  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% of
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.
