A building permit system for smart cities: A cloud-based framework

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 turnaround 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.

BiGeo: A Foundational PaaS Framework for Efficient Storage, Visualization, Management, Analysis, Service, and Migration of Geospatial Big Data—A Case Study of Sichuan Province, China

“…We need to further observe the stability and efficiency of BiGeo after it has been scaled up, allowing it to be verified under more scenarios; (2) BiGeo currently focuses more on the technology implementation used in data management. The next step is to organize the logical organization of the data such that the data organization model can fully utilize the underlying capabilities of the BiGeo framework; (3) The back-end capabilities of BiGeo services lack a front-end performance and need to be further integrated with various front-end technologies [79] to enhance its overall application; (4) The tool side should allow new tools to be added according to the ETL data requirements [80,81]. Finally, (5) the frameworks and components of the call interface should be further optimized based on feedback from application system developers.…”

A time-frequency analysis of the housing construction time as the basis for making decisions on the construction market (the case study of Poland)

“…Based on the findings identified in urban economics and public finance literature, it is possible to state that local politicians exert an influence on creating a number of building permits, which then translates onto balanced development of the real estate market and optimizing migration processes (De Witte, Geys, and Schönhage, 2018). Other studies revealed that the significance of building permits as well as the complicated legal situation of obtaining them resulted in developing a cloud-based framework for activities supporting inhabitants as well as clerks in the process of issuing building permits (Eirinaki, Dhar, Mathur, Kaley, Patel, Joshi, and Shah, 2018). Another important study determined the relation between land price and administrative processes in the domain of the real estate market (such as decisions about the number of granted building permits) (Gluszak and Zygmunt, 2018), while (van Vuuren, Kjellander, and Nilsson, 2019) discuss another important issue, i.e.…”

Smart city big data analytics: An advanced review

“…According to literature review, ICT is an essential component in smart city governance to gain necessary intelligence that provides basis for evidence‐based decision‐making. At the core of ICT based solutions are three main components: (i) data collection through sensors, Internet‐of‐Things, smart phones, remote sensing (e.g., satellite or in‐situ) and city databases; (ii) data processing and/or pre‐processing (e.g., filtering, data quality and format translations); (iii) data analysis using machine learning, data mining and other statistical algorithms to generate new knowledge in various cross‐thematic applications such as mobility (Docherty, Marsden, & Anable, ; Peters‐Anders et al, ; Rathore et al,), energy (Antonić, Marjanović, Pripužić, & Žarko, ; Carli, Albino, Dotoli, Mummolo, & Savino, ; Silva, Khan, & Han, ), health (Anisetti et al, ; Farahani et al, ), environment (Antonić et al, ), public services (Pérez‐González & Díaz‐Díaz, ; Zhang et al, ), economy (Chatfield & Reddick, ; Saggi & Jain, ; Zaman et al, ), waste management (Digiesi, Facchini, Mossa, Mummolo, & Verriello, ), social analysis (Kousiouris et al, ; Terroso‐Saenz, Gonzalez‐Vidal, Cuenca‐Jara, & Skarmeta, ), waste water management (Edmondson et al, ), urban planning (Eirinaki et al, ; Pettit et al, ; Rathore, Ahmad, Paul, & Rho, ), tourism and cultural heritage (Sun, Song, Jara, & Bie, ), buildings (Linder, Vionnet, Bacher, & Hennebert, ), agriculture (Kamilaris, Gao, Prenafeta‐Boldu, & Ali, ), emergency response (Abu‐Elkheir, Hassanein, & Oteafy, ), etc. The above three components—with some variations—are common among the most of smart city data analytics literature for example, Zhang et al (); Khan et al (); Rathore et al ().…”

“…For good city governance, monitoring and real‐time decision making can play an important role as highlighted by Malik et al () where raw data is transformed in JSON and RDF for semantic inferencing and querying. For instance, Eirinaki et al () use a multi‐tier architecture (Angular JS, NodeJS, Apache Mahout, MySQL with interfaces with Socrata Open Data Platform) in Amazon EC2 cloud to handle online building permit applications. This not only facilitates citizens to easily communicate with complex planning permission processes but it also performs cross‐departmental data collection and data analytics to enable city planners get the likelihood of building constructions in specific parts of a city.…”