While Big Data is now routinely used in some industries, its value has yet to be exploited in regenerative planning matters. While various cities are adopting smart and innovative solutions to strengthen the sustainability and resilience of urban fabric [5], they often lack a more holistic approach. In many cases, they are based on sectoral initiatives, for example, isolated transport solutions such as smart intersections, instead of introducing integrated frameworks leading to more sustainable mobility patterns.
Moreover, a strongly technocratic narrative still dominates, which is evidenced by initiatives often being limited to enhancing the efficiency of the distribution systems and developing the market for smart appliances [6] instead of providing a change in response to the needs of the users.
Knowledge of Big Data is still spread and unstructured, and therefore, its usability in supporting both planning processes and decisions is not yet fully recognised. So far, many Big-Data- based urban solutions have been tested [11]. However, access to numerous databases that could be used in supportive tools for urban development simulations is still limited, so their use in shaping sustainable, regenerative solutions is not fully embraced.
To provide a model to assess numerous Big-Data-based urban projects to facilitate novel solutions, both the process and criteria for case study selection were designed based on previous research [3]. The objective is to identify tools using large datasets, which – by combining existing solutions – can help to achieve a more holistic approach and facilitate the regenerative design.
The main criteria for case study selection were the presence of key elements defining the regenerative approach. However, additional aspects such as (a) the scale of implementation, (b) planning phases and (c) project scope were considered to analyse the diversity of existing solutions. For each case, data sources were studied and classified using the Thakuriah (et al.) [14] typology (sensor systems, user-generated content, administrative, the private sector and hybrid data). It was proven that any type of such data could support regenerative planning and design processes.
BIG DATA IN REGENERATIVE URBAN
Selected case studies are presented in the matrix (Figure 20), where examples corresponding with the aims of sustainable development are presented in rows, while the type of data used in analysed projects is presented in columns. An additional (last) column indicates the scale of implementation. This matrix shows the diverse possibilities of using Big Data sources.
BIG-DATA-BASED SOLUTIONS SUPPORTING A REGENERATIVE DESIGN APPROACH
As numerous research points out [13, 5], there are opportunities to enhance regenerative planning by using assessment tools combining clear cases of regenerative implementation with data mining analysis or surveys. The use of user-generated content provides an opportunity for access to opinions of the city dwellers.
Therefore, tools based on such data and methods can often be considered an essential source of information for introducing supportive urban solutions. In particular, they are useful for:
- Shaping policies and strategies aimed at improving safety or health conditions – e.g. Street Score [17] where the users’
perception of urban space is measured based on photos posted online,
- Strengthening planning participation by introducing tools for collecting opinions posted online, e.g. project of M-Participation by Erito [18],
- Providing well-organised, efficient transportation systems, e.g. MobiliCities [19],
- Evaluating city connectivity and accessibility to public services, e.g. analysis of the Dakar Metropolitan Area by Fetzer and Sy [20],
- Recognising environmental sensitivity by using sensor systems to measure changes of local microclimate when implementing new projects for public spaces, e.g. studies by Huang et al. [21],
- Supporting decision-making processes to build social equity in the living space within urbanised areas, e.g. Data-Pop [22]
project,
- Measuring the digital economy, e.g. project tested in Latin America [23] by linking business (private sector) data with statistical information,
- Introducing tools for improving the energy efficiency of the built environment, e.g. Google Project SunRoof [24].
synthetic) data sources
1. social & cultural active, inclusive & safe
predictive policing preventing crime with data and analytics
scale of implementation
neighborhood/city
neighborhood/city
city/region
neighborhood
neighborhood
city
neighborhood m-participation the emergence of
participatory planning applications 2. governance well run
mobilicities 3. transportation &
connectivity well connected
the dakar diamniadio toll highway &
increase of human mobility 4. services well served
6. equity fair for everyone data-pop big data and people
selected projects
gps spatial
information personal
microdata social
media user-generated
content administrative
(governmental data) private sector data
hybrid data (linked and sensor
systems
5. environment environmentally sensitive microclimate, outdoor thermal comfort the human experiment
digital economy in latin america and the caribbean
7. economy thriving
solar potential of your community, U.S.
project by google
8. housing & built environment well designed & built
neighborhood/city Figure 20
Research summary - case studies, data sources and the scale of implementation. Source: Authors’
elaboration [3]. The figure presents a matrix for sectoral use of Big Data sources. Based on selected case studies, it shows in which field such data can be supportive and on what scale it can be considered for achieving reliable results. The figure proves the lack of a holistic approach, which is necessary to help to implement regenerative oriented solutions.
The list above shows only chosen cases. The present study together with the author’s previous research [3] confirms the findings of Zanella [1], stressing that there are already existing systems based on Big Data capable of capturing and monitoring human- ecosystem relations in urban space such as the occupancy-based model for efficient reduction of HVAC energy, created by Erickson, Carreira-Perpiñán & Cerpa [23]. At the same time, the range of the analysed project is, in the majority of cases, sectoral; i.e. connected with participation, transport or energy efficiency. No holistic projects using Big-Data-based tools to integrate different systems of the city were found among the analysed cases. As Big Data describes the phenomenon continuously rather than at a given point in time, it can be considered an appropriate tool to assess a circular approach (based on reuse of space and monitoring urban flows), instead of states. The analysed case studies focus not only on the mitigation of negative phenomena but also on the creation of positive balance - a feature of the regenerative approach.
CONCLUSIONS ON THE INTEGRATION OF BIG DATA WITHIN THE REGENERATIVE DESIGN
Introducing examples of projects where Big Data is used as a supportive tool shows that sustainable development processes can be enhanced in all phases: planning (recognising needs), designing (programming solutions) and evaluation (improving the design). However, only when integrating data sources and different sectoral Big-Data-based urban interventions as described in the collection of the case studies can planners introduce a holistic approach that allows the support of regenerative development of human settlements. At the same time, the research shows that, unfortunately, not many integrated urban solutions have yet been implemented or tested. There is a strong need to change the sectoral approach and adopt more holistic solutions that utilise Big Data.
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One of the most important developments in the area of Architecture, Engineering, and Construction (AEC) will be the way designers relate to climate change and the notion of climate adaptation as a primary response to the environmental pressures from climate change. Before we move into solutions, there is a need to understand and critically reflect on what adaptation, within the realm of design, really means and how it is apparent in nature, which is a constant inspiration for environmental design innovation.
A critical reflection always begins with an attempt to clarify and define important aspects of a problem. There are currently three major misconceptions in the AEC industry, nested within three important processes of design: Parametric Design Optimisation, Ecological Design and Evolution, Climate Adaptation.
PARAMETRIC DESIGN OPTIMISATION, DESIGNING WITH PARTIAL INFORMATION AS THE NORM
Parametric Design (PD) is defined as ‘a process based on algorithmic thinking that enables the expression of parameters and rules that, together, define, encode and clarify the relationships between design intent and design response’ [1]. The word parameter is at the core of parametric design, but the ‘relationship between design intent and design response’ is as crucial. It is this relationship that parametric design is supposed to uncover to inform the design and allow it to be optimised iteratively and in a quantitative manner.
Unfortunately, in practice, this relationship is rarely adequately explored. The misconception here is that we think this partial understanding is enough to properly inform the design process in a ‘better less than nothing’ approach. Practitioners claim to have ‘parametrically optimised’ their designs even if a handful of scenarios, a fraction of the possible design space, are assessed.
The industry is being short-sighted here in a very literal sense;
while it can ‘see’ the most minute of design spaces, it is unable to explore any significantly sized design space, at least under most performance-based evaluations.