Copyright © 2022 by the author(s). Published here under license by the Resilience Alliance. Łaszkiewicz, E., M. Wolff, E. Andersson, J. Kronenberg, D. N. Barton, D. Haase, J. Langemeyer, F. Baró, and P. McPhearson. 2022. Greenery in urban morphology: a comparative analysis of differences in urban green space accessibility for various urban structures across European cities. Ecology and Society 27(3):22. https://doi.org/10.5751/ES-13453-270322 Research, part of a Special Feature on Holistic Solutions Based on Nature: Unlocking the Potential of Green and Blue Infrastructure Greenery in urban morphology: a comparative analysis of differences in urban green space accessibility for various urban structures across European cities Edyta Łaszkiewicz 1, Manuel Wolff 2,3, Erik Andersson 4,5, Jakub Kronenberg 1, David N. Barton 6, Dagmar Haase 2,7, Johannes Langemeyer 2,8, Francesc Baró 9,10,11 and Timon McPhearson 4,12,13 ABSTRACT. The understanding of urban social-ecological systems requires integrated and interdisciplinary methods. This paper explores differences in the accessibility of urban green spaces (UGS) based on urban morphology. In contrast to other comparative analyses that followed simplified quantification of UGS provision and/or omitted the impact of morphological properties of urban space, this study proposes three improvements. First, it uses the share of UGS in the service area of 300 m walking distance around each residential building in a city as a measure of UGS provision. Second, it includes the potential physical accessibility of UGS as warranted by key actors, such as owners or managers, who decide whether UGS are open or not to potential users. Third, it links UGS accessibility and heterogeneous urban structures. We developed a mixed-methods analysis that combines multiple data sources regarding UGS, the spatial distribution of residential buildings, and street networks. We conducted our analysis in five case-study cities (Barcelona, Halle, Lodz, Oslo, and Stockholm). Our findings suggest that the urban structures where the human–environment interaction transformed the space (such as in the core city areas) are characterized by limited UGS in the service area. Urban structures that are less transformed by human activity (especially suburbia) have the highest share of selected UGS in the service area. In addition, even if the share of UGS in the service area is high, many of them might have limited physical accessibility. In the broader sense, this highlights that social-ecological processes are linked to urban form and cannot be separated in an analysis. Therefore, social-ecological systems could be better understood through the lens of urban morphology. Key Words: GIS; land use; landscape metrics; social-ecological systems; spatial analysis; urban green space management INTRODUCTION urban ecosystem services (Kabisch 2019, Barton et al. 2020). At The understanding of urban social-ecological systems requires the same time, studies that focused on quantifying UGS the integration of data and methods from various fields. One of disparities typically neglected the urban morphology perspective the promising—but still insufficiently explored—areas of such (Zhang et al. 2020a). integration is to link the disparities in the provision of urban green There are several potential advantages of incorporating urban spaces (UGS) (Andersson et al. 2019, Pauleit et al. 2019, Herreros- morphology into the analysis of disparities in UGS provision Cantis and McPhearson 2021) and urban morphology (Dennis (Zou and Wang 2021). One of the roles of urban morphology is et al. 2018, Marcus et al. 2019, Riechers et al. 2020). In general to identify the recurring spatial patterns in the structure, terms, urban morphology refers to the study of both urban form, formation, and transformation of the built environment i.e., the physical elements that structure and shape cities like (Fleischmann et al. 2021a). A combined perspective of urban streets, buildings, and plots, and the stakeholders and processes morphology and UGS provision can also foster the debate on the that shape it (Kropf 2018, Oliveira 2019b). As a source of density dilemma and the related duality of making cities more knowledge on the organization of cities, urban morphology livable and sustainable (Neuman 2005, Wolff and Haase 2019). informs various aspects of life in cities, such as the flow of benefits Urban morphology can be used to delimit homogeneous urban from UGS to the inhabitants (Oliveira 2019b, Andersson et al. structures that could be used for intra-city comparisons of UGS 2019). provision and disparities (Grafius et al. 2018). In addition, linking As Zou and Wang (2021) highlighted, in the last decade, UGS UGS-related disparities and urban structures could help to better have been studied through the lens of urban morphology understand the background of these disparities and provide an (Šćitaroci and Marić 2019, Whitehand 2019). Although there have informed background for UGS provision standards (Badiu et al. been attempts to integrate the analysis of urban forms and urban 2016, Boulton et al. 2018). Such an integrated analysis could help inequalities (Oliveira 2021), studies conducted by urban avoid oversimplification when interpreting UGS-related morphologists did not discuss the deeper disparities in UGS disparities and their potentially biased quantification. provision (Kabisch et al. 2016) and benefits provided by UGS— 1 Social-Ecological Systems Analysis Lab, Faculty of Economics and Sociology, University of Lodz, Lodz, Poland, 2Department of Geography, Humboldt Universität zu Berlin, Germany, 3Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany, 4Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden, 5Research Unit for Environmental Sciences, North-West University, Potchefstroom, South Africa, 6Norwegian Institute for Nature Research (NINA), Oslo, Norway, 7Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany, 8Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona, Barcelona, Spain, 9Department of Geography, Vrije Universiteit Brussel (VUB), Brussels, Belgium, 10Department of Sociology, Vrije Universiteit Brussel (VUB), Brussels, Belgium, 11Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona (UAB), Spain, 12Urban Systems Lab, The New School, New York, New York, USA, 13Cary Institute of Ecosystem Studies, Millbrook, New York, USA Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ This study contributes to the emerging debate on how to better data came from open geographical data sources, such as understand social-ecological systems (Marcus et al. 2019, OpenStreet Map or Copernicus, making our analysis replicable Riechers et al. 2020). In contrast to other comparative analyses in other cities. The list of databases, each with a brief description, that followed simplified quantification of UGS provision and/or is presented in Table 2. omitted the impact of morphological properties of urban space, this study proposes three improvements. First, it uses the share of Fig. 1. Case study cities and UGS coverage. UGS in the service area of 300 m walking distance around each residential building in a city as a measure of UGS provision. Second, it includes the potential physical accessibility of UGS as warranted by key actors, such as owners or managers, who decide on whether UGS are open or not to potential users. Third, it links UGS accessibility and heterogeneous urban structures. We aim to quantify the disparities in the provision of UGS, characterized by different levels of potential accessibility, for various urban structures, delimited through the lens of urban morphology. We hypothesize that UGS provision and the inequalities regarding its provision differ between urban structures. We applied mixed- methods analysis for multisource spatially explicit data on UGS, residential buildings, and street networks in five case-study cities (Barcelona, Halle, Lodz, Oslo, and Stockholm). The remainder of this article is divided into five sections. The “Materials and Methods” section justifies the selection of case- study cities, data sources, and methods used to quantify disparities in UGS provision for various urban structures. The “Results” section contains the findings, which are then discussed in the subsequent section in the broader context of using multiple spatial scales, such as the level of residential buildings and urban structures, to evaluate UGS provision. We also discuss the results from the perspective of the three systemic filters that mediate the Methods flow of benefits that UGS provide to city inhabitants (Andersson The identification of spatially explicit disparities in UGS et al. 2019, 2021) and suggest how urban morphology could provision through the lens of urban morphology consists of seven improve the understanding of social-ecological systems. We end phases: (1) classifying UGS according to their potential physical with some concluding remarks. accessibility, (2) quantifying spatially explicit UGS provision, (3) operationalizing the urban structure, (4) delimiting urban blocks, MATERIALS AND METHODS (5) quantifying urban form characters, (6) identifying urban structures, and (7) quantifying spatially explicit inequalities in Case-study Cities UGS provision for different urban structures. In the following Our analysis involves five cities that represent different subsections, each of these phases is described in detail. geographical locations in Europe: Barcelona (Spain), Halle (Germany), Lodz (Poland), Oslo (Norway), and Stockholm Classifying urban green spaces according to their potential (Sweden). They differ in terms of population density and size, physical accessibility and the challenges to UGS planning. This diversity makes them We aimed to obtain the most comprehensive categorization of representative of different urban forms. In particular, at the spatial UGS to consider their potential physical accessibility (Biernacka level of residential buildings, the case-study cities vary from and Kronenberg 2018, 2019) and to discuss their provision from 51,342 to 83,605 observations for Halle and Oslo, respectively. In the broader perspective of the three systemic filters that affect the terms of UGS coverage, the cities differ from 33.3% to 73.4% for flow of benefits from UGS to city inhabitants: institutions, Barcelona and Oslo, respectively (Table 1). In Oslo, UGS includes infrastructures, and preferences (Andersson et al. 2019, 2021; see the protected peri-urban forest of the Marka area. Protection of also the subsection “The context of the mediating filters the greenbelt has constrained urban growth, leading to framework”). Using ArcGIS 10, we combined data on UGS from densification within the built zone, which makes it a relatively multiple sources (Table 2) and classified them into 21 UGS compact city, but not as compact as, for example, Barcelona. This categories (for data processing details, see Append. 1). makes our sample of cities diverse enough to represent European differences in UGS coverage and the pan-European context of We classified UGS based on their potential physical accessibility, UGS planning (Kabisch et al. 2016, Artmann et al. 2017, Wolff which is a particular aspect of UGS provision (Biernacka and and Haase 2019). A brief comparison of the case-study cities is Kronenberg 2018, 2019). Based on previous studies (Biernacka presented in Fig. 1 and Table 1. and Kronenberg 2018, 2019), we assigned different levels of physical accessibility to each of the 21 UGS categories to account Databases for the influence that key actors, e.g., owners or managers, have This study required data on UGS, the spatial distribution of on whether they are open or not to potential users. We residential buildings, and the street network. The majority of the approximated the different levels of physical accessibility by Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 1: Main characteristics of case study cities. Indicator / City Barcelona Halle Lodz Oslo Stockholm Location Southwestern Europe, Central Europe, Central-Eastern North Europe, North Europe, Sweden Spain Germany Europe, Poland Norway Population¹ 1,620,343 239,173 687,702 673,469 962,154 Total area¹ (km²) 102 135 293 454 188 Population density (population/km²) 15,886 1,772 2,347 1,483 5,118 Number of residential buildings² 61,228 51,342 52,122 83,605 62,218 UGS coverage (% of city area)² 33.6 69.8 69.8 73.4 50.4 ¹ Eurostat, Instituto Nacional de Estadistica, Statistics Sweden. ² According to the database described in Table 2. categorizing UGS into public (high physical accessibility), semi- described as a morphological region, i.e., “an area that has a unit public (medium physical accessibility), and private (low physical in respect of its form, that distinguishes it from surrounding accessibility) (see Table 3). areas” (Oliveira 2019a), and urban tissue, i.e., “an organic whole that can be seen according to different levels of resolution” (Kropf Quantifying spatially explicit urban green space provision 1996, Oliveira 2022). We quantified the provision of UGS at the finest spatial resolution possible, i.e., separately for each residential building. For each of Delimiting urban blocks the 310,515 residential buildings in the five cities, we calculated It was necessary to delimit each city into our basic spatial units the percentage of each of the 21 UGS categories within a service —urban blocks—to identify urban structures. To separate one area of 300 m walking distance around each residential building’s urban block from another, we mainly used the street network centroid. We chose the service area approach as it is often (Grippa et al. 2018). To identify urban blocks for suburbs, we considered particularly meaningful because it refers to where supplement the street network with railways and/or natural people walk, which is how they typically reach their local UGS. borders, such as the coastline. In particular, in contrast to a circular buffer, it does not Delimiting urban blocks in each city followed the following steps: overestimate the area that can be reached by pedestrians, hence, (1) we cleaned the street network of bridleways, cycleways, compared with other methods, it represents an improved measure footways, and paths as they do not often act as a physical barrier of UGS provision (Lin et al. 2020, Wolff 2021). that separates areas, (2) we supplemented the street network with The service area of 300 m refers to the concept of “pedestrian rail and tram lines, and natural borders because they usually shed” promoted by sustainable urbanism and interpreted as a core divide space into disconnected parts, and (3) we added the city catchment area, i.e., the area of access to the most important, borders. daily-used facilities (Vale et al. 2018). Additionally, it is in line Based on the above set of polylines, we created the initial polygons, with Natural England’s recommendation that each person should which represented urban residential blocks (with at least one have green space no further than 300 m or a 5 min walk from their residential building located within a block) and urban non- home (Mears et al. 2019). For this purpose, we used Network residential blocks (which were mostly polygons with transport Analysis Toolbox in ArcGIS 10.5. infrastructure). Then, we provided a visual inspection of the Operationalizing the urban structure initial urban blocks and cleaned them of undesirable polygons Urban morphology addresses the various aspects of urban form, that result from multilane roads, functional roads near crossroads, ranging from physical components of urban space, through their or highway ramps. This resulted in the final set of residential and interactions with inhabitants, to land uses and functions non-residential urban blocks. Only the former was further (Fleischmann et al. 2021b). We focused on selected physical considered when identifying urban structures. aspects of urban form for which we applied methods from Quantifying urban form characters landscape ecology (spatial metrics) and statistics (dimensionality- We quantified urban form characters for each urban block using reduction and cluster analysis) (Clifton et al. 2008, Zhang et al. 15 spatial metrics (Zhang et al. 2019, Li et al. 2021) (Table 4). 2019). Spatial metrics derived from landscape ecology might fail to To avoid terminological inconsistency and to overcome comprehensively cover urban form characters (Vanderhaegen and simplifications in quantitative analysis of urban form Canters 2017). To avoid this limitation, in this study, the rationale (Fleischmann et al. 2021b), in this study, we use the term “urban for selecting the urban form character indicators was as follows: structure” to define an area within a city that is homogeneous in (1) to cover urban form characters in three scales: building, street terms of selected urban form characters and that differs from the network, and urban block, and (2) to include general surrounding areas. We defined urban form character, following (“ontological”) categories of indicators: complexity, shape, Fleischmann et al. (2021b), as a “characteristic (or feature) of one intensity, connectivity, and dimension (Fleischmann et al. 2021b). kind of urban form that distinguishes it from another kind.” The We included indicators of urban form character for individual basic spatial unit used to delimit urban structures was the urban buildings located within an urban block because, following block—an area bounded on each side by roads. Our Vanderhaegen and Canters (2017), “individual building understanding of urban structure is similar to what others characteristics contribute most to urban form at block level”. In Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 2. Description of data sources. Information Case-study cities Data source Comments Urban green spaces All Urban Atlas, Street Tree Layer, European Data for 2012 (Urban Atlas), 2011–2013 (Street Settlement Map from Copernicus; OpenStreet Tree Layer) 2010–2013 (European Settlement Map) Map and 2017 (OpenStreet Map); Urban Atlas contains objects of at least 0.25 ha; Street Tree Layers were based on SPOT 5 Supermode data also used for the Urban Atlas; European Settlement Map was based on SPOT5 and SPOT6 satellite imagery with a spatial resolution of 2.5 m Spatial distribution of Barcelona Barcelona’s City Hall Open Data Service Data for 2012 residential buildings Halle Land and property register Data for 2012 Lodz Land and property register Data for 2012 obtained from the data set for 2018 by eliminating all buildings erected after 2012 Oslo Land and property register Data for 2014 Stockholm GDS Property Map from Lantmateriet Data for 2012 obtained from the data set for 2017 by eliminating all buildings erected after 2012 Street network All OpenStreet Map Data for 2017; street network without roads that are not used by pedestrians (motorways, trunks, primary and secondary roads) addition, we used the indicators for the street network because cluster were interpreted as one urban structure. Such an urban the urban form is shaped by the configuration of streets in space structure could be described by urban form characters using (Boeing 2018, Lang et al. 2020). average values of the synthesized or raw indicators. Any two different urban structures (clusters from the cluster analysis) We omitted land use cover, which is also considered in quantitative should differ in the indicators of urban form characters. Each studies on urban form (Fleischmann et al. 2021b), because it refers urban block must belong to one cluster only. We did not pre-define to UGS. This could further lead to a UGS-driven identification the total number of urban structures and allowed the two-step of urban structures. Similarly, Olivera (2021) omitted social, cluster analysis to select the optimum number based on the Akaike economic, and environmental aspects from the quantification of Information Criterion. To validate the division of urban blocks urban form characters to correlate them with urban structures. into urban structures, we used the Silhouette coefficient of the We calculated indicators of urban form characters for buildings cluster’s cohesion and separation (Arrenberg 2020). We and urban blocks in ArcGIS 10.5 using ZonalMetrics (Adamczyk conducted the PCA and two-step cluster analysis in IBM SPSS and Tiede 2017) and V-LATE (Lang and Tiede 2003) Toolboxes. Statistics 25. The indicators for the street network were calculated in Python using the OSMnx package (Boeing 2017). Quantifying disparities in the provision of urban green spaces in To reduce the complexity and correlation of urban form character accordance with the urban structures indicators, we conducted principal component analysis (PCA) We quantified the disparities in UGS provision using the median (Arrenberg 2020). The PCA enabled us to obtain uncorrelated and interquartile range of UGS provision in the service area of synthesized indicators of urban form, which captured a high 300 m around each residential building. We used the medians and degree of the variance contained in the original set of data in interquartile ranges rather than means and standard deviations fewer indicators. These synthesized indicators described the urban as they are more robust measures of central tendency and form of each urban block. We conducted the PCA separately for variability due to the non-normal and highly skewed frequency each city as the interrelation between indicators of urban form distributions of the UGS provision measures for high spatial may be city-specific. We employed Kaiser’s criterion to select the resolution data (Tan and Samsudin 2017). Also, we calculated the number of principal components that represent synthesized percentage of residential buildings with UGS provision in a indicators of urban form, choosing only principal components service area of 300 m higher than 10%. We applied Mood’s median with eigenvalues higher than one. To optimize the statistical test (Desu and Raghavarao 2019) to compare the medians of UGS performance of the PCA, we used a varimax orthogonal rotation provision in urban structures and to check for disparities between of eigenvectors, which additionally ensures that the results are those urban structures. interpretable. RESULTS Identifying urban structures The results are presented in order, consistent with the steps of our We applied a two-step cluster analysis (Arrenberg 2020) for analysis (see “Methods” for the list of steps). First, as the output synthesized indicators of urban form, approximated by the of the first step of our analysis, we present the classification of principal component (factor) scores as the output from the PCA. UGS using the share of UGS area in the city’s total area. Next, Using two-step cluster analysis, we identified urban structures as we provide results of the provision of UGS categories, quantified the groups of urban blocks that are homogeneous in terms of at the level of residential buildings for the whole city. This urban form characters. The urban blocks that belonged to one corresponds to the second step of our analysis. Then, we presented the urban structures identified in each case-study city as the Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 3. Potential physical accessibility of UGS categories. Name of the UGS category Potential physical accessibility ensured by the actors with the greatest impact on UGS management Forests High physical accessibility Urban forestry areas (not classified as forests) UGS are accessible to all inhabitants; accessibility is ensured by public Green urban areas institutions such as city council or local planning authority through local Trees in urban parks zoning plans Greenery other than tree canopy in urban parks Trees accompanying roads and transportation areas Greenery other than tree canopy in allotment gardens Medium physical accessibility Trees in allotment gardens UGS are accessible to a given group of inhabitants, or all inhabitants but Greenery other than tree canopy in cemeteries with limitations; accessibility is ensured mostly by allotment garden councils, Trees in cemeteries urban green space authorities, city councils etc. Greenery other than tree canopy in sports and leisure green areas Trees in sports and leisure green areas Trees on private land and inner court trees Low physical accessibility Other greenery on private land and inner court greening Accessibility of UGS is limited to individual owners who may—but do not Arable land, permanent crops, pastures and wetlands have to—ensure UGS accessibility Trees in industrial and commercial areas Other greenery in industrial and commercial areas Trees accompanying railway areas Trees on mineral extractions Trees on construction sites Brownfield trees output of steps three to six. We end with the results for the city area. Depending on the city, UGS of medium physical inequalities in UGS provision for different urban structures as accessibility mainly consist of allotment gardens (Halle, Lodz) the output of the seventh step of our analysis. and UGS in sports and leisure spaces (Barcelona, Oslo, Stockholm). The dominant lowly accessible UGS types are those Urban Green Space Coverage on private land, inner courtyard greenery, agricultural land, and We quantified the UGS coverage separately for each level of those in industrial and commercial areas. In Oslo and Stockholm, potential accessibility and summarized it using four indicators the share of UGS on private land in the city area is higher than (Table 5 and Append. 2). The results show that, even when UGS the share of other UGS categories characterized by low physical coverage is high in a given city, a large share of UGS might be accessibility—7.5% and 13.3%, respectively. In Halle and Lodz, characterized by limited physical accessibility. In Barcelona, the shares of private land are similar (8.3% and 10.9%). However, highly and lowly accessible UGS represent 18% and 12% of the in these two cities, the dominant lowly accessible UGS is total city area, respectively. Other cities noted larger differences agricultural areas. In Halle, its share in the total city area is 34.3%, in the percentage of UGS in the total area of the city (indicator whereas in Lodz, it is 36.4%. In summary, the results show A). In Halle and Lodz, UGS of low potential physical accessibility between-city variation in UGS structure and differences in UGS have the largest share in the city area—47% and 51%, respectively potential physical accessibility. —whereas UGS of high potential accessibility account for 16% and 15% only. Only in Oslo, due to the omitted public transport, The Spatially Explicit Provision of Urban Green Spaces does the share of UGS that is characterized by potentially high Table 5 summarizes the results for UGS provision based on physical accessibility account for a much higher share of the city potential physical accessibility and differentiation between tree area (56%) than UGS whose accessibility is low (15%). canopy and other greenery. A detailed provision of each UGS category is presented in Append. 2. In each city, the highly accessible UGS mainly consist of tree canopy, whereas lowly accessible UGS are dominated by grass Again, the between-city variation in UGS provision is visible, cover. The share of highly accessible tree canopy in the total area especially for highly and lowly accessible UGS. For example, the of tree canopy in a city varies from 65% (Halle) to 92% (Oslo). median percentage of lowly accessible UGS in the service area of In contrast, the share of highly accessible grass cover in its total 300 m differs between cities, from 4% in Barcelona to 39% in Oslo. area is scarce—from 2–3% (Halle, Lodz, Oslo) to 13% and 21% The median provision of highly accessible UGS is similar in for Barcelona and Stockholm, respectively. This division is closely Barcelona (0.85%) and Lodz (1%), as well as in Halle (1.4%) and linked to the physical accessibility of particular UGS categories. Oslo (1.5%). The between-city differences are even greater when we compare the share of residential buildings with more than 10% In all cities, the main source of highly accessible UGS is forests of UGS in their service areas. For example, in Stockholm, the (Append. 2). Forests cover 11–12% of the city area in Barcelona, share of buildings characterized by >10% provision of highly Halle, and Lodz, and 54% of Oslo. In Stockholm, forests and accessible UGS is 55%. In contrast, in Oslo, Halle, and Lodz, it urban forestry areas (not classified as forests) cover 20% of the is 21–23%, whereas in Barcelona, it is only 11%. Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 4. Indicators of urban form characters. Scale Category Definition Literature Residential building Dimension Mean area of residential buildings Fleischmann et al. 2021b Shape Mean fractal dimension index for residential buildings Ma et al. 2020 Intensity Edge density for residential buildings Weber et al. 2014 Spatial distribution Average Euclidean nearest-neighbor distance for residential buildings Fleischmann et al. 2021b Connectivity Percentage of shared edges in total edge length Wu and Murray 2008 Street network Dimension Average street segment length* Boeing 2017 Shape Average circuity, the ratio of network distances to straight-line distances* Boeing 2019 Spatial distribution Average closeness centrality* Boeing 2018 Intensity Street density* Boeing 2017 Connectivity Weighted average clustering coefficient* Boeing 2018 Urban block Dimension Urban block area Annunziata and Garau 2021 Shape Fractal dimension index for urban block Zhang et al. 2019 Spatial distribution Squared Euclidean distance between urban block and its neighbors in terms of Fleischmann et al. 2021b building density** Intensity Number of residential buildings per urban block area Oliveira et al. 2020 Connectivity Number of adjacent urban blocks Hermosilla et al. 2014 *Streets in a buffer of 200 m around urban block. **We referred to continuity as spatial distribution feature and assumed that the more similar are neighboring urban blocks, the more contiguous is urban space. The results show that the general information on UGS coverage urban blocks using 15 indicators of urban form characters (Table is not enough to capture the provision of UGS, especially if 4) and then used them as input data in the PCA. In each case- differences in physical access to UGS are taken into account. For study city, the PCA yielded five synthesized indicators of urban example, Oslo, which is characterized by UGS coverage equal to form characters. The exception is Halle, for which four 73% (Table 1), has almost eight times lower provision of highly synthesized indicators of urban form character were extracted accessible UGS than Stockholm, which has a lower UGS coverage from the PCA (see Append. 3 for details). The synthesized (50%). This is mainly due to the location of the “Oslomarka” peri- indicators of urban form characters explain around 60% of the urban forest which is protected against residential development, variability of information from the original data sets. concentrating densification to the urban built zone between the In line with our expectations (see “Quantifying urban form coastline and the forest. characters”), the results of the PCA show that the indicators of The provision of tree canopy and other greenery is not the same urban form characters are related, especially the indicators of the in each of the five case-study cities. The provision of tree canopy street network. For example, in Barcelona, synthesized indicator is much lower when only highly accessible UGS are taken into 5, obtained from the PCA (Append. 3), reflects the shape and account. The lowest difference between the median provision of intensity of the street network. In Halle, synthesized indicator 4 highly and lowly accessible tree canopy is observed in Stockholm. combines the dimension, intensity, and connectivity of the street In comparison, the median provision of greenery other than tree network and the urban block shape. Similarly, in Oslo, synthesized canopy is much lower when only highly accessible UGS are indicator 2 is based on the spatial distribution, intensity, and considered, and this corresponds to a low share of such UGS that connectivity of the street network and the urban block shape. is only around 0.4–3% of the city’s areas. Similarly, the median These synthesized indicators of urban form characters show that provision of other greenery might be limited due to potential the urban block shape is linked to the structure of the street restrictions in physical accessibility observed for UGS, for network around such an urban block. example arable land and private gardens. Interestingly, in each The two-step cluster analysis, which is based on the synthesized case-study city, these two categories of UGS are characterized by indicators of urban form characters, grouped the urban blocks the highest median provision for residential buildings among all into different urban structures. The total number of urban UGS categories (Append. 2). structures delimited in each city is similar and equals three (for The provision of UGS varies within each city among residential Halle and Stockholm), four (for Barcelona) and five (for Lodz buildings. This is captured by the high values of the interquartile and Oslo). For each two-step cluster analysis, the Silhouette range and could be interpreted as a signal that UGS provision is coefficient of the cluster’s cohesion and separation was at least unevenly distributed among residential buildings in each city. 0.4; thus, the urban structures are of sufficient quality. The urban Also, the highly skewed distributions of UGS provision for structures are presented in Figs. 2–6. residential buildings reveal the high disparities in UGS provision A detailed characterization of each urban structure can be based between residential buildings, indicating the need to consider on the mean values of synthesized indicators. However, this would more deeply within-city differences in UGS provision. require an additional interpretation of each principal component Extraction and Description of Urban Structures from the PCA, separately for each city. Instead, we provided a For each case-study city, we obtained urban blocks with at least general description of the identified urban structures based on one residential building using the extended street network. The the mean values of the three raw indicators of urban form numbers of blocks were 4,421 (Barcelona), 969 (Halle), 2,148 character (Table 6). (Lodz), 1,059 (Oslo), and 3,474 (Stockholm). We described these Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 5. Urban green space (UGS) provision in the five case-study Fig. 2. Urban structures in Barcelona (number of urban blocks cities (in %) in brackets). UGS and Indica­ Barcelona Halle Lodz Oslo Stockholm physical tor accessibility High physical A 17.80 16.20 14.90 56.40 26.50 accessibility B 0.85 1.40 0.99 1.49 11.59 (total) C 3.52 8.60 7.94 8.01 17.37 D 11.31 23.08 21.21 22.46 55.11 Other A 1.70 1.10 1.00 0.40 3.30 greenery B 0.00 0.00 0.00 0.00 0.73 C 0.12 1.09 0.44 0.23 2.87 D 1.04 3.03 2.31 1.35 3.02 Tree canopy A 16.10 15.10 13.90 56.00 23.20 B 0.72 1.08 0.71 1.33 9.45 C 2.51 5.95 5.07 6.56 15.45 D 8.52 17.11 16.63 19.61 48.12 Medium A 3.60 6.9 4.20 2.00 5.10 physical B 0.00 0.00 0.00 0.00 0.00 accessibility C 0.00 4.74 0.00 0.00 1.25 (total) D 2.87 15.48 6.17 6.00 6.20 Other A 3.30 4.90 2.30 1.50 3.40 greenery B 0.00 0.00 0.00 0.00 0.00 C 0.00 2.76 0.00 0.00 0.71 D 2.69 10.24 2.69 4.86 4.04 Tree canopy A 0.30 2.00 1.90 0.50 1.70 B 0.00 0.00 0.00 0.00 0.00 C 0.00 1.95 0.00 0.00 0.05 D 0.00 8.68 1.51 0.86 1.22 Low physical A 12.30 46.70 50.70 15.10 18.80 accessibility B 3.96 29.90 34.14 38.64 32.29 (total) C 6.27 24.17 28.47 19.57 22.30 Fig. 3. Urban structures in Lodz (number of urban blocks in D 20.39 88.15 92.77 92.48 87.38 Other A 8.40 40.60 46.00 11.00 9.20 brackets). greenery B 0.00 13.09 13.53 16.35 18.91 C 2.53 19.41 22.94 17.27 15.28 D 9.03 59.75 60.30 68.47 77.56 Tree canopy A 3.90 6.10 4.70 4.10 9.60 B 2.78 14.36 17.62 18.45 12.31 C 3.85 10.16 10.15 13.39 11.17 D 9.35 70.34 83.85 80.83 61.93 A - Percentage of UGS in the total area of the city (in %); B and C - Median (B) and interquartile range (C) of UGS provision in the service area of 300 m around each residential building; D - Percentage of residential buildings with UGS provision in the service area of 300 m greater than 10% (in %). The results from the two-step cluster analysis show similarities and differences between our case study cities located across Europe. What Barcelona, Halle, Lodz, and Oslo have in common is the division of the cities into core part and suburbia, represented by different urban structures. The central parts of Lodz and Halle are represented by urban structure 1. In Lodz, in particular, this urban structure corresponds to the mainly high-density, historic city center. In Halle, the limited number of urban structures obtained in the two-step cluster analysis results in a sharp division into a high-density central city area and suburbia. By contrast, Lodz is delimited into more urban structures, and so the core- suburbia gradient involves more steps. In Lodz, structures 2 and 3 could be described as areas with mixed residential and commercial functions. Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ In Oslo, the division of the city into urban structures is the most concentric of all the case-study cities. The central part of the city Fig. 5. Urban structures in Oslo (number of urban blocks in mainly features urban structures 1 and 2, encircled by urban brackets). structure 3. Urban structures 4 and 5 form the most peripheral part of the city. This is also true of Barcelona, where urban structures 1 and 2 are characterized by a lower mean Euclidean distance between neighboring residential buildings and smaller urban block sizes than urban structures 3 and 4 (Table 6). This means that the density of residential buildings in these urban structures is high. What both Oslo and Barcelona have in common is an urban development area concentrated between a protected peri-urban forest and a coastline and a port area. Fig. 4. Urban structures in Halle (number of urban blocks in brackets). Table 6. Characterization of urban structures in case-study cities Urban Indicator Barcelona Halle Lodz Oslo Stockholm structure of urban Urban structures 4 and 5 in Oslo, 2 and 3 in Halle, 3 and 4 in form Barcelona, and 4 and 5 in Lodz represent suburbia, dominated character by single-family houses rather than multi-family dwellings. What Urban A 0.34 1.65 2.81 0.66 10.03 they have in common is the high size of the urban blocks and the structure 1 B 0.04 0.04 0.06 0.07 0.04 distance between residential buildings, further suggesting low C 1.31 4.23 5.88 2.64 3.71 building density. In summary, the results show variation in the Urban A 0.05 2.71 7.90 18.32 0.86 structure 2 B 0.09 0.03 0.03 0.06 0.06 urban structures, which could be related at a general level to the C 0.46 12.26 2.65 6.55 1.43 historical background of the cities and their land use dynamics. Urban A 64.21 16.75 15.33 6.47 36.93 Interestingly, in Stockholm, the urban structures are mixed and structure 3 B 0.90 0.05 0.07 0.04 0.05 do not constitute a continuous area with the same urban structure. C 40.70 63.59 5.38 35.36 19.05 In contrast to the other cities, in which the division into urban Urban A 7.68 20.29 9.11 structure 4 B 0.07 0.04 0.03 structures visually suggests a concentric zone model, a visual C 2.56 52.53 10.47 inspection of the urban structures for Stockholm suggests a Urban A 24.88 105.36 multiple nuclei model of the city’s spatial development. This is structure 5 B 0.02 0.04 partly an effect of the slightly complicated geography of C 36.68 1 409 Stockholm with several islands and waterways. A - The mean Euclidean nearest-neighbor distance for residential buildings (in m); B - The mean street network density in urban block (in m/m²); C - The mean urban block size (in ha). Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Disparities in the Provision of Urban Green Spaces in Different urban structures in this city. The median percentage of UGS cover Urban Structures in the service area of 300 m around residential buildings for Lodz’s Table 7 shows the median provision of UGS in different urban central part is 30%. This value rises for urban structures 2 and 3, structures. Mood’s median test has a p value < 0.01 for each UGS, which correspond to mixed residential and commercial functions confirming statistically significant disparities between urban (37 and 45%). However, the highest median provision of UGS is structures in UGS provision. These differences are observed in observed for urban structures 4 and 5, which represent the each city and for each level of UGS physical accessibility. This outskirts (46 and 70%, respectively). Similar growth of the median further extends the general findings regarding the disparities provision of UGS in a city’s core–suburbia gradient is noticed in observed between European cities and the limited physical access Oslo. The results for Stockholm are the exception. In particular, to the various UGS categories. The results show that UGS the median provision of UGS does not vary much across the three provision for different levels of potential physical accessibility urban structures. depends on the type of urban structure (Table 7). Despite the above, there are differences between and within cities, In general, in each city and urban structure, there is a similar mainly in the disproportion between the median provision of variation of UGS provision related to the level of physical lowly accessible tree canopy and other greenery. In the case of accessibility. Interestingly, in all cities, the lowest provision is highly accessible UGS, in all urban structures and cities, we noticed for UGS characterized by medium physical accessibility, observe a higher median provision of tree canopy than other which is the result of the small number of those UGS in cities (see greenery. This is not observed for lowly accessible tree canopy and Table 5). Also, in each urban structure, the median provision of other greenery, however. For example, in Halle, only in urban lowly accessible UGS is much higher than the median provision structure 1 (city center) is the median provision of lowly accessible of highly accessible UGS. The only exception is urban structure tree canopy higher than other greenery. In two other urban 3 in Barcelona, with a median provision of highly and lowly structures, the median provision of tree canopy and other accessible UGS in the service area of 300 m around residential greenery is almost equal. Similar equalization is observed for the buildings, both of around 10%. In addition, in all cities, except outskirts (urban structure 4) of Barcelona. This contrasts with Stockholm, the median percentage of highly accessible UGS in Lodz and Stockholm. The former notices higher median the service area of residential buildings is not higher than 10%. provision of other greenery (39%) than tree canopy (17%) in the outskirts (urban structure 5). The latter is characterized by a Fig. 6. Urban structures in Stockholm (number of urban blocks higher median provision of other greenery (21%) than tree canopy in brackets). (13%) for urban structure 1. In summary, the results demonstrate that the disparities in the median provision of UGS vary across urban structures. Also, without further consideration of potential physical accessibility and division into vegetation structure, the provision of UGS gives only general information on UGS-related inequalities. DISCUSSION The results refer to multiple challenges discussed by urban ecologists and planners. In particular, we propose linking UGS provision at the spatial level of residential buildings and urban structures delimited based on urban blocks, further contributing to the broader discussion on the proper spatial (dis)aggregation of data when analyzing disparities in UGS provision. We also propose considering different levels of potential physical accessibility of UGS when quantifying UGS provision for various urban structures. This further contributes to the ongoing discussion on the role of institutions (e.g., property rights) and infrastructures (e.g., landscape composition) in the flow of benefits from UGS to urban inhabitants. In the broader sense, our findings contribute to the discussion on integrating an analysis of social-ecological systems and urban morphology. What is also common for our case-study cities, excluding Multiple Spatial Scales and the Quantification of Urban Green Stockholm, is the lower median provision of UGS in those urban Space Provision structures that reflect the city’s core, and a higher median We argue for a better understanding of the oversimplifications of provision of UGS in the urban structures that correspond to the findings regarding disparities in UGS provision, which are the city’s suburbia. For example, in Barcelona, the median provision result of using spatially aggregated data. In particular, we have of UGS for urban structures 1 and 2 (the central part of the city) demonstrated that UGS provision quantified at a city scale, is around 5–6%, whereas for urban structures 3 and 4 (suburbia), without considering spatially disaggregated units such as urban it is 30–37%. Similarly, the central part of Lodz (urban structure structures, provides only general and often overestimated 1) is characterized by the lowest median provision of UGS among information on UGS provision. Our results show that the lower Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Table 7. Median provision of urban green spaces in urban structures (in %) and interquartile range in brackets. UGS and potential Urban Barcelona Halle Lodz Oslo Stockholm physical accessibility structure UGS (total) 1 5.61 (8.28) 26.77 (25.71) 30.00 (24.88) 23.02 (22.19) 52.58 (19.17) 2 4.69 (7.84) 45.82 (23.12) 44.89 (20.20) 27.96 (25.02) 41.02 (40.06) 3 37.45 (58.46) 49.98 (21.28) 36.52 (15.20) 48.37 (16.73) 57.38 (18.37) 4 30.01 (41.93) 46.53 (39.78) 48.82 (14.51) 5 69.94 (29.99) 50.78 (20.05) High physical 1 0.73 (2.76) 3.40 (10.63) 0.90 (6.32) 1.69 (9.15) 11.46 (17.20) accessibility (total) 2 0.54 (2.32) 0.81 (5.95) 1.08 (7.81) 0.97 (4.87) 11.20 (16.11) 3 9.66 (27.19) 3.39 (14.83) 2.25 (8.54) 1.59 (8.60) 16.84 (22.63) 4 3.84 (14.43) 1.06 (9.57) 1.01 (1.80) 5 0.69 (9.91) 5.29 (18.91) Other greenery 1 0.00 (0.06) 0.41 (2.74) 0.00 (0.97) 0.00 (1.07) 0.85 (3.02) 2 0.00 (0.11) 0.00 (0.34) 0.00 (0.40) 0.00 (0.57) 0.30 (2.32) 3 0.00 (0.57) 0.00 (1.83) 0.38 (3.27) 0.00 (0.25) 0.58 (2.51) 4 0.00 (0.51) 0.00 (0.31) 0.00 (0.00) 5 0.00 (0.00) 0.00 (0.00) Tree canopy 1 0.66 (2.15) 2.36 (6.97) 0.62 (3.26) 1.43 (7.76) 9.12 (15.04) 2 0.50 (1.58) 0.73 (4.01) 0.79 (5.15) 0.83 (3.96) 9.72 (15.29) 3 6.74 (26.05) 2.08 (10.86) 0.89 (3.18) 1.40 (6.92) 14.43 (20.10) 4 2.87 (11.03) 0.82 (6.21) 0.95 (1.63) 5 0.61 (8.72) 4.34 (18.26) Medium physical 1 0.00 (0.00) 0.00 (2.81) 0.00 (0.04) 0.00 (0.67) 0.00 (1.23) accessibility (total) 2 0.00 (0.00) 0.00 (5.52) 0.00 (0.00) 0.00 (0.00) 0.00 (1.49) 3 0.00 (0.00) 0.00 (5.66) 0.00 (0.95) 0.00 (0.00) 0.00 (0.81) 4 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 5 0.00 (0.00) 0.00 (0.00) Other greenery 1 0.00 (0.00) 0.00 (1.50) 0.00 (0.02) 0.00 (0.50) 0.00 (0.66) 2 0.00 (0.00) 0.00 (3.25) 0.00 (0.00) 0.00 (0.00) 0.00 (0.92) 3 0.00 (0.00) 0.00 (3.17) 0.00 (0.50) 0.00 (0.00) 0.00 (0.45) 4 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 5 0.00 (0.00) 0.00 (0.00) Tree canopy 1 0.00 (0.00) 0.00 (0.85) 0.00 (0.00) 0.00 (0.00) 0.00 (0.06) 2 0.00 (0.00) 0.00 (2.58) 0.00 (0.00) 0.00 (0.00) 0.00 (0.02) 3 0.00 (0.00) 0.00 (2.31) 0.00 (0.00) 0.00 (0.00) 0.00 (0.02) 4 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 5 0.00 (0.00) 0.00 (0.00) Low physical accessibility 1 3.57 (4.55) 14.44 (16.82) 22.24 (19.25) 12.68 (15.97) 34.26 (20.83) (total) 2 2.93 (3.92) 34.67 (21.02) 37.25 (19.24) 19.41 (24.55) 22.21 (29.59) 3 10.02 (27.63) 34.73 (23.81) 28.43 (11.85) 40.48 (14.97) 33.83 (19.70) 4 17.31 (26.73) 32.93 (33.89) 44.59 (14.54) 5 58.83 (35.16) 37.99 (15.56) Other greenery 1 0.87 (1.86) 3.04 (11.27) 5.79 (10.40) 4.22 (7.91) 20.63 (14.07) 2 0.66 (1.44) 16.15 (18.27) 15.33 (13.25) 5.28 (12.48) 10.66 (16.05) 3 3.88 (17.91) 17.97 (20.19) 8.32 (7.85) 19.02 (16.48) 19.38 (15.69) 4 7.48 (16.16) 14.04 (28.53) 20.18 (17.27) 5 39.18 (40.41) 15.48 (12.70) Tree canopy 1 2.56 (2.88) 9.41 (7.83) 14.66 (10.14) 7.44 (6.84) 12.96 (10.27) 2 2.24 (2.42) 16.21 (10.11) 20.75 (8.24) 11.48 (12.03) 8.88 (14.34) 3 5.06 (8.49) 14.62 (7.99) 18.87 (8.46) 19.45 (11.80) 12.17 (10.32) 4 8.56 (8.63) 16.19 (10.25) 22.51 (13.74) 5 17.40 (10.85) 20.69 (9.58) the spatial level of data aggregation, the more heterogeneous the the more detailed the picture of the disparities in UGS provision. UGS provision. The disparities in UGS provision noticed at the microscale may be higher (due to the relatively higher spatial data variability) than Based on our results, in each case-study city, we can observe that disparities measured at a higher spatial scale (Łaszkiewicz et al. the variability of UGS provision (which results from differences 2021, Carvalho et al. 2022). Our results confirm this rule and between urban structures) is further complicated and nuanced highlight the need to link conclusions from various spatial data due to the high inequalities in the provision of UGS between aggregation levels. residential buildings (see the values of the interquartile range in Table 7). This is in line with previous studies, which suggested that We found that the provision of UGS differs among urban using the proper spatial data scale affects the outcomes of any structures. This is visible especially when we compare the results analysis of UGS disparities (Tan and Samsudin 2017, Rüttenauer for the urban structures that reflect the core city area with those 2018, Schaeffer and Tivadar 2019). The smaller the spatial scale, for suburbia. Furthermore, the highly uneven spatial distribution Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ of UGS between city centers and green suburbia does not of real UGS provision, especially for those benefits provided by necessarily correspond to where people live. This is visible in each UGS that are highly dependent on the physical accessibility of of our case-study cities, except for Stockholm. For example, in UGS, such as active recreation (Biernacka and Kronenberg 2018, Barcelona, the median provision of UGS for urban structure 4, 2019). which corresponds to the city’s suburbia, is five times higher than In particular, we show that a high share of UGS in the total area for urban structure 1, which reflects the city center. In Halle, Lodz, of a city does not always guarantee that all residential buildings and Oslo, the median provision of UGS in suburbia is around have it in their immediate surroundings. For example, in Oslo, the two times higher than in those cities’ centers. Similar results on coverage of potentially highly accessible UGS is much higher than the differences between the central and peri-urban areas in UGS in Stockholm—56% and 26.5%, respectively. However, the accessibility were obtained by others using big geodata (Ma et al. median provision of these UGS in the service area of 300 m 2020, Chen et al. 2020). Through this study, we propose around residential buildings is much lower in Oslo (1.5%) than in quantifying UGS provision using spatially explicit data and Stockholm (12%). It is even more visible when comparing Oslo linking it further with urban structures to improve the and Halle. The medians of UGS provision in the service area of understanding of UGS distribution. Analyzing disparities in 300 m around each residential building (indicator B in Table 5) UGS provision through the lens of urban morphology and using are almost the same (around 1.5%), although the cities vary in properly selected spatial scales could improve the mapping of terms of UGS coverage. When it comes to Oslo, the low median UGS supply and demand (Whitehand 2017, 2019, Pauleit et al. provision of these UGS in the service area of 300 m is partly offset 2019). by access to peri-urban nature by public transport, which was not We are aware that the indicators of urban form characters are not considered in this study. purely independent of each other and might overlap Our results suggest that the provision of arable land and UGS on (Fleischmann et al. 2021b). In particular, when it comes to the private land and inner courtyard greenery, which represent lowly street network, the assignment of an indicator to a given category, accessible UGS, is the highest among all UGS categories in each such as connectivity, intensity, or spatial distribution, is vague, case-study city. In Barcelona, half of the residential buildings have and there is no consistency in the literature as to which category a share of those UGS in their service area of 300 m below 3%. such indicators represent. For example, Boeing (2018, 2019) This contrasts with the other case-study cities. For example, in suggested that an average closeness centrality and weighted Halle and Lodz, the median provision of arable land and UGS average clustering coefficient indicate both connectivity and on private land and inner courtyard greenery equals 27% and spatial distribution of the street network. Moreover, he argued 31%, respectively. Although most of the data supporting our that those two indicators, as well as average circuity, are measures analysis came from 2012, we expect that over the last decade the of street network complexity that are potentially interchangeable. median provision of UGS in our case-study cities could have Also, following Fleischmann et al. (2021b), the meaning of spatial decreased due to the replacement of informal UGS by new distribution is broad as it may reflect distance, continuity, or residential or commercial developments. concentration of buildings or urban blocks. However, distance- based indicators used to capture the spatial distribution of The limited physical accessibility of UGS excludes those who do buildings might correlate with measures of building connectivity. not have property rights from having pleasant, green surroundings Although Fleischmann et al. (2021b) assigned connectivity for active recreation, such as walking, cycling, or practicing sport indicators only to street networks, we have also added indicators (Biernacka and Kronenberg 2019). In the case of arable land, of connectivity for buildings and urban blocks. This might also UGS on private land, and inner courtyard greenery, we may expect result in the correlation between indicators. that at least their private owners, who have those UGS in their service area of 300 m, can gain all of those benefits that require The Physical Accessibility of Urban Green Spaces physical contact with greenery. Other city inhabitants, who cannot Our results contribute to the emerging need for a deeper enter UGS on private plots, can still gain benefits from those UGS consideration of the potential limitation in gaining benefits from related to visual appreciation or air purification, to mention just UGS due to potentially restricted physical accessibility in cities a few. (Biernacka et al. 2020) and barriers (Barber et al. 2021). We demonstrate that, without including UGS diversity and the The Context of the Mediating Filters Framework broader understanding of physical accessibility that goes beyond According to the research framework of the three systemic filters, walking distance metrics (Wolff 2021), analyzing disparities in the flows of benefits from UGS to urban inhabitants are mediated UGS provision may lead to an oversimplification of the findings. by three filters: institutions (e.g., property rights, social norms), Our results show that high UGS coverage does not guarantee that infrastructures (e.g., landscape composition), and perceptions all inhabitants have UGS in their surroundings. (Andersson et al. 2019, 2021). Our results highlight the relevance of two of these filters in particular: institutions and Even in cities characterized by high UGS coverage, the median infrastructures. provision of UGS provided by highly accessible UGS could be low. Using extensive cross-study spatial analysis, we showed that Institutions reflect policy intentions, social norms, ownership, the discussion on UGS needs to acknowledge and incorporate and user rights. In this study, this filter is represented by the the diversity of physical accessibility of UGS that shapes the diversified physical accessibility of UGS and reflects institutions possibility of city inhabitants benefiting from UGS. Our results (ownership, social norms, and property rights). We included show that omitting the physical accessibility of UGS in an analysis institutions by assigning to each UGS category the potential of disparities in UGS provision may lead to the overestimation physical accessibility ensured by actors with the greatest influence Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ on a given UGS, based on the framework proposed by Biernacka advantages of such an integration by demonstrating how and Kronenberg (2018, 2019) and Biernacka et al. (2020). disparities in UGS provision are shaped by heterogeneous urban structures. Our results show that the possibilities of benefiting from UGS could be sharply limited because of changes in UGS accessibility. Considered from the morphological perspective, intra-city This could be further interpreted as the impact of institutions that differences reflect the processes that occur during a city’s can restrict the potential delivery of benefits from UGS. Our development, or past and present urban planning decisions, results reflect the sharp between-cities differences in the median among others. For these reasons, urban planners cannot treat the provision of UGS, such as those in private land and inner whole urban area in the same way, as the different parts may not courtyard greenery, which could further provide opportunities for be comparable (Grafius et al. 2018, Oliveira 2021). Accordingly, active recreation (Hanson et al. 2021). In Stockholm, the median in this study, we propose “comparing the comparable” and provision of such UGS is around 18%, in Halle and Lodz, 7% quantifying the inequalities in UGS provision in accordance with and 5%, respectively, whereas in Barcelona, it is only 1% (see differences between urban structures. In addition, we treat the Append. 2). At the same time, UGS on private land and inner division of cities into urban structures as the approximation of courtyard greenery have low physical accessibility, which is limited infrastructures as mediating factors that may affect the flow of mainly to the landowners. Such an important impact of UGS UGS-related benefits. accessibility on the provision of cultural UGS-related benefits to Urban form, as a product of human–environment interactions, city inhabitants could be interpreted as a result of institutional shapes the spatial distribution of UGS. Our results demonstrate barriers. this fact as the differences among urban structures in (1) UGS Infrastructures are understood as the composition and coverage, (2) the median provision of UGS for residential configuration of the urban landscape that capture the buildings, and (3) the intensity of disparities in UGS provision. interrelations between supply and demand areas (Andersson et Similar conclusions were reached by others who attempted to link al. 2019, 2021). The present study includes the role of UGS and heterogeneous urban space (Grafius et al. 2018, Marcus infrastructures twofold—by using the service area to quantify et al. 2019). Ossola and Hopton (2018) suggested that urban UGS provision at the level of each residential building, and by morphology was one of the main drivers of urban tree cover in allowing for the variation of UGS provision in different urban Denver, and Zhang et al. (2020b) demonstrated that urban form structures. Using the service area for each residential building, we influenced biomass loss for the Yangtze River Delta cities in are able to realistically capture whether the inhabitants can reach China. Our results enable us to draw a similar conclusion. The UGS to gain benefits. This is important, especially when it comes provision of UGS varies not only between cities but also between to recreation. We found that the possibility of benefiting from different urban structures within each city, which further confirms UGS thanks to daily, direct contact with greenery is highly other findings (Ossola and Hopton 2018, Ossola et al. 2019, diversified between cities and within them. However, for our study, Zhang et al. 2020b). it was crucial to account for the heterogeneous urban structures, Although we found that the provision of UGS and UGS-related which could be treated as an approximation of infrastructures. disparities are affected by the urban form of cities, the question Our results confirm the hypothesis that the median provision of of reverse causality arises. In this study, we did not account for UGS varies among urban structures. This could be further linked land-use patterns when identifying urban structures (see with the concept of the spatial production function of UGS “Quantifying urban form characters”). It does not mean that the benefits (Andersson et al. 2021). In particular, urban structures, urban structures we obtained are not partly affected by UGS. The as a product of the human–environment interaction, influence location of small pieces of UGS within urban blocks, or even the the distribution and physical accessibility of UGS and, in the configuration of urban blocks, to mention just a few examples, broader context, the delivery of benefits for human wellbeing. may affect the indices of urban form characters and indirectly According to our results, the urban structures where the human– affect the identification of urban structures. This would be in line environment interactions transformed the space to the largest with Guyot et al. (2021), who proposed including UGS cover in extent (such as in the core city areas) are characterized by the the identification of urban structures. This could further explain limited provision of UGS. Urban structures that are less the differences in UGS coverage among urban structures. In the transformed by human activity (especially suburbia) have the broader context, this means reciprocity in the relationship highest provision of selected UGS. This could further supplement between UGS and urban form. the ongoing discussion on how to manage UGS to support the The importance of urban morphology in the diversification of flow of benefits from UGS to city inhabitants (Sikorski et al. UGS provision is linked to the spatial distribution of UGS 2021). categories. Each UGS category is differently distributed across Toward an Integrated Social-Ecological Urban Morphology urban structures, which results in a variation in UGS provision. The need for broader integration of urban morphology and In other words, within-city configuration determines the spatial social-ecological systems has been highlighted several times both distribution of environmental characteristics, which has further by urban morphologists (Whitehand 2017, 2019, Marcus et al. implications for UGS (Steele and Wolz 2019). In particular, this 2019) and urban ecologists (Marcus and Colding 2014). As a is highly visible in the gradient from the city center to the suburbs. result, the number of attempts to combine both fields for a Nevertheless, for all urban structures, the provision of potentially broader understanding of social-ecological processes has been highly accessible UGS is much lower than those characterized by growing in recent years (Sharifi 2019, Li et al. 2021, Oliveira 2021). low physical accessibility. At the same time, further disaggregation This study contributes to the ongoing discussion on the of UGS into tree canopy and other greenery demonstrates that, Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ in a few cases, the provision of potentially highly accessible tree proposals, by the national funders: the Swedish Research Council canopy is higher than the median provision of potentially lowly for Environment, Agricultural Sciences, and Spatial Planning, the accessible tree canopy. Swedish Environmental Protection Agency, the German Aeronautics and Space Research Centre, the National Science In the broader context, our results highlight that social-ecological Centre (Poland) (grant no. 2016/22/Z/NZ8/00003), the Research processes are linked to urban morphology, which means that they Council of Norway, and the Spanish Ministry of Economy and cannot be analyzed separately. Thus, social-ecological systems Competitiveness. could be better understood through the lens of urban morphology. This is in line with the recent literature, which emphasizes human–environment interactions, which result in the Data Availability: social-ecological change expressed by urban morphology. Data on the provision of 21 urban green space categories for each Perhaps then, to better understand social-ecological processes, it residential building are publicly available in the WFS format. would be relevant to continue integrating the knowledge and tools Barcelona: https://dservices1.arcgis.com/Y0GEUoQU0oZOJlz6/ used in landscape ecology and urban morphology (Marcus et al. arcgis/services/Barcelona/WFSServer?service=wfs&request=getcapabilities 2019, Whitehand 2019, Oliveira 2021). Halle: https://dservices1.arcgis.com/Y0GEUoQU0oZOJlz6/arcgis/ CONCLUSION services/Halle/WFSServer?service=wfs&request=getcapabilities Lodz: This study demonstrates the advantages of linking the disparities https://dservices1.arcgis.com/Y0GEUoQU0oZOJlz6/arcgis/services/ in UGS provision with the heterogeneous intra-city urban Lodz/WFSServer?service=wfs&request=getcapabilities Oslo: https:// structures. We applied mixed-methods for multiple spatially dservices1.arcgis.com/Y0GEUoQU0oZOJlz6/arcgis/services/Oslo/ explicit data to comprehensively analyze the provision of UGS WFSServer?service=wfs&request=getcapabilities Stockholm: https:// and their disparities for each residential building in five European dservices1.arcgis.com/Y0GEUoQU0oZOJlz6/arcgis/services/Stockholm/ cities (Barcelona, Halle, Lodz, Oslo, Stockholm). We linked them WFSServer?service=wfs&request=getcapabilities Other data used to urban structures delimited through the lens of urban in our paper came from open sources listed in Appendix 1, such as morphology, using indices of urban form characters. OpenStreet Map and Urban Atlas. Our results show that the general information on UGS coverage is not enough to capture the provision of UGS. Also, physical LITERATURE CITED access to UGS differentiates the amount of UGS in the service Adamczyk, J., and D. Tiede. 2017. ZonalMetrics - a Python area around residential buildings. Even when the UGS coverage toolbox for zonal landscape structure analysis. Computers and is high in a given city, in reality, a large share might be Geosciences 99:91-99. https://doi.org/10.1016/j.cageo.2016.11.005 characterized by limited physical accessibility. The results show both between-city and within-city differences in the provision of Andersson, E., S. Borgström, D. Haase, J. Langemeyer, A. highly and lowly accessible UGS. This further suggests the need Mascarenhas, T. McPhearson, M. Wolff, E. Łaszkiewicz, J. to consider deeply the use of spatially disaggregated data and Kronenberg, D. Barton, and P. Herreros-Cantis. 2021. A context- multiple spatial scales to go beyond general and often sensitive systems approach for understanding and enabling overestimated information on the amount of UGS for city ecosystem service realization in cities. Ecology and Society 26(2): inhabitants. 35. https://doi.org/10.5751/ES-12411-260235 Our findings suggest that the urban structures where the human– Andersson, E., J. Langemeyer, S. Borgström, T. McPhearson, D. environment interaction has transformed the space (such as in the Haase, J. Kronenberg, D. N. Barton, M. Davis, S. Naumann, L. core city areas) are characterized by limited UGS provision. Röschel, and F. Baró. 2019. Enabling green and blue Urban structures that are less transformed by human activity infrastructure to improve contributions to human well-being and (especially suburbia) have the highest provision of selected UGS. equity in urban systems. BioScience 69(7):566-574. https://doi. In the broader sense, this means that social-ecological processes org/10.1093/biosci/biz058 and urban form cannot be separated; thus, we advocate linking Annunziata, A., and C. Garau. 2021. A literature review on the both. With this study, we support the proposal highlighted by assessment of vitality and its theoretical framework. Emerging urban morphologists and urban ecologists to support the better perspectives for geodesign in the urban context. Pages 305-322 in understanding of social-ecological systems through the lens of O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Blecic, D. Taniar, urban morphology and vice versa. B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, and C. M. Torre, editors. Computational science and its applications— Responses to this article can be read online at: ICCSA 2021. Springer International Publishing, Cham, https://www.ecologyandsociety.org/issues/responses. Switzerland. php/13453 Arrenberg, J. 2020. Analysis of multivariate data with SPSS: workbook with detailed examples. BoD - Books on Demand,, Norderstedt, Germany. Artmann, M., X. Chen, C. Iojă, A. Hof, D. Onose, L. Poniży, A. Z. Lamovšek, and J. Breuste. 2017. The role of urban green spaces Acknowledgments: in care facilities for elderly people across European cities. Urban This research was carried out within the ENABLE project funded Forestry and Urban Greening 27:203-213. https://doi. through the 2015–2016 BiodivERsA COFUND call for research org/10.1016/j.ufug.2017.08.007 Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Badiu, D. L., C. I. Iojă, M. Pătroescu, J. Breuste, M. Artmann, Dennis, M., D. Barlow, G. Cavan, P. A. Cook, A. Gilchrist, J. M. R. Niță, S. R. Grădinaru, C. A. Hossu, and D. A. Onose. 2016. Handley, P. James, J. Thompson, K. Tzoulas, C. P. Wheater, and Is urban green space per capita a valuable target to achieve cities’ S. Lindley. 2018. Mapping urban green infrastructure: a novel sustainability goals? Romania as a case study. Ecological landscape-based approach to incorporating land use and land Indicators 70:53-66. https://doi.org/10.1016/j.ecolind.2016.05.044 cover in the mapping of human-dominated systems. Land 7(1):17. https://doi.org/10.3390/land7010017 Barber, A., D. Haase, and M. Wolff. 2021. Permeability of the Desu, M. M., and D. Raghavarao. 2019. Nonparametric city—physical barriers of and in urban green spaces in the city of statistical methods for complete and censored data. CRC Press, Halle, Germany. Ecological Indicators 125: 107555. https://doi. Boca Ratron, Florida, USA. https://doi.org/10.1201/9781482285895 org/10.1016/j.ecolind.2021.107555 Barton, D. N., N. Gulsrud, N. Kabisch, and T. B. Randrup. 2020. Fleischmann, M., A. Feliciotti, and W. Kerr. 2021a. Evolution of Urban open space valuation for policymaking and management. urban patterns: urban morphology as an open reproducible data Pages 129-148 in M. Janssen and T. Randrup, editors. Urban open science. Geographical Analysis. https://doi.org/10.1111/gean.12302 space governance and management. Routledge, Abingdon-on- Thames, UK. https://doi.org/10.4324/9780429056109-10 Fleischmann, M., O. Romice, and S. Porta. 2021b. Measuring urban form: overcoming terminological inconsistencies for a Biernacka, M., and J. Kronenberg. 2018. Classification of quantitative and comprehensive morphologic analysis of cities. institutional barriers affecting the availability, accessibility and Environment and Planning B: Urban Analytics and City Science attractiveness of urban green spaces. Urban Forestry and Urban 48(8):2133-2150. https://doi.org/10.1177/2399808320910444 Greening 36:22-33. https://doi.org/10.1016/j.ufug.2018.09.007 Grafius, D. R., R. Corstanje, and J. A. Harris. 2018. Linking Biernacka, M., and J. Kronenberg. 2019. Urban green space ecosystem services, urban form and green space configuration availability, accessibility and attractiveness, and the delivery of using multivariate landscape metric analysis. Landscape Ecology ecosystem services. Cities and the Environment (CATE) 12(1): 5. 33:557-573. https://doi.org/10.1007/s10980-018-0618-z Biernacka, M., J. Kronenberg, and E. Łaszkiewicz. 2020. An Grippa, T., S. Georganos, S. Zarougui, P. Bognounou, E. Diboulo, integrated system of monitoring the availability, accessibility and Y. Forget, M. Lennert, S. Vanhuysse, N. Mboga, and E. Wolff. attractiveness of urban parks and green squares. Applied 2018. Mapping urban land use at street block level using Geography 116:102152. https://doi.org/10.1016/j.apgeog.2020.102152 OpenStreetMap, remote sensing data, and spatial metrics. ISPRS International Journal of Geo-Information 7(7): 246. https://doi. Boeing, G. 2017. OSMnx: new methods for acquiring, org/10.3390/ijgi7070246 constructing, analyzing, and visualizing complex street networks. Guyot, M., A. Araldi, G. Fusco, and I. Thomas. 2021. The urban Computers, Environment and Urban Systems 65:126-139. https:// form of Brussels from the street perspective: the role of vegetation doi.org/10.1016/j.compenvurbsys.2017.05.004 in the definition of the urban fabric. Landscape and Urban Boeing, G. 2018. Measuring the complexity of urban form and Planning 205: 103947. https://doi.org/10.1016/j.landurbplan.2020.103947 design. URBAN DESIGN International 23(4):281-292. https:// doi.org/10.1057/s41289-018-0072-1 Hanson, H. I., E. Eckberg, M. Widenberg, and J. Alkan Olsson. 2021. Gardens’ contribution to people and urban green space. Boeing, G. 2019. The morphology and circuity of walkable and Urban Forestry ad Urban Greening 63:127198. https://doi. drivable street networks. Pages 271-287 in L. D’Acci, editor. The org/10.1016/j.ufug.2021.127198 mathematics of urban morphology. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-0­ Hermosilla, T., J. Palomar-Vázquez, Á. Balaguer-Beser, J. Balsa- 30-12381-9_12 Barreiro, and L. A. Ruiz. 2014. Using street based metrics to characterize urban typologies. Computers, Environment and Boulton, C., A. Dedekorkut-Howes, and J. Byrne. 2018. Factors Urban Systems 44:68-79. https://doi.org/10.1016/j. shaping urban greenspace provision: a systematic review of the compenvurbsys.2013.12.002 literature. Landscape and Urban Planning 178:82-101. https:// doi.org/10.1016/j.landurbplan.2018.05.029 Herreros-Cantis, P., and T. McPhearson. 2021. Mapping supply of and demand for ecosystem services to assess environmental Carvalho, C., A. G. Del Campo, and D. de Carvalho Cabral. 2022. justice in New York City. Ecological Applications: e02390. https:// Scales of inequality: the role of spatial extent in environmental doi.org/10.1002/eap.2390 justice analysis. Landscape and Urban Planning 221: 104369. https://doi.org/10.1016/j.landurbplan.2022.104369 Kabisch, N. 2019. Urban ecosystem service provision and social- environmental justice in the city of Leipzig, Germany. Pages Chen, Y., W. Yue, and D. La Rosa. 2020. Which communities have 347-352 in M. Schröter, A. Bonn, S. Klotz, R. Seppelt, and C. better accessibility to green space? An investigation into Baessler, editors. Atlas of ecosystem services: drivers, risks, and environmental inequality using big data. Landscape and Urban societal responses. Springer International Publishing, Cham, Planning 204:103919. https://doi.org/10.1016/j.landurbplan.2020.103919 Switzerland. https://doi.org/10.1007/978-3-319-96229-0_53 Clifton, K., R. Ewing, G. Knaap, and Y. Song. 2008. Quantitative Kabisch, N., M. Strohbach, D. Haase, and J. Kronenberg. 2016. analysis of urban form: a multidisciplinary review. Journal of Urban green space availability in European cities. Ecological Urbanism: International Research on Placemaking and Urban Indicators 70:586-596. https://doi.org/10.1016/j.ecolind.2016.02.029 Sustainability 1(1):17-45. https://doi.org/10.1080/17549170801903496 Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ Kropf, K. 1996. Urban tissue and the character of towns. International Research on Placemaking and Urban Sustainability. URBAN DESIGN International 1(3):247-263. https://doi. https://doi.org/10.1080/17549175.2021.2011378 org/10.1057/udi.1996.32 Oliveira, V. M. 2022. The study of urban form: different Kropf, K. 2018. The handbook of urban morphology. Wiley, New approaches. Pages 141-197 in V. M. Oliveira, editor. Urban York, New York, USA. https://doi.org/10.1002/9781118747711 morphology: an introduction to the study of the physical form of cities. Springer International Publishing, Cham, Switzerland. Lang, S., and D. Tiede. 2003. vLATE Extension für ArcGIS - https://doi.org/10.1007/978-3-319-32083-0_6 vektorbasiertes Tool zur quantitativen Landschaftsstrukturanalyse. Paper presented at ESRI European User Conference 2003, Oliveira, V., V. Medeiros, and J. Corgo. 2020. The urban form of Innsbruck, Austria. Portuguese cities. Urban Morphology 24(2):145-166. Lang, W., E. C. M. Hui, T. Chen, and X. Li. 2020. Understanding Ossola, A., and M. E. Hopton. 2018. Measuring urban tree loss livable dense urban form for social activities in transit-oriented dynamics across residential landscapes. Science of The Total development through human-scale measurements. Habitat Environment 612:940-949. https://doi.org/10.1016/j.scitotenv.2017.08.103 International 104: 102238. https://doi.org/10.1016/j.habitatint.2020.102238 Ossola, A., D. Locke, B. Lin, and E. Minor. 2019. Greening in Łaszkiewicz, E., J. Kronenberg, and S. Marcińczak. 2021. style: urban form, architecture and the structure of front and Microscale socioeconomic inequalities in green space availability backyard vegetation. Landscape and Urban Planning in relation to residential segregation: the case study of Lodz, 185:141-157. https://doi.org/10.1016/j.landurbplan.2019.02.014 Poland. Cities 111: 103085. https://doi.org/10.1016/j.cities.2020.103085 Pauleit, S., B. Ambrose-Oji, E. Andersson, B. Anton, A. Buijs, D. Haase, B. Elands, R. Hansen, I. Kowarik, J. Kronenberg, T. Li, F., T. Zhou, and F. Lan. 2021. Relationships between urban Mattijssen, A. Stahl Olafsson, E. Rall, A. P. N. van der Jagt, and form and air quality at different spatial scales: a case study from C. Konijnendijk van den Bosch. 2019. Advancing urban green northern China. Ecological Indicators 121: 107029. https://doi. infrastructure in Europe: outcomes and reflections from the org/10.1016/j.ecolind.2020.107029 GREEN SURGE project. Urban Forestry and Urban Greening Lin, D., R. Zhu, J. Yang, and L. Meng. 2020. An open-source 40:4-16. https://doi.org/10.1016/j.ufug.2018.10.006 framework of generating network-based transit catchment areas Riechers, M., Á. Balázsi, D. Abson, and J. Fischer. 2020. The by walking. ISPRS International Journal of Geo-Information 9 influence of landscape change on multiple dimensions of human- (8): 467. https://doi.org/10.3390/ijgi9080467 nature connectedness. Ecology and Society 25(3): 3. https://doi. Ma, L., H. Zhang, and M. Lu. 2020. Building’s fractal dimension org/10.5751/ES-11651-250303 trend and its application in visual complexity map. Building and Rüttenauer, T. 2018. Neighbours matter: A nation-wide small- Environment 178: 106925. https://doi.org/10.1016/j.buildenv.2020.106925 area assessment of environmental inequality in Germany. Social Science Research 70:198-211. https://doi.org/10.1016/j. Marcus, L., M. Berghauser Pont, and S. Barthel. 2019. Towards ssresearch.2017.11.009 a socio-ecological spatial morphology: integrating elements of urban morphology and landscape ecology. Urban morphology Schaeffer, Y., and M. Tivadar. 2019. Measuring environmental 23(2):115-124. inequalities: insights from the residential segregation literature. Ecological Economics 164: 106329. https://doi.org/10.1016/j. Marcus, L., and J. Colding. 2014. Toward an integrated theory of ecolecon.2019.05.009 spatial morphology and resilient urban systems. Ecology and Society 19(4): 55. https://doi.org/10.5751/ES-06939-190455 Šćitaroci, M., and M. Marić. 2019. Morphological characteristics of green spaces in fortified towns and cities. Urban Morphology Mears, M., P. Brindley, R. Maheswaran, and A. Jorgensen. 2019. 23.1:27-44. Understanding the socioeconomic equity of publicly accessible greenspace distribution: the example of Sheffield, UK. Geoforum Sharifi, A. 2019. Urban form resilience: a meso-scale analysis. 103:126-137. https://doi.org/10.1016/j.geoforum.2019.04.016 Cities 93:238-252. https://doi.org/10.1016/j.cities.2019.05.010 Neuman, M. 2005. The compact city fallacy. Journal of Planning Sikorski, P., B. Gawryszewska, D. Sikorska, J. Chormański, A. Education and Research 25(1):11-26. https://doi. Schwerk, A. Jojczyk, W. Ciężkowski, P. Archiciński, M. org/10.1177/0739456X04270466 Łepkowski, I. Dymitryszyn, A. Przybysz, M. Wińska-Krysiak, B. Zajdel, J. Matusiak, and E. Łaszkiewicz. 2021. The value of doing Oliveira, V. 2019a. An historico-geographical theory of urban nothing—how informal green spaces can provide comparable form. Journal of Urbanism: International Research on ecosystem services to cultivated urban parks. Ecosystem Services Placemaking and Urban Sustainability 12(4):412-432. https://doi. 50: 101339. https://doi.org/10.1016/j.ecoser.2021.101339 org//10.1080/17549175.2019.1626266 Steele, M. K., and H. Wolz. 2019. Heterogeneity in the land cover Oliveira, V. 2019b. Urban forms, agents, and processes of change. composition and configuration of US cities: implications for Pages 529-535 in L. D’Acci, editor. The mathematics of urban ecosystem services. Landscape Ecology 34(6):1247-1261. https:// morphology. Springer International Publishing, Cham, doi.org/10.1007/s10980-019-00859-y Switzerland. https://doi.org/10.1007/978-3-030-12381-9_28 Tan, P. Y., and R. Samsudin. 2017. Effects of spatial scale on Oliveira, V. 2021. Urban form and the socioeconomic and assessment of spatial equity of urban park provision. Landscape environmental dimensions of cities. Journal of Urbanism: Ecology and Society 27(3): 22 https://www.ecologyandsociety.org/vol27/iss3/art22/ and Urban Planning 158:139-154. https://doi.org/10.1016/j. landurbplan.2016.11.001 Vale, D. S., C. M. Viana, and M. Pereira. 2018. The extended node-place model at the local scale: evaluating the integration of land use and transport for Lisbon’s subway network. Journal of Transport Geography 69:282-293. https://doi.org/10.1016/j. jtrangeo.2018.05.004 Vanderhaegen, S., and F. Canters. 2017. Mapping urban form and function at city block level using spatial metrics. Landscape and Urban Planning 167:399-409. https://doi.org/10.1016/j. landurbplan.2017.05.023 Weber, N., D. Haase, and U. Franck. 2014. Assessing modelled outdoor traffic-induced noise and air pollution around urban structures using the concept of landscape metrics. Landscape and Urban Planning 125:105-116. https://doi.org/10.1016/j. landurbplan.2014.02.018 Whitehand, J. 2017. How we view cities: a green-space enigma? Urban Morphology 21(2):103-104. Whitehand, J. 2019. Green space in urban morphology: a historico-geographical approach. Urban Morphology 23(1):5-17. Wolff, M. 2021. Taking one step further—advancing the measurement of green and blue area accessibility using spatial network analysis. Ecological Indicators 126: 107665. https://doi. org/10.1016/j.ecolind.2021.107665 Wolff, M., and D. Haase. 2019. Mediating sustainability and liveability—turning points of green space supply in European cities. Frontiers in Environmental Science 7: 61. https://doi. org/10.3389/fenvs.2019.00061 Wu, X., and A. T. Murray. 2008. A new approach to quantifying spatial contiguity using graph theory and spatial interaction. International Journal of Geographical Information Science 22 (4):387-407. https://doi.org/10.1080/13658810701405615 Zhang, A., C. Xia, J. Chu, J. Lin, W. Li, and J. Wu. 2019. Portraying urban landscape: a quantitative analysis system applied in fifteen metropolises in China. Sustainable Cities and Society 46:101396. https://doi.org/10.1016/j.scs.2018.12.024 Zhang, J., Z. Yu, Y. Cheng, C. Chen, Y. Wan, B. Zhao, and H. Vejre. 2020a. Evaluating the disparities in urban green space provision in communities with diverse built environments: the case of a rapidly urbanizing Chinese city. Building and Environment 183:107170. https://doi.org/10.1016/j.buildenv.2020.107170 Zhang, T., S. S. Chen, and G. Li. 2020b. Exploring the relationships between urban form metrics and the vegetation biomass loss under urban expansion in China. Environment and Planning B: Urban Analytics and City Science 47(3):363-380. https://doi.org/10.1177/2399808318816993 Zou, H., and X. Wang. 2021. Progress and gaps in research on urban green space morphology: a review. Sustainability 13 (3):1202. https://doi.org/10.3390/su13031202 Appendix 1. Multiple-source data on urban green spaces for the case study cities. Name of the UGS category Code Data source and data processing Urban forestry areas (not Green urban areas in UA, intersected with the tree GS4 classified as forests) layer from COPERNICUS Forests GS5 Forests in UA Greenery other than tree Green urban areas in UA, intersected with OSM GS18 canopy in urban parks urban parks Green urban areas in UA, intersected with OSM Trees in urban parks GS16 urban parks and with the tree layer from COPERNICUS Green urban areas in UA other than forest urban Green urban areas GS8 areas, forests, urban parks, and trees in urban parks Road, rail and associated land in UA (CODE2012 = Trees accompanying roads and '12210' OR CODE2012 = '12220' OR CODE2012 = GS7 transportation areas '12400' OR CODE2012 = '12300'), intersected with the tree layer from COPERNICUS Mineral extractions, dump sites in UA, intersected Trees on mineral extractions GS11 with the tree layer from COPERNICUS Construction sites in UA, intersected with the tree Trees on construction sites GS17 layer from COPERNICUS Land without current use in UA, intersected with the Brownfield trees GS2 tree layer from COPERNICUS Trees in industrial areas: industrial, commercial, Tree canopy in industrial and GS10 military in UA, intersected with the tree layer from commercial areas COPERNICUS Greenery other than tree Greenery other than trees: industrial, commercial, canopy in industrial and GS21 military in UA, intersected with green coverage from commercial areas European Settlement Map Road, rail and associated land in UA (CODE2012 = Trees accompanying railway GS6 '12230'), intersected with tree layer from areas COPERNICUS Tree canopy in private land Trees on private land: urban fabric in UA, intersected and inner court trees (e.g., GS9 with the tree layer from COPERNICUS house gardens) Greenery other than tree Greenery other than trees: urban fabric in UA, canopy in private land and GS20 intersected with green coverage from European inner court greenery (e.g., Settlement Map house gardens) Arable land, permanent crops, Arable land (annual crops), permanent crops, GS19 pastures and wetlands herbaceous vegetation, pastures and wetlands in UA Greenery other than tree Green urban areas in UA, intersected with OSM GS3 canopy in cemeteries cemeteries 1 Green urban areas in UA, intersected with OSM Trees in cemeteries GS15 cemeteries and with the tree layer from COPERNICUS Greenery other than tree canopy in sports and leisure GS12 Sport/leisure areas in UA green areas Trees in sports and leisure Sport/leisure areas in UA, intersected with the tree GS13 green areas layer from COPERNICUS Greenery other than tree Sport/leisure areas in UA, intersected with OSM GS1 canopy in allotment gardens allotment gardens Sport/leisure areas in UA, intersected with OSM Trees in allotment gardens GS14 allotment gardens and with the tree layer from COPERNICUS 2 Appendix 2. Urban green space coverage and its spatially-explicit provision. UGS and physical Indicator Barcelona Halle Lodz Oslo Stockholm accessibility A 17.80 16.20 14.90 56.40 26.50 High physical B 0.85 1.40 0.99 1.49 11.59 accessibility (total) C 3.52 8.60 7.94 8.01 17.37 D 11.31 23.08 21.21 22.46 55.11 A 12.20 11.20 11.50 54.40 9.60 B 0.00 0.00 0.00 0.00 0.00 Forests C 0.00 0.00 0.00 0.00 0.00 D 2.91 5.26 10.45 9.62 6.67 A 0.90 1.90 0.60 0.80 10.60 Urban forestry B 0.00 0.00 0.00 0.00 4.32 areas (not classified C 0.00 0.88 0.04 0.06 12.25 as forests) D 0.57 3.99 1.42 5.18 31.30 A 1.70 1.10 0.90 0.40 3.30 B 0.00 0.00 0.00 0.00 0.73 Green urban areas C 0.12 1.09 0.44 0.23 2.87 D 1.04 3.03 2.31 1.35 3.02 A 0.90 0.70 1.00 0.20 1.50 Trees in urban B 0.00 0.00 0.00 0.00 0.00 parks C 0.00 0.00 0.00 0.00 0.50 D 0.58 1.56 2.19 0.47 3.66 A 1.30 0.70 0.60 0.30 1.00 Greenery other than B 0.00 0.00 0.00 0.00 0.00 tree canopy in C 0.00 0.00 0.00 0.00 0.16 urban parks D 1.24 1.90 0.77 2.17 0.80 Trees A 0.90 0.50 0.20 0.20 0.60 accompanying B 0.46 0.44 0.20 0.35 0.52 roads and C 1.06 0.75 0.52 0.90 1.10 transportation areas D 0.08 0.00 0.00 0.03 0.01 A 3.60 6.90 4.20 2.00 5.10 Medium physical B 0.00 0.00 0.00 0.00 0.00 accessibility (total) C 0.00 4.74 0.00 0.00 1.25 D 2.87 15.48 6.17 6.00 6.20 A 0.00 3.00 0.90 0.10 0.50 Greenery other than B 0.00 0.00 0.00 0.00 0.00 tree canopy in C 0.00 0.00 0.00 0.00 0.00 allotment gardens D 0.00 5.67 0.73 0.41 0.47 A 0.00 0.60 1.40 0.00 0.40 Trees in allotment B 0.00 0.00 0.00 0.00 0.00 gardens C 0.00 0.00 0.00 0.00 0.00 D 0.00 0.54 1.97 0.00 0.20 A 0.20 0.10 0.50 0.20 0.20 Greenery other than B 0.00 0.00 0.00 0.00 0.00 tree canopy in C 0.00 0.00 0.00 0.00 0.00 cemeteries D 0.21 0.00 0.31 0.35 0.00 A 0.00 0.60 0.10 0.10 0.60 B 0.00 0.00 0.00 0.00 0.00 Trees in cemeteries C 0.00 0.00 0.00 0.00 0.00 D 0.00 1.08 0.10 0.25 0.20 A 3.10 1.80 0.80 1.30 2.70 Greenery other than B 0.00 0.00 0.00 0.00 0.73 tree canopy in C 0.12 1.09 0.44 0.23 2.87 1 sports and leisure D 1.04 3.03 2.31 1.35 3.02 green areas A 0.20 0.90 0.40 0.30 0.80 Trees in sports and B 0.00 0.00 0.00 0.00 0.00 leisure green areas C 0.00 0.63 0.00 0.00 0.42 D 2.46 3.55 1.00 3.97 3.55 A 12.30 46.70 50.70 15.10 18.80 Low physical B 3.96 29.90 34.14 38.64 32.29 accessibility (total) C 6.27 24.17 28.47 19.57 22.30 D 20.39 88.15 92.77 92.48 87.38 A 3.30 4.10 3.60 3.60 8.10 Tree canopy in B 0.00 0.00 0.00 0.00 0.00 private land and C 0.00 0.18 0.00 0.00 0.00 inner court trees D 0.00 0.86 0.27 0.14 0.33 A 3.20 4.20 7.30 3.9 5.20 Other greenery on B 0.81 7.16 5.45 12.49 17.58 private land and C 2.21 13.76 10.17 15.89 15.12 inner court greenery D 6.93 39.20 30.13 58.48 73.46 Arable land, A 3.80 34.30 36.40 6.30 2.90 permanent crops, B 2.44 12.69 15.86 17.04 11.58 pastures and C 3.46 10.41 10.35 13.49 11.49 wetlands D 7.77 62.29 77.53 76.12 57.87 A 0.60 1.60 0.90 0.50 1.30 Tree canopy in B 0.00 0.00 0.00 0.00 0.00 industrial and C 0.00 2.45 17.26 0.00 0.00 commercial areas D 1.53 18.51 29.88 6.72 2.52 A 1.30 3.10 2.30 0.80 1.10 Other greenery in B 0.00 0.00 0.00 0.00 0.00 industrial and C 0.00 0.96 0.63 0.83 0.79 commercial areas D 0.12 1.05 0.39 0.84 0.42 A 0.00 0.20 0.10 0.00 0.00 Trees B 0.00 0.38 0.57 0.19 0.21 accompanying C 0.38 1.84 2.17 1.59 0.96 railway areas D 0.21 1.03 1.58 1.81 0.21 A 0.00 0.00 0.10 0.00 0.00 Trees in mineral B 0.00 0.00 0.00 0.00 0.00 extractions C 0.00 0.00 0.00 0.00 0.00 D 0.00 0.08 0.00 0.00 0.00 A 0.00 0.00 0.00 0.00 0.00 Trees in B 0.00 0.00 0.00 0.00 0.00 construction sites C 0.00 0.00 0.00 0.00 0.00 D 0.00 0.00 0.00 0.00 0.00 A 0.00 0.20 0.20 0.00 0.00 B 0.00 0.00 0.00 0.00 0.00 Brownfield trees C 0.00 0.00 0.00 0.00 0.00 D 0.00 0.05 0.00 0.00 0.00 A – percentage of UGS in the total area of the city [in %]; B and C – median (B) and interquartile range (C) of UGS provision in the service area of 300m around residential building; D – percentage of residential buildings with UGS provision in the service area of 300m higher than 10% [in %]. 2 Appendix 3. The results from PCA. Barcelona Synthesized indicators of urban form characters Category Scale Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Dimension Residential building -0.541 0.102 -0.115 0.207 0.156 Shape Residential building 0.029 0.011 -0.290 0.726 0.030 Intensity Residential building 0.900 0.171 -0.114 0.038 0.027 Spatial distribution Residential building -0.166 -0.147 0.290 0.597 -0.111 Connectivity Residential building 0.769 -0.020 -0.304 -0.205 -0.050 Dimension Street network -0.022 0.138 -0.624 0.095 -0.100 Shape Street network 0.002 0.099 0.164 -0.094 -0.754 Spatial distribution Street network 0.377 0.619 0.088 -0.013 0.171 Intensity Street network -0.003 0.093 0.194 -0.142 0.581 Connectivity Street network -0.045 0.059 0.598 0.037 -0.102 Dimension Urban block 0.015 -0.750 0.164 0.156 0.029 Shape Urban block 0.427 0.286 0.609 -0.024 0.128 Spatial distribution Urban block 0.658 0.086 0.259 0.070 0.117 Intensity Urban block 0.922 0.148 0.089 0.046 0.038 Connectivity Urban block -0.019 -0.838 -0.126 -0.053 0.073 Halle Synthesized indicators of urban form characters Category Scale Indicator 1 Indicator 2 Indicator 3 Indicator 4 Dimension Residential building -0.024 -0.046 -0.690 0.261 Shape Residential building -0.198 -0.016 0.774 -0.025 Intensity Residential building 0.757 0.339 0.231 0.229 Spatial distribution Residential building -0.512 0.112 0.064 0.058 Connectivity Residential building 0.782 -0.022 -0.083 0.059 Dimension Street network 0.249 0.371 0.118 -0.615 Shape Street network -0.099 0.218 0.147 -0.002 Spatial distribution Street network 0.334 0.581 0.064 0.454 Intensity Street network 0.375 0.233 -0.109 0.715 Connectivity Street network -0.147 0.160 0.103 0.366 Dimension Urban block -0.091 -0.790 0.155 -0.108 Shape Urban block 0.316 0.165 -0.058 0.613 Spatial distribution Urban block 0.274 -0.117 0.620 0.317 Intensity Urban block 0.643 0.324 0.583 0.194 Connectivity Urban block 0.017 -0.866 -0.002 -0.092 1 Lodz Synthesized indicators of urban form characters Category Scale Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Dimension Residential building 0.176 -0.323 0.713 -0.008 -0.140 Shape Residential building -0.261 0.416 0.188 -0.192 0.506 Intensity Residential building 0.498 0.695 0.009 0.109 -0.063 Spatial distribution Residential building -0.153 -0.541 0.001 -0.055 0.231 Connectivity Residential building 0.009 0.858 0.000 0.098 0.030 Dimension Street network 0.219 -0.102 -0.765 0.002 -0.061 Shape Street network 0.198 -0.072 -0.292 0.088 0.464 Spatial distribution Street network 0.778 0.121 0.017 -0.119 0.114 Intensity Street network 0.438 0.199 0.756 -0.044 -0.051 Connectivity Street network 0.126 -0.178 0.012 0.081 0.681 Dimension Urban block -0.665 -0.148 -0.051 0.304 -0.068 Shape Urban block 0.637 0.056 0.134 0.018 -0.080 Spatial distribution Urban block -0.151 0.055 -0.030 0.874 0.098 Intensity Urban block -0.213 0.152 -0.016 0.879 -0.004 Connectivity Urban block -0.630 0.015 0.150 0.237 -0.244 Oslo Synthesized indicators of urban form characters Category Scale Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Dimension Residential building -0.257 0.388 -0.190 0.001 -0.562 Shape Residential building -0.039 0.039 -0.016 -0.039 0.692 Intensity Residential building 0.809 0.333 0.249 -0.164 -0.148 Spatial distribution Residential building -0.223 -0.026 0.028 0.625 -0.065 Connectivity Residential building 0.829 -0.114 -0.193 -0.079 -0.053 Dimension Street network 0.144 -0.155 0.770 0.126 -0.066 Shape Street network -0.010 -0.039 0.054 0.396 0.027 Spatial distribution Street network 0.260 0.703 0.291 -0.129 -0.233 Intensity Street network 0.143 0.842 -0.268 -0.132 -0.096 Connectivity Street network -0.053 0.375 0.245 0.346 0.170 Dimension Urban block 0.067 -0.259 -0.261 0.681 -0.068 Shape Urban block 0.093 0.709 0.115 -0.142 0.096 Spatial distribution Urban block 0.527 0.321 -0.159 0.130 0.369 Intensity Urban block 0.846 0.166 0.278 -0.128 0.213 Connectivity Urban block 0.066 -0.307 -0.608 0.132 -0.133 2 Stockholm Synthesized indicators of urban form characters Category Scale Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5 Dimension Residential building -0.292 0.226 0.038 -0.679 -0.128 Shape Residential building 0.040 0.064 -0.566 -0.110 0.022 Intensity Residential building 0.768 0.296 0.180 -0.069 -0.101 Spatial distribution Residential building -0.250 -0.086 -0.043 0.039 0.625 Connectivity Residential building 0.732 -0.030 0.004 -0.137 -0.198 Dimension Street network -0.011 0.198 0.043 0.565 -0.468 Shape Street network 0.072 0.178 0.033 0.108 0.593 Spatial distribution Street network 0.182 0.655 0.087 0.079 0.106 Intensity Street network 0.261 0.267 0.602 -0.234 -0.129 Connectivity Street network -0.119 0.206 0.062 0.441 0.160 Dimension Urban block -0.058 -0.739 -0.115 -0.049 0.124 Shape Urban block 0.195 0.005 0.826 0.025 0.111 Spatial distribution Urban block 0.796 0.023 0.079 0.088 0.092 Intensity Urban block 0.873 0.174 0.094 0.230 -0.025 Connectivity Urban block -0.064 -0.695 0.407 0.030 -0.139 3