Literature Review on the Smart City Resources Analysis with Big Data Methodologies

Abstract

This article provides a systematic literature review on applying different algorithms to municipal data processing, aiming to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification, correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types, ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long Short-Term Memory (LSTM) for timeseries prediction and classification.

Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis

“…In numerous instances, smart city data may exhibit sparsity, noise, or incompleteness. It is imperative to enhance data collection infrastructure, deploy data cleaning techniques, and explore methods to address missing data [31][32][33].…”