Construction projects have unavoidable impacts on surrounding environment, such as public safety, emergency response, noise, and dust. These impacts are especially significant when construction projects are located in urban settings with tight space and high population constraints. Current means to evaluate construction impacts on the quality of life in urban settings are ineffective, because the current methods are reactive (in response to complaints reported to related city agencies) instead of being proactive. The broader availability of public city data from metropolitans such as New York City provides opportunities for practitioners, researchers, and civic organizations to extract knowledge from historical data and take proactive actions. Current efforts on analyzing data using open data platforms are mainly focused on tackling travel delays, predicting climate for hazard mitigation, and understanding the energy usage profiles. Continuous construction and rehabilitation in urban settings are part of the city life, yet with limited tools to understand their impacts. This paper investigates the impact of construction projects on urban quality of life by analyzing open city datasets through a method containing data selection, analysis and prediction model creation. The analysis uses road reconstruction projects as testbeds, complaints due to construction as measurement and leverages a machine-learning technique, hidden Markov models (HMM), to predict the future impact of similar construction projects.