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Cities and urbanized regions are complex, dynamic, and highly integrated systems linking social, ecological, and technical infrastructure domains in ways that create deep challenges for good governance, policymaking, and planning. The combination of impacts from climate change in cities, air pollution, rapid population growth, multiple sources of development pressure and overall urban system complexity make it difficult for decision-makers to develop and guide development trajectories along more livable, equitable, and at the same time, more resilient pathways. Advancing urban sustainability and resilience agendas requires expanding the scope of inter- and trans-disciplinarity approaches, moving beyond the historically separate social–ecological and socio-technical approaches to jointly study social–ecological–technical infrastructure systems in cities. We take urban complexity as a given and suggest that in both research and practice we need to better capture and understand feedbacks, interdependencies, and non-linearities which create uncertainties and challenge the efficacy of governance practices to achieve normative goals for society. Here, we explore new methods, tools, and approaches to advance our understanding of urban system complexity through a series of journal special issue articles that examine urban structure–function relationships, urban sustainability transitions, green space availability, social–ecological memory, functional traits, and urban land use scenarios. [ABSTRACT FROM AUTHOR]

The narrative of urban sustainability transformations epitomises the hope that urban governance can create the conditions to plan and govern cities in a way that they contribute to local and global sustainability and resilience. So far, urban governance is not delivering: novel governance approaches are emerging in cities worldwide, yet are unable to transform conventional policymaking and planning to allow for innovative, co-beneficial and long-term solutions and actions to emerge and institutionalise. We present a capacities framework for urban transformations governance, starting from the need to fulfil distinct output functions ('what needs to happen') for mobilising and influencing urban transformation dynamics. The framework helps to diagnose and inform urban governance for responding to disturbances (stewarding capacity), phasing-out drivers of path-dependency (unlocking capacity), creating and embedding novelties (transformative capacity) and coordinating multi-actor processes (orchestrating capacity). Our case study of climate governance in New York City exemplifies the framework's applicability and explanatory power to identify conditions and activities facilitating transformation (governance), and to reveal gaps and barriers of these vis-à-vis the existing governance regime. Our framework thereby functions as a tool to explore what new forms of urban transformation governance are emerging, how effective these are, and how to strengthen capacities. • Existing urban governance is unable to address persistent problems. • Effective governance support of transformation lies in collective capacities. • We develop a framework of four capacities for urban transformation governance (UTG). • We use the framework to explain the development of UTG capacities in New York City. • The framework enables explaining, evaluating and supporting UTG. [ABSTRACT FROM AUTHOR]

In New York City (NYC), the early period of the COVID-19 pandemic in the spring of 2020 induced a significant shift in the use and accessibility of urban green spaces (UGS). To understand the impact of COVID-19 on the access to UGS, we conducted a spatial analysis of geographic access to UGS and perceived access based on data collected from a social survey deployed from May 13 to June 15, 2020. We examine geographical accessibility to UGS and how this compares to perceived accessibility, or the ease which residents feel they can access a UGS. We further explored the correlation between spatial access to UGS and fifteen social vulnerability variables including economic status, household composition, minority status, and housing type for different zip codes. The results show that geographical proximity variables can predict a number of the perceived access variables, particularly those related to COVID-19 measures. Although lower-income communities were found to have higher spatial access to UGS, many of the same communities, including people living in crowded and multi-unit buildings, on average only have access to smaller green spaces, suggesting an uneven distribution of larger quality parks. This observation is further confirmed by survey results. These findings have implications for policies surrounding the distribution of UGS and whether equitable access is provided to NYC residents, with implications for similar patterns that may exist in other cities. • We compare geographical access to urban green spaces (UGS) with perceived access. • Geographical accessibility can predict perceived concerns regarding social distancing. • UGS environmental justice implications are related to perceptions of UGS qualities. • We overlap COVID-19 vulnerabilities with the availability of UGS at the zip code level. • Many working-class neighborhoods are highly impacted by COVID-19 and have smaller UGS. [ABSTRACT FROM AUTHOR]

As cities face increasing pressure from densification trends, green roofs represent a valuable source of ecosystem services for residents of compact metropolises where available green space is scarce. However, to date little research has been conducted regarding the holistic benefits of green roofs at a citywide scale, with local policymakers lacking practical guidance to inform expansion of green roofs coverage. The study addresses this issue by developing a spatial multi-criteria screening tool applied in Barcelona, Spain to determine: 1) where green roofs should be prioritized in Barcelona based on expert elicited demand for a wide range of ecosystem services and 2) what type of design of potential green roofs would optimize the ecosystem service provision. As inputs to the model, fifteen spatial indicators were selected as proxies for ecosystem service deficits and demands (thermal regulation, runoff control, habitat and pollination, food production, recreation, and social cohesion) along with five decision alternatives for green roof design (extensive, semi-intensive, intensive, naturalized, and allotment). These indicators and alternatives were analyzed probabilistically and spatially, then weighted according to feedback from local experts. Results of the assessment indicate that there is high demand across Barcelona for the ecosystem services that green roofs potentially might provide, particularly in dense residential neighborhoods and the industrial south. Experts identified habitat, pollination and thermal regulation as the most needed ES with runoff control and food production as the least demanded. Naturalized roofs generated the highest potential ecosystem service provision levels for 87.5% of rooftop area, apart from smaller areas of central Barcelona where intensive rooftops were identified as the preferable green roof design. Overall, the spatial model developed in this study offers a flexible screening based on spatial multi-criteria decision analysis that can be easily adjusted to guide municipal policy in other cities considering the effectiveness of green infrastructure as source of ecosystem services. Unlabelled Image • The article addresses the question of where to build green roofs most effectively with regard to citizen needs. • A spatial multi-criteria screening tool for the creation of green roofs is developed. • Ecosystem service deficits are spatially defined by combined social-ecological evaluation criteria. • Finally, the optimal green roof design for an effective ecosystem service provision is determined. [ABSTRACT FROM AUTHOR]

Understanding why some parks are used more regularly or intensely than others can inform ways in which urban parkland is developed and managed to meet the needs of a rapidly expanding urban population. Although geolocated social media (GSM) indicators have been used to examine park visitation rates, studies applying this approach are generally limited to flagship parks, national parks, or a small subset of urban parks. Here, we use geolocated Flickr and Twitter data to explore variation in use across New York City's 2143 diverse parks and model visitation based on spatially-explicit park characteristics and facilities, neighborhood-level accessibility features and neighborhood-level demographics. Findings indicate that social media activity in parks is positively correlated with proximity to public transportation and bike routes, as well as particular park characteristics such as water bodies, athletic facilities, and impervious surfaces, but negatively associated with green space and increased proportion of minority ethnicity and minority race in neighborhoods in which parks are located. Contrary to previous studies which describe park visitation as a form of nature-based recreation, our findings indicate that the kinds of green spaces present in many parks may not motivate visitation. From a social equity perspective, our findings may imply that parks in high-minority neighborhoods are not as accessible, do not accommodate as many visitors, and/or are of lower quality than those in low-minority neighborhoods. These implications are consistent with previous studies showing that minority populations disproportionately experience barriers to park access. In applying GSM data to questions of park access, we demonstrate a rapid, big data approach for providing information crucial for park management in a way that is less resource-intensive than field surveys. Highlights • We analyze geographic human visitation dynamics in all New York City parks using Twitter and Flickr data • Park visitation increases with proximity to public transportation, water bodies, athletic fields and impervious surfaces • Park visitation decreases with green space and proportion of minority ethnicity and minority race in neighborhoods of parks • Location-based social media data can be used as a rapid indicator for citywide park visitation to inform management [ABSTRACT FROM AUTHOR]

Defining landscape structure and key relationships between landscape structure and function is challenging in urban areas characterized by density and patchy spatial patterns. In order to trace the spatial and temporal patterns of urban landscape structures, compare patterns across cities, or inform urban design principles, we need to classify the landscape in a way that captures context and landscape heterogeneity, but can be broadly applied across different cities or landscape variations within a city. In this study, we introduce a simple and reproducible approach for classifying the structure of urban landscapes (STURLA) that utilizes heterogeneous, composite classes which represent combinations of built and natural features, and examine the response of a landscape function – surface temperature. This classification approach is unique in that it develops composite (as opposed to homogeneous) classes, which are defined a posteriori, based on compositions of adjacent structural elements that emerge in the urban landscape, using a cellular grid to define units of analysis. We test the separability of classes that emerge from this approach, and find that it is possible to discern classes – comprised of the mix of land and building covers common in urban areas – which have meaningfully distinct temperature signatures. This classification approach may be extended to multiple cities and ecological indicators in order to offer insight into the relationship between urban landscape structure and ecosystem response, in a way that accounts for interactions among different types of urban landscape surfaces. We suggest that this approach can support spatial prioritization of landscape function needs in urban development and design approaches for improving particular types of functioning, such as reductions in urban heat. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to classifying urban structure using land cover and building height. The goal of the study was to improve comparability of urban structure–function relationships across cities through development of a novel classification framework that can facilitate urban studies of ecological patterns and processes. We tested the suitability of the classification in two very different urban settings – continental Berlin and coastal New York City. Using Landsat temperature data as an ecological function variable, we compared how urban structures in both cities relate to temperature. Results show that in both cities a large range of urban structure classes show similar trends with respect to land surface temperature, despite differences in climate and structure of the two cities. We found that approximately 68% of the area of Berlin and 79% of the area of New York City can be represented with the same fifteen urban structure classes. Results indicate that these common classes share very similar temperature patterns and may indicate broader utility of the classification framework. Among the classes which have the most dissimilar temperature trends between the two cities, we find large differences in inner-class composition and neighboring classes. Findings also show that the presence of water has a strong influence on temperature regulation, as classes containing water have the lowest surface temperatures, indicating a need for prioritizing aquatic ecosystems in urban planning and management. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models. In this article, we introduce a novel multi-chain multi-objective MCMC (mc-MO-MCMC) CA model to simulate LUCC. Unlike the classical MCMC, the proposed mc-MO-MCMC is a multiple chains method that imports crossover operation from classical evolutionary optimization algorithms. In each new chain, after the initial one, the crossover operator generates the initial solution. The selection of solutions to be crossed over are made according to their fitness score. In this paper, we chose the example of New York City (USA) to apply our model to simulate three conflicting objectives of changes from non-urban to low-, medium- or high-density urban between 2001 and 2016 using USA National Land Cover Database (NLCD). Elevation, slope, Euclidean distance to highways and local roads, population volume and average household income are used as LUCC causative factors. Furthermore, to demonstrate the efficiency of our proposed model, we compare it with the multi-objective genetic algorithm (MO-GA) and standard single-chain multi-objective MCMC (sc-MO-MCMC). Our results demonstrate that mc-MO-MCMC produces accurate simulations of land use dynamics featured by faster convergence to the Pareto frontier comparing to MO-GA and sc-MO-MCMC. The proposed multi-objective cellular automata model should efficiently help to simulate a trade-off among multiple and, possibly, conflicting land use change dynamics at once. • This paper presents a multi-objective CA land use/cover change model. • The model uses a novel multi-objective Markov chain Monte Carlo (MO-MCMC). • We compare MO-MCMC with a multi-objective genetic algorithm (MO-GA). • We apply our model to simulate the expansion of multi-density urban areas in New York. • The results show that the MO-MCMC outperformed MO-GA in terms of accuracy and speed. [ABSTRACT FROM AUTHOR]

The urban heat island effect creates warmer and drier conditions in urban areas than in their surrounding rural areas. This effect is predicted to be exacerbated in the future, under a climate change scenario. One way to mitigate this effect is to use the urban green infrastructure as a way to promote the cooling island effect. In this study we aimed to model, with a high spatial resolution, how Mediterranean urban parks can be maximized to be used as cooling islands, by answering the following questions: i) which factors influence the cooling effect and when?; ii) what type of green spaces contributes the most to the cooling effect?; iii) what is the cooling distance of influence? To answer these questions we established a sampling design where temperature and relative humidity were measured in different seasons, in locations with contrasting characteristics of green and grey cover. We were able to model the effect of green and grey spaces in the cooling island effect and build high spatial resolution predicting maps for temperature and relative humidity. Our study showed that even green spaces with reduced areas can regulate microclimate, alleviating temperature by 1–3 °C and increasing moisture by 2–8%, on average. Green spaces with a higher density of trees were more efficient in delivering the cooling effect. The morphology, aspect and level of exposure of grey surfaces to the solar radiation were also important features included in the models. Green spaces influenced temperature and relative humidity up to 60 m away from the parks' limits, whereas grey areas influenced in a much lesser range, from 5 m up to 10 m. These models can now be used by citizens and stakeholders for green spaces management and human well-being impact assessment. Unlabelled Image • Urban planning needs high spatial resolution information to mitigate the urban heat island effect. • Land-cover type (green and grey spaces) and urban morphology strongly influence the cooling island effect. • Parks with high density of trees reduce temperature (1-3° C) and increase relative humidity (2-8%) mostly during summer. • Tree canopy area influences temperature and relative humidity as far as 60 m. [ABSTRACT FROM AUTHOR]