Abstract
Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.
Highlights
Cities—in the context of a Smart City, where most data are available to understand CriticalInfrastructures (CI)—play an important role in ensuring the livability, safety, security, and health of citizens
Following the beginning of the COVID-19 pandemic, it became clear that the Smart City should be considered an overall Critical Infrastructure composed of a set of infrastructures that are, in turn, critical and part of the same system, with strong interdependencies
Smart Cities are definitely enriched by big data technologies, and data-driven methods are fundamental in this context to extract patterns for more informed decision-making
Summary
Cities—in the context of a Smart City, where most data are available to understand CriticalInfrastructures (CI)—play an important role in ensuring the livability, safety, security, and health of citizens. Modern critical infrastructures are becoming increasingly smarter, leading to the birth of. Following the beginning of the COVID-19 pandemic, it became clear that the Smart City should be considered an overall Critical Infrastructure composed of a set of infrastructures that are, in turn, critical and part of the same system, with strong interdependencies. In this perspective, the failure of one of its components can lead to a series of internal cascading effects, such as compromising the functioning of the Smart City itself. Making existing infrastructures smarter is usually associated with making them more complex, but it can make them more vulnerable and subject to cascading effects
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