The Uses of Big Data in Cities

Area of Science:

• Urban Studies
• Computational Social Science
• Data Science

Background:

• Cities generate vast amounts of data, creating opportunities for improved management and planning.
• Traditional urban planning faces challenges due to the complexity and scale of modern cities.

Purpose of the Study:

• To explore the utility of big data in urban planning by framing it as a computational problem.
• To investigate how data-driven urban policy can address persistent urban issues.
• To reconcile the need for urban planning with its computational intractability in large cities.

Main Methods:

• Formalizing urban planning as a computational problem.
• Analyzing the application of big data and urban policy through engineering principles.
• Conceptualizing cities as self-organizing social networks.

Main Results:

• Big data, combined with urban policy, can offer novel solutions to urban problems.
• Comprehensive urban planning is computationally intractable for large cities, irrespective of data volume.
• Cities function as self-organizing networks where big data's primary role is to enhance information flow and coordination.

Conclusions:

• Big data facilitates learning and coordination among individuals in self-organizing urban networks.
• Urban self-organization and service improvement require general principles for sustainable city evolution.
• Data and information technologies are crucial for effective urban policy, fostering development and innovation.