Located at the complex intersection of economic development and environmental change, cities play a central role in our efforts to move towards sustainability. Reducing air and water pollution, improving energy efficiency while securing energy supply, and minimizing vulnerabilities to disruptions and disturbances are interconnected and pose a formidable challenge, with their dynamic interactions changing in highly complex and unpredictable manners.
Data collection, analysis, and integration, therefore, is critical in enabling informed and robust decision making for urban sustainability. In addressing the eleventh goal of the Sustainable Development Goals (SDGs), which aims at making cities inclusive, safe, resilient and sustainable, the Global Pulse program has been initiated by the United Nations to explore opportunities and challenges in utilizing big data and analytics. The Data Integration and Analysis System (DIAS) maintains a vast amount of data dealing with diverse issues concerning urban sustainability, including climate/weather, air, water, energy, building, land use, disaster risk management, agriculture, biodiversity, health, and economy. The Beijing City Lab demonstrates the usefulness of open urban data in mapping urbanization with a fine spatiotemporal scale and reflecting social and environmental dimensions of urbanization through visualization at multiple scales.
The basic principle of open data will generate significant opportunities for promoting inter-disciplinary and inter-organizational research, producing new data sets through the integration of different sources, avoiding duplication of research, facilitating the verification of previous results, and encouraging citizen scientists and crowdsourcing approaches. Open data also is expected to help governments promote transparency, citizen participation, and access to information in policy-making processes.
Despite a significant potential, however, there still remain numerous challenges in facilitating innovation for urban sustainability through open data. The scope and amount of data collected and shared are still limited, and the quality control, error monitoring, and cleaning of open data is also indispensable in securing the reliability of the analysis. Also, the organizational and legal frameworks of data sharing platforms are often not well-defined or established, and it is critical to address the interoperability between various data standards, balance between open and proprietary data, and normative and legal issues such as the data ownership, personal privacy, confidentiality, law enforcement, and the maintenance of public safety and national security.
Institutional conditions and frameworks influence to what extent the benefits arising from open data can be realized and secured. Distinct regimes can be identified with regard to the characteristics of data in fields concerning smart cities, ranging from energy, air, and water to health, building, and transportation. A schematic typology of the data regimes would help assess and design appropriate public policies and instruments, including formal legislation and administrative regulations in the public sector as well as waivers, common-use licenses, and contract in the private sector.
To benefit from advanced data systems for making better decisions, we need to foster collaboration, create safe spaces for experimentation, develop iterative methodologies, and engage evaluation communities. Capacity building is also important to nurture the skills and expertise that would be necessary for making the best use of the data-intensive approaches to creating innovation for sustainable smart cities.
These findings are described in the article entitled Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities, published in the journal Sustainability Science. This work was led by Masaru Yarime from the City University of Hong Kong.
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