The new metric is applied first to a synthetic dataset demonstrating consistency with existing classifications of urban change patterns. Next, the metric is applied country by country on the European CORINE land cover dataset. The results indicate a striking contrast in change patterns between Western and Eastern European counties. The method can be further elaborated in many different ways and can therefore be the first in a family of spatio-temporal descriptive statistics.

Spatial interaction models commonly use discrete zones to represent locations. The computational requirements of the models normally arise with the square of the number of zones or worse. For computationally intensive models, such as land usetransport interaction models and activity-based models for city regions, this dependency of zone size is a long-standing problem that has not disappeared even with increasing computation speed in PCsit still forces modelers to compromise on the spatial resolution and extent of model coverage as well as on the rigor and depth of model-based analysis. This article introduces a new type of discrete zone system, with the objective of reducing the time for estimating and applying spatial interaction models while maintaining their accuracy. The premise of the new system is that the appropriate size of destination zones depends on the distance to their origin zone: at short distances, spatial accuracy is important and destination zones must be small; at longer distances, knowing the precise location becomes less important and zones can be larger. The new method defines a specific zone map for every origin zone; each origin zone becomes the focus of its own map, surrounded by small zones nearby and large zones farther away. We present the theoretical formulation of the new method and test it with a model of commuting in England. The results of the new method are equivalent to those of the conventional model, despite reducing the number of zone pairs by 96% and the computation time by 70%.

Increasingly, the application of models in urban hydrology has undergone a shift toward integrated structures that recognize the interconnected nature of the urban landscape and both the natural and engineered water cycles. Improvements in computational processing during the past few decades have enabled the application of multiple, connected model structures that link previously disparate systems together, incorporating feedbacks and connections. Many applications of integrated models look to assess the impacts of environmental change on physical dynamics and quality of landscapes. Whilst these integrated structures provide a more robust representation of natural dynamics, they often place considerable data requirements on the user, whereby data are required at contrasting spatial and temporal scales which can often transcend multiple disciplines. Concomitantly, our ability to observe complex, natural phenomena at contrasting scales has improved considerably with the advent of increasingly novel monitoring technologies. This has provided a pathway for reducing model uncertainty and improving our confidence in modeled outputs by implementing suitable monitoring regimes. This commentary assesses how component models of an exemplar integrated model have advanced over the past few decades, with a critical focus on the role of monitoring technologies that have enabled better identification of the key physical process. This reduces the uncertainty of processes at contrasting spatial and temporal scales, through a better characterization of feedbacks which then enhances the utility of integrated model applications.

We investigated the determinants of personal exposure concentrations black carbon (BC), ultrafine particle number concentrations (PNC), and particulate matter (PM1, PM2.5 and PM10) in different travel modes. We quantified the contribution of key factors that explain the variation of the previous pollutants in four commuting routes in London, each covered by four transport modes (car, bus, walk and underground). Models were performed for each pollutant, separately to assess the effect of meteorology (wind speed) or ambient concentrations (with either high spatial or temporal resolution). Concentration variations were mainly explained by wind speed or ambient concentrations and to a lesser extent by route and period of the day. In multivariate models with wind speed, the wind speed was the common significant predictor for all the pollutants in the above-ground modes (i.e., car, bus, walk); and the only predictor variable for the PM fractions. Wind speed had the strongest effect on PM during the bus trips, with an increase in 1 m s-1 leading to a decrease in 2.25, 2.90 and 4.98 μg m-3 of PM1, PM2.5 and PM10, respectively. PM2.5 and PM10 concentrations in car trips were better explained by ambient concentrations with high temporal resolution although from a single monitoring station. On the other hand, ambient 32 concentrations with high spatial coverage although lower temporal resolution predicted better the concentrations in bus trips, due to bus routes passing through streets with a high variability of traffic intensity. In the underground models, wind speed was not significant and line and type of windows on the train explained 42% of the variation of PNC and 90% of all PM fractions. Trains in the district line with openable windows had an increase in concentrations of 1684 cm-3 for PNC and 40.69 μg m-3 for PM2.5 compared with trains that has non-openable windows. The results from this work can be used to target efforts to reduce personal exposures of London commuters.

The physical and mental health benefits of cycling are well established. During the COVID-19 pandemic cycling has also presented additional health benefits by enabling social distancing compared to public transport modes. In low-cycling countries these benefits are unevenly realised, with substantial differences in cycling mode share by age and gender. In England and Wales women are four times less likely to commute by bicycle than men; and commuters aged 35–49 cycle more than other age categories. This paper explores these demographic effects and their interactions. It uses logit models to examine the relationship between 17 determinants of cycling mode share and cycling rates for six demographic groups (males and females in age categories of 18-34, 35–49 and 50–74) across 29,694 small geographic units in England and Wales. The determinants comprise: distance; population density; cycle paths; cycle lanes; traffic density; hilliness; temperature; sun; rain; wind; wealth; lower social status; children; green votes; bicycle performance; traffic risk and parking costs. Determinants associated with physical effort (hilliness and distance) and traffic (traffic density and cycle lanes) are more important in the older age groups for both men and women. More important than the qualitative mix of determinants is their combined effect, or utility. Women require a higher threshold of utility to start cycling than men; and in higher utility environments gender differences are almost non-existent. Differences in cycling rates by age-group also reduce in higher utility environments, although the effects are less pronounced and older commuters still cycle less than other age-groups even in the highest utility environemnts. The results provide insight into the relative importance of gender versus age, and illustrate that cycling rates are more strongly associated with gender than age. For both dimensions, better cycling environments are shown to be more equal cycling environments. •The analysis examines interactions between age, gender and determinants of cycling.•Gender has a greater influence on commuter cycling behaviours than age.•Physical and risk factors may be more important for older commuters.•More supportive cycling environments are more equal in terms of both age and gender.

Transport modelling and in particular transport assignment is a well-known bottleneck in computation cost and time for urban system models. The use of Transport Analysis Zones (TAZ) implies a trade-off between computation time and accuracy: practical computational constraints can lead to concessions to zone size with severe repercussions for the quality of the transport representation in urban models. This paper investigates how a recently developed geographical topology called adaptive zoning can be used to obtain more favorable trade-offs between computational cost and accuracy than traditional TAZ. Adaptive zoning was developed specifically for representing spatial interactions; it makes use of a nested zone hierarchy to adapt the model resolution as a function of both the origin and destination location. In this paper the adaptive zoning method is tied to an approach to trip assignment that uses high spatial accuracy (small zones) at one end of the route and low spatial accuracy (large zones) at the other end of the route. Opportunistic use of either the first or second half of such routes with asymmetric accuracy profiles leads to a method of transport assignment that is more accurate than traditional TAZ based assignment at reduced computational cost. The method is tested and demonstrated on the well-known Chicago Regional test problem. Compared with an assignment using traditional zoning, an adaptive-zoning-based assignment that uses the same computation time reduces the bias in travel time by a factor 16 and link level traffic volume RMSE by a factor 6.4.

•Cities interact with their surroundings from local to global scales in many domains.•Here we define watersheds, airsheds, biodiversitysheds, resourcesheds and peoplesheds.•Each shed can operate at multiple scales, which do not neatly overlap.•There are interactions among these environmental, economic and social sheds.•This understanding can help plan NBS to provide greater benefits and fewer trade-offs. Cities are highly complex, inter-connected social-ecological systems, encompassing social, built and natural/semi-natural components. They interact with their surrounding extra-urban areas at varying scales, from peri-urban and rural to global. Space is a valuable commodity in cities. However, in most instances, city planners tend to think about interventions only within cities and rarely about the wider connected domains outside. Yet, considering the wider spatial context, including space outside of the city boundaries, may open up opportunities to achieve substantially greater benefit for city residents without sacrificing valuable space, leading to more sustainable city design for people and the environment. In this paper we discuss the intra-extra-urban flows which connect cities to their wider airsheds, watersheds, biosheds and resourcesheds, which in turn interact with their peoplesheds. For each domain, we illustrate the processes and the scales they operate at, and discuss the implications for optimum location of nature-based solutions (NBS) to address urban challenges. We suggest that integrating knowledge about these multiple sheds can inform holistic design of NBS to deliver greater benefit for city residents. This takes into account the synergies and multi-functional co-benefits which arise from a careful consideration of place and people, while minimising potential disbenefits and trade-offs.