A Systematic Review for Smart City Data Analytics

Abstract

Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to “connect the pieces” among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the “D Taxonomy” for the data production, the “M Taxonomy” for data analytics methods, and the “S Taxonomy” for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing.

Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case

“…However, there is a trend on an increasing use of the composite index methodology-first detailed and explained by the European Commission (EC) in 2002 [20]-for There have been some trials to establish a formal classification of top-layer services for smart cities (we have clarified that smart port-cities are a subset of the latter. For the paper, authors decided to use the taxonomy proposed in [52]. This classification was chosen because it enables the combination of smart services dealing with more than one smart city dimension, which is aligned with the hypothesis of our work.…”

Data4UrbanMobility: Towards Holistic Data Analytics for Mobility Applications in Urban Regions

“…In this section we discuss related work in the area of smart city mobility systems as well as approaches to predict individual aspects of urban mobility. [24] conducted a systematic review of smart city data analysis approaches and provide taxonomies for data sources, data analytic methods and smart city services. Urban CPS [38] is a cyber physical system that integrates floating car data, cellphone data and public transportation data to make prediction about real time traffic speeds.…”

Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

“…To date, and due to the increasing power processing capabilities of the different mobile devices, the Adaboost method is one of the most used methods, and it reports reliable results [24][25][26][27][28][29][30][31][32]. The motivation of this systematic review is to evaluate the reliability of the Adaboost method for daily activities and environment recognition using the sensors available in mobile devices for further implementation of a framework [33][34][35][36][37][38][39][40][41][42].…”