Collective decision making is often a customary action taken in government crowdsourcing. Through ensemble of opinions (popularly known as judgment analysis), governments can satisfy majority of the people who provided opinions. This has various real-world applications like urban planning or participatory budgeting that require setting up {\em facilities} based on the opinions of citizens. Recently, there is an emerging interest in performing judgment analysis on opinions that are constrained. We consider a new dimension of this problem that accommodate background constraints in the problem of judgment analysis, which ensures the collection of more responsible opinions. The background constraints refer to the restrictions (with respect to the existing infrastructure) to be taken care of while performing the consensus of opinions. In this paper, we address the said kind of problems with efficient unsupervised approaches of learning suitably modified to cater to the constraints of urban planning. We demonstrate the effectiveness of this approach in various scenarios where the opinions are taken for setting up ATM counters and sewage lines. Our main contributions encompass a novel approach of collecting data for smart city planning (in the presence of constraints), development of methods for opinion aggregation in various formats. As a whole, we present a new dimension of judgment analysis by adding background constraints to the problem.