A building permit system for smart cities: A cloud-based framework. (July 2018)

• Record Type: Journal Article
• Authors: Eirinaki, Magdalini; Dhar, Subhankar; Mathur, Shishir; Kaley, Adwait; Patel, Arpit; Joshi, Akshar; Shah, Dhvani
• Is Part Of: Computers, environment and urban systems. Volume 70(2018)
• Page Start: 175
• Page End: 188
• Publication Date: 2018-07
• Subjects: Building permit -- Data analytics -- Data mining -- Recommender system -- Urban planning -- Cloud computing -- Smart city; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2018.03.006
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on their location, time, and type of request. The novelty of the proposed framework lies in the integration of a pre-permit processing front-end with permit processing and data analytics & mining modules, along with utilization of techniques for extracting knowledge from the data generated through the use of the system. The proposed framework is completely cloud-based, such that any city can deploy it with lower initial as well as maintenance costs. We also present a proof-of-concept use case, using real permit data from New York City. Highlights: A novel, cloud-based online permit system framework is proposed. Data visualizations allow city officials to analyze the data and identify trends. The interactive pre-permitting process is supported by a rule-based decision tree that guides users. Collaborative filtering used to generate permit recommendations to end users. A proof-of-concept using NYC open permit data set is presented.