Informed Urban Environments

The chapter sketches out a route for developing informed urban environments as an effective approach towards addressing complex sustainability problems especially for urban areas. The main thesis is that this can be initiated by linking the fields of sustainability science, urban science, and architectural science, based on shared inter- and transdisciplinary, as well as data-integrated approach with the aim to arrive at a framework that is both instrumental and problem-oriented. To commence this effort focus is placed on reviewing the main features, discourses and data-related approaches in sustainability science, urban science, and architectural science with the aim to identify potential connecting points and overlaps between these fields.

Data has an immense role in the design and planning of urban environments. Data, however, is not neutral, nor is it without a domain. With increasing frequency, designers are awakening to the datasets and databases of other fields such as urban ecology, environmental management, and public health. Designers are presented with a vast opportunity that momentarily obscures the role of practice-specific databases for a much larger prize—the agency of making data-mosaics. A data-mosaic is a multi-domain, unstructured data complex. Its contributors might originate from multiple professions with different aims and ambitions. Designers who work to make data-mosaics see the gaps in their collective understanding of urban environments as quickly as they see the opportunities to participate in transdisciplinary collaborations. This chapter of Informed Urban Environments incorporates a practice-based perspective on the opportunities, challenges, limitations, and applications of data and data-driven design. KieranTimberlake, a Philadelphia-based practice, discusses modeling practices associated with vegetation, life cycle assessment, and occupant comfort to demonstrate the profession's increasing awareness of mosaic-making.

Over the past decades the understanding of urban environment has undergone profound changes. This has been accelerated by the advent of information science and big data, and the ever-increasing quantity of data produced by smart devices located in urban areas and remote-sensing. This development has been accompanied by advanced information and communication technologies designed to take advantage of this data deluge. Computational ontologies have been proposed to turn this data into knowledge that can be exploited for a variety of tasks ranging from information retrieval to decision support. In this chapter, we review how recent approaches for information modelling in urban environments implement computational ontologies. This survey highlights the problems these approaches intend to solve, as well as their limitations. Based on this analysis, a roadmap of future research is drawn that needs to meet the environmental and ecological challenges raised by urbanization.

This chapter addresses and reframes urban adaptation through frontier coevolutionary information physics and complex system dynamic approaches, with an interdisciplinary perspective seamlessly articulating frontier natural, social and technical sciences into a coherent manner through a novel mathematical lingua franca articulating manifold disciplines. In doing so, insights on urban adaptation depart from rethinking to reframing adaptation into full-fledged system dynamic coevolution, thereby reshaping concepts, strengthening procedures, empowering choices to turn insights into actions, to bestow into urban adaptation a novel mathematically robust interdisciplinary framework able to brave the emerging challenges facing the urban socio-environmental dynamics along with its interdependencies across manifold scales and domains ultimately linking to the overall coevolutionary Earth System Dynamics. In methodological terms, a novel robust mathematical edifice is thus provided to both data based and process based system dynamic understanding, design, analytics and decision support. In this sense, the methodological foundations are set to empower a robust synergistic coalition among cutting-edge information physics, nonlinear data analytics and model design, along with expert knowledge of those on the field, leveraging and complementing their voices, insights and operations with innovative robust mathematical, physical, numerical, computational, novel tools and data analytics to drive the design, decision and operation processes in multissectorial urban adaptation terms.

Cities are composed of a multitude of interconnected interactive layers and systems. The contemporary urban discourse has seen the utilization of Open data in decoding and understanding complex urban patterns that have eluded researchers for decades. Different layers of raw data from historical city cores up to the atmospheric climate have become more accessible, opening new horizons for multidisciplinary research. The rising complexity of cities calls for emerging approaches that can address the relationship between different layers of data—existing or emerging. In this regard, the current chapter is introducing and applying a methodology to use historical, spatial, and temporal datasets from Open Street Map (OSM) processed by Space Syntax superimposed on simulated urban microclimate dataset to find correlating patterns on how urban morphology has shaped the cities and the microenvironments over time. The outcomes for the case of Munich, illustrate the typologies that can be utilized in planning and developing design strategies to address micro-climate and accessibility in cities.

Autonomous vehicles are anticipated to be a widespread and well-adopted aspect of urban infrastructure by the end of the decade. With research and development scaling up over the past few years, studies on autonomous technology’s built environment impacts are still in their infancy—but we can look to the history of urban mobility to inform our understanding of its future trajectory. In this chapter, we add to the discussion of how mobility has and continues to shape infrastructure in cities, presenting research projects from MIT’s Senseable City Lab along with their accompanying historical contexts. First, offering a brief overview of the urban planning histories of both Amsterdam and New Amsterdam, or present-day New York City, we then examine how software and hardware innovations in the AV industry could transform citizens’ movement through urban areas and interactions with physical infrastructure. Looking to Amsterdam’s famous canals, we investigate how the development of autonomous vehicles serves as an example of programmable infrastructure that responds in real-time to human behaviour. Following this, we propose how implementing autonomous ridesharing systems in cities could provide opportunities for repurposing present-day automobile infrastructure, i.e., parking. These case studies shed light on the possibilities AVs present for expanding infrastructure’s capabilities as dynamic, responsive conduits of city residents and resources, which raise questions about how we define infrastructure versus transit and whether such a distinction will exist in future urban mobility.

Attractive urban space is fundamental for creating safe and healthy cities. In the context of the climate crisis, microclimate becomes a determining factor for the use of public space in pursuing a just and equal society. Shifting to non-motorized modes of individual transport has manifold effects on the quality of urban environments in terms of safety, health, and spatial justice. Considering the need for quantifying microclimatic conditions in urban space, this chapter presents a methodology applied to a case study in the Boston Back Bay Area that develops a factor to indicate spatiotemporal outdoor comfort availability. The factor is based on a simulation workflow that generates datasets and maps, to be employed to quantify outdoor comfort availability at the pedestrian level with a high spatiotemporal resolution in adaptive spatial domains. The maps can be employed to compare different scenarios and neighbourhoods, and can serve as a base to put into evidence the influence of comfort and to formulate indications to increase outdoor thermal comfort in urban ecosystems. For promoting a tangible improvement of the city, pleasant environmental conditions are fundamental to accommodate pedestrian flows and to facilitate the implementation of social justice and public health.

Growing urban areas are facing significant challenges in terms of ecosystem health, human wellbeing, and environmental quality. Ecosystems are composed of biotic and abiotic components and their interactions. Healthy urban ecosystems can provide numerous so-called ecosystem services, which are benefits provided by nature to society and the economy. They are classified into provisioning services, regulating services, cultural services, and support services. As demonstrated by several studies and stated by the European Commission, nature-based solutions, depending on urban areas’ characteristics, can enhance the provision of relevant ecosystem services in cities. Therefore, nature-based solutions can be planned, designed, and integrated as a means to improve urban ecosystem health. However, a systematic approach to optimizing ecosystem service provision is frequently overlooked. Data collection and modeling allow for the evaluation of biotic and abiotic interactions, as well as the effects of anthropogenic activities and modifications. In this field, the chapter mainly focuses on the role of data in an integrated design process, aimed at the optimization of the performance of nature-based solutions to improve urban ecosystem health.

The urban forest, i.e. the stock of urban trees, is a major component of urban green spaces. It can make significant contributions to urban sustainability and climate change adaptation. Urban forest governance and management play a key role in the extent to which these contributions are realized for good. This chapter presents a selection of promising new technologies in support of urban forestry. Techniques and applications are introduced in the domains of remote sensing, modeling and citizen science. These technology-driven developments offer new potentials for ‘smart’ urban forestry but may also create new risks of a shift towards techno-managerialism as opposed to more open and democratic processes.

21st century agricultural production faces major challenges including climate change, environmental degradation, land use change, and population growth leading to food shortage. Addressing these challenges can benefit substantially from inclusive approaches that consider both rural and urban agriculture. As the exchange of insights and approaches in food production between rural and urban contexts intensifies it is useful to map related development in both contexts, with the aim to highlight present overlaps, exchanges, and research foci, as well as to identify research gaps in developing novel solutions to the existing challenges. In this context this chapter focuses on charting the increasing role of Big Data, data-related methods and decision support in seeking to face these challenges.