The Use of Big Data in Regenerative Planning
- Can Big Data, when used considering an eco-systemic approach, help in shaping policies and support the development of cities?
- What is the potential of Big Data-based tools in supporting and assessing the regenerative design of urban spaces?
- Under what conditions can Big Data be integrated into regenerative design and sustainable planning?
2. Background—Regenerative Sustainability, Its Assessment, and the Opportunity to Use Big Data
- self-sufficiency in most aspects, leading to a judicious use of the local resources within the context of community lives;
- the wide introduction of conservation technologies in areas of water, energy, and appropriate building materials (positive energy and material flows);
- respecting natural conditions, avoiding loss of the natural landscape and most importantly, of all un-checked consumption, including fighting the urban sprawl (sustainable land use);
- promoting the mixed-land use and multi-level use in urban areas to reduce the energy consumption of the transportation sector while at the same time;
- adding to vibrant urban life, which is a base for community development and an important aspect in building social cohesion;
3. Methodological Approach
- The first phase of the study is based on gap analysis, allowing the identification of opportunities and barriers that could possibly prevent or foster the use of Big Data in relation to the emerging regenerative sustainability paradigm. While exploring features of regenerative settlements (described in Section 2), the authors identify which frameworks can be considered as the most useful for such an approach in designing and assessing planning processes. The paper specifically connects the above mentioned frameworks to the possibilities emerging from the use of Big Data.
- Further, a review of current processes where Big Data-based tools are used in the projects aiming to improve urban sustainability has been performed. As a result, based on the typology by Thakuriah et al. , a classification of data sources potentially supporting regenerative planning has been introduced (Section 6).
- To answer the questions outlined in the introduction, a comparative analysis of the mechanisms supporting regenerative planning using Big Data was conducted. As a result, the crucial factors determining the usability of those tools for the assessment of projects which support regenerative planning were identified in order to create a base framework for the model of analysis.
- The next part of the research is based on case studies analysis. The criteria for selecting projects were identified according to the approach introduced by Seawright and Gerring , who indicated seven methods of cross-case selection and analysis allowing the definition of: typical, diverse, extreme, deviant, influential, most similar, and most different cases. For this research, the first two—typicality and diversity of studied projects—were identified as the crucial factors for allowing researchers to get a full picture of how the planning of regenerative human settlements can be supported by Big Data-based tools.
- As a next step of the research, the following criteria for the analysis of case studies were adopted:
- (1) Scale—Depending on what kind of action is needed, the area defined for intervention can vary; therefore, selected case studies are supposed to give the possibility of implementation on various scales.
- (2) Type of data—For each planning process, individual studies and solutions are designed; therefore, each project requires a different type of data from different sources and owners.
- (3) Country development phase—Each place has a different specificity and capacity, so the case studies present projects from different continents and countries: still developing, in transition, and developed.
- (4) Phase of planning—The circular approach requires that not only tools for planning and implementation, but also for evaluation and finally for improving existing solutions, are analyzed.
- (5) Thematic area—Most importantly, based on the holistic approach, all aspects of city planning should be covered; therefore, the case studies described below address all the goals of the selected framework (introduced in Section 4).
- Using Urban informatics term as a key word in the conducted literature study, the authors, taking into consideration the above mentioned criteria, studied over 50 examples of projects applying Big Data tools. The search was conducted within projects and publications of or related to the most known data centres, such as the UCL Centre for Advanced Spatial Analysis, MIT Media Lab, MIT Senseable City Lab, Future Cities Laboratory, and Urban Big Data Centre.
- To further the aim of recognizing the potential usability of Big Data in supporting regenerative planning projects, the conceptual framework for a model was introduced and tested as the main result of the research. The most suitable Big Data-based projects allowing for analysis aimed at enhancing design sustainability were mapped upon these aspects according to the developed model.
- Finally, the test run for a model’s usability allowed us to draw conclusions on how projects using Big Data-based tools support a regenerative approach in the present, as well as in which areas their role could be enhanced.
4. Evolution of Frameworks for Assessing Sustainability
- (1) active, inclusive, and safe—fair, tolerant, and cohesive with a strong local culture and other shared community activities;
- (2) well-run—with effective and inclusive participation, representation, and leadership;
- (3) well-connected—with good transport services and communication linking people to jobs, schools, and health and other services;
- (4) well-served—with public, private, community, and voluntary services that are appropriate to people’s needs and accessible to all;
- (5) environmentally sensitive—providing places for people to live that are considerate of the environment;
- (6) equity—fair for everyone;
- (7) thriving—with a flourishing, diverse, and innovative local economy;
- (8) well-designed and -built—featuring quality built and natural environment.
5. Review of Current Processes, Tools, and Mechanism of Using Big Data in the Built Environment
6. Classification of Data Sources Supporting Regenerative Planning
- Sensor systems gathered data (infrastructure-based or moving object sensors)—most often including information on environmental issues, as well as blue and green infrastructure, provided by the public sector and completed by researchers.
- User-Generated Content (“social” or “human” sensors)—information generated by online activity, including social media, but also when using GPS systems. Databases are organized by private business; however, quite often with open access, since collected data is provided by Internet users.
- Administrative (governmental) data, both open and confidential micro-data—accessible in Europe due to the implementation of the INSPIRE directive, managed by public institutions, consisting of information such as: (bank) transactions, taxes etc.; but also confidential (on the level of the individual micro-data), concerning, e.g., employment and health. The important aspect of such data is spatial information infrastructure supporting most urban analyses.
- Private Sector Data (customer and transactions records)—with limited access provided by the private business, e.g., financial institutions, including information on business records and customer profile.
- Hybrid data (linked and synthetic data)—linked with Big Data although completed with surveys or census-administrative records conducted by, e.g., planning institutions, as well as governmental organizations.
7. Model of Analysis to Implement Big Data Use in the Sustainable Design and Planning Process
Conflicts of Interest
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Kamrowska-Zaluska, D.; Obracht-Prondzyńska, H. The Use of Big Data in Regenerative Planning. Sustainability 2018, 10, 3668. https://doi.org/10.3390/su10103668
Kamrowska-Zaluska D, Obracht-Prondzyńska H. The Use of Big Data in Regenerative Planning. Sustainability. 2018; 10(10):3668. https://doi.org/10.3390/su10103668Chicago/Turabian Style
Kamrowska-Zaluska, Dorota, and Hanna Obracht-Prondzyńska. 2018. "The Use of Big Data in Regenerative Planning" Sustainability 10, no. 10: 3668. https://doi.org/10.3390/su10103668