Leveraging Data Driven Approaches to Quantify the Impact of Construction Projects on Urban Quality of Life
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According to the World Bank, more than half of the world's population now lives in cities, creating burdens on the degraded city infrastructures and driving up the demand for new ones. Construction sites are abundant in already dense cities and have unavoidable impacts on surrounding environments and residents. However, such impacts were rarely quantified and made available to construction teams and local agencies to inform their planning decisions. A challenge in achieving this was the lack of availability of data that can provide insights about how urban residents respond to changes in their environment due to construction projects. Wider availability of data from city agencies nowadays provides opportunities for having such analysis possible. This paper provides the details of a generic data-driven approach that enables the analysis of impact of construction projects on quality of life in urban settings through the quantification of change on widely accepted quality of life indicators in cities. This paper also evaluated the approach using data from publicly construction projects' information and open city data portals from New York City. Historical 311 Service Requests along with 27 road reconstruction projects were used as testbeds. The results showed that 61 the projects analyzed in this testbed experienced higher 311 requests after the commencement of construction, with main complaints of 'noise', 'air quality', and 'sewer' at the beginning of construction, and 'sanitation' and 'waste' towards the end. Prediction models, built using regression machine learning algorithms, achieved an R-Squared value of 0.67. The approach is capable of providing insights for government agencies and construction companies to take proactive actions based on expected complaint types through different phases of construction.
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