Controllo Dynamico is a dashboard for internal use, accessible to politician, decision-makers and practitioners.

Link to Controllo Dynamico: Microsoft Power BI

The Project Process

The urban monitoring and benchmarking framework favoured data provided in open mode by national or international institutes. The framework for monitoring neighbourhoods and in-depth analysis of different parts of the city considered the indicators developed by the Authority and its investee companies, favouring data collected in the framework of other projects, in particular the “Dossier Grandi Comuni” (Dossier of the Major Municipalities) and the Authority’s Joint Planning Paper (DUP, Documento Unico di Programmazione).

The development of the Controllo Dynamico Framework took place in several subsequent stages, with multiple steps at each stage.

In the first step, the University identified the dataset of elementary indicators, selecting data available in open mode, collected, or elaborated by institutes and research bodies of proven authority, indicating the direct source, and specifying some notes on the availability and granularity of the data, or on the need to identify more adequate proxies to measure that specific theme.

In the second step, the University, with the operational support of the Statistics Office of the Municipality of Genova, identified the territorial and temporal limits of the research in the two different granularities: urban and local.[1]

At this stage, the scale of the urban comparison has been set at the 12 Italian cities with a population ≥ 250,000, and the granularity chosen for the data is the municipal or provincial level (for Genova, it coincides with the metropolitan scale). The territorial dimension, for which the framework is currently being developed, aims instead to understand the dynamics of the neighbourhoods of the Municipality of Genova, and the preferred granularity is that of the urban unit, which can be appropriately aggregated into neighbourhoods.

In order to ensure the robustness of the model even when the available granularity differs from the optimal one, a computational algorithm has been chosen to normalise the data to guarantee the functionality and fluidity of the model even in the presence of some data sources with a granularity different from the required one.

The time horizon considered appropriate for the verification of the model in the medium term was set at five years: a time horizon adequate both to achieve time series and to activate some simple forecasting models for development.

The theoretical model developed by the University is therefore the converse of the Controllo Dynamico Dashboard developed by Liguria Digitale (inhouse IT company of the Municipality of Genoa), with the support of the Statistics Office of the Municipality of Genoa, which extracted and verified the data selected by the University and, on the basis of the framework’s data model, built a matrix of 160 indicators for 5 years for 12 Italian cities with a population of more than 250,000.

Each of the 160 indicators has been assigned a unique alphanumeric code, which facilitates the continuous implementation of the model and also promotes its interoperability with other urban monitoring tools in the future.

Urban Comparison: Large major Municipalities. These are the 12 cities with over 250,000 inhabitants, many of which, like Genoa, are also Metropolitan Cities: Turin, Milan, Venice, Verona, Genoa, Bologna, Florence, Rome, Naples, Bari, Catania, Palermo.