## 4 Dataset Description  
Using deep learning to analyze large, high-quality datasets is crucial and often difficult to obtain. In this study, the data source for the dataset is a significant challenge. This dataset was organized by the Italian telecom operator TIM in 2015 [42]. This dataset is one of the most comprehensive collections from operators and is publicly available. It mainly includes telecommunications, weather, news, and social networks for Milan and Trentino from November to December 2013. We focus on the telecom record in Milan city for mobile internet forecasts. First, we define a geographic grid for data recording, and the city is divided into \(100\times 100\) areas. The Milan grid's aggregated call data record (CDR) is shown in Figure 1. Each grid has a unique square ID covering an area of \(235\times 235\). The data are in the standard WGS84 (EPSG: 4326) GeoJson format, as shown in Figure 0(a). Figure 0(b) overlays this grid on a map of Milan to visually represent how the grid corresponds to the city's layout. Figure 0(c) is a heat map that displays network traffic intensity across Milan. The axes of the heat map correspond to grid cell IDs along the horizontal and vertical directions. At the same time, the color intensity represents the volume of network traffic, with red indicating higher traffic areas. The blue square highlights a zone of particular interest, which is discussed further in our analysis. Additionally, the dataset contains the following information used in this project:  
**Internet traffic:** internet traffic (INT) in Milan.  
* Square ID: the identifier of the square in the Milan grid.
* Time interval: The beginning of the time interval of the record. The end interval time can be obtained by adding 10 min to this value.
* Internet traffic activity: The number of internet activities generated during the time interval in this square id.  
**Milano Today:** The news in Milan.
* Data: the content of the news articles available online ([[http://www.milanotoday.it](http://www.milanotoday.it)]([http://www.milanotoday.it](http://www.milanotoday.it)), accessed on 6 August 2015).
* Date: the time at which the news event occurred.
* Geometry: the coordinates where the news event happened.  
**Social Pulse:** Tweet records in Milan.
* Data: the locations of tweets sent within the Milan and Trentino area.
* Date: the time at which the tweet was sent.
* Geometry: the coordinates from where the tweet was sent.  
The weather data describe the type and intensity of meteorological phenomena in Milan city. The data for Milan were collected by the Agenzia Regionale per la Protezione dell'Ambiente (ARPA). Weather data are divided into two datasets: legend datasets and weather phenomena. In this project, we mainly use the latest dataset. The sensors can measure the following meteorological phenomena: wind direction, wind speed, temperature, relative humidity, precipitation, global radiation, atmospheric pressure, and net radiation.  
**Weather Phenomena:**  
* _Sensor ID:_ identification string of the sensor.
* _Time Instant_: the time instant of the measurement, which is expressed as a date/time with the following format: YYYY/MM/DD HH24:MI (e.g., 2013/07/22 15:30)
* _Measurement_: The value of meteorological phenomenon intensity measured at the time instant by the sensor. The unit of measurement (UOM) of the value recorded by the given sensor is specified in the legend dataset.  
In this paper, we select four types of weather data: temperature, rainfall, wind speed, and air pollution.