Urban Science: Putting the “Smart” in Smart Cities

Abstract

Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize.

The influence of smart technologies: A comparative study on developing Large to small scale smart cities and smart campuses.

“…A Smart city engages efficient urban services through be Many examples are found of cities one of the earliest smart city proje grew public due to their benefits at t Anyang's city in Korea stated its fi examples are Stockholm city that h Janeiro city that adopted smart tec 2009, Chicago city that is considere working on smart city projects sinc community, Barcelona's famous sm city projects in 2011 and by 2014 i projects in India where about 100 c taking serious action as Nagpur ca Although there is no strict agreed-u smart cities only state the aims or m "… a municipality that uses info efficiency, share information with t citizen welfare." [9].…”

Organizing Theories for Disasters into a Complex Adaptive System Framework

“…In experimentation with complex adaptive systems, computer simulations provide the necessary tools to magnify small differences, making them observable (e.g., [240,241]). They also enable collaborative processes that develop shared-risk models for community stakeholders and policy-makers (e.g., [4,[242][243][244][245]), transforming data into information and then knowledge. Analysis of complex adaptive systems in disaster requires a different approach and new methodological tools that can manage large, heterogeneous datasets; identify and calculate power laws; run the high sample sizes of events in simulations needed to generate a range of expected outcomes; simulate events and implement theories that cannot be tested with available field data; identify and explore dynamics with multi-level dependencies; and apply machine learning and other computational techniques for latent variable analysis.…”