Urban Planning Tools Research Articles

Forecasting Urban Land Use Dynamics Through Patch-Generating Land Use Simulation and Markov Chain Integration: A Multi-Scenario Predictive Framework

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally.

Factors hindering the implementation of urban planning tools in Central Africa: Case study of reference urban plan for Sarh Town (Chad)

The reference urban plan is an urban planning tool often used to orient the development of Chadian cities. However, expanding Chadian urban centers, such as Sarh, face challenges in implementing urban planning orientations of their urban plans within the set deadlines. The objective of this study is to identify the factors impeding the effective implementation of the reference urban plan for Sarh town. The methodology employed encompasses a literature review, individual interviews with urban planning experts, geographic information system (GIS) data, household surveys and statistical analysis. The results revealed that less than a quarter (19.72%) of the households surveyed were aware of the reference urban plan. The applied logistic regression model identified age, occupation and level of education as the main factors influencing public participation in the preparation of the reference urban plan. On average, 33.33% of the urban planning guidelines and 21.74% of the projected urban projects were implemented, with a difference of 1631.28 hectares (ha) between the projected plan and the actual plan for the town. Five factors were identified as contributing to the failure to implement the reference urban plan for Sarh town, including low funding, inadequate land management, a lack of political will, weak governance and poor communication. Consequently, participatory and inclusive planning approaches, effective financial mobilisation, strong governance, and the use of modern technologies such as GIS tools are recommended to enhance the implementation of urban planning tools.

A novel tucker decomposition driven taxi travel demand forecasting algorithm

<p>This research aims to enhance taxi travel demand forecasting for sustainable urban traffic management and planning. We extend the Seasonal Autoregressive Integrated Moving Average model into a high-dimensional tensor form by treating the urban transport network as a Euclidean space and introducing Tucker decomposition. This novel approach, both theoretically significant and practically applicable, represents time series data as tensors to better capture multimodal structures and correlations, improving predictive accuracy. Tucker decomposition reduces computational complexity and memory requirements, making it ideal for large-scale urban traffic network prediction. Experiments on six real-world datasets show that the model’s MAE and RMSE are reduced by about 39.43% and 27.01% on average, respectively, compared to the baseline model. Notably, the model is computationally very efficient and takes only a relatively short time to train., suitable for real-time traffic management, congestion mitigation, and resource optimization. In summary, this work innovates time series analysis, providing an efficient and precise tool for urban traffic management and planning, contributing to sustainable urban transportation advancement.<strong></strong></p>

Ecology of urban decay. Unintentional landscapes of enhanced biodiversity in Rome

This research explores the relationship between urban environments and unintentional landscapes in Rome, emphasizing the significance of spontaneous nature within the city’s cultural narrative. To do so, it empirically hypothesizes that the temporal dimension, considering the historical context, plays a crucial role in shaping public acceptance and legitimization of these landscapes. By drawing on urban ecology experiences from Central Europe, the research aims to provide a modern interpretation of unintentional landscapes as valuable urban planning tools for Rome. Rome has many gardens, parks, and villas, but it also has a unique relationship with spontaneous nature, a phenomenon that is deeply rooted in the cultural narrative. It is visible in a number of historical engravings, paintings, and academic studies, which illustrate the intricate interactions between urban neglect and the unintentional landscapes. These landscapes contribute to a diffuse and heterogeneous natural fabric that enhances the ecological potential of today’s existing green spaces. The paper posits that, over time, spontaneous nature has blurred the boundaries between the natural and built environments, fostering a paradigm of coexistence that promotes a more-than-human city.

Cross-sectoral interventions to promote health-supportive urban environments

Abstract Background As dealing with the complexity of urban living conditions becomes more and more evident and urgent, it is fundamental to promote cross-sectoral interventions that allow cities to become more inclusive and supportive of health. The Emilia-Romagna region, Italy, has promoted a participatory initiative to structurally integrate health into urban planning tools, identifying a framework of tailored urban indicators linked to locally perceived population health issues. Methods Through internal discussion and systematic literature research, a set of urban planners, epidemiologists, and public health experts from regional offices, local health services, universities, and environmental protection agencies identified the urban and health dimensions most suitable for embedding population health into city planning. The result was summarised in a preliminary working paper, which included a matrix of associations between urban features and health dimensions framed according to the DPSEEA framework. Its applicability was then tested in a participatory process (March-May 2024) involving urban planners and public health departments of 7 different sized municipalities. Results A group of epidemiologists identified local-level computable indicators for the health dimensions already included in the matrix or added at request. Each municipality focused on specific topics based on local priorities: 3 groups assessing their already-approved urban plans and looking for computable urban indicators; the others integrating their under-construction plans with local health indicators. Conclusions The need for professional contamination and cross-sectoral interventions is significant. Epidemiological support is pivotal in integrating different professional languages and reading health data. The outputs of this experience will contribute to a technical document outlining how to incorporate health promotion into urban planning tools and practice while actively considering the local context. Key messages • Although the development of a common vocabulary can be difficult, multidisciplinary collaboration is essential in promoting a model for integrating health promotion into urban planning. • Local municipalities’ involvement in the experimentation can improve the model’s applicability by taking into account territorial specificities.

Green horizons: Exploring the potential of vertical green walls

The term “Green walls” also known as vertical gardens or living walls has emerged as a promising solution to combat the environmental challenges posed by rapid urbanization. This review explores the concept ornamental, plant choice suitable and benefits associated with green walls in urban settings. Utilising vegetation to cover vertical surfaces, green walls offer several benefits to the environment, society and economy. They contribute to improved air quality, noise reduction, energy conservation and biodiversity enhancement. Vertical greenery allows for the utilization of unused wall surfaces, creating green areas without occupying valuable ground space, which is often scarce in urban environments. Additionally, green walls offer aesthetic appeal, promote urban agriculture and create opportunities for community engagement. However, their implementation requires careful consideration of factors such as structural integrity, irrigation and maintenance. Despite these challenges, green walls offer a sustainable and innovative approach to enhancing the quality of urban life while mitigating the negative impacts of urban development on the environment. The current emphasis highlights the significance of green walls as an effective tool in sustainable urban planning, underscoring the need for further research and their integration into urban planning practices.

The evolution of urban ecological resilience: An evaluation framework based on vulnerability, sensitivity and self-organization

Ecological resilience assessment has become a key link in urban sustainable governance. This study introduces a new evaluation framework to inform policy-making and practical applications. Based on the structural and functional dimensions of landscape patterns, it integrates the vulnerability, sensitivity and self-organization of resilience to point to desirable directions of ecological resilience. A composite ecological resilience index is compiled based on six indices of landscape diversity, landscape disturbance, source-sink patch distance, habitat quality, minimum cumulative resistance, and landscape restoration. The framework is particularly applicable to cities located in ecologically sensitive areas. Hence, Nanchang City, China was selected as a case study. Using 1km2 hexagonal grids, the framework is applied to map spatiotemporal changes and to analyze various natural and anthropogenic driving forces of ecological resilience in Nanchang from 2000 to 2020. Research findings confirm the feasibility and value of the urban ecological resilience analysis framework. They also highlight the advantages of the framework in revealing spatially dynamic processes and ecological resilience contributing factors, making it a valuable and practical tool for sustainable urban planning and refined management decision-making.

Towards A City of Good Dwelling

One of the main issues in contemporary urban planning is the fragmentation of urban spaces (Batty 2009) due to the relentless processes of land transformation. This phenomenon can lead to the creation of isolated urban islands and a lack of high-quality public areas. Such weaknesses particularly affect the socio-economic conditions of marginal areas within the existing city, especially those on the outskirts near rural areas. Public spaces (Low et al 2006) are essential for social interaction, community building, and improving the overall quality of urban life. They are spaces where cultural, economic, and social activities converge, contributing to the city's vibrancy and inclusiveness. Are considered the key of the new processes of urban regeneration and it contributes significantly to the making of the city of "good dwelling" (Beauregard 2020). To obtain the conditions for a good dwelling, it is proposed to apply the seven principles contained in the "Manifesto della Città del Buon Abitare"(Colarossi et al 2023): the city of good dwelling must assume the general principles of hospitality, urbanity and beauty of its public spaces. The paper proposes as innovative approach, the use of an urban planning tool with which it is possible to apply the principles of the Manifesto of the city of good living and which is called "Plan-program of public space planning and public and private services" (P.A.S.S.i.). The effort of this research is to identify a general planning tool for the redevelopment to the existing city. The multi-scalar approach of the research tool simultaneously allows an overall vision of the program and a qualitative control of each individual project.

Understanding the Transformations of San Lorenzo, Rome: An Attempt at Conceptual Order between Gentrification and Urban Policy

This paper is the result of a preliminary research phase aimed at identifying the phenomenon of gentrification in Rome’s San Lorenzo neighborhood, examining the perception of the phenomenon and the urban planning tools that have been adopted until today to contain it. The experimental framework proposed, based on qualitative research methods, aims to discuss if some urban transformations can be related only to market phenomenon inherent to gentrification or to the lack of planning tools able to manage—through the construction of a shared view on neighborhoods development—the negative externalities resulting from the real estate investments induced by the tourism sector and linked to the presence of universities in the reference territory. The research combines an international theoretical framework; the analysis of the policies and of the urban transformation during the contemporary history of the neighborhood; and interviews to key informants such as representatives of local institutions and associations, researchers, inhabitants, and others. The research approach intends to deepen the general perception and the comprehension of the phenomenon of gentrification from the local community, in relation to his own involvement in decision-making processes about the strategic regulatory–prescriptive development of the neighborhood. The primary objective of the paper is to advocate for an analytical framework that facilitates a better understanding of gentrification within the national context and explores potential institutional responses. It seeks to clarify the concept of gentrification, which is often misunderstood when interpreted solely through its Anglo-Saxon definition. By doing so, the paper aims to contribute to informed discussions and effective policy-making regarding urban development in San Lorenzo and beyond.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Voluntary Planning and City Networks: A Systematic Bibliometric Review Addressing Current Issues for Sustainable and Climate-Responsive Planning

Over the years, energy saving and climate change have sparked a challenge in developing innovative spatial and urban planning tools, methods, and approaches for urban areas and territorial management. This challenge has pointed out a gap in the traditional planning framework in tackling energy and climate issues, highlighting the need for renovated spatial planning practices. The current effective measures carried out by the complex network of stakeholders (public and private) are fragmented, and the role of city networks in shaping decisions and interventions has emerged. In particular, among the approaches for urban and territorial planning and the development of climate-responsive measures, this research adopts a specific focus on voluntary planning. The authors, through a literature review and an analysis of nine city networks, investigated how this approach addresses energy and climate issues and how it is linked to the city networks in designing investments and interventions within the urban context. From the authors’ perspective, city networks are the operative component in designing local experiences and applications, contributing to achievement of the global energy and climate targets according to the current policies. In this context, successful results with respect to climate and energy challenges is expressed in the degree of the awareness and the commitment achieved by the local communities. Moreover, reaching the climate and energy goals is a representation of the outcome of the interaction between the (public or private) actors involved and the degree of a city’s climate-responsiveness, rather than from a normative constraint or an institutional framework.

Mapping the intrinsic potential of water infiltration in urban subsurface: feedback from France

In a context of increasing urbanization, with strong soil sealing, and with an increase in extreme weather events, the management of rainwater in urban areas is becoming a major issue. In order to improve water resource management and to prevent urban floods, more and more cities are considering or already implementing water infiltration systems. Infiltration of water is not possible anywhere due to natural and/or anthropic reasons. In this frame, the intrinsic infiltration capacity of the subsurface is one key natural parameter. Global maps are needed to build territorial strategies. In France, a lot of studies are available but a national methodological framework for mapping this index does not exist. In this paper, we analyse various studies carried out in France for such mapping and compare the methods with examples in other countries. Most of the French studies combine a Multi-Criteria Analysis (MCA) and a Geographic Information System (GIS) spatial analysis. The criteria include geological, hydrogeological and geomorphological parameters. The PHOEBUS method developed on Rennes Metropolis seems the most relevant and replicable one to provide a common framework at French scale. It takes into account 7 criteria, including rock/soil permeability, topographic slope and thickness of the unsaturated zone. The obtained maps may be used as such, modified by integrating sealed surfaces or crossed with other criteria linked to the urban environment (e.g. sealed surfaces, soil pollution, heat islands). They provide a decision support tool for urban planning. In particular, they are useful to improve rainwater management, but also in the elaboration of desealing or renaturation strategies. It seems essential to act in concertation with local stakeholders to identify their needs and the specificities of the territory.

Measuring physical disorder of architectural façades for informing better urban renewal using deep learning and space syntax

With the emergence of human-centered urban development goals and the increasing pursuit of a better quality of life, the architectural façades of cities are receiving growing attention. However, during the process of urban development, architectural façades often experience physical disorder. This phenomenon tends to be overlooked in targeted urban management practices or lacks cohesive urban renewal planning at a macro scale. This oversight can negatively impact the livability and attractiveness of a region. This study aims to quantify the architectural façades encountered daily by urban residents by measuring the physical disorder of architectural façades to inform better urban renewal using deep learning and space syntax. First, streetscape images of architectural façades were collected using the Baidu Maps Street View service. Subsequently, an evaluation system for architectural façades was developed, and machine learning was employed to conduct high-resolution measurements and assessments of these façades. Simultaneously, street network data is extracted and analyzed using space syntax to quantify the accessibility of architecture on each street. Finally, by integrating the analysis of architectural façades and accessibility, the study identifies priority areas for building renewal, thus providing a decision-support tool for sustainable urban renewal planning. Overall, the paper presents an innovative method that combines image data, deep learning, and space syntax-derived architectural accessibility for a quadrant analysis. It offers designers and decision makers new perspectives and enhances the livability of residents by focusing on the physical condition of architectural façades, thereby making urban renewal practices more human-centered and better aligned with the actual needs of city dwellers.

Virtual Reality in planning, design, and management of urban green and blue infrastructure

There is a need for new tools in urban planning, design, and management (urban PDM) of green-blue infrastructure (GBI) to better support resilience and sustainable development. Virtual Reality (VR) emerges as a potential tool in this context. GBI provides a diversity of ecosystem services that increase the capacity of urban environments to absorb change while continuing to develop (adaptive capacity), a key factor in resilience and sustainability. However, there is a lack of tools capable of effectively capturing and communicating the designs, functions, and uses of GBI. Despite this need, research on the contributions of VR as a tool in urban PDM of GBI remains limited. To address this gap, an exploratory experiment using inductive qualitative data analysis was conducted. Participants of different ages and with different expertise viewed 360-degree video clips from urban areas through VR, completed a questionnaire, and participated in interviews. The results showed that the 360-degree video VR experience enhanced participants’ understanding of the viewed sites. Specifically, participants paid attention to details, captured the context, responded by generating emotional engagement, identified site development potential, and described an immersive experience. These results align with key principles for the sustainable management of ecosystem services, such as participation, learning, diversity, place-making, and stewardship; and adaptive capacity aspects such as processes and results grounded in complexity-embracing creativity. Our analysis shows that 360-degree video VR can increase the understanding of complexities, connections, and nuances in the urban environment. We conclude that 360-degree video VR could serve as a valuable complement to traditional tools in urban PDM, enriching decision-making with information that can support adaptive capacity, resilience, and sustainability.

Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.

Urban design and pollution using AI: Implications for urban development in China

The primary aim of this study is to explore the role of AI in urban design and its potential to reduce pollution in Chinese cities. The study investigates how AI-driven urban planning tools can be applied to create more sustainable, efficient, and functional urban environments. PM2.5 and PM10 show high concentrations with peaks between 2014 and 2017, indicating simple pollution actions. Post-2017, there is a noticeable decline in pollution levels, possibly due to improved regulations or the global impact of the COVID-19 pandemic. Specific years, like 2016, show extreme spikes, possibly due to industrial activities or natural events. The overall trend suggests improved air quality and moderate to strong positive correlations exist between PM2.5 and PM10, NO2, SO2, and CO, indicating shared bases or co-occurrence. However, there is no significant correlation between PM2.5 and O3, suggesting different bases and behaviors. Bi-directional causality is observed between PM2.5 and PM10, PM2.5 and O3, PM2.5 and NO2, PM2.5 and SO2, and PM2.5 and CO. This mutual cause suggests interrelated impressive processes and shared bases. The results of the causality analysis suggest the existence of complex interactions, where high levels of pollution can predict changes in others. AI in urban design play vital role for identifying the most effective strategies for reducing pollution and helping to build more sustainable and functional urban environments in China.

BP neural network multi-module green roof thermal performance prediction model optimized based on sparrow search algorithm

Green roofs, as a passive building energy-saving technology, offer a range of benefits including reduced energy consumption and mitigation of the urban heat island effect. To analyze the thermal performance of green roofs more accurately and efficiently, this study introduces a novel predictive model that utilizes a Sparrow Search Algorithm (SSA) optimized Backpropagation Neural Network (BPNN) for forecasting the thermal performance of green roofs in subtropical regions. Compared to traditional BPNN, the SSA-BPNN model enhances the network's convergence and generalization capabilities by optimizing initial weights and thresholds. The model, established using meteorological data from Guangzhou, China, and simulated green roof temperature data, underwent rigorous training and validation. The results indicate that predictive accuracy has significantly improved across all seasons, with substantial reductions in all 12 evaluation metrics, and the model GR3 (SSA-BP) emerging as the optimal model. Particularly in summer, the SSA-BPNN model exhibits the highest accuracy, underscoring its effectiveness for subtropical climates. The findings suggest that the SSA-BPNN model is a powerful tool for sustainable urban planning and green roof design in subtropical regions, marking a significant advancement in the integration of artificial intelligence in environmental engineering.

Incorporating public perception of Renewable Energy Landscapes in local spatial planning tools: A case study in Mediterranean countries

The European energy transition requirements have been posing many questions on the deployment of renewable energy sources. The development of renewable energy infrastructures entails landscape transformations affecting the perceived landscape quality and local acceptance. Sustainable energy spatial planning considers environmental, cultural, ecological needs but often neglect community perception of landscape transformations including both the physical landscape structures and the meanings associated to them. To address this issue, the paper aims to explore public perception and incorporate it in the planning tools. The research draws on a survey of residents of Arcos de la Frontera, Spain, conducted with the visual Q methodology, and on structured interviews with local experts. A selection of 36 different photovoltaic applications in urban and rural areas was evaluated by 21 citizens. The analysis identified four distinct viewpoints on photovoltaic applications in urban and rural landscapes. Local experts provided feedback on the current local spatial planning tools and on their consideration of landscape transformations. Considering both citizens and experts, we provided landscape integration strategies linked to siting and landscape design of solar power plants to be included in urban planning tools.

Hybrid Model for Multistep-Ahead Rainfall Forecast in Northeast India: A Comparative Study

Abstract Among all hydrometeorological parameters, rainfall strongly correlates with hydrometeorological disasters. The rainfall forecast process remains challenging due to the nonlinear, nonstationary nature and multiscale variability of rainfall. Moreover, the unique microclimate in different regions further complicates the forecasting process. This study proposes a hybrid model employing multivariate singular spectrum analysis (MSSA) and long short-term memory (LSTM) for multistep-ahead hourly rainfall forecasting in urban areas of northeast India. The model is trained and evaluated using high-resolution (12 km) hourly meteorological data from the Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset for Guwahati (plain) and Aizawl (hilly) regions from 2015 to 2019. The hybrid model outperforms the single LSTM model in both plain and hilly regions, with an average percentage gain of 47.99% and 43.88% for symmetric mean absolute percentage error (SMAPE) and root-mean-square error (RMSE) in the case of the Guwahati dataset and 84.59% and 82.27% in the case of the Aizawl dataset, respectively. The performance of the LSTM model significantly improves as the zero values in the observed data are eliminated after reconstruction by MSSA. This enables the model to discern essential patterns and relationships in the data, which leads to more accurate forecasts. However, the hybrid model underestimates the rainfall, which can be tackled by hypertuning the parameters. The study highlights the importance of considering the interplay between rainfall and meteorological parameters for accurate rainfall forecasting in urban areas. The proposed MSSA–LSTM model can be used as a decision support tool for urban planning and disaster management. Significance Statement This study addresses a critical need in multistep-ahead hourly rainfall forecasting in urban areas, with a focus on the unique conditions of northeast India. Our research has identified the presence of red noise in the rainfall data, shedding light on the complexities of the underlying rainfall patterns. Furthermore, we delve into the intricate interplay between rainfall and meteorological parameters, providing valuable insights into the factors influencing rainfall dynamics. Notably, our study underscores the region-specific challenges in rainfall forecasting. While the hybrid model demonstrates reasonable accuracy in the plains, its performance in the hilly region falls short of expectations. This highlights the nuanced nature of rainfall prediction in areas with varying topography and emphasizes the need for tailored forecasting approaches.

Addressing the sustainable urbanism paradox: tipping points for the operational reconciliation of dense and green morphologies

To improve biodiversity and human living conditions in the Anthropocene, urban forms must reconcile density with vegetation to meet the dual sustainability-liveability challenge. This paradox poses a dilemma for urban planners and is a critical research issue requiring comprehensive analyses. Multi-family residential housing holds the potential to achieve balanced density-greening, proximity ecosystem services and human-nature connectedness, but meeting such objectives relies on finding balanced morphologies and metrics at an operational scale. Analysing 11,593 plots in the Lyon metropolitan area (France) using a systemic approach, we identified critical tipping points in morphology and greening. Density explained only 6% of Plot Greening, while morphology and landscaping accounted for 94%. We identified an open-space ratio (unbuilt area/floor area) >0.3 as a morphological threshold to achieve sustainable green supply. Operational morphologies balancing density and greening were modelled and illustrated across building heights, providing guidelines for emerging regulatory tools in sustainable urban planning.