Urban blue and green spaces in the UK: Distribution, equity and ecological implications

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Morgan, Rodney Forster, Charlotte R. Hopkins, Africa Gómez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7156944/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in npj Urban Sustainability → Version 1 posted 9 You are reading this latest preprint version Abstract Cities are closely linked to the 'triple planetary crisis', climate change, pollution, and biodiversity loss, and urbanisation impacts human health through the removal of natural cover. Urban blue and green spaces offer mitigating effects, but research is traditionally green-focused. Here, we investigate blue space availability and land cover patterns across 500 cities in Great Britain, and for the first time, rank and compare cities by blue cover. City-scale habitat data were paired with deprivation indices to compare equality of blue space, green space, and urban habitat diversity. We found that blue space cover is lower than green space but more evenly distributed across socioeconomic gradients. Additionally, land cover diversity can be higher in deprived areas, suggesting that urban regeneration could result in land cover homogenisation. These findings emphasise the potential of underutilised blue spaces to address environmental injustices and highlight how underexplored land-use patterns can contribute to advancing urban sustainability. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Scientific community and society/Geography Social science/Geography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Urban areas, characterised by high population densities and built infrastructure (hereafter also referred to as cities), contain more than 55% of the global population 1 and play a critical role in driving the 'triple planetary crises', climate change, pollution, and biodiversity loss 2 , 3 . As cities expand to accommodate increasing human populations, they make major contributions to greenhouse gas emissions and drive habitat loss 4 , 5 . Extensive impervious surfaces and limited natural cover contribute to numerous urban-specific challenges, including higher than average temperatures ‘ urban heat island effec t’ 6 , increased vulnerability to flooding 7 , and poor air quality 8 . In addition, limited access to outdoor natural spaces in cities can lead to increased prevalence of physical and mental health conditions, exacerbated in deprived areas due to environmental inequalities 9 , 10 . As continued urban expansion is predicted (urban sprawl), research around sustainable urban design is required to create environments that hold adaptive, absorptive and transformative capacity 11 . One of the strongest cases for meeting these demands is through the integration of natural spaces, which can provide critical ecosystem services whilst supporting human health and well-being 12 , 13 . Natural outdoor environments within urban areas can be split into two broad categories, blue and green spaces, Green spaces, land characterised by vegetation, such as parks, gardens, and unused marginal land (road verges and railway sidings), have been well studied across geospatial, ecological and social contexts 14 – 16 . Blue spaces, natural or manmade environments containing water 17 , such as park lakes or urban rivers, have received less attention than green spaces, despite their equal relevance 18 , and are yet to fulfil their potential roles in urban sustainability 19 . Research often prioritises a green-focused agenda, including water as a subcategory 15 , 20 . Collectively, blue and green spaces provide essential ecosystem services, including water absorption, environmental cooling, air purification and general improvements to human health and well-being 21 – 23 . Due to the strong water dependencies of urbanisation 24 , human development often occurs around coastal and inland water bodies 25 , where water facilitates industrial activities, transportation and food provision 26 , 27 . As a result, major urban areas are often built alongside rivers, canals and coasts, providing residents with opportunities for blue space exposure 28 , either by proximity (e.g., river bank walk, viewpoints) or directly (e.g., swimming, kayaking). However, urbanisation generally degrades blue spaces and wetland habitats 29 , and their ecological and social potential is not always utilised 19 , with many being viewed as industrial areas 30 . Although urban water bodies are usually severely modified, due to culverting, straightening, and reclamation 31 , 32 , they still have a role to play in the biodiversity crisis, providing refuge for endangered species 33 . The importance of urban blue spaces becomes especially relevant when considering that 25% of freshwater fauna is threatened with extinction 34 and wetland habitats have declined by 23% globally 29 . Urban development poses significant risks to blue spaces, which consist of highly productive and regionally restricted habitats, such as estuaries 35 , and hold significant social, cultural, and ecological value 36 . However, current research around natural spaces in urban areas typically focuses on green spaces and fails to capture the nuances and trends of urban blue spaces across multiple contexts. As far as we are aware, there has been no other research targeting both ecological and social aspects of urban blue spaces at scale in Great Britain, as there has been with green space cover 16 . Therefore, establishing baseline data for urban blue spaces and understanding their socio-ecological associations is essential to ensuring they are valued and managed as effectively as their green counterparts. In this study, we investigate urban blue space trends across the 500 largest towns and cities in Great Britain, where wetlands have experienced approximately an 80% decline over the past 300 years 29 . Our primary aims were to (1) assess the current extent of urban blue spaces and how blue space could be affected by the pressures of urbanisation (2) evaluate and compare blue spaces with grey and green spaces, and (3) explore socio-economic links between major land cover types (blue, grey and green) and land cover diversity. To achieve this, we buffered standardised urban boundaries by 200 m and quantified their respective areas into 21 land cover types, taking advantage of high-resolution land use and land cover datasets with national coverage. We then integrated data from the Index of Multiple Deprivation Decile (IMDD) to examine the distribution of blue spaces and other land cover types within social contexts. Results Quantifying Urban Land Cover Across Great Britain, the 500 largest urban areas, identified with built-up area shape files (Fig. 2 a), were extended by 200 m and classified into 21 land cover types (Fig. 2 b). Quality checks, comparing classified areas against unclassified areas, confirmed that, on average, 100% of the land in each area was accounted for (range: 99.58–100.51%). Urban and Suburban land cover were the most abundant, accounting for 46.3% and 20.5% of the summed classified area, respectively (Fig. 2 c). This was followed by Improved grassland (13.5%), Deciduous woodland (7.2%), Arable land (6.5%), Freshwater (1.4%) and Neutral grassland (1.4%). The remaining 14 land cover types, of which eight were blue and six were grey, all had < 1% coverage each and were unevenly distributed across locations (Fig. 2 d). Following classification, five urban areas were excluded from the study due to insufficient or limited grey space, rendering them unsuitable for urban categorisation. This resulted in a final sample of 495 cities for the remainder of the study After grouping land cover types into categories based on shared characteristics: blue ( n = 9), spaces ( n = 10), and grey space ( n = 2), blue spaces had the lowest overall mean cover at 3.56% (Min: 00.06, Max: 25.49, SD: 3.98). As expected, grey space was the most dominant component of urban cover, with an overall mean of 64.61% (Min: 38.94, Max: 97.87, SD: 8.99), followed by green space at 31.82% (Min: 25.06, Max: 36.77, SD: 8.08), see Supplementary Table 1 full results. Urban areas ranked by blue space Urban areas were categorised into coastal ( n = 71), estuarine ( n = 27), and inland ( n = 397) based on their location (Fig. 3 a) and ranked by blue space cover using a decile scale (Fig. 3 b top). Coastal areas had the highest proportionate blue space overall, comprising 75% (38/50) and 50% (25/50) of decile one (ranks 1–50) and decile two (ranks 51–100), respectively (Fig. 3 b). Estuarine areas ranked second for blue space with consistently high blue space values placing them within deciles one to four, as with coastal areas. Inland urban areas were the least blue overall, making up 100% of deciles five to ten, meaning 75% of inland areas had less blue space than any coastal or estuarine areas included in the study. However, some inland areas were present in deciles one to four, highlighting the variability of blue space within this category. From the bluest decile (1), to the least blue (10), grey space remained relatively constant (Min: 62.06, Max: 67.57, SD: 1.58) with a mean of 64.60% (Fig. 3 c). In contrast, blue space was highly variable (Min: 0.28, Max: 12.95, SD: 3.56) with a mean of 3.56%, as was green space (Min: 24.99, Max: 36.50, SD: 3.99) with a mean of 31.84% (see Supplementary Table 2 for full results). Average blue space cover decreased from a high of 12.9% in decile one to 2.3% by decile five, and 0.3% by decile 10; it was mostly replaced by green space, which increased from 25% in decile one to 35.6% by decile 10. This either-or pattern between green and blue space allowed grey space to remain comparatively stable (see Fig. 3 c). As shown in Fig. 3 d, the bluest cities overall were all located on the coast, which included Great Yarmouth, Peterhead and Fleetwood, ranked 1st, 2nd and 3rd, respectively. However, some inland and estuarine areas also had high proportions of blue space, such as Staines-upon-Thames (ranked 4th) and Canvey Island (ranked 6th). A full list of ranked cities can be seen in Supplementary Table 7. Land cover differences across coastal, estuarine, and inland urban areas The relative proportions of grey, green, and blue space were compared across coastal, estuarine, and inland urban areas using a Kruskal-Wallis test followed by Dunn’s test (see Fig. 4 below). Blue space cover was significantly higher in coastal areas when compared to both inland (Z = 12.68, adj. p < 0.001) and estuarine areas (Z = 2.17, adj. p = 0.045), and estuarine areas had more blue space cover than inland areas (Z = 6, adj. p < 0.001). Green space cover was significantly higher across inland areas, when compared to coastal (Z = -6.24, adj. p < 0.001) and estuarine areas (Z = -3.65, p < 0.001), but there was no difference between coastal and estuarine areas (Z = -0.45, adj. p = 0.976). Grey space cover was not significantly different across any comparisons ( p = 0.31), although estuarine areas exhibited a slight trend toward higher grey space cover. Full test results are listed in Supplementary Table 4. Associations between the blue space cover and urban area size, population density, green space and grey space Bivariate associations between blue space (%), city size (m 2 ), grey space (%), green space (%), and population size were tested using Spearman’s rank correlation across each geographic category (See Table 1 ). In coastal cities, blue space had a moderate negative correlation with size (rho − 0.33, p = 0.005), grey space (rho − 0.227, p = 0.012) and population size (rho − 0.132, p = 0.009). In inland cities, blue space had a weak positive correlation with size (rho 0.154, p = 0.002) and population size (rho 0.156, p = 0.002) but a negative correlation with green space (rho − 0.250, p < 0.001). In estuarine cities, no associations between blue space and the variables were significant. Table 1 Summary of Spearman’s rank correlation comparing associations between blue space, green space, grey space, and population size. Variable 1 Variable 2 Rho P-Value Coastal Blue space (%) Size (m 2 ) -0.33 0.005 Green Space (%) -0.227 0.056 Grey Space (%) -0.295 0.012 Population Size -0.307 0.009 Inland Blue space (%) Size (m 2 ) 0.154 0.002 Green Space (%) -0.250 < 0.001 Grey Space (%) -0.049 0.328 Population Size 0.156 0.002 Estuarine Blue space (%) Size (m 2 ) -0.124 0.517 Green Space (%) -0.866 0.653 Grey Space (%) -0.3 0.114 Population Size -0.231 0.905 Environmental predictors of deprivation General additive models (GAM) were used to explore the predictive power of environmental variables for deprivation indices, whilst controlling for latitude, longitude, class (coastal, estuarine, inland), population counts, and size (see Supplementary Material Table 6 for permutations). The best fitting GAM, indicated that green space, blue space, Simpson’s Index and latitude were strong predictors of socio-economic deprivation (Adj. R 2 = 0.52, GCV = 1.82, n = 435), best described using smooth terms (thin plate regression splines) due to their varying degrees of linearity (See Fig. 6 ). Using 10-fold cross-validation, the model achieved an average RMSE of 1.37, indicating a moderate prediction error (~ 16%) with the dependent variable ranging from 1.56 to 9.95 with a mean of 5.75. Green space had a positive, almost linear trend (edf = 1.280) with deprivation decreasing as green space cover increased ( p < 0.001). Blue space had a non-linear trend with deprivation (edf = 1.909) with negligible effects at lower values, but slightly positive effects at higher values ( p < 0.001). Simpson's diversity had a complex non-linear relationship with deprivation (edf = 7.184), which was consistently negative across the majority of data points ( p < 0.001). Latitude had a non-linear effect with deprivation (edf = 4.380, p < 0.001), with area between 51° and 52° being the least deprived (including London, Cambridge and Oxford). Overall, the results indicate that natural cover tends to increase as deprivation decreases, while overall land cover diversity tends to decline. Table 2 General Additive Model summary table. Family Link Function Formula Adjusted R-squared Deviance explained (%) Gaussian identity imdd_weight_av ~ s(total_green, k = 9, bs = "tp", fx = FALSE) + s(log(total_blue), bs = "tp", k = 9, fx = FALSE) + s(simpsons, k = 9, bs = "tp", fx = FALSE) + s(lat, k = 9, bs = "tp", fx = FALSE) 0.523 53.9 A. Parametric coefficients Term Estimate Std Error t-value p-value (Intercept) 5.75331 0.06343 90.7 < 0.001 B. Smooth terms Term edf Ref.df F p-value s(total_green) 1.280 1.516 143.223 < 0.001 s(log(total_blue) 1.909 2.438 6.785 < 0.001 s(simpsons) 7.184 7.799 11.580 < 0.001 Latitude 4.380 5.344 20.255 < 0.001 GCV = 1.8161 Scale est. = 1.7503 n = 435 Discussion Here, we developed a comprehensive method to quantify urban blue spaces at scale across Great Britain, including both freshwater and marine environments, and explored urban land use patterns from both ecological and social perspectives. We found that blue spaces are more prominent in coastal and estuarine cities than in inland cities, but they represent a minority land cover type compared to grey and green spaces. However, unlike green space coverage, which has strong associations with socio-economic deprivation, blue space coverage remains comparatively even across deprivation gradients. Our results also indicate that the most deprived cities tend to have the greatest land cover diversity, suggesting urban ecosystems could be simplified during regeneration. Collectively, our findings address current knowledge gaps in urban blue space baselines and offer new perspectives on blue, green and grey space land cover patterns, vital for creating sustainable, equitable cities. We created a highly detailed composite blue space map by combining land cover and land use data, overcoming the issue of underrepresented blue spaces in each of their original formats. This method particularly improved the representation of small waterbodies, commonly missed by 10 m resolution land cover data 37 , whilst retaining important blue space habitats which are only available from these datasets (e.g., saltmarsh). Overall, our urban land cover results aligned with previous assessments, indicating that ~ 30% of urban areas are made up of natural cover (grass, trees, water etc.) 38 . But our study included a 200 m extension to urban boundaries, offering a ‘doorstep scale’ perspective 39 of blue and green spaces which surround cities. By assessing land beyond standardised urban geometries and including highly relevant coastal blue spaces (including the sea), which are not always included in existing blue space maps, we provide a more realistic and holistic assessment of “accessible” urban blue spaces. For example, Natural England’s Green Infrastructure Map 39 , which lists blue space, is based exclusively on inland waterbodies from land use data sets. This means the Blue Infrastructure listings do not include major blue spaces, such as intertidal zones (including beaches or foreshores) or fen, bog or salt marsh. Similarly, an assessment of blue space accessibility carried out by the Canal and Rivers Trust 40 based on land use data, was restricted to blue spaces found within a 20 m buffer of public rights of way, dismissing relevant blue spaces beyond this distance, and the fact that blue space can be experienced from a distance, such as coastal walks and viewpoints. These previous blue space assessments, which do not incorporate land cover data, ignore important blue space habitats which are relevant to both biodiversity 41 and social well-being 18 . Therefore, we suggest that blue space maps and assessments should have a holistic approach, combining land use and land cover data, to include all habitats that can be justified as blue space, as done here. Overall, coastal cities and seaside towns had the highest proportion of blue space, as a result of being in close proximity to marine environments. However, unlike inland areas, the relative blue space of coastal urban areas decreased as factors of urbanisation increased (e.g., the size and population of a city), with larger coastal urban areas having proportionately less blue space than smaller ones. This decrease could be linked to landward urban expansion, driven by coastal erosion 42 , the provision of setback zones 43 or coastal squeeze 44 . If this is the case, any urban development in coastal areas which does not involve the coastline would reduce the relative land-water interface and therefore decrease the proportionate blue space in that urban area. Seaward expansion via land reclamation could also share a similar effect, unless artificial bays, marinas and lagoons suitable for beachgoers are created, such as those in the UAE 45 . In contrast, blue space coverage had a slight positive relationship with the size of an urban area and population size across inland cities. This unexpected pattern may be explained by the provision of man-made blue spaces having a notable impact on blue space proportions in these areas, such as park lakes and reservoirs 46 . For example, we found high blue space cover across Staines-upon-Thames due to nearby reservoirs, which were created as part of London's water infrastructure 47 . Therefore, in some circumstances, blue spaces could remain stable and even increase in cover during urbanisation and utilitarian water management. We found estuarine cities had more blue space than inland cities, but less than coastal cities, reflecting their natural role as transitional zones between land and sea. We also found evidence for estuarine cities having high grey space coverage and restricted blue space accessibility, primarily due to heavy industrialisation. Estuarine habitats have faced huge declines as a result of sprawling coastal developments 48 and land reclamation of wetlands 35 . Here, 10 out of 14 urban boundaries that required modification to exclude completely inaccessible and non-residential urban zones (e.g., refineries and cargo ports) were estuarine, suggesting that industrial land use in estuarine cities puts constraints on blue space accessibility. However, Ghomeshi and Walczak 49 highlight the potential of post-industrial cities for blue space regeneration, as do Burda and Nyka 50 , who promote waterfront regeneration in locations where there has been withdrawal of port and shipyard industries from city centres. Examples of this can be seen in Kingston Upon Hull, which has multiple access points to the Humber Estuary and docks which have been converted into commercial hubs 51 . Our results show that the blue space cover is heavily dependent on the location of the urban area, with geographic biases having a strong influence on blue space quantities, and that the effect of urbanisation on blue space availability can be highly varied across different regions. Furthermore, we show that blue spaces are less abundant than green spaces, presented here at scale and in a previous case study on Bristol, England 32 . The scarcity of urban wetlands, particularly inland freshwater systems, makes them disproportionately vulnerable to land cover change during urbanisation, highlighting the importance of prioritising their preservation. We also observed a trade-off between green and blue space cover in urban areas, finding that as green and blue space replace one another, grey space remains relatively stable. This can be seen in coastal cities, which have less green space cover than inland cities, but no difference in grey space cover. Previous research exploring low green space within coastal cities highlights that environmental conditions on exposed coasts, including strong winds and sea salt aerosol 52 , can create challenging conditions for trees, resulting in stunted growth, increased dieback and less amenity value 53 . In addition, trees can block desirable views of water bodies, and can be removed or opposed by communities 53 , showing that the presence of a blue space can indirectly affect green space cover in urban areas. By examining social data, we found that blue space does not exhibit the same associations with deprivation as green space, which typically increases as deprivation decreases, shown here and in previous studies 16 , 54 . We also found deprivation was lower in the south of England, as shown in previous studies 55 , suggesting the trends captured in our model are reliable. Unlike green space, we found blue space availability remains comparatively stable across levels of deprivation, with some weak associations when cover is high. This finding highlights that blue space inequity is less notable than that of green spaces, building on the growing interest in the role of blue spaces in mitigating environmental inequalities across deprived communities 56 . The stability of blue space cover could be explained by their geographic and physical constraints, often being fixed features of the landscape (e.g. a river or coastline) which do not necessarily increase with urban expansion or regeneration. In contrast, green spaces, which are more easily introduced or expanded, are commonly provided for in urban design. In addition, we found that general land cover diversity increases with increasing deprivation, meaning the most deprived communities also have the most heterogeneous land cover. This suggests that the process of urban regeneration, associated with increasing wealth, could lead to homogenisation of urban areas, alongside the more commonly studied cultural and social homogenisation 57 – 59 . For example, the removal of brownfield sites (“wastelands”) during regeneration, known to impact urban biodiversity 60 , could also reduce both the number and extent of urban habitats. With this in mind, and considering our results, the process of revitalising a city may have negative impacts on ecosystem resilience if the management of habitats is not well thought out 61 . To conclude, as the interest in creating accessible natural spaces in cities increases, a comprehensive perspective on land cover is essential if urban planners are going to address both ecological and social demands of urban environments. Here, we found that blue spaces are ubiquitous with urbanisation, as with green space, but have much less cover, particularly across inland areas, and provide a robust baseline for blue space cover, suitable for comparison in future studies. Furthermore, in addition to reaffirming known patterns of socio-economic deprivation and green space, we provide new land cover perspectives, which show blue space remains relatively stable across levels of deprivation, and that the diversity of land cover increases with deprivation. This indicates that urban land cover may become homogenised with urban regeneration, thus having counteractive effects on biodiversity and sustainability goals. Collectively, our results highlight the need to incorporate blue spaces, alongside green spaces, into holistic ecological perspectives of the urban environment to better understand landscape-scale land-use patterns. If all land cover types, particularly threatened wetlands, are accounted for within both social and ecological contexts, future urban development has the potential to be more sustainable, inclusive, and responsive to growing urban pressures. Methods Study area Great Britain (GB), made up of England, Wales and Scotland, is the ninth largest island in the world and home to over 65 million people 62 . The population is highly urbanised, with 83% (56 million) of England's population living in built-up areas 63 . Across all three countries, the composition, location and size of built-up areas are highly varied, ranging from small coastal towns to extensive urban agglomerations. Data sources Five GB datasets were used to gather geospatial, ecological, and socioeconomic perspectives: (1) a layer detailing built up area boundaries provided by the Office of National Statistics 64 , (2) a 10 m resolution land cover map detailing 21 habitats, created by the Centre of Ecology and Hydrology 65 , (3) Ordnance Survey Vector Map land use boundaries 66 , (4) UK government population census results 62 , and (5) government index of multiple deprivation indices 67 . Data handling Built-up urban areas were identified from the Built-Up Areas layer (n = 8545), and sub-sampled into the 500 largest by area (maintaining ONS names). Original geometries were then extended by 200 m using the QGIS buffer tool to include highly accessible spaces (either visually or physically) falling outside of the original geometries but relevant to the area. For example, the sea, “accessible” in most coastal regions, will always fall outside of statistical zonations, meaning original geometries would introduce strong boundary effects in assessments of blue space cover. An evaluation of general accessibility was carried out for all coastal and estuarine regions using satellite imagery and Google Street View. This process consisted of checking for access points and identifying disconnected built-up areas, which were part of an urban agglomeration, but not residential or publicly accessible (see Supplementary Fig. 1). In total, 14 urban areas were modified to remove disconnected (no public roads or paths) and inaccessible industrial zones, including refineries and ports (see Supplementary Table 5). Land cover types within urban areas were quantified with CEH land cover data 65 and OS land use data. Although very accurate (82.6% overall), the primary focus of the CEH land cover map was terrestrial cover, meaning coastal and intertidal zones have lower accuracy (Marston et al. 2022). Similarly, smaller water bodies (< 0.5 ha or < 40 m wide) have less accuracy when compared to large water bodies 65 , meaning narrow but notable blue spaces (such as rivers and canals) are often missing. As blue space was our focus, we assessed other cartographical resources to improve accuracy, retaining the CEH land cover for all other habitats. Three highly accurate OS land use layers (~ 1 m resolution) were identified for blue space improvements, consisting of Tidal water, Surface water and Foreshore (delineated by low and high tide water marks). CEH and OS data sets were then combined following the workflow below. Compiling land cover and land use data for blue spaces Land cover data was downloaded from the Digimap portal ( digimap.edina.ac.uk ) in 10 m resolution raster format (.tif) and processed using QGIS (v.3.36) as batches using five steps: (1) clip original file to extended BUA boundaries ( processing toolbox > clip raster by extent tools ), (2) convert raster data to polygons maintaining resolution ( processing toolbox > vector creation > raster pixels to polygons ), (3) merge land cover polygons by shared attributes ( processing toolbox > vector geometry > dissolve ), (4) assign location names to new shapes ( processing toolbox > vector general > join attributes by location ), (5) calculate areas of land cover ( field calculator > geometry >$area ). Land use data was downloaded from the OS Data Hub, within a GB Local District Data package. The three layers of interested were then processed individually following these steps: (1) merge data ( edit > merge selected features ), (2) clip with extended BUA geometries ( vector > geoprocessing tools > clip ), (3) assign names, (4) calculate area (method for steps 3–4 same as 4–5 above). Overlap between land cover and land use layers was fixed with geoprocessing tools. First, OS tidal water and surface water layers were used to replace CEH land cover classifications when overlap occurred (vector > geoprocessing > difference). Second, CEH littoral layers (isolated layers) replaced OS foreshore cover when overlap occurred (as before). To retain ecological classifications, land use data were reclassified as follows: tidal water to saltwater, surface water to freshwater, and foreshore to littoral sediment. See Supplementary Fig. 2. and Fig. 3 for examples. The accuracy of the new dataset was checked using the topology checker plugin, and by summing the area of all new classified geometries and comparing them against the unclassified extended BUA. Defining blue, green and grey space When defining blue spaces, we adopted the definition of Grellier et al 17 “outdoor environments either natural or manmade that prominently feature water and are accessible to humans either proximally (being in, on or near water) or distally/virtually (being able to see hear or other-wise sense water)” which acknowledges the fact blue spaces can be experienced with and without direct contact 68 . Based on this, we have therefore assumed that all blue land covers listed in Fig. 7 falling within our extended urban areas have the potential to offer some form of physical or sensory exposure, therefore making them all blue spaces. To define green spaces, we grouped all non-blue natural habitats found within urban areas listed in Fig. 7 , acknowledging the fact that people experience greenness across an entire built-up area and not just in the official “public green spaces” 69 . As with blue space, either visual or physical accessibility was assumed for all land falling within urban areas. Grey spaces, areas of land characterised by impervious surfaces, were defined by combining Suburban and Urban land cover data. Defining habitat diversity Habitat diversity metrics using all 21 land cover classifications were calculated using the Shannon Diversity Index and Simpson’s Diversity Index, often used as a proxy for habitat heterogeneity 70 , 71 . Built-up area geographic categorisation During the conception of the study and data preparation, it was noted that many of the selected urban areas were from distinct geographic regions, which were classified into three categories: coastal - when seaward; estuarine - when located along an estuary; and inland - when not connected to any major tidal systems. Ranking built-up areas by blue space Built-up areas were ranked by proportionate blue space cover (1-495) and grouped into deciles (n = 50) to characterise and describe “the bluest areas” and understand how blue space compares to other types of urban land cover (namely grey and green space). Social data Deprivation assessments were restricted to England due to variability in deprivation calculation between England, Wales and Scotland. Our analyses used the Indices of Multiple Deprivation (IMD), which are calculated from multiple socio-economic indicators at a national level 67 . The IMD uses a decile ranking system (IMDD) with 1 being the most deprived and 10 being the least. To calculate the overall deprivation scores of the urban areas used in this study, we used the original BUA layer (not extended) and followed these steps: (1) IMD datasets were downloaded from the government portals as spatial data (aggregated into statistical parcels) (2) statistical parcels containing IMDD information were then refitted into the original built up areas using a cookie cutter approach ( vector > geoprocessing > clip ), (3) names of each bounding BUA were assigned to new shapes ( processing toolbox > vector general > join attributes by location ), (4) a weighted average per urban area was then calculated by multiplying the area of each land parcel by its IMDD score (decile), summing these values, and dividing by the sum all land parcels (total area of that built-up area). Statistical analysis and visualisations All statistical analyses were carried out in R Studio 72 , using base R version 4.2.2 73 unless otherwise specified. Before analysis, data distribution and normality were assessed visually using histograms, box plots, and quantile-quantile plots. Parametric and non-parametric methods were used based on data distribution. Kruskal-Wallis and Dunn’s test were used to test differences between blue, green and grey space cover across geographic categorisation. Spearman’s rank correlation coefficient was used to test associations between blue space cover and the size of a built-up area, population counts, grey space and green space. Data manipulation and plots were created with tidyverse 74 . A General Additive Model (GAM) from the mgcv R package 75 was used to test the predictive power of environmental variables for deprivation. The GAM was chosen due to its flexibility in handling non-linear relationships between predictors and the response variable. To address multicollinearity, we assessed the correlation between predictor variables using a correlation matrix 76 , with a threshold of > 0.8 indicating significant collinearity (see Supplementary Fig. 4). This analysis revealed strong correlations between grey space and green space, and between population size and urban area size. Specifically, grey space and green space were inversely correlated, while population size and urban area size were positively correlated, meaning the response of the removed variables could be reasonably inferred based on those retained (see Supplementary Fig. 5). Our final model used a Gaussian family with an identity link function, appropriate for continuous response variables. Smoothing splines were applied to account for non-linearity among predictors, with the best smoothing parameters and variables selected via a stepwise approach (see Supplementary Table 6). To preserve model interpretability and maximise stability, grey space and population size were omitted. We used the gam.check function from the mgcv package to assess model fit, examining residual plots and k-index values. This final step confirmed that the model fit was appropriate and that the smoothing terms were correctly specified, with no indications of overfitting or other major issues. Model diagnostics can be seen in Fig. 5 and Fig. 6 . Declarations Author Contribution All authors contributed to the conceptualisation of the study. MM was responsible for all data handling, processing, analysis, and visualisation. MM led all writing and formatting. All authors reviewed the manuscript. Acknowledgement We gratefully acknowledge the University of Hull for funding this research. Data Availability All raw data and R code used to produce results in this study are available on github https://github.com/MCMorgan06/Bluest_Cities_UK and zonodo (10.5281/zenodo.16038672) to promote transparency and reproducibility. References United Nations. World Cities Report: Envisaging the Future of Cities . (2022). Simkin, R. D., Seto, K. C., McDonald, R., I. & Jetz, W. Biodiversity impacts and conservation implications of urban land expansion projected to 2050. Proc. Natl. Acad. Sci. U. S. A. 119, (2022). Espey, J., Keith, M., Parnell, S., Schwanen, T. & Seto, K. C. Designing policy for Earth’s urban future. Science 383, 364–367 (2024). McKinney, M. L. Urbanization, Biodiversity, and ConservationThe impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bioscience 52, 883–890 (2002). Singh, N., Singh, S. & Mall, R. K. 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The underappreciated value of brownfield sites: motivations and challenges associated with maintaining biodiversity. J. Environ. Plan. Manag. 1–19 (2022) doi: 10.1080/09640568.2022.2050683 . Colding, J. ‘Ecological land-use complementation’ for building resilience in urban ecosystems. Landsc. Urban Plan. 81, 46–55 (2007). Office for National Statistics. Population estimates for the UK, England, Wales, Scotland, and Northern Ireland: mid-2022. (2024). DEFRA. Statistical Digest of Rural England . (2021). Office for National Statistics. Towns and cities, characteristics of built-up areas, England and Wales: Census 2021. (2021). Marston, C., Rowland, C. S., O’Neil, A. W. & Morton, R. D. Land Cover Map 2021 (10m classified pixels, GB). NERC EDS Environmental Information Data Centre https://doi.org/ 10.5285/a22baa7c-5809-4a02-87e0-3cf87d4e223a (2022). Ordnance Survey. OS VectorMap District. (2023). Ministry of Housing, Communities and Local Government. Indices of Multiple Deprivation (IMD). (2019). Gao, T., Zhang, T., Zhu, L., Gao, Y. & Qiu, L. Exploring Psychophysiological Restoration and Individual Preference in the Different Environments Based on Virtual Reality. Int. J. Environ. Res. Public Health 16, (2019). Slater, S. J., Christiana, R. W. & Gustat, J. Recommendations for Keeping Parks and Green Space Accessible for Mental and Physical Health During COVID-19 and Other Pandemics. Prev. Chronic Dis. 17, E59 (2020). Karimi, A. & Raymond, C. M. Assessing the diversity and evenness of ecosystem services as perceived by residents using participatory mapping. Appl. Geogr. 138, 102624 (2022). Sultana, M., Corlatti, L. & Storch, I. The interaction of imperviousness and habitat heterogeneity drives bird richness patterns in south Asian cities. Urban Ecosyst. 24, 335–344 (2021). R Studio Team. RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. (2025). R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing , Vienna, Austria, (2025). Wickham, H. et al. Welcome to the tidyverse. Journal of Open Source Software 4(43), 1686, (2019). Wood, S. N. Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC. 2, (2011). Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix. (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialBluestCitiesMMorgan.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 31 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 18 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7156944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511408235,"identity":"04773860-d0a0-468d-b0fd-392536163a06","order_by":0,"name":"Matthew C. Morgan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3RsUrEMBzH8X8NxCU45xB6TyD8jwNx6r3Kv2S4pcXBpYMcOYSOulZ8iW7iFil4S8Q1g0gPX6CTOB3aDoLQ0hsF890K+ZRfCIDP98cLaspkiMDWpvsW44RhY8/mCIHem/DJbZ7F5Rg50aw+cNlbdHOn4VhwubyX8Te5jACt6SWnhiNL7IUqXg3MhZDpQ9GSJwX4rAeIAJbmpMCdN0pImZauJdwAvvQP+yFTR1AJlEvsyG6cROgoWBckqSNBboaHVRyrxBLNHDFojJyVdqtNfK3EZOj6m6vte5LRInR0+BnvVlPcqMe6+YjCI0v9yxhA+7P49wja4yEXYwd8Pp/vH/cFmuZjetI0uioAAAAASUVORK5CYII=","orcid":"","institution":"University of Hull","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"C.","lastName":"Morgan","suffix":""},{"id":511408236,"identity":"9a05cd16-159b-4f70-bb1c-57bcee790894","order_by":1,"name":"Rodney Forster","email":"","orcid":"","institution":"University of Hull","correspondingAuthor":false,"prefix":"","firstName":"Rodney","middleName":"","lastName":"Forster","suffix":""},{"id":511408239,"identity":"94158d22-dcd6-4920-8193-2f48ff90f017","order_by":2,"name":"Charlotte R. Hopkins","email":"","orcid":"","institution":"University of Hull","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"R.","lastName":"Hopkins","suffix":""},{"id":511408240,"identity":"5980b341-5a35-4cf3-a0d6-c8b80d7bc0ed","order_by":3,"name":"Africa Gómez","email":"","orcid":"","institution":"University of Hull","correspondingAuthor":false,"prefix":"","firstName":"Africa","middleName":"","lastName":"Gómez","suffix":""}],"badges":[],"createdAt":"2025-07-18 10:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7156944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7156944/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42949-026-00349-6","type":"published","date":"2026-03-17T15:59:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90854006,"identity":"169ed33b-d286-42dd-a298-3ebd39ddd364","added_by":"auto","created_at":"2025-09-09 04:04:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":511705,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram summarising the main research conducted, resources used, methods applied, and data analysis.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/98d1cc689082b36c16296696.png"},{"id":90854009,"identity":"b2ec85b1-b144-4ba8-ace0-f21c6e4f70e9","added_by":"auto","created_at":"2025-09-09 04:05:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1195606,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover in urban areas, colour-coded by grey, green, or blue land cover groupings (see Supplementary Table 3). a. Satellite image showing one of the 500 urban geometries (outlined in yellow) before classification. b. Urban geometry after classification with land cover and land use data (100% coverage). c. Total coverage (y-axis) of each land cover type (x-axis, n = 21) across all urban areas (n = 495), with overlap removed. d\u003cem\u003e.\u003c/em\u003eBox plot showing the percentage (y-axis) of each land cover type (x-axis) per urban area.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/22bb9b1787853a3452a86e47.png"},{"id":90854026,"identity":"b22e7e07-828f-4a28-8c9c-00635ea917ec","added_by":"auto","created_at":"2025-09-09 04:05:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":417563,"visible":true,"origin":"","legend":"\u003cp\u003ea. Distribution of inland (\u003cem\u003en\u003c/em\u003e = 397), estuarine (\u003cem\u003en\u003c/em\u003e = 27), and coastal (\u003cem\u003en\u003c/em\u003e = 71) areas. b. Composition of inland, estuarine and coastal areas within each decile (ranked by blue space). c\u003cem\u003e.\u003c/em\u003e Average cover of grey space, green space, and blue space per decile. Dashed lines represent overall means: grey space 64.6% (top line), green space 31.8% (middle line), and blue space 3.6% (bottom line). d. Urban areas are colour-coded by their relative blue space (%).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/2de8e4b72153d08fc38a0132.png"},{"id":90854027,"identity":"908891b5-f4af-47aa-8e84-8ce7ad55677f","added_by":"auto","created_at":"2025-09-09 04:05:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180598,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover percentages of blue space (left), green space (middle), and grey space (right) are shown for all urban areas, grouped by region: coastal (n = 71), estuarine (n = 27), and inland (n = 397). Significant differences identified by Dunn’s test are indicated (* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/7dde681401db0275b738cbe0.png"},{"id":90854499,"identity":"098ad82a-e70f-4254-8d48-5cbfcf8c2105","added_by":"auto","created_at":"2025-09-09 04:13:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":158181,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between blue space and area size across geographic classifications. Spearman's rank correlation coefficients and \u003cem\u003ep\u003c/em\u003e values are listed above each plot. Note that inland (middle) and estuarine (right) are on a log scale.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/e8c432bf5cbcf0bb24fb63be.png"},{"id":90854024,"identity":"13a5f82b-57f4-422f-bd04-afe7b44fdd25","added_by":"auto","created_at":"2025-09-09 04:05:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":151101,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of four significant predictors of deprivation: green space (top left), blue space (top right), Simpson's diversity index (bottom left) and latitude (bottom right), as produced by the GAM smooth terms (thin plate splines, K = 9). Estimated effects are shown by the solid lines with 95% confidence intervals within dashed lines. The Y-axis represents the deprivation index, with higher values indicating less deprivation.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/da1698ff0fa143c669000857.png"},{"id":90854014,"identity":"615b8c4d-ae78-4dd3-b21d-ff97c695e9cd","added_by":"auto","created_at":"2025-09-09 04:05:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":711384,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Plymouth as an example, the figure shows how land coverage was quantified and grouped across urban areas. A. Original BUA dimensions (top and middle) and the same area after the 200 m extension (bottom). B. UKCEH land cover data, the corresponding legend can be seen in section D. C. Blue space land use data from OS. D. Visualisation of how land use and land cover data were combined for the composite map and analysis. E. Composite map after combining datasets coloured by green, grey or green classifications.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/52e96215ec90b7a861307549.png"},{"id":105223802,"identity":"196992cb-b435-45ce-8574-f5f27ab1d476","added_by":"auto","created_at":"2026-03-23 16:11:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3798153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/670bba83-ec28-466f-82c6-5075c08aacab.pdf"},{"id":90854498,"identity":"2c4d8a6b-2f6c-40cc-a0ae-dee5bfc34974","added_by":"auto","created_at":"2025-09-09 04:13:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2772646,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialBluestCitiesMMorgan.docx","url":"https://assets-eu.researchsquare.com/files/rs-7156944/v1/5ce437b2f58b6864aa5a60fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban blue and green spaces in the UK: Distribution, equity and ecological implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrban areas, characterised by high population densities and built infrastructure (hereafter also referred to as cities), contain more than 55% of the global population \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and play a critical role in driving the 'triple planetary crises', climate change, pollution, and biodiversity loss \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As cities expand to accommodate increasing human populations, they make major contributions to greenhouse gas emissions and drive habitat loss \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Extensive impervious surfaces and limited natural cover contribute to numerous urban-specific challenges, including higher than average temperatures ‘\u003cem\u003eurban heat island effec\u003c/em\u003et’ \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, increased vulnerability to flooding \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and poor air quality \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, limited access to outdoor natural spaces in cities can lead to increased prevalence of physical and mental health conditions, exacerbated in deprived areas due to environmental inequalities \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. As continued urban expansion is predicted (urban sprawl), research around sustainable urban design is required to create environments that hold adaptive, absorptive and transformative capacity \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. One of the strongest cases for meeting these demands is through the integration of natural spaces, which can provide critical ecosystem services whilst supporting human health and well-being \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNatural outdoor environments within urban areas can be split into two broad categories, blue and green spaces, Green spaces, land characterised by vegetation, such as parks, gardens, and unused marginal land (road verges and railway sidings), have been well studied across geospatial, ecological and social contexts \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Blue spaces, natural or manmade environments containing water \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, such as park lakes or urban rivers, have received less attention than green spaces, despite their equal relevance \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and are yet to fulfil their potential roles in urban sustainability \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Research often prioritises a green-focused agenda, including water as a subcategory \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Collectively, blue and green spaces provide essential ecosystem services, including water absorption, environmental cooling, air purification and general improvements to human health and well-being \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDue to the strong water dependencies of urbanisation \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, human development often occurs around coastal and inland water bodies \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, where water facilitates industrial activities, transportation and food provision \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. As a result, major urban areas are often built alongside rivers, canals and coasts, providing residents with opportunities for blue space exposure \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, either by proximity (e.g., river bank walk, viewpoints) or directly (e.g., swimming, kayaking). However, urbanisation generally degrades blue spaces and wetland habitats \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and their ecological and social potential is not always utilised \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, with many being viewed as industrial areas \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Although urban water bodies are usually severely modified, due to culverting, straightening, and reclamation \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, they still have a role to play in the biodiversity crisis, providing refuge for endangered species \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The importance of urban blue spaces becomes especially relevant when considering that 25% of freshwater fauna is threatened with extinction \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and wetland habitats have declined by 23% globally \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUrban development poses significant risks to blue spaces, which consist of highly productive and regionally restricted habitats, such as estuaries \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and hold significant social, cultural, and ecological value \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. However, current research around natural spaces in urban areas typically focuses on green spaces and fails to capture the nuances and trends of urban blue spaces across multiple contexts. As far as we are aware, there has been no other research targeting both ecological and social aspects of urban blue spaces at scale in Great Britain, as there has been with green space cover \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Therefore, establishing baseline data for urban blue spaces and understanding their socio-ecological associations is essential to ensuring they are valued and managed as effectively as their green counterparts.\u003c/p\u003e\u003cp\u003eIn this study, we investigate urban blue space trends across the 500 largest towns and cities in Great Britain, where wetlands have experienced approximately an 80% decline over the past 300 years \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our primary aims were to (1) assess the current extent of urban blue spaces and how blue space could be affected by the pressures of urbanisation (2) evaluate and compare blue spaces with grey and green spaces, and (3) explore socio-economic links between major land cover types (blue, grey and green) and land cover diversity. To achieve this, we buffered standardised urban boundaries by 200 m and quantified their respective areas into 21 land cover types, taking advantage of high-resolution land use and land cover datasets with national coverage. We then integrated data from the Index of Multiple Deprivation Decile (IMDD) to examine the distribution of blue spaces and other land cover types within social contexts.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eQuantifying Urban Land Cover\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross Great Britain, the 500 largest urban areas, identified with built-up area shape files (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), were extended by 200 m and classified into 21 land cover types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Quality checks, comparing classified areas against unclassified areas, confirmed that, on average, 100% of the land in each area was accounted for (range: 99.58\u0026ndash;100.51%). \u003cem\u003eUrban\u003c/em\u003e and \u003cem\u003eSuburban land\u003c/em\u003e cover were the most abundant, accounting for 46.3% and 20.5% of the summed classified area, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This was followed by \u003cem\u003eImproved grassland\u003c/em\u003e (13.5%), \u003cem\u003eDeciduous woodland\u003c/em\u003e (7.2%), \u003cem\u003eArable land\u003c/em\u003e (6.5%), \u003cem\u003eFreshwater\u003c/em\u003e (1.4%) and \u003cem\u003eNeutral grassland\u003c/em\u003e (1.4%). The remaining 14 land cover types, of which eight were blue and six were grey, all had\u0026thinsp;\u0026lt;\u0026thinsp;1% coverage each and were unevenly distributed across locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Following classification, five urban areas were excluded from the study due to insufficient or limited grey space, rendering them unsuitable for urban categorisation. This resulted in a final sample of 495 cities for the remainder of the study\u003c/p\u003e\u003cp\u003eAfter grouping land cover types into categories based on shared characteristics: blue (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), spaces (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), and grey space (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), blue spaces had the lowest overall mean cover at 3.56% (Min: 00.06, Max: 25.49, SD: 3.98). As expected, grey space was the most dominant component of urban cover, with an overall mean of 64.61% (Min: 38.94, Max: 97.87, SD: 8.99), followed by green space at 31.82% (Min: 25.06, Max: 36.77, SD: 8.08), see Supplementary Table\u0026nbsp;1 full results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUrban areas ranked by blue space\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUrban areas were categorised into coastal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71), estuarine (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27), and inland (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;397) based on their location (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and ranked by blue space cover using a decile scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb top). Coastal areas had the highest proportionate blue space overall, comprising 75% (38/50) and 50% (25/50) of decile one (ranks 1\u0026ndash;50) and decile two (ranks 51\u0026ndash;100), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Estuarine areas ranked second for blue space with consistently high blue space values placing them within deciles one to four, as with coastal areas. Inland urban areas were the least blue overall, making up 100% of deciles five to ten, meaning 75% of inland areas had less blue space than any coastal or estuarine areas included in the study. However, some inland areas were present in deciles one to four, highlighting the variability of blue space within this category.\u003c/p\u003e\u003cp\u003eFrom the bluest decile (1), to the least blue (10), grey space remained relatively constant (Min: 62.06, Max: 67.57, SD: 1.58) with a mean of 64.60% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In contrast, blue space was highly variable (Min: 0.28, Max: 12.95, SD: 3.56) with a mean of 3.56%, as was green space (Min: 24.99, Max: 36.50, SD: 3.99) with a mean of 31.84% (see Supplementary Table\u0026nbsp;2 for full results). Average blue space cover decreased from a high of 12.9% in decile one to 2.3% by decile five, and 0.3% by decile 10; it was mostly replaced by green space, which increased from 25% in decile one to 35.6% by decile 10. This either-or pattern between green and blue space allowed grey space to remain comparatively stable (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, the bluest cities overall were all located on the coast, which included Great Yarmouth, Peterhead and Fleetwood, ranked 1st, 2nd and 3rd, respectively. However, some inland and estuarine areas also had high proportions of blue space, such as Staines-upon-Thames (ranked 4th) and Canvey Island (ranked 6th). A full list of ranked cities can be seen in Supplementary Table\u0026nbsp;7.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLand cover differences across coastal, estuarine, and inland urban areas\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relative proportions of grey, green, and blue space were compared across coastal, estuarine, and inland urban areas using a Kruskal-Wallis test followed by Dunn\u0026rsquo;s test (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below). Blue space cover was significantly higher in coastal areas when compared to both inland (Z\u0026thinsp;=\u0026thinsp;12.68, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and estuarine areas (Z\u0026thinsp;=\u0026thinsp;2.17, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), and estuarine areas had more blue space cover than inland areas (Z\u0026thinsp;=\u0026thinsp;6, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Green space cover was significantly higher across inland areas, when compared to coastal (Z = -6.24, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and estuarine areas (Z = -3.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but there was no difference between coastal and estuarine areas (Z = -0.45, adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.976). Grey space cover was not significantly different across any comparisons (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31), although estuarine areas exhibited a slight trend toward higher grey space cover. Full test results are listed in Supplementary Table\u0026nbsp;4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations between the blue space cover and urban area size, population density, green space and grey space\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBivariate associations between blue space (%), city size (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), grey space (%), green space (%), and population size were tested using Spearman\u0026rsquo;s rank correlation across each geographic category (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In coastal cities, blue space had a moderate negative correlation with size (rho \u0026minus;\u0026thinsp;0.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), grey space (rho \u0026minus;\u0026thinsp;0.227, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and population size (rho \u0026minus;\u0026thinsp;0.132, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). In inland cities, blue space had a weak positive correlation with size (rho 0.154, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and population size (rho 0.156, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) but a negative correlation with green space (rho \u0026minus;\u0026thinsp;0.250, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In estuarine cities, no associations between blue space and the variables were significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Spearman\u0026rsquo;s rank correlation comparing associations between blue space, green space, grey space, and population size.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRho\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eCoastal Blue space (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eInland Blue space (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eEstuarine Blue space (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrey Space (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnvironmental predictors of deprivation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGeneral additive models (GAM) were used to explore the predictive power of environmental variables for deprivation indices, whilst controlling for latitude, longitude, class (coastal, estuarine, inland), population counts, and size (see Supplementary Material Table\u0026nbsp;6 for permutations). The best fitting GAM, indicated that green space, blue space, Simpson\u0026rsquo;s Index and latitude were strong predictors of socio-economic deprivation (Adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52, GCV\u0026thinsp;=\u0026thinsp;1.82, n\u0026thinsp;=\u0026thinsp;435), best described using smooth terms (thin plate regression splines) due to their varying degrees of linearity (See Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Using 10-fold cross-validation, the model achieved an average RMSE of 1.37, indicating a moderate prediction error (~\u0026thinsp;16%) with the dependent variable ranging from 1.56 to 9.95 with a mean of 5.75. Green space had a positive, almost linear trend (edf\u0026thinsp;=\u0026thinsp;1.280) with deprivation decreasing as green space cover increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Blue space had a non-linear trend with deprivation (edf\u0026thinsp;=\u0026thinsp;1.909) with negligible effects at lower values, but slightly positive effects at higher values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Simpson's diversity had a complex non-linear relationship with deprivation (edf\u0026thinsp;=\u0026thinsp;7.184), which was consistently negative across the majority of data points (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Latitude had a non-linear effect with deprivation (edf\u0026thinsp;=\u0026thinsp;4.380, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with area between 51\u0026deg; and 52\u0026deg; being the least deprived (including London, Cambridge and Oxford). Overall, the results indicate that natural cover tends to increase as deprivation decreases, while overall land cover diversity tends to decline.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral Additive Model summary table.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLink Function\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjusted R-squared\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDeviance explained (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaussian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eidentity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eimdd_weight_av\u0026thinsp;~\u0026thinsp;s(total_green, k\u0026thinsp;=\u0026thinsp;9, bs = \"tp\", fx\u0026thinsp;=\u0026thinsp;FALSE)\u0026thinsp;+\u0026thinsp;\u003c/p\u003e\u003cp\u003es(log(total_blue), bs = \"tp\", k\u0026thinsp;=\u0026thinsp;9, fx\u0026thinsp;=\u0026thinsp;FALSE)\u0026thinsp;+\u0026thinsp;s(simpsons,\u003c/p\u003e\u003cp\u003ek\u0026thinsp;=\u0026thinsp;9, bs = \"tp\", fx\u0026thinsp;=\u0026thinsp;FALSE)\u0026thinsp;+\u0026thinsp;s(lat,\u003c/p\u003e\u003cp\u003ek\u0026thinsp;=\u0026thinsp;9, bs = \"tp\", fx\u0026thinsp;=\u0026thinsp;FALSE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA. Parametric coefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTerm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eEstimate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eStd Error\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003et-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.75331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB. Smooth terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTerm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eedf\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eRef.df\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003es(total_green)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003es(log(total_blue)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003es(simpsons)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCV\u0026thinsp;=\u0026thinsp;1.8161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale est. = 1.7503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we developed a comprehensive method to quantify urban blue spaces at scale across Great Britain, including both freshwater and marine environments, and explored urban land use patterns from both ecological and social perspectives. We found that blue spaces are more prominent in coastal and estuarine cities than in inland cities, but they represent a minority land cover type compared to grey and green spaces. However, unlike green space coverage, which has strong associations with socio-economic deprivation, blue space coverage remains comparatively even across deprivation gradients. Our results also indicate that the most deprived cities tend to have the greatest land cover diversity, suggesting urban ecosystems could be simplified during regeneration. Collectively, our findings address current knowledge gaps in urban blue space baselines and offer new perspectives on blue, green and grey space land cover patterns, vital for creating sustainable, equitable cities.\u003c/p\u003e\u003cp\u003eWe created a highly detailed composite blue space map by combining land cover and land use data, overcoming the issue of underrepresented blue spaces in each of their original formats. This method particularly improved the representation of small waterbodies, commonly missed by 10 m resolution land cover data \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, whilst retaining important blue space habitats which are only available from these datasets (e.g., saltmarsh). Overall, our urban land cover results aligned with previous assessments, indicating that ~ 30% of urban areas are made up of natural cover (grass, trees, water etc.) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. But our study included a 200 m extension to urban boundaries, offering a ‘doorstep scale’ perspective \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e of blue and green spaces which surround cities. By assessing land beyond standardised urban geometries and including highly relevant coastal blue spaces (including the sea), which are not always included in existing blue space maps, we provide a more realistic and holistic assessment of “accessible” urban blue spaces. For example, Natural England’s Green Infrastructure Map \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, which lists blue space, is based exclusively on inland waterbodies from land use data sets. This means the Blue Infrastructure listings do not include major blue spaces, such as intertidal zones (including beaches or foreshores) or fen, bog or salt marsh. Similarly, an assessment of blue space accessibility carried out by the Canal and Rivers Trust \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e based on land use data, was restricted to blue spaces found within a 20 m buffer of public rights of way, dismissing relevant blue spaces beyond this distance, and the fact that blue space can be experienced from a distance, such as coastal walks and viewpoints. These previous blue space assessments, which do not incorporate land cover data, ignore important blue space habitats which are relevant to both biodiversity \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and social well-being \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Therefore, we suggest that blue space maps and assessments should have a holistic approach, combining land use and land cover data, to include all habitats that can be justified as blue space, as done here.\u003c/p\u003e\u003cp\u003eOverall, coastal cities and seaside towns had the highest proportion of blue space, as a result of being in close proximity to marine environments. However, unlike inland areas, the relative blue space of coastal urban areas decreased as factors of urbanisation increased (e.g., the size and population of a city), with larger coastal urban areas having proportionately less blue space than smaller ones. This decrease could be linked to landward urban expansion, driven by coastal erosion \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, the provision of setback zones \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e or coastal squeeze \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. If this is the case, any urban development in coastal areas which does not involve the coastline would reduce the relative land-water interface and therefore decrease the proportionate blue space in that urban area. Seaward expansion via land reclamation could also share a similar effect, unless artificial bays, marinas and lagoons suitable for beachgoers are created, such as those in the UAE \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In contrast, blue space coverage had a slight positive relationship with the size of an urban area and population size across inland cities. This unexpected pattern may be explained by the provision of man-made blue spaces having a notable impact on blue space proportions in these areas, such as park lakes and reservoirs \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. For example, we found high blue space cover across Staines-upon-Thames due to nearby reservoirs, which were created as part of London's water infrastructure \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Therefore, in some circumstances, blue spaces could remain stable and even increase in cover during urbanisation and utilitarian water management.\u003c/p\u003e\u003cp\u003eWe found estuarine cities had more blue space than inland cities, but less than coastal cities, reflecting their natural role as transitional zones between land and sea. We also found evidence for estuarine cities having high grey space coverage and restricted blue space accessibility, primarily due to heavy industrialisation. Estuarine habitats have faced huge declines as a result of sprawling coastal developments \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and land reclamation of wetlands \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Here, 10 out of 14 urban boundaries that required modification to exclude completely inaccessible and non-residential urban zones (e.g., refineries and cargo ports) were estuarine, suggesting that industrial land use in estuarine cities puts constraints on blue space accessibility. However, Ghomeshi and Walczak \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e highlight the potential of post-industrial cities for blue space regeneration, as do Burda and Nyka \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, who promote waterfront regeneration in locations where there has been withdrawal of port and shipyard industries from city centres. Examples of this can be seen in Kingston Upon Hull, which has multiple access points to the Humber Estuary and docks which have been converted into commercial hubs \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur results show that the blue space cover is heavily dependent on the location of the urban area, with geographic biases having a strong influence on blue space quantities, and that the effect of urbanisation on blue space availability can be highly varied across different regions. Furthermore, we show that blue spaces are less abundant than green spaces, presented here at scale and in a previous case study on Bristol, England \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The scarcity of urban wetlands, particularly inland freshwater systems, makes them disproportionately vulnerable to land cover change during urbanisation, highlighting the importance of prioritising their preservation. We also observed a trade-off between green and blue space cover in urban areas, finding that as green and blue space replace one another, grey space remains relatively stable. This can be seen in coastal cities, which have less green space cover than inland cities, but no difference in grey space cover. Previous research exploring low green space within coastal cities highlights that environmental conditions on exposed coasts, including strong winds and sea salt aerosol \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, can create challenging conditions for trees, resulting in stunted growth, increased dieback and less amenity value \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In addition, trees can block desirable views of water bodies, and can be removed or opposed by communities \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, showing that the presence of a blue space can indirectly affect green space cover in urban areas.\u003c/p\u003e\u003cp\u003eBy examining social data, we found that blue space does not exhibit the same associations with deprivation as green space, which typically increases as deprivation decreases, shown here and in previous studies \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. We also found deprivation was lower in the south of England, as shown in previous studies \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, suggesting the trends captured in our model are reliable. Unlike green space, we found blue space availability remains comparatively stable across levels of deprivation, with some weak associations when cover is high. This finding highlights that blue space inequity is less notable than that of green spaces, building on the growing interest in the role of blue spaces in mitigating environmental inequalities across deprived communities \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The stability of blue space cover could be explained by their geographic and physical constraints, often being fixed features of the landscape (e.g. a river or coastline) which do not necessarily increase with urban expansion or regeneration. In contrast, green spaces, which are more easily introduced or expanded, are commonly provided for in urban design. In addition, we found that general land cover diversity increases with increasing deprivation, meaning the most deprived communities also have the most heterogeneous land cover. This suggests that the process of urban regeneration, associated with increasing wealth, could lead to homogenisation of urban areas, alongside the more commonly studied cultural and social homogenisation \u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e–\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. For example, the removal of brownfield sites (“wastelands”) during regeneration, known to impact urban biodiversity \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, could also reduce both the number and extent of urban habitats. With this in mind, and considering our results, the process of revitalising a city may have negative impacts on ecosystem resilience if the management of habitats is not well thought out \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo conclude, as the interest in creating accessible natural spaces in cities increases, a comprehensive perspective on land cover is essential if urban planners are going to address both ecological and social demands of urban environments. Here, we found that blue spaces are ubiquitous with urbanisation, as with green space, but have much less cover, particularly across inland areas, and provide a robust baseline for blue space cover, suitable for comparison in future studies. Furthermore, in addition to reaffirming known patterns of socio-economic deprivation and green space, we provide new land cover perspectives, which show blue space remains relatively stable across levels of deprivation, and that the diversity of land cover increases with deprivation. This indicates that urban land cover may become homogenised with urban regeneration, thus having counteractive effects on biodiversity and sustainability goals. Collectively, our results highlight the need to incorporate blue spaces, alongside green spaces, into holistic ecological perspectives of the urban environment to better understand landscape-scale land-use patterns. If all land cover types, particularly threatened wetlands, are accounted for within both social and ecological contexts, future urban development has the potential to be more sustainable, inclusive, and responsive to growing urban pressures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGreat Britain (GB), made up of England, Wales and Scotland, is the ninth largest island in the world and home to over 65\u0026nbsp;million people \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The population is highly urbanised, with 83% (56\u0026nbsp;million) of England's population living in built-up areas \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Across all three countries, the composition, location and size of built-up areas are highly varied, ranging from small coastal towns to extensive urban agglomerations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFive GB datasets were used to gather geospatial, ecological, and socioeconomic perspectives: (1) a layer detailing built up area boundaries provided by the Office of National Statistics \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, (2) a 10 m resolution land cover map detailing 21 habitats, created by the Centre of Ecology and Hydrology \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, (3) Ordnance Survey Vector Map land use boundaries \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, (4) UK government population census results \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, and (5) government index of multiple deprivation indices \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData handling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBuilt-up urban areas were identified from the Built-Up Areas layer (n = 8545), and sub-sampled into the 500 largest by area (maintaining ONS names). Original geometries were then extended by 200 m using the QGIS buffer tool to include highly accessible spaces (either visually or physically) falling outside of the original geometries but relevant to the area. For example, the sea, “accessible” in most coastal regions, will always fall outside of statistical zonations, meaning original geometries would introduce strong boundary effects in assessments of blue space cover. An evaluation of general accessibility was carried out for all coastal and estuarine regions using satellite imagery and Google Street View. This process consisted of checking for access points and identifying disconnected built-up areas, which were part of an urban agglomeration, but not residential or publicly accessible (see Supplementary Fig.\u0026nbsp;1). In total, 14 urban areas were modified to remove disconnected (no public roads or paths) and inaccessible industrial zones, including refineries and ports (see Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003cp\u003eLand cover types within urban areas were quantified with CEH land cover data \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and OS land use data. Although very accurate (82.6% overall), the primary focus of the CEH land cover map was terrestrial cover, meaning coastal and intertidal zones have lower accuracy (Marston et al. 2022). Similarly, smaller water bodies (\u0026lt; 0.5 ha or \u0026lt; 40 m wide) have less accuracy when compared to large water bodies \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, meaning narrow but notable blue spaces (such as rivers and canals) are often missing. As blue space was our focus, we assessed other cartographical resources to improve accuracy, retaining the CEH land cover for all other habitats. Three highly accurate OS land use layers (~ 1 m resolution) were identified for blue space improvements, consisting of Tidal water, Surface water and Foreshore (delineated by low and high tide water marks). CEH and OS data sets were then combined following the workflow below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCompiling land cover and land use data for blue spaces\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLand cover data was downloaded from the Digimap portal (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edigimap.edina.ac.uk\u003c/span\u003e) in 10 m resolution raster format (.tif) and processed using QGIS (v.3.36) as batches using five steps: (1) clip original file to extended BUA boundaries (\u003cem\u003eprocessing toolbox \u0026gt; clip raster by extent tools\u003c/em\u003e), (2) convert raster data to polygons maintaining resolution (\u003cem\u003eprocessing toolbox \u0026gt; vector creation \u0026gt; raster pixels to polygons\u003c/em\u003e), (3) merge land cover polygons by shared attributes (\u003cem\u003eprocessing toolbox \u0026gt; vector geometry \u0026gt; dissolve\u003c/em\u003e), (4) assign location names to new shapes (\u003cem\u003eprocessing toolbox \u0026gt; vector general \u0026gt; join attributes by location\u003c/em\u003e), (5) calculate areas of land cover (\u003cem\u003efield calculator \u0026gt; geometry \u0026gt;$area\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eLand use data was downloaded from the OS Data Hub, within a GB Local District Data package. The three layers of interested were then processed individually following these steps: (1) merge data (\u003cem\u003eedit \u0026gt; merge selected features\u003c/em\u003e), (2) clip with extended BUA geometries (\u003cem\u003evector \u0026gt; geoprocessing tools \u0026gt; clip\u003c/em\u003e), (3) assign names, (4) calculate area (method for steps 3–4 same as 4–5 above).\u003c/p\u003e\u003cp\u003eOverlap between land cover and land use layers was fixed with geoprocessing tools. First, OS tidal water and surface water layers were used to replace CEH land cover classifications when overlap occurred (vector \u0026gt; geoprocessing \u0026gt; difference). Second, CEH littoral layers (isolated layers) replaced OS foreshore cover when overlap occurred (as before). To retain ecological classifications, land use data were reclassified as follows: tidal water to saltwater, surface water to freshwater, and foreshore to littoral sediment. See Supplementary Fig.\u0026nbsp;2. and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for examples. The accuracy of the new dataset was checked using the topology checker plugin, and by summing the area of all new classified geometries and comparing them against the unclassified extended BUA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefining blue, green and grey space\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen defining blue spaces, we adopted the definition of Grellier et al \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e “outdoor environments either natural or manmade that prominently feature water and are accessible to humans either proximally (being in, on or near water) or distally/virtually (being able to see hear or other-wise sense water)” which acknowledges the fact blue spaces can be experienced with and without direct contact \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Based on this, we have therefore assumed that all blue land covers listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e falling within our extended urban areas have the potential to offer some form of physical or sensory exposure, therefore making them all blue spaces.\u003c/p\u003e\u003cp\u003eTo define green spaces, we grouped all non-blue natural habitats found within urban areas listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, acknowledging the fact that people experience greenness across an entire built-up area and not just in the official “public green spaces” \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. As with blue space, either visual or physical accessibility was assumed for all land falling within urban areas. Grey spaces, areas of land characterised by impervious surfaces, were defined by combining \u003cem\u003eSuburban\u003c/em\u003e and \u003cem\u003eUrban\u003c/em\u003e land cover data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefining habitat diversity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHabitat diversity metrics using all 21 land cover classifications were calculated using the Shannon Diversity Index and Simpson’s Diversity Index, often used as a proxy for habitat heterogeneity \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBuilt-up area geographic categorisation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the conception of the study and data preparation, it was noted that many of the selected urban areas were from distinct geographic regions, which were classified into three categories: coastal - when seaward; estuarine - when located along an estuary; and inland - when not connected to any major tidal systems.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRanking built-up areas by blue space\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBuilt-up areas were ranked by proportionate blue space cover (1-495) and grouped into deciles (n = 50) to characterise and describe “the bluest areas” and understand how blue space compares to other types of urban land cover (namely grey and green space).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDeprivation assessments were restricted to England due to variability in deprivation calculation between England, Wales and Scotland. Our analyses used the Indices of Multiple Deprivation (IMD), which are calculated from multiple socio-economic indicators at a national level \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The IMD uses a decile ranking system (IMDD) with 1 being the most deprived and 10 being the least. To calculate the overall deprivation scores of the urban areas used in this study, we used the original BUA layer (not extended) and followed these steps: (1) IMD datasets were downloaded from the government portals as spatial data (aggregated into statistical parcels) (2) statistical parcels containing IMDD information were then refitted into the original built up areas using a cookie cutter approach (\u003cem\u003evector \u0026gt; geoprocessing \u0026gt; clip\u003c/em\u003e), (3) names of each bounding BUA were assigned to new shapes (\u003cem\u003eprocessing toolbox \u0026gt; vector general \u0026gt; join attributes by location\u003c/em\u003e), (4) a weighted average per urban area was then calculated by multiplying the area of each land parcel by its IMDD score (decile), summing these values, and dividing by the sum all land parcels (total area of that built-up area).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis and visualisations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll statistical analyses were carried out in R Studio \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, using base R version 4.2.2 \u003csup\u003e73\u003c/sup\u003e unless otherwise specified. Before analysis, data distribution and normality were assessed visually using histograms, box plots, and quantile-quantile plots. Parametric and non-parametric methods were used based on data distribution. Kruskal-Wallis and Dunn’s test were used to test differences between blue, green and grey space cover across geographic categorisation. Spearman’s rank correlation coefficient was used to test associations between blue space cover and the size of a built-up area, population counts, grey space and green space. Data manipulation and plots were created with tidyverse \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA General Additive Model (GAM) from the mgcv R package \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e was used to test the predictive power of environmental variables for deprivation. The GAM was chosen due to its flexibility in handling non-linear relationships between predictors and the response variable. To address multicollinearity, we assessed the correlation between predictor variables using a correlation matrix \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, with a threshold of \u0026gt; 0.8 indicating significant collinearity (see Supplementary Fig.\u0026nbsp;4). This analysis revealed strong correlations between grey space and green space, and between population size and urban area size. Specifically, grey space and green space were inversely correlated, while population size and urban area size were positively correlated, meaning the response of the removed variables could be reasonably inferred based on those retained (see Supplementary Fig.\u0026nbsp;5). Our final model used a Gaussian family with an identity link function, appropriate for continuous response variables. Smoothing splines were applied to account for non-linearity among predictors, with the best smoothing parameters and variables selected via a stepwise approach (see Supplementary Table\u0026nbsp;6). To preserve model interpretability and maximise stability, grey space and population size were omitted. We used the gam.check function from the mgcv package to assess model fit, examining residual plots and k-index values. This final step confirmed that the model fit was appropriate and that the smoothing terms were correctly specified, with no indications of overfitting or other major issues. Model diagnostics can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conceptualisation of the study. MM was responsible for all data handling, processing, analysis, and visualisation. MM led all writing and formatting. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge the University of Hull for funding this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll raw data and R code used to produce results in this study are available on github https://github.com/MCMorgan06/Bluest_Cities_UK and zonodo (10.5281/zenodo.16038672) to promote transparency and reproducibility.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations. \u003cem\u003eWorld Cities Report: Envisaging the Future of Cities\u003c/em\u003e. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimkin, R. D., Seto, K. C., McDonald, R., I. \u0026amp; Jetz, W. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7156944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7156944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCities are closely linked to the 'triple planetary crisis', climate change, pollution, and biodiversity loss, and urbanisation impacts human health through the removal of natural cover. Urban blue and green spaces offer mitigating effects, but research is traditionally green-focused. Here, we investigate blue space availability and land cover patterns across 500 cities in Great Britain, and for the first time, rank and compare cities by blue cover. City-scale habitat data were paired with deprivation indices to compare equality of blue space, green space, and urban habitat diversity. We found that blue space cover is lower than green space but more evenly distributed across socioeconomic gradients. Additionally, land cover diversity can be higher in deprived areas, suggesting that urban regeneration could result in land cover homogenisation. These findings emphasise the potential of underutilised blue spaces to address environmental injustices and highlight how underexplored land-use patterns can contribute to advancing urban sustainability.\u003c/p\u003e","manuscriptTitle":"Urban blue and green spaces in the UK: Distribution, equity and ecological implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 04:04:55","doi":"10.21203/rs.3.rs-7156944/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T14:22:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T16:48:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T11:11:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290503063012363340469109088755051156871","date":"2025-09-09T08:33:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284899946690838871970508355069217936012","date":"2025-09-04T19:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T18:59:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T16:03:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T10:12:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2025-07-18T10:50:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-urban-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjurbansustain","sideBox":"Learn more about [npj Urban Sustainability](https://www.nature.com/npjurbansustain/)","snPcode":"42949","submissionUrl":"https://submission.springernature.com/new-submission/42949/3","title":"npj Urban Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94f84403-5c86-4da2-95da-d5bd03fedac9","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54317563,"name":"Biological sciences/Ecology"},{"id":54317564,"name":"Earth and environmental sciences/Ecology"},{"id":54317565,"name":"Earth and environmental sciences/Environmental sciences"},{"id":54317566,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":54317567,"name":"Social science/Environmental studies"},{"id":54317568,"name":"Scientific community and society/Geography"},{"id":54317569,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-03-23T16:07:49+00:00","versionOfRecord":{"articleIdentity":"rs-7156944","link":"https://doi.org/10.1038/s42949-026-00349-6","journal":{"identity":"npj-urban-sustainability","isVorOnly":false,"title":"npj Urban Sustainability"},"publishedOn":"2026-03-17 15:59:08","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-09-09 04:04:55","video":"","vorDoi":"10.1038/s42949-026-00349-6","vorDoiUrl":"https://doi.org/10.1038/s42949-026-00349-6","workflowStages":[]},"version":"v1","identity":"rs-7156944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7156944","identity":"rs-7156944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}