Urban Morphology Links Green Space and Compact City Policy in a Shrinking Japanese Metropolis

Urban Morphology Links Green Space and Compact City Policy in a Shrinking Japanese Metropolis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Urban Morphology Links Green Space and Compact City Policy in a Shrinking Japanese Metropolis Taher Osman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8006965/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The "compact city" model, a central tenet of sustainable urbanism, creates critical policy tensions in post-industrial nations, where land use intensification often conflicts with urban shrinkage and the need to preserve urban green space (UGS). This study addresses a significant research gap by moving beyond city-level metrics to conduct a fine-grained morphological analysis of urban form and UGS provision at the urban block level. Using Nagoya, Japan, as an archetypal case study of a city facing simultaneous compaction and shrinkage, we develop a comprehensive urban block typology through a GIS-based hierarchical classification. The analysis reveals a stark spatial polarization: block types embodying the compact city ideal (high-density commercial and residential) are the most green-deficient, characterized by small, fragmented, and morphologically inefficient non-built-up areas. Conversely, peripheral and specialized blocks (e.g., woodlands, farmland) function as the city's primary green reservoirs. These findings provide high-resolution evidence for the compact city-green space paradox, identifying the morphology of non-built-up space as a key mechanism of green exclusion. We contribute a transferable methodological framework that can be repurposed as a Planning Support System (PSS). Policy Implications: This typology enables a targeted "right-sizing" and greening strategy, allowing planners and policymakers to use morphological analysis to identify specific blocks for green infrastructure acquisition and land use interventions. This provides a practical tool for land use governance, helping to reconcile densification policies with the need for equitable and resilient urban environments. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Scientific community and society/Geography Social science/Geography Compact City Urban Shrinkage Land Use Policy Urban Green Space (UGS) Morphological Analysis Planning Support Systems (PSS) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The 21st-century urban planning agenda has been largely dominated by the pursuit of the compact city (OECD, 2012 ). Promoted by influential international organizations, this land use paradigm advocates for high-density, mixed-use, and transit-oriented development as a principal strategy for achieving urban sustainability (Dantzig and Saaty, 1973 ; Couret, 2022 ; Abdullahi and Pradhan, 2018 ). The theoretical underpinnings are compelling: by intensifying urban form, cities can theoretically reduce automobile dependency, curb energy consumption, protect agricultural and natural hinterlands from sprawl, and foster economic vitality (OECD, 2012 ). This vision has been widely adopted in policy discourses and master plans globally, shaping the physical trajectory of metropolitan areas from Europe to Asia. However, this prevailing narrative of densification is increasingly colliding with a powerful countertrend: urban shrinkage. Defined as a multidimensional process of sustained population loss, often coupled with economic restructuring, urban shrinkage is a defining feature of many post-industrial cities, particularly in Japan, Germany, and the American Rust Belt (Haase et al., 2017 ; Dewar and Thomas, 2013 ). The UN-Habitat World Cities Report 2022 highlights that nearly half of all cities in developed regions are experiencing population decline, generating vast governance challenges related to under-utilized infrastructure, urban blight, and fiscal constraints (UN-Habitat, 2022 ). This demographic shift presents a fundamental challenge to the growth-oriented logic embedded in many compact city policies. It raises a critical question for land use policy: how can, and should, cities plan for a "compact" future when they are simultaneously destined to "shrink"? (Yoon, 2020 ). This paradox is most acutely manifested in the conflict between urban densification and the provision of Urban Green Space (UGS). UGS—encompassing parks, gardens, woodlands, and other vegetated areas—is no longer considered a mere amenity but a critical component of urban infrastructure (Jim and Chen, 2003 ). It provides essential ecosystem services, enhances climate resilience, and is strongly linked to improved public health and social well-being (Wolch, Byrne and Newell, 2014 ; Cai et al., 2023 ; Besir and Cuce, 2018 ). Yet, the intensification of land use inherent in compact city strategies often leads to the direct loss and fragmentation of UGS, creating a direct trade-off between density and livability (Artmann, Inostroza, and Fan, 2019 ). Recent research confirms this dilemma. A 2021 study of Amsterdam and Brussels, for example, empirically demonstrated that densification policies led to a significant decrease in the quantity, average size, and connectivity of urban green spaces, suggesting that development plans often dominate and override stated UGS policies (Balikci et al., 2021 ). This tension between "gray" and "green" intensification is a central problem in contemporary land use planning (Mouratidis, 2021 ). Despite its widespread adoption in policy circles, the compact city thesis has been subject to sustained academic critique, leading some scholars to identify a "Compact City Fallacy" (Neuman, 2005 ; Gordon and Richardson, 1997 ). This critique argues that the empirical evidence supporting the model's purported benefits is often equivocal or context-dependent (Neuman, 2005 ). A primary criticism focuses on the unintended consequences of densification, including decreased housing affordability and complex travel behaviors (Westernick et al., 2012 ). This includes the "barbecue effect," where residents of dense cores, deprived of private green space, undertake more frequent long-distance recreational travel, potentially offsetting transport savings (Breheny, 1995 ). Most pertinent to this study is the well-documented negative correlation between urban compactness and the quantity and quality of UGS (Jim and Chen, 2003 ; Rrat, 2012 ). The debate, therefore, has shifted from whether density is "good" or "bad" to how land use policy can reconcile its benefits with the essential functions of urban green infrastructure. This requires moving beyond simple density metrics to focus on urban form, morphology, and design (Eizenberg et al., 2019 ; Mouratidis, 2021 ). Parallel to the compact city debate, a distinct body of literature has emerged to address urban shrinkage. For decades, the dominant planning response was denial or a pursuit of growth-oriented regeneration (Dewar and Thomas, 2013 ). A paradigm shift is now underway, moving towards an acceptance of shrinkage as a durable condition requiring new land use policies (Pallagst and Wiechmann, 2005 ). This new paradigm is variously termed "smart decline," "planning for shrinkage," or, most commonly, "right-sizing" (Hollander and Németh, 2011 ; Sousa and Pinho, 2015 ). The core principle of rightsizing is the strategic adaptation of the city's physical fabric to the needs of a smaller population (Pallagst and Wiechmann, 2005 ). A central element is the reconceptualization of urban vacancies. Instead of viewing vacant lots as liabilities, right-sizing strategies treat them as assets—opportunities for strategic greening interventions (Schilling and Logan, 2008 ). In this context, UGS is not an amenity to be traded off against development but a fundamental tool for managing decline and building a more resilient and livable urban future. To operate and analyze these complex relationships, this study draws on urban morphology—the systematic study of the physical form of cities (Larkham, 2002 ). This approach is particularly salient in the Japanese context, where the urban form is a complex mosaic shaped by historical legacies (Jinnai, 1995 ). A morphological approach allows for a nuanced characterization of this fabric at the scale of the urban block, the fundamental unit of urban design. Recent studies confirm that the spatial morphology of UGS (e.g., shape, connectivity, fragmentation) is as critical as its total quantity in determining its ecological and social benefits (Haq, 2011 ; Tan et al., 2022 ; Gong et al., 2022 ). The integration of these complex spatial analyses into planning practice is facilitated by Planning Support Systems (PSS). PSS are computer-based geo-information instruments that combine data, models, and visualization tools to support planners in evaluating alternative scenarios (Klosterman, 1997 ; Batty, 2009 ). A morphological typology, such as the one developed in this study, can serve as the analytical engine of a PSS, providing a conceptual framework for guiding greening interventions at the block level (Ghent, 2022 ). This study investigates this complex interplay through an in-depth case study of Nagoya, Japan. As Japan's fourth-largest city, Nagoya provides an archetypal setting. The city is actively implementing its Master Plan, which champions high-density redevelopment to bolster its global competitiveness (Nagoya City, 2011 , 2019 ). Concurrently, official forecasts project that the city is at the precipice of sustained population decline (Nagoya City, 2019 , 2023 ). Nagoya is thus positioned at the critical policy nexus of compaction and shrinkage, making it an exemplary laboratory for examining the future of urban land use. While the relationship between urban form and green space has been widely studied, much of the research relies on aggregated data at the city or district level (Lan et al., 2021 ). Such analyses often mask significant heterogeneity at the micro-scale. A notable gap exists in the literature for systematic, morphological studies at the urban block level that can quantitatively link land use patterns, physical form, and green space provision. This study aims to fill this gap. The central objective is to analyze the intricate relationship between urban block typology and green space provision in Nagoya, using this analysis to illuminate the land use policy challenges and opportunities of planning for a city that is simultaneously compacting and shrinking. This objective is guided by the following research questions: What are the dominant urban block typologies in a post-industrial Japanese city, and what are their characteristic morphological and green space profiles? How does the spatial distribution of these typologies reveal patterns of green space provision and potential land use conflicts across the urban fabric? What are the implications of these block-level patterns for land use policies and governance strategies that seek to reconcile densification with strategic greening in the context of demographic decline? By addressing these questions, this paper seeks to provide not only a detailed empirical account of Nagoya's urban structure but also a transferable methodological framework and critical insights for planners and policymakers grappling with similar land use challenges worldwide. 2. Methodology and Data 2.1. Study Area: Nagoya, Japan The study focuses on the entire administrative area of Nagoya City, Japan. Nagoya is the capital of Aichi Prefecture and the center of Japan's third-largest metropolitan area. Historically, the city's development originated from its 17th-century castle structure, which has left an imprint on its central layout (Jinnai, 1995 ). Following extensive damage in World War II, Nagoya underwent a massive reconstruction that established its modern grid of wide arterial roads. It evolved into a major industrial hub, particularly for the automotive and aerospace industries. The contemporary planning context of Nagoya is defined by a dual policy agenda. On one hand, the Nagoya City Master Plan 2028 explicitly promotes a "compact and networked" urban structure, encouraging high-density, mixed-use redevelopment in key urban centers and along public transit corridors (Nagoya City, 2023 ). This is exemplified by massive ongoing projects such as the redevelopment of the Nagoya Station area (Nagoya City, 2011 , 2019 ). On the other hand, the Master Plan acknowledges the city's challenging demographic future. After decades of modest growth, Nagoya's population is projected to peak and enter a period of sustained decline (Nagoya City, 2019 , 2023 ). This places Nagoya at the forefront of cities that must navigate the inherent tensions between land use intensification, strategic management of decline, and the enhancement of urban green infrastructure. 2.2. Data Sources and Baseline Justification The analysis is based on two primary vector data sets provided by the City of Nagoya: Nagoya Urban Planning Basic Research (2006) : This comprehensive GIS dataset contains polygons for city blocks, building footprints (with attributes for use and number of floors), and detailed land use parcels. Present Data of Green of Nagoya (2020) : This dataset contains polygons delineating all areas of green coverage, including parks, woodlands, farmland, and vegetated private lots. A critical methodological consideration is the temporal difference in these datasets. This study frames this 2006–2020 timeframe as a unique analytical strength. The 2006 data capture the city's built form at or near its peak population, immediately preceding both the acceleration of major central-area redevelopment projects and the onset of demographic decline. The 2020 data provide a contemporary snapshot of green coverage. This analysis therefore establishes a vital historical baseline of the "legacy" urban form—the morphological and green space conditions that contemporary and future land use policies must contend with as the city transitions into its new phase of simultaneous densification and shrinkage. All data were integrated and processed within an ArcGIS environment. 2.3. Analytical Framework The methodology follows a systematic, multi-stage process designed to classify every urban block in Nagoya and analyze its characteristics. The entire process, from data preparation to final analysis, is depicted in the analytical workflow diagram (Fig. 1 ). This workflow ensures a transparent and reproducible approach to developing the urban block typology. 2.4. Derivation of Indices and Block Classification A set of quantitative indices was calculated for each urban block to capture its land use composition, physical morphology, and green space characteristics. These indices, defined in Table 1 , form the statistical basis for the classification. Table 1 Land Use Indices Used in the Analysis Indicator Definition Formula Data Source Block Green Coverage Ratio (BGCR) The proportion of the total block area is covered by vegetation. BGCR = (Σ Total green coverage area in the block) / Block area Green Coverage Data (Nagoya City, 2020) Block non-built-up Area Ratio The proportion of the block area not covered by building footprints. BNAR = (Block area - Σ Total building footprint area) / Block area Building Footprint Data (Nagoya City, 2006) Block Land Use Composition (BLUC) The proportion of the block area occupied by specific land use (e.g., commercial, residential). BLUC i = (Σ Total area of land use i) / Block area Land Use Data (Nagoya City, 2006) Floor Area Ratio (FAR) The ratio of the total floor space of all buildings to the block area. FAR = (Σ (Building footprint area × Floors)) / Block area Building Footprint Data (Nagoya City, 2006) Non-built-up Openness Index A measure of the openness of the non-built-up space within a block. (Proprietary index from Nagoya City) Derived from Nagoya City (2006) datasets Compact Ratio A measure of the shape efficiency of the non-built-up space within a block. (Proprietary index from Nagoya City) Derived from Nagoya City (2006) datasets Using these indices, a three-stage hierarchical classification method was employed to assign each block to one of 26 mutually exclusive Block Land Use Composition (BLUC) types. This process was chosen for its theoretical and practical rigor, allowing for a logical 'sieve' that first isolates blocks defined by non-development (Stage 1), then by public function (Stage 2), before analyzing the heterogeneous private urban fabric (Stage 3). This ensures typological clarity and policy relevance. Stage 1 (Green Dominant Classification) : Blocks with a cumulative area of "positive-for-green" land uses (farmland, woodland, parks, vacant land, and temples) exceeding 50% of the total block area were identified and classified first. Stage 2 (Public and Institutional Classification) : From the remaining pool, blocks dominated by specific public or institutional uses (e.g., parking lots, public buildings, schools) with a coverage of > 75% were extracted. Stage 3 (Private Urban Fabric Classification) : The final set of blocks, representing the core private urban fabric, was classified based on the relative proportions of residential, commercial, and industrial land uses, using a ternary system. These types were further subdivided by density, using thresholds for FAR and the proportion of multiple-dwelling housing. 2.5. Spatial Analysis of Land Use Governance To understand how the derived block types are distributed, their spatial concentration was analyzed within two distinct geographical frameworks from Nagoya's official planning documents: Geographical/Urbanization Zones : The city was divided into five zones: Downtown (A), Urbanized Flatland (B), Newly Developed Flatland (C), Urbanized Hillside (D), and Newly Developed Hillside (E). Road Proximity Zones : All blocks were categorized as either being adjacent to a wide lane road (width > 15 meters) or not. The degree of spatial concentration for each BLUC type within each zone was quantified using a Specialization Coefficient (SC). This coefficient compares the proportion of a specific block type within a given zone to its proportion in the city. A value greater than 1.0 indicates that the block type is disproportionately concentrated, or "specialized," in that zone. The coefficient is calculated as follows: First, the block area composition rate (Uij​) for each BLUC type i in a zone j is calculated: U i ⱼ = S i ⱼ / Σ (from i = 1 to 26) S i ⱼ, where S i ⱼ is the total area of block type i in zone j. Next, the total block area composition rate (Vj​) for zone j is calculated: Vⱼ = (Σ (from i = 1 to 26) S i ⱼ) / Total City Area. The Specialization Coefficient (SCij​) is then the ratio of these two values: SC i ⱼ = U i ⱼ / Vⱼ, This analysis allows for a robust, quantitative assessment of the spatial logic of the city's morphology, linking specific block types to distinct parts of the urban landscape. 3. Results 3.1. A Typology of Nagoya's Urban Blocks The three-stage classification process yielded a comprehensive typology of 26 distinct Block Land Use Composition (BLUC) types, which collectively account for the entire urban fabric of Nagoya. This typology provides a fine-grained decomposition of the city, moving beyond monolithic land use categories. Table 2 summarizes the key characteristics of these 26 types, grouped into broader functional categories, highlighting the vast differences in physical form and green space provision. Table 2 The Block Land Use Composition (BLUC) Typology of Nagoya Category BLUC Symbol BLUC Type Name Total Area (ha) Mean BGCR (%) Mean FAR (%) High-Density Core 01HDC High Density Commercial 147 3.1 > 300 02HDC/R High Density Commercial/Residential 1,049 4.5 > 100 05HDR High Density Residential 2,309 8.9 > 100 Low-Density Built-up 03LDC Low Density Commercial 695 6.2 < 100 04LDC/R Low Density Commercial/Residential 1,045 7.5 < 100 06MDH Multiple Dwelling House 519 15.3 Variable 07LDR Low Density Residential 6,606 12.1 < 100 Industrial & Mixed 08C/I Commercial/Industrial 403 5.8 Variable 09In Industrial 2,149 6.5 Variable 10R/I Residential/Industrial 1,204 9.7 Variable 11C/R/I Commercial/Residential/Industrial 1,536 7.1 Variable Public & Institutional 12P Parking Lot 91 2.5 N/A 13Sh School 725 18.2 Variable 14PB Public Building 131 11.4 Variable 15OPB Other Public Building 92 14.6 Variable Green Dominant 16Te Temple 143 25.1 Variable 17Te/Bu Temple/Building Mixed 190 19.8 Variable 18VA Vacant Land 408 33.5 N/A 19VA/Bu Vacant Land/Building Mixed 474 24.3 Variable 20PA Park 1,123 65.8 N/A 21PA/Bu Park/Building Mixed 290 41.2 Variable 22FA Farmland 564 78.3 N/A 23FA/Bu Farmland/Building Mixed 321 55.7 Variable 24W Woodland 314 82.4 N/A 25W/Bu Woodland/Building Mixed 630 60.1 Variable Other 26ONB Other Non-Building Mixed 1,088 13.5 N/A 3.2. The Morphological Signature of Land Use The analysis reveals a strong, systematic relationship between a block's land use composition and the morphology of its non-built-up space. Figure 2 visualizes this relationship by plotting the average non-built-up Openness Index against the average Compact Ratio for each of the 26 BLUC types. A distinct polarization is evident. Block types associated with intense urban development (e.g., High Density Commercial (01HDC), Industrial (09In), Commercial/Residential/Industrial Mix (11C/R/I)) are clustered in the bottom-left quadrant. These blocks exhibit low value for both openness and compactness, indicating that their non-built-up areas (e.g., courtyards, alleys, setbacks) are small, fragmented, and have complex, inefficient shapes. In stark contrast, block types dominated by "positive-for-green" land uses (e.g., Farmland (22FA), Woodland (24W), Park (20PA)) occupy the top-right quadrant. These types have high values for both indices, signifying that their non-built-up spaces are large, contiguous, and morphologically simple. This quantitative result provides clear evidence that the underlying land use function of a block imparts a distinct morphological signature on its open spaces, directly influencing their suitability for functional greening. 3.3. Spatial Patterns of Specialization and Green Inequality The specialization coefficient analysis reveals a highly structured spatial distribution of the BLUC types, creating distinct zones of morphological character and green space provision. The key findings are summarized in Table 3 and visualized in Figs. 3 and 4 . Table 3 Specialization Coefficients and Block Green Coverage Ratios for Key BLUC Types BLUC Type Zone of Highest Specialization Specialization Coefficient Mean BGCR in Zone (%) 01HDC (High Density Commercial) Downtown (A) 16.9 2.0 02HDC/R (High Density Comm/Res) Downtown (A) 5.5 5.0 05HDR (High Density Residential) Downtown (A) 1.4 10.0 09In (Industrial) Urbanized Flatland (B) 1.9 6.0 12P (Parking Lot) Urbanized Flatland (B) 1.9 3.0 22FA (Farmland) Newly Developed Flatland (C) 4.9 75.0 24W (Woodland) Newly Developed Hillside (E) 2.7 85.0 03LDC (Low Density Commercial) Along Wide Lane Roads 1.8 2.0 The spatial analysis covers several distinct patterns that define the city's socio-ecological structure (visualized in Table 4 , Figs. 5 and 6 ): Table 4 Specialization coefficient and the block green coverage ratio The Compact, "Gray" Core : High-density commercial (01HDC, 02HDC/R) and residential (05HDR) blocks are overwhelmingly specialized in the Downtown (A) zone. This zone exhibits the city's lowest green coverage, confirming a strong spatial correlation between the most intense urban forms and a profound deficit of green space. The Industrial Belt : Industrial (09In) and mixed industrial blocks show a strong specialization in the Urbanized Flatlands (B), historically the locus of the city's manufacturing economy. The Suburban and Peri-Urban Fringe : Low-density residential blocks (07LDR) are the dominant type in the newly developed areas. These zones, particularly the flatlands, also show a high specialization of farmland (22FA) and vacant land (18VA), resulting in high green coverage. The Green Reservoirs : The city's primary green assets are spatially segregated. Woodland blocks (24W) are heavily concentrated in the eastern hillside zones (E), forming a large greenbelt. Parks (20PA) and temples (16Te) are more prevalent in the urbanized hillside and newly developed areas than in the central core. Linear "Gray" Corridors : A distinct pattern emerges along major arterial roads. Low-density commercial (03LDC) and parking lot (12P) blocks show a strong specialization in these transport corridors, creating linear zones of extremely low green coverage that fragment the urban green fabric. Collectively, these results paint a picture of a city with significant green space inequality, where the highest levels of access to green amenities are found at the periphery, while the densest, most populated central areas are the most deprived. 4. Discussion 4.1. The Compact City Paradox: A Micro-Scale Policy Critique The findings provide robust, block-level empirical evidence that substantiates key critiques of the compact city model (Neuman, 2005 ; Westernick et al., 2012 ). The results demonstrate a clear and inverse relationship between urban compactness and green space provision in Nagoya. The very block types that closely align with the compact city ideal—high-density, mixed-use commercial and residential cores (01HDC, 02HDC/R, 05HDR)—are precisely those with the lowest green coverage and the poorest quality of non-built-up space. This analysis moves beyond a simple correlation by identifying urban morphology as the critical intervening mechanism. The low openness and compactness scores of the dense central blocks (see Fig. 2 ) reveal why they are green-deficient. The economic logic of densification, particularly on the fine-grained plot structure typical of Japanese cities, incentivizes maximizing floor area. This process treats non-built-up space as a residual, resulting in fragmented, poorly configured, and inaccessible leftover areas like lightwells, service alleys, and shaded slivers between buildings. These spaces are morphologically unsuitable for supporting functional UGS, such as small parks or even meaningful tree canopy. This creates a condition of "morphological lock-in," where the physical form established by high-density development inherently precludes the integration of effective green infrastructure. The implication for land use policy is profound: policies that focus solely on increasing density metrics (e.g., Floor Area Ratio), without concurrently regulating the quality, contiguity, and usability of the resulting open space, will exacerbate green space deficits and undermine livability. This finding, derived from a non-Western context, echoes research from European cities like Amsterdam and Brussels, where densification policies were found to degrade UGS quantity and connectivity (Balikci et al., 2021 ). Achieving a "green compact city" requires a form-based approach that actively designs for high-quality open space as an integral component of densification, not as an afterthought (Mouratidis, 2021 ). 4.2. A Typology-Based PSS for 'Smart Decline' and Land Use Governance The impending onset of urban shrinkage in Nagoya demands a strategic shift from purely growth-oriented planning to one that also manages decline. Population loss will not be spatially uniform; it is likely to disproportionately affect specific areas, leading to increased vacancy in block types like older industrial zones (09In) or low-density residential areas (07LDR). This challenge, however, presents a unique opportunity for land use policy. The BLUC typology developed in this paper can be repurposed from a descriptive, diagnostic tool into a prescriptive Planning Support System (PSS) to guide a targeted "right-sizing" strategy (Dewar and Thomas, 2013 ; Hollander and Németh, 2011 ). This framework moves beyond reactive vacancy management and allows for proactive, strategic land use governance. The process involves a spatial overlay of vulnerability and needs: Diagnostic Tool : The typology map (Fig. 6 ) acts as a green equity audit, immediately identifying block types suffering from severe green space deficits (e.g., 01HDC, 05HDR). Prescriptive Tool : The typology also identifies blocks with characteristics suggesting a higher risk of future vacancy and lower land values (e.g., 09In Industrial, 10R/I Residential/Industrial). By identifying areas where these two conditions are in proximity, planners can develop a highly targeted land acquisition and redevelopment strategy. For example, a cluster of declining industrial blocks (09In) adjacent to a green-deficient high-density residential neighborhood (05HDR) becomes a prime candidate for transformation into a new district park or green corridor. This approach, rooted in the "smart decline" literature (Schilling and Logan, 2008 ), allows the city to leverage the process of shrinkage in one area to address a long-standing quality-of-life deficit in another. This methodology provides a practical, block-level implementation framework for national-level policies, such as Japan's "location normalization plan," which aims to create more compact urban structures in the face of depopulation (Yoon, 2020 ). The typology acts as a dynamic tool for strategic urban acupuncture, enabling planners to make targeted, efficient, and equitable decisions about where to invest in green infrastructure as the city adapts to a smaller population. 4.3. Limitations and Methodological Transferability This study's primary strength lies in its fine-grained, block-level analysis. By disaggregating the city into 26 distinct morphological types, the methodology reveals nuanced land use patterns obscured by aggregated approaches (Lan et al., 2021 ). The main limitation is the use of 2006 building/land use data. However, as argued, this is intentionally framed as a methodological strength, providing a critical baseline of Nagoya's urban form at its demographic peak. This baseline is essential for future longitudinal studies to accurately measure the morphological impacts of both densification and shrinkage. Future research should replicate this study with contemporary datasets to track these changes over time. Furthermore, integrating fine-grained socioeconomic data would allow for a deeper analysis of the environmental justice dimensions of green space access across the block typologies. The methodological framework itself is highly transferable. The multi-stage classification process—integrating land use composition, building density, and open space morphology—is not specific to Nagoya. The availability of increasingly detailed, building-level geospatial data in cities worldwide makes this type of analysis more feasible than ever. Applying this framework to other cities in Europe, North America, and Asia would enable robust comparative urban morphological research, helping to identify common patterns and context-specific variations in the land use-sustainability relationship. 5. Conclusion and Policy Implications This research has dissected the urban fabric of Nagoya, Japan, to reveal the intricate and often contradictory relationship between urban form, density, and the provision of green space. In doing so, it offers three primary contributions to land use policy and planning. First, on an empirical level, it presents a novel 26-category urban block typology, quantitatively demonstrating the stark spatial polarization of green space in a major post-industrial city. Second, on a theoretical level, it provides high-resolution evidence for the "compact city paradox," identifying the morphology of non-built-up space as a key mechanism through which densification policies can lead to green space exclusion. Third, on a practical and policy level, it demonstrates how a morphological typology can transcend its descriptive function to become a proactive Planning Support System for implementing targeted "right-sizing" and greening strategies in cities facing demographic decline. The findings yield several actionable recommendations for land use policy and governance in Nagoya and other cities facing similar trajectories: Adopt Form-Based Codes to Complement Density-Based Zoning : Municipalities should move beyond simplistic land use and density controls (e.g., Floor Area Ratio) and develop form-based codes that regulate the qualitative attributes of non-built-up space. This could include establishing minimum standards for openness, contiguity, and solar access of open spaces created within new high-density developments, ensuring they are functional for greening and recreation, not just residual artifacts. Implement Typology-Based Green Equity Audits : Planners can use the BLUC typology as a framework to conduct "green equity audits." By mapping the distribution of block types against demographic data, they can identify which communities are most affected by green space deficits (e.g., residents of 05HDR blocks) and prioritize land acquisition and green investments accordingly, thereby advancing environmental justice. Develop Proactive Land Acquisition Policies for 'Right-Sizing' : In shrinking cities, municipalities should adopt proactive land banking and acquisition policies focused on block types identified as vulnerable to decline (e.g., aging industrial or low-density residential areas). This would allow cities to assemble land strategically over time, creating a network of future green infrastructure that can serve the needs of adjacent, green-deficient neighborhoods, effectively using shrinkage as a tool for urban regeneration. This study opens several avenues for future research. A crucial next step is to conduct a longitudinal analysis, replicating this study with updated data to measure the morphological transformations that have occurred as Nagoya has undergone its recent wave of redevelopment. Further research should also integrate fine-grained socioeconomic data to explore the environmental justice dimensions of green space access and assess the ecological connectivity of the UGS network. By refining our understanding of the city at this fine-grained scale, we can better equip planners to formulate land use policies that create cities that are not only compact but also green, equitable, and resilient. Declarations Conflict of Interest The author declares that there is no conflict of interest regarding the publication of this paper. Ethical Approval This article does not contain any studies with human participants or animals performed by the author. Informed Consent Not applicable, as this study does not involve human participants. Consent to Publish The author consents to the publication of this manuscript in the Journal of Humanities and Social Sciences , should it be accepted. Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution T.O. is the sole author of this work and was responsible for all aspects of the study, including conceptualization, methodology, analysis, visualization, and writing. Data Availability Statement All data generated or analyzed during this study are available from the corresponding author upon reasonable request. The spatial datasets used for morphological classification were derived from publicly available sources. References Abdullahi S, Pradhan B (2018) Sustainable urban development: a review of the concept of compact city. Int J Urban Sci 22(2):135–154 Artmann M, Inostroza L, Fan P (2019) Urban sprawl, compact urban development and green cities. How much do we know, how much do we agree? Ecol Ind 96:3–9 Balikci S, Giezen M, Arundel R (2021) The paradox of planning the compact and green city: Analyzing land-use change in Amsterdam and Brussels. J Environ Planning Manage 65(11):1994–2016 Batty M (2009) Planning support systems: A new perspective on computer-aided planning. Planning Support Systems for Cities and Regions. Lincoln Institute of Land Policy, pp 21–42 Besir AB, Cuce E (2018) Green roofs and facades: A comprehensive review. Renew Sustain Energy Rev 82:915–939 Breheny MJ (1995) The compact city and transport energy consumption. Trans Inst Br Geogr 20(1):81–101 Cai W et al (2023) Urban green space and public health: A review of reviews. Environ Res 216:114547 Couret D (2022) The compact city: an analysis of the concept and its implications for urban sustainability. J Plann Literature 37(1):22–38 Dantzig GB, Saaty TL (1973) Compact city: A plan for a livable urban environment. WH Freeman Dewar M, Thomas JM (eds) (2013) The city after abandonment. University of Pennsylvania Eizenberg E, Sasson O, Shilon M (2019) Urban Morphology and Qualitative Topology: Open Green Spaces in High-Rise Residential Developments. Urban Plann 4(4):160–173 Ghent A (2022) Greening Blocks: A Conceptual Typology of Practical Design Interventions to Integrate Health and Climate Resilience Co-Benefits. Sustainability 14(19):12157 Gong C, Chen J, Yu S (2022) How does the morphology of urban green space affect visual perception? A study based on deep learning and structural equation modeling. Land 11(8):1279 Gordon P, Richardson HW (1997) Are compact cities a desirable planning goal? J Am Plann Association 63(1):95–106 Haase A et al (2017) Conceptualizing urban shrinkage. Shrinking Cities. Routledge, pp 15–32 Haq SM (2011) Urban green spaces and an integrative approach to sustainable environment. J Environ Prot 2(5):601–608 Hollander JB, Németh J (2011) The bounds of smart decline: A foundational theory for planning shrinking cities. Hous Policy Debate 21(3):349–367 Jim CY, Chen WY (2003) Comprehensive greenspace planning based on landscape ecology principles in compact city: The case of Hong Kong. Landsc Urban Plann 66(1):41–56 Jinnai H (1995) Tokyo: A spatial anthropology. University of California Press Klosterman RE (1997) Planning support systems: A new perspective on computer-aided planning. J Plann Educ Res 17(1):45–54 Lan W et al (2021) Measuring urban compactness: A systematic literature review. Cities 116:103282 Larkham PJ (2002) Misunderstanding urban morphology. Urban Morphology 6(1):59 Mouratidis K (2021) Urban planning and quality of life: A review of pathways linking the built environment to subjective well-being. Cities 115:103229 Nagoya City (2011) City master plans on Nagoya city . Nagoya City Nagoya City (2019) Nagoya City Master Plan 2023 . Nagoya City Nagoya City (2023) Nagoya City Master Plan 2028 (Draft) . Nagoya City Neuman M (2005) The compact city fallacy. J Plann Educ Res 25(1):11–26 OECD (2012) Compact City Policies: A Comparative Assessment. OECD Publishing Pallagst K, Wiechmann T (2005) Planning for shrinking cities. Town Plann Rev 76(4):iii–ix Rrat P (2012) The new demographic growth of cities: The case of reurbanisation in Switzerland. Urban Stud 49(5):1107–1125 Schilling J, Logan J (2008) Greening the rust belt: A green infrastructure model for right sizing America's shrinking cities. J Am Plann Association 74(4):451–466 Sousa S, Pinho P (2015) Planning for shrinkage: Paradox or paradigm? Eur Plan Stud 23(1):12–32 Tan Y et al (2022) Spatiotemporal dynamics of urban green space and its driving factors: A case study of Hefei city, China. Front Ecol Evol 10:998111 UN-Habitat (2022) World Cities Report 2022: Envisaging the Future of Cities. United Nations Human Settlements Programme Westernick J et al (2012) The compact city and the sustainable city: a review of literature. Built Environ 38(2):185–203 Wolch JR, Byrne J, Newell JP (2014) Urban green space, public health, and environmental justice: The challenge of making cities just green enough. Landsc Urban Plann 125:234–244 Yoon CJ (2020) A Study on the Application of Compact City Policies in Japanese Shrinking Cities. Sustainability 12(3):989 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8006965","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597208145,"identity":"b1d4edab-ef32-4fa9-ae95-975cd49a3750","order_by":0,"name":"Taher Osman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACHjiL8QGYOkBQA0ILswHJWtgkiNJiz3724OMKBht73fazxyp+tjHI8d1IYHz4BZ8tPHnJhmcY0hK3nclLu9nbxmAseSOB2VgGr8NyzCQbGA4nmB3IMbvN2MaQuOFGApu0BD4t/G/MfwK12Judf2NWDNRSD9TC/huvFokcM0agFsZtN3LMmIFaEgyAtjB+wKflxhtjyQYDoF9AjJ5zEoYzzzxslsajg4G9P8fwY0OFDdBhOYYffpTZyPMdTz748Qc+PWBgAGeBPMHYwMyDWy0OwEjYllEwCkbBKBhBAAAw40ngMyA7ogAAAABJRU5ErkJggg==","orcid":"","institution":"Cairo University","correspondingAuthor":true,"prefix":"","firstName":"Taher","middleName":"","lastName":"Osman","suffix":""}],"badges":[],"createdAt":"2025-11-01 16:38:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8006965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8006965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103575936,"identity":"27cb81d7-d9b5-4e91-a03c-fe690b0d70a1","added_by":"auto","created_at":"2026-02-27 09:12:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":954930,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical Workflow for Urban Block Typology and Spatial Analysis\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/7371db7831c80dfd16b2d3d0.jpeg"},{"id":103575931,"identity":"61682337-c722-47ab-aad7-3a88dea93ce7","added_by":"auto","created_at":"2026-02-27 09:12:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211948,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Compact Ratio and Non-built-up Openness Index\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/818be3a6f7aaaf2dcc236171.jpeg"},{"id":103575835,"identity":"829da720-61fd-492e-9593-2f6ae340f1c6","added_by":"auto","created_at":"2026-02-27 09:11:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135859,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution and Specialization of Key Urban Block Typologies\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/5d20f0684ffe174e5621e665.jpeg"},{"id":103575892,"identity":"f93535ea-41bc-4be8-a673-e9c85ab4fd26","added_by":"auto","created_at":"2026-02-27 09:11:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399409,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial Distribution by wide lane road\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/dec97b4881181c7e16cc195d.jpeg"},{"id":103575909,"identity":"1b58ac2e-8998-4289-87d0-c93eaf110455","added_by":"auto","created_at":"2026-02-27 09:11:56","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1904271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/cd36e1c1996072d00b35c44d.jpeg"},{"id":103575946,"identity":"f7202235-06e8-45c3-8998-e185aca23f2b","added_by":"auto","created_at":"2026-02-27 09:12:10","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3592046,"visible":true,"origin":"","legend":"\u003cp\u003eThe Block Land Use Coverage Types\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/885263b4b7ec990fcac6415d.jpeg"},{"id":103576112,"identity":"e25f270b-bc5c-4436-9267-0cefad85105c","added_by":"auto","created_at":"2026-02-27 09:12:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8624706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8006965/v1/92a0bd24-f5a1-4091-ad60-0bb28d0c00b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Morphology Links Green Space and Compact City Policy in a Shrinking Japanese Metropolis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe 21st-century urban planning agenda has been largely dominated by the pursuit of the compact city (OECD, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Promoted by influential international organizations, this land use paradigm advocates for high-density, mixed-use, and transit-oriented development as a principal strategy for achieving urban sustainability (Dantzig and Saaty, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Couret, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abdullahi and Pradhan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The theoretical underpinnings are compelling: by intensifying urban form, cities can theoretically reduce automobile dependency, curb energy consumption, protect agricultural and natural hinterlands from sprawl, and foster economic vitality (OECD, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This vision has been widely adopted in policy discourses and master plans globally, shaping the physical trajectory of metropolitan areas from Europe to Asia.\u003c/p\u003e \u003cp\u003eHowever, this prevailing narrative of densification is increasingly colliding with a powerful countertrend: urban shrinkage. Defined as a multidimensional process of sustained population loss, often coupled with economic restructuring, urban shrinkage is a defining feature of many post-industrial cities, particularly in Japan, Germany, and the American Rust Belt (Haase et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dewar and Thomas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The UN-Habitat \u003cem\u003eWorld Cities Report 2022\u003c/em\u003e highlights that nearly half of all cities in developed regions are experiencing population decline, generating vast governance challenges related to under-utilized infrastructure, urban blight, and fiscal constraints (UN-Habitat, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis demographic shift presents a fundamental challenge to the growth-oriented logic embedded in many compact city policies. It raises a critical question for land use policy: how can, and should, cities plan for a \"compact\" future when they are simultaneously destined to \"shrink\"? (Yoon, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paradox is most acutely manifested in the conflict between urban densification and the provision of Urban Green Space (UGS). UGS\u0026mdash;encompassing parks, gardens, woodlands, and other vegetated areas\u0026mdash;is no longer considered a mere amenity but a critical component of urban infrastructure (Jim and Chen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). It provides essential ecosystem services, enhances climate resilience, and is strongly linked to improved public health and social well-being (Wolch, Byrne and Newell, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Besir and Cuce, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Yet, the intensification of land use inherent in compact city strategies often leads to the direct loss and fragmentation of UGS, creating a direct trade-off between density and livability (Artmann, Inostroza, and Fan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research confirms this dilemma. A 2021 study of Amsterdam and Brussels, for example, empirically demonstrated that densification policies led to a significant decrease in the quantity, average size, and connectivity of urban green spaces, suggesting that development plans often dominate and override stated UGS policies (Balikci et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This tension between \"gray\" and \"green\" intensification is a central problem in contemporary land use planning (Mouratidis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its widespread adoption in policy circles, the compact city thesis has been subject to sustained academic critique, leading some scholars to identify a \"Compact City Fallacy\" (Neuman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gordon and Richardson, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This critique argues that the empirical evidence supporting the model's purported benefits is often equivocal or context-dependent (Neuman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A primary criticism focuses on the unintended consequences of densification, including decreased housing affordability and complex travel behaviors (Westernick et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This includes the \"barbecue effect,\" where residents of dense cores, deprived of private green space, undertake more frequent long-distance recreational travel, potentially offsetting transport savings (Breheny, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost pertinent to this study is the well-documented negative correlation between urban compactness and the quantity and quality of UGS (Jim and Chen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Rrat, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The debate, therefore, has shifted from whether density is \"good\" or \"bad\" to how land use policy can \u003cem\u003ereconcile\u003c/em\u003e its benefits with the essential functions of urban green infrastructure. This requires moving beyond simple density metrics to focus on urban form, morphology, and design (Eizenberg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mouratidis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParallel to the compact city debate, a distinct body of literature has emerged to address urban shrinkage. For decades, the dominant planning response was denial or a pursuit of growth-oriented regeneration (Dewar and Thomas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A paradigm shift is now underway, moving towards an acceptance of shrinkage as a durable condition requiring new land use policies (Pallagst and Wiechmann, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This new paradigm is variously termed \"smart decline,\" \"planning for shrinkage,\" or, most commonly, \"right-sizing\" (Hollander and N\u0026eacute;meth, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sousa and Pinho, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe core principle of rightsizing is the strategic adaptation of the city's physical fabric to the needs of a smaller population (Pallagst and Wiechmann, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A central element is the reconceptualization of urban vacancies. Instead of viewing vacant lots as liabilities, right-sizing strategies treat them as assets\u0026mdash;opportunities for strategic greening interventions (Schilling and Logan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this context, UGS is not an amenity to be traded off against development but a fundamental tool for managing decline and building a more resilient and livable urban future.\u003c/p\u003e \u003cp\u003eTo operate and analyze these complex relationships, this study draws on urban morphology\u0026mdash;the systematic study of the physical form of cities (Larkham, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This approach is particularly salient in the Japanese context, where the urban form is a complex mosaic shaped by historical legacies (Jinnai, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). A morphological approach allows for a nuanced characterization of this fabric at the scale of the urban block, the fundamental unit of urban design. Recent studies confirm that the spatial \u003cem\u003emorphology\u003c/em\u003e of UGS (e.g., shape, connectivity, fragmentation) is as critical as its total \u003cem\u003equantity\u003c/em\u003e in determining its ecological and social benefits (Haq, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gong et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of these complex spatial analyses into planning practice is facilitated by Planning Support Systems (PSS). PSS are computer-based geo-information instruments that combine data, models, and visualization tools to support planners in evaluating alternative scenarios (Klosterman, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Batty, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A morphological typology, such as the one developed in this study, can serve as the analytical engine of a PSS, providing a conceptual framework for guiding greening interventions at the block level (Ghent, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates this complex interplay through an in-depth case study of Nagoya, Japan. As Japan's fourth-largest city, Nagoya provides an archetypal setting. The city is actively implementing its Master Plan, which champions high-density redevelopment to bolster its global competitiveness (Nagoya City, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Concurrently, official forecasts project that the city is at the precipice of sustained population decline (Nagoya City, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nagoya is thus positioned at the critical policy nexus of compaction and shrinkage, making it an exemplary laboratory for examining the future of urban land use.\u003c/p\u003e \u003cp\u003eWhile the relationship between urban form and green space has been widely studied, much of the research relies on aggregated data at the city or district level (Lan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such analyses often mask significant heterogeneity at the micro-scale. A notable gap exists in the literature for systematic, morphological studies at the urban block level that can quantitatively link land use patterns, physical form, and green space provision.\u003c/p\u003e \u003cp\u003eThis study aims to fill this gap. The central objective is to analyze the intricate relationship between urban block typology and green space provision in Nagoya, using this analysis to illuminate the land use policy challenges and opportunities of planning for a city that is simultaneously compacting and shrinking. This objective is guided by the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the dominant urban block typologies in a post-industrial Japanese city, and what are their characteristic morphological and green space profiles?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow does the spatial distribution of these typologies reveal patterns of green space provision and potential land use conflicts across the urban fabric?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the implications of these block-level patterns for land use policies and governance strategies that seek to reconcile densification with strategic greening in the context of demographic decline?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy addressing these questions, this paper seeks to provide not only a detailed empirical account of Nagoya's urban structure but also a transferable methodological framework and critical insights for planners and policymakers grappling with similar land use challenges worldwide.\u003c/p\u003e"},{"header":"2. Methodology and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area: Nagoya, Japan\u003c/h2\u003e \u003cp\u003eThe study focuses on the entire administrative area of Nagoya City, Japan. Nagoya is the capital of Aichi Prefecture and the center of Japan's third-largest metropolitan area. Historically, the city's development originated from its 17th-century castle structure, which has left an imprint on its central layout (Jinnai, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Following extensive damage in World War II, Nagoya underwent a massive reconstruction that established its modern grid of wide arterial roads. It evolved into a major industrial hub, particularly for the automotive and aerospace industries.\u003c/p\u003e \u003cp\u003eThe contemporary planning context of Nagoya is defined by a dual policy agenda. On one hand, the Nagoya City Master Plan 2028 explicitly promotes a \"compact and networked\" urban structure, encouraging high-density, mixed-use redevelopment in key urban centers and along public transit corridors (Nagoya City, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is exemplified by massive ongoing projects such as the redevelopment of the Nagoya Station area (Nagoya City, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, the Master Plan acknowledges the city's challenging demographic future. After decades of modest growth, Nagoya's population is projected to peak and enter a period of sustained decline (Nagoya City, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This places Nagoya at the forefront of cities that must navigate the inherent tensions between land use intensification, strategic management of decline, and the enhancement of urban green infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Sources and Baseline Justification\u003c/h2\u003e \u003cp\u003eThe analysis is based on two primary vector data sets provided by the City of Nagoya:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNagoya Urban Planning Basic Research (2006)\u003c/b\u003e: This comprehensive GIS dataset contains polygons for city blocks, building footprints (with attributes for use and number of floors), and detailed land use parcels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePresent Data of Green of Nagoya (2020)\u003c/b\u003e: This dataset contains polygons delineating all areas of green coverage, including parks, woodlands, farmland, and vegetated private lots.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eA critical methodological consideration is the temporal difference in these datasets. This study frames this 2006\u0026ndash;2020 timeframe as a unique analytical strength. The 2006 data capture the city's built form at or near its peak population, immediately preceding both the acceleration of major central-area redevelopment projects and the onset of demographic decline. The 2020 data provide a contemporary snapshot of green coverage. This analysis therefore establishes a vital \u003cem\u003ehistorical baseline\u003c/em\u003e of the \"legacy\" urban form\u0026mdash;the morphological and green space conditions that contemporary and future land use policies must contend with as the city transitions into its new phase of simultaneous densification and shrinkage. All data were integrated and processed within an ArcGIS environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analytical Framework\u003c/h2\u003e \u003cp\u003eThe methodology follows a systematic, multi-stage process designed to classify every urban block in Nagoya and analyze its characteristics. The entire process, from data preparation to final analysis, is depicted in the analytical workflow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This workflow ensures a transparent and reproducible approach to developing the urban block typology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Derivation of Indices and Block Classification\u003c/h2\u003e \u003cp\u003eA set of quantitative indices was calculated for each urban block to capture its land use composition, physical morphology, and green space characteristics. These indices, defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, form the statistical basis for the classification.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Use Indices Used in the Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock Green Coverage Ratio (BGCR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe proportion of the total block area is covered by vegetation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBGCR = (Σ Total green coverage area in the block) / Block area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreen Coverage Data (Nagoya City, 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock non-built-up Area Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe proportion of the block area not covered by building footprints.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBNAR = (Block area - Σ Total building footprint area) / Block area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Footprint Data (Nagoya City, 2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock Land Use Composition (BLUC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe proportion of the block area occupied by specific land use (e.g., commercial, residential).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBLUC\u003csub\u003ei\u003c/sub\u003e = (Σ Total area of land use i) / Block area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand Use Data (Nagoya City, 2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFloor Area Ratio (FAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe ratio of the total floor space of all buildings to the block area.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAR = (Σ (Building footprint area \u0026times; Floors)) / Block area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Footprint Data (Nagoya City, 2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-built-up Openness Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA measure of the openness of the non-built-up space within a block.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Proprietary index from Nagoya City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived from Nagoya City (2006) datasets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompact Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA measure of the shape efficiency of the non-built-up space within a block.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Proprietary index from Nagoya City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived from Nagoya City (2006) datasets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing these indices, a three-stage hierarchical classification method was employed to assign each block to one of 26 mutually exclusive Block Land Use Composition (BLUC) types. This process was chosen for its theoretical and practical rigor, allowing for a logical 'sieve' that first isolates blocks defined by non-development (Stage 1), then by public function (Stage 2), before analyzing the heterogeneous private urban fabric (Stage 3). This ensures typological clarity and policy relevance.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStage 1 (Green Dominant Classification)\u003c/b\u003e: Blocks with a cumulative area of \"positive-for-green\" land uses (farmland, woodland, parks, vacant land, and temples) exceeding 50% of the total block area were identified and classified first.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStage 2 (Public and Institutional Classification)\u003c/b\u003e: From the remaining pool, blocks dominated by specific public or institutional uses (e.g., parking lots, public buildings, schools) with a coverage of \u0026gt;\u0026thinsp;75% were extracted.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStage 3 (Private Urban Fabric Classification)\u003c/b\u003e: The final set of blocks, representing the core private urban fabric, was classified based on the relative proportions of residential, commercial, and industrial land uses, using a ternary system. These types were further subdivided by density, using thresholds for FAR and the proportion of multiple-dwelling housing.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Spatial Analysis of Land Use Governance\u003c/h2\u003e \u003cp\u003eTo understand how the derived block types are distributed, their spatial concentration was analyzed within two distinct geographical frameworks from Nagoya's official planning documents:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGeographical/Urbanization Zones\u003c/b\u003e: The city was divided into five zones: Downtown (A), Urbanized Flatland (B), Newly Developed Flatland (C), Urbanized Hillside (D), and Newly Developed Hillside (E).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRoad Proximity Zones\u003c/b\u003e: All blocks were categorized as either being adjacent to a wide lane road (width\u0026thinsp;\u0026gt;\u0026thinsp;15 meters) or not.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe degree of spatial concentration for each BLUC type within each zone was quantified using a Specialization Coefficient (SC). This coefficient compares the proportion of a specific block type within a given zone to its proportion in the city. A value greater than 1.0 indicates that the block type is disproportionately concentrated, or \"specialized,\" in that zone. The coefficient is calculated as follows:\u003c/p\u003e \u003cp\u003eFirst, the block area composition rate (Uij​) for each BLUC type i in a zone j is calculated: U\u003csub\u003ei\u003c/sub\u003eⱼ = S\u003csub\u003ei\u003c/sub\u003eⱼ / Σ (from i\u0026thinsp;=\u0026thinsp;1 to 26) S\u003csub\u003ei\u003c/sub\u003eⱼ, where S\u003csub\u003ei\u003c/sub\u003eⱼ is the total area of block type i in zone j. Next, the total block area composition rate (Vj​) for zone j is calculated: Vⱼ = (Σ (from i\u0026thinsp;=\u0026thinsp;1 to 26) S\u003csub\u003ei\u003c/sub\u003eⱼ) / Total City Area. The Specialization Coefficient (SCij​) is then the ratio of these two values: SC\u003csub\u003ei\u003c/sub\u003eⱼ = U\u003csub\u003ei\u003c/sub\u003eⱼ / Vⱼ, This analysis allows for a robust, quantitative assessment of the spatial logic of the city's morphology, linking specific block types to distinct parts of the urban landscape.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. A Typology of Nagoya's Urban Blocks\u003c/h2\u003e \u003cp\u003eThe three-stage classification process yielded a comprehensive typology of 26 distinct Block Land Use Composition (BLUC) types, which collectively account for the entire urban fabric of Nagoya. This typology provides a fine-grained decomposition of the city, moving beyond monolithic land use categories. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the key characteristics of these 26 types, grouped into broader functional categories, highlighting the vast differences in physical form and green space provision.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Block Land Use Composition (BLUC) Typology of Nagoya\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBLUC Symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBLUC Type Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Area (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean BGCR (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean FAR (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Density Core\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01HDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Density Commercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02HDC/R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Density Commercial/Residential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05HDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Density Residential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-Density Built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e03LDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Density Commercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e04LDC/R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Density Commercial/Residential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e06MDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple Dwelling House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e07LDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow Density Residential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustrial \u0026amp; Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e08C/I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial/Industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e09In\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndustrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10R/I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential/Industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11C/R/I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial/Residential/Industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic \u0026amp; Institutional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParking Lot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13Sh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublic Building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15OPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther Public Building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen Dominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16Te\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17Te/Bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemple/Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18VA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVacant Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19VA/Bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVacant Land/Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21PA/Bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePark/Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22FA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23FA/Bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarmland/Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25W/Bu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWoodland/Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26ONB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther Non-Building Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The Morphological Signature of Land Use\u003c/h2\u003e \u003cp\u003eThe analysis reveals a strong, systematic relationship between a block's land use composition and the morphology of its non-built-up space. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes this relationship by plotting the average non-built-up Openness Index against the average Compact Ratio for each of the 26 BLUC types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA distinct polarization is evident. Block types associated with intense urban development (e.g., High Density Commercial (01HDC), Industrial (09In), Commercial/Residential/Industrial Mix (11C/R/I)) are clustered in the bottom-left quadrant. These blocks exhibit low value for both openness and compactness, indicating that their non-built-up areas (e.g., courtyards, alleys, setbacks) are small, fragmented, and have complex, inefficient shapes.\u003c/p\u003e \u003cp\u003eIn stark contrast, block types dominated by \"positive-for-green\" land uses (e.g., Farmland (22FA), Woodland (24W), Park (20PA)) occupy the top-right quadrant. These types have high values for both indices, signifying that their non-built-up spaces are large, contiguous, and morphologically simple. This quantitative result provides clear evidence that the underlying land use function of a block imparts a distinct morphological signature on its open spaces, directly influencing their suitability for functional greening.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Spatial Patterns of Specialization and Green Inequality\u003c/h2\u003e \u003cp\u003eThe specialization coefficient analysis reveals a highly structured spatial distribution of the BLUC types, creating distinct zones of morphological character and green space provision. The key findings are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and visualized in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecialization Coefficients and Block Green Coverage Ratios for Key BLUC Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLUC Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZone of Highest Specialization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecialization Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean BGCR in Zone (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01HDC (High Density Commercial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDowntown (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02HDC/R (High Density Comm/Res)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDowntown (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05HDR (High Density Residential)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDowntown (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e09In (Industrial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized Flatland (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12P (Parking Lot)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanized Flatland (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22FA (Farmland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNewly Developed Flatland (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24W (Woodland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNewly Developed Hillside (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03LDC (Low Density Commercial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlong Wide Lane Roads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe spatial analysis covers several distinct patterns that define the city's socio-ecological structure (visualized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eTable 4 Specialization coefficient and the block green coverage ratio\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"722\" height=\"440\"\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Compact, \"Gray\" Core\u003c/b\u003e: High-density commercial (01HDC, 02HDC/R) and residential (05HDR) blocks are overwhelmingly specialized in the Downtown (A) zone. This zone exhibits the city's lowest green coverage, confirming a strong spatial correlation between the most intense urban forms and a profound deficit of green space.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Industrial Belt\u003c/b\u003e: Industrial (09In) and mixed industrial blocks show a strong specialization in the Urbanized Flatlands (B), historically the locus of the city's manufacturing economy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Suburban and Peri-Urban Fringe\u003c/b\u003e: Low-density residential blocks (07LDR) are the dominant type in the newly developed areas. These zones, particularly the flatlands, also show a high specialization of farmland (22FA) and vacant land (18VA), resulting in high green coverage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Green Reservoirs\u003c/b\u003e: The city's primary green assets are spatially segregated. Woodland blocks (24W) are heavily concentrated in the eastern hillside zones (E), forming a large greenbelt. Parks (20PA) and temples (16Te) are more prevalent in the urbanized hillside and newly developed areas than in the central core.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLinear \"Gray\" Corridors\u003c/b\u003e: A distinct pattern emerges along major arterial roads. Low-density commercial (03LDC) and parking lot (12P) blocks show a strong specialization in these transport corridors, creating linear zones of extremely low green coverage that fragment the urban green fabric.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCollectively, these results paint a picture of a city with significant green space inequality, where the highest levels of access to green amenities are found at the periphery, while the densest, most populated central areas are the most deprived.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The Compact City Paradox: A Micro-Scale Policy Critique\u003c/h2\u003e \u003cp\u003eThe findings provide robust, block-level empirical evidence that substantiates key critiques of the compact city model (Neuman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Westernick et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The results demonstrate a clear and inverse relationship between urban compactness and green space provision in Nagoya. The very block types that closely align with the compact city ideal\u0026mdash;high-density, mixed-use commercial and residential cores (01HDC, 02HDC/R, 05HDR)\u0026mdash;are precisely those with the lowest green coverage and the poorest quality of non-built-up space.\u003c/p\u003e \u003cp\u003eThis analysis moves beyond a simple correlation by identifying \u003cem\u003eurban morphology\u003c/em\u003e as the critical intervening mechanism. The low openness and compactness scores of the dense central blocks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reveal \u003cem\u003ewhy\u003c/em\u003e they are green-deficient. The economic logic of densification, particularly on the fine-grained plot structure typical of Japanese cities, incentivizes maximizing floor area. This process treats non-built-up space as a residual, resulting in fragmented, poorly configured, and inaccessible leftover areas like lightwells, service alleys, and shaded slivers between buildings. These spaces are morphologically unsuitable for supporting functional UGS, such as small parks or even meaningful tree canopy.\u003c/p\u003e \u003cp\u003eThis creates a condition of \"morphological lock-in,\" where the physical form established by high-density development inherently precludes the integration of effective green infrastructure. The implication for land use policy is profound: policies that focus solely on increasing density metrics (e.g., Floor Area Ratio), without concurrently regulating the quality, contiguity, and usability of the resulting open space, will exacerbate green space deficits and undermine livability. This finding, derived from a non-Western context, echoes research from European cities like Amsterdam and Brussels, where densification policies were found to degrade UGS quantity and connectivity (Balikci et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Achieving a \"green compact city\" requires a form-based approach that actively designs for high-quality open space as an integral component of densification, not as an afterthought (Mouratidis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. A Typology-Based PSS for 'Smart Decline' and Land Use Governance\u003c/h2\u003e \u003cp\u003eThe impending onset of urban shrinkage in Nagoya demands a strategic shift from purely growth-oriented planning to one that also manages decline. Population loss will not be spatially uniform; it is likely to disproportionately affect specific areas, leading to increased vacancy in block types like older industrial zones (09In) or low-density residential areas (07LDR). This challenge, however, presents a unique opportunity for land use policy.\u003c/p\u003e \u003cp\u003eThe BLUC typology developed in this paper can be repurposed from a descriptive, diagnostic tool into a prescriptive Planning Support System (PSS) to guide a targeted \"right-sizing\" strategy (Dewar and Thomas, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hollander and N\u0026eacute;meth, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This framework moves beyond reactive vacancy management and allows for proactive, strategic land use governance. The process involves a spatial overlay of vulnerability and needs:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiagnostic Tool\u003c/b\u003e: The typology map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) acts as a green equity audit, immediately identifying block types suffering from severe green space deficits (e.g., 01HDC, 05HDR).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrescriptive Tool\u003c/b\u003e: The typology also identifies blocks with characteristics suggesting a higher risk of future vacancy and lower land values (e.g., 09In Industrial, 10R/I Residential/Industrial).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy identifying areas where these two conditions are in proximity, planners can develop a highly targeted land acquisition and redevelopment strategy. For example, a cluster of declining industrial blocks (09In) adjacent to a green-deficient high-density residential neighborhood (05HDR) becomes a prime candidate for transformation into a new district park or green corridor. This approach, rooted in the \"smart decline\" literature (Schilling and Logan, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), allows the city to leverage the process of shrinkage in one area to address a long-standing quality-of-life deficit in another.\u003c/p\u003e \u003cp\u003eThis methodology provides a practical, block-level implementation framework for national-level policies, such as Japan's \"location normalization plan,\" which aims to create more compact urban structures in the face of depopulation (Yoon, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The typology acts as a dynamic tool for strategic urban acupuncture, enabling planners to make targeted, efficient, and equitable decisions about where to invest in green infrastructure as the city adapts to a smaller population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and Methodological Transferability\u003c/h2\u003e \u003cp\u003eThis study's primary strength lies in its fine-grained, block-level analysis. By disaggregating the city into 26 distinct morphological types, the methodology reveals nuanced land use patterns obscured by aggregated approaches (Lan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main limitation is the use of 2006 building/land use data. However, as argued, this is intentionally framed as a methodological strength, providing a critical \u003cem\u003ebaseline\u003c/em\u003e of Nagoya's urban form at its demographic peak. This baseline is essential for future longitudinal studies to accurately measure the morphological impacts of both densification and shrinkage. Future research should replicate this study with contemporary datasets to track these changes over time. Furthermore, integrating fine-grained socioeconomic data would allow for a deeper analysis of the environmental justice dimensions of green space access across the block typologies.\u003c/p\u003e \u003cp\u003eThe methodological framework itself is highly transferable. The multi-stage classification process\u0026mdash;integrating land use composition, building density, and open space morphology\u0026mdash;is not specific to Nagoya. The availability of increasingly detailed, building-level geospatial data in cities worldwide makes this type of analysis more feasible than ever. Applying this framework to other cities in Europe, North America, and Asia would enable robust comparative urban morphological research, helping to identify common patterns and context-specific variations in the land use-sustainability relationship.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Policy Implications","content":"\u003cp\u003eThis research has dissected the urban fabric of Nagoya, Japan, to reveal the intricate and often contradictory relationship between urban form, density, and the provision of green space. In doing so, it offers three primary contributions to land use policy and planning. First, on an empirical level, it presents a novel 26-category urban block typology, quantitatively demonstrating the stark spatial polarization of green space in a major post-industrial city. Second, on a theoretical level, it provides high-resolution evidence for the \"compact city paradox,\" identifying the morphology of non-built-up space as a key mechanism through which densification policies can lead to green space exclusion. Third, on a practical and policy level, it demonstrates how a morphological typology can transcend its descriptive function to become a proactive Planning Support System for implementing targeted \"right-sizing\" and greening strategies in cities facing demographic decline.\u003c/p\u003e \u003cp\u003eThe findings yield several actionable recommendations for land use policy and governance in Nagoya and other cities facing similar trajectories:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAdopt Form-Based Codes to Complement Density-Based Zoning\u003c/b\u003e: Municipalities should move beyond simplistic land use and density controls (e.g., Floor Area Ratio) and develop form-based codes that regulate the \u003cem\u003equalitative attributes\u003c/em\u003e of non-built-up space. This could include establishing minimum standards for openness, contiguity, and solar access of open spaces created within new high-density developments, ensuring they are functional for greening and recreation, not just residual artifacts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImplement Typology-Based Green Equity Audits\u003c/b\u003e: Planners can use the BLUC typology as a framework to conduct \"green equity audits.\" By mapping the distribution of block types against demographic data, they can identify which communities are most affected by green space deficits (e.g., residents of 05HDR blocks) and prioritize land acquisition and green investments accordingly, thereby advancing environmental justice.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDevelop Proactive Land Acquisition Policies for 'Right-Sizing'\u003c/b\u003e: In shrinking cities, municipalities should adopt proactive land banking and acquisition policies focused on block types identified as vulnerable to decline (e.g., aging industrial or low-density residential areas). This would allow cities to assemble land strategically over time, creating a network of future green infrastructure that can serve the needs of adjacent, green-deficient neighborhoods, effectively using shrinkage as a tool for urban regeneration.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study opens several avenues for future research. A crucial next step is to conduct a longitudinal analysis, replicating this study with updated data to measure the morphological transformations that have occurred as Nagoya has undergone its recent wave of redevelopment. Further research should also integrate fine-grained socioeconomic data to explore the environmental justice dimensions of green space access and assess the ecological connectivity of the UGS network. By refining our understanding of the city at this fine-grained scale, we can better equip planners to formulate land use policies that create cities that are not only compact but also green, equitable, and resilient.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe author declares that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants or animals performed by the author.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003eNot applicable, as this study does not involve human participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eThe author consents to the publication of this manuscript in the \u003cem\u003eJournal of Humanities and Social Sciences\u003c/em\u003e, should it be accepted.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.O. is the sole author of this work and was responsible for all aspects of the study, including conceptualization, methodology, analysis, visualization, and writing.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are available from the corresponding author upon reasonable request. The spatial datasets used for morphological classification were derived from publicly available sources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullahi S, Pradhan B (2018) Sustainable urban development: a review of the concept of compact city. Int J Urban Sci 22(2):135\u0026ndash;154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArtmann M, Inostroza L, Fan P (2019) Urban sprawl, compact urban development and green cities. How much do we know, how much do we agree? Ecol Ind 96:3\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalikci S, Giezen M, Arundel R (2021) The paradox of planning the compact and green city: Analyzing land-use change in Amsterdam and Brussels. J Environ Planning Manage 65(11):1994\u0026ndash;2016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBatty M (2009) Planning support systems: A new perspective on computer-aided planning. 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Sustainability 12(3):989\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Compact City, Urban Shrinkage, Land Use Policy, Urban Green Space (UGS), Morphological Analysis, Planning Support Systems (PSS)","lastPublishedDoi":"10.21203/rs.3.rs-8006965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8006965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe \"compact city\" model, a central tenet of sustainable urbanism, creates critical policy tensions in post-industrial nations, where land use intensification often conflicts with urban shrinkage and the need to preserve urban green space (UGS). This study addresses a significant research gap by moving beyond city-level metrics to conduct a fine-grained morphological analysis of urban form and UGS provision at the urban block level. Using Nagoya, Japan, as an archetypal case study of a city facing simultaneous compaction and shrinkage, we develop a comprehensive urban block typology through a GIS-based hierarchical classification. The analysis reveals a stark spatial polarization: block types embodying the compact city ideal (high-density commercial and residential) are the most green-deficient, characterized by small, fragmented, and morphologically inefficient non-built-up areas. Conversely, peripheral and specialized blocks (e.g., woodlands, farmland) function as the city's primary green reservoirs. These findings provide high-resolution evidence for the compact city-green space paradox, identifying the morphology of non-built-up space as a key mechanism of green exclusion. We contribute a transferable methodological framework that can be repurposed as a Planning Support System (PSS). Policy Implications: This typology enables a targeted \"right-sizing\" and greening strategy, allowing planners and policymakers to use morphological analysis to identify specific blocks for green infrastructure acquisition and land use interventions. This provides a practical tool for land use governance, helping to reconcile densification policies with the need for equitable and resilient urban environments.\u003c/p\u003e","manuscriptTitle":"Urban Morphology Links Green Space and Compact City Policy in a Shrinking Japanese Metropolis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 09:09:36","doi":"10.21203/rs.3.rs-8006965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-11T08:47:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T17:20:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T10:45:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6245614110595413459135798435815043106","date":"2026-02-28T02:30:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213635063499541047669674389356194564285","date":"2026-02-26T00:36:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32720059233079159150238321329126372991","date":"2026-02-25T18:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T18:12:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-23T18:05:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T07:34:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T02:23:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-11-01T16:22:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d93f97ca-ce4c-40f5-a26c-814074ff70a2","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63549920,"name":"Earth and environmental sciences/Environmental sciences"},{"id":63549921,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":63549922,"name":"Social science/Environmental studies"},{"id":63549923,"name":"Scientific community and society/Geography"},{"id":63549924,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-04-20T14:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 09:09:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8006965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8006965","identity":"rs-8006965","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}