The operation and management of a municipality generate large amounts of complex data, enclosing information that is not easy to infer or extract. Their analysis is challenging and requires specialized approaches and tools, usually based on statistical techniques or on machine learning and artificial intelligence algorithms. These Big Data is often created by combining many data sources that correspond to different operational groups in the city, such as transport, energy consumption, water consumption, maintenance, and many others. Each group exhibits unique characteristics that are usually not shared by others. This paper provides a detailed systematic literature review on applying different algorithms to urban data processing. The study aims to figure out how this kind of information was collected, stored, pre-processed, and analyzed, to compare various methods, and to select feasible solutions for further research. The review finds that clustering, classification, correlation, anomaly detection, and prediction algorithms are frequently used. Moreover, the interpretation of relevant and available research results is presented.
