## 2 Materials and Methods
Std: Latitude, longitude, Areas_int, issue_int, year, month, day, hour, minute, issue_Probability: Represents the standard deviation, a measure of the amount of variation or dispersion in each numeric column. Min: Latitude, longitude, Areas_int, issue_int, year, month, day, hour, minute, issue_Probability: Represents the minimum value in each numeric column. - 25%, 50%, 75%: Latitude, longitude, Areas_int, issue_int, year, month, day, hour, minute, issue_Probability: These values represent the quartiles, indicating the distribution of the data. For example, 25% of the reported dates and times fall on or before the 57 th minute. Max: Latitude, longitude, Areas_int, issue_int, year, month, day, hour, minute, issue_Probability: Represents the maximum value in each numeric column. From this summary, someone can identify patterns such as the common areas and issues and can also observe the distribution and variability of numeric columns. However, identifying outliers might require additional visualization techniques, like box plots or \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**stat\_report[numeric\_cols] = stat\_report[numeric\_cols].applymap(lambda x: f\({}^{\prime\prime}\)(int(x) if pd.notna(x) else “/”)**} \\ \hline count & 93,053 & 93,053 & 93,043 & 93,053 & 93,053 & 93,053 & 93,053 & 93,053 & 93,053 & 93,053 & 93,053 & 93,053 \\ unique & 162 & 8 & 89,003 & & & & & & & & & \\ top & Ayvia & garbage & 14/10/2019 & 7:32:00 & & & & & & & & & \\ freq & 5881 & 31,836 & 4 & & & & & & & & & & \\ mean & 38 & 21 & NaN & 90 & NaN & 2 & nan & 2020 & 6 & 15 & 10 & 29 & 69 \\ std & 0 & 0 & NaN & 53 & NaN & 1 & nan & 1 & 3 & 8 & 4 & 17 & 17 \\ min & 38 & 21 & NaN & 1 & NaN & 1 & nan & 2018 & 1 & 1 & 0 & 0 & 23 \\
25\% & 38 & 21 & NaN & 41 & NaN & 1 & nan & 2019 & 4 & 8 & 7 & 15 & 57 \\
50\% & 38 & 21 & NaN & 89 & NaN & 2 & nan & 2021 & 7 & 16 & 9 & 30 & 72 \\
75\% & 38 & 21 & NaN & 144 & NaN & 4 & nan & 2022 & 9 & 23 & 12 & 45 & 84 \\ max & 38 & 21 & NaN & 171 & NaN & 8 & nan & 2023 & 12 & 31 & 23 & 59 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistical summary of the generated data. scatter plots. The summary provides a good overview, but further exploration through data visualization and more advanced statistical techniques may be needed for a deeper understanding. Figure 7 provides boxplots for the \"Latitude\", \"Longitude\", \"Areas_int\", and \"issue_int\" columns. The boxplots, which show the distribution of the number of citizen reports received on each day, are very useful in highlighting some observations: The boxes are relatively wide, which indicates that there is a lot of variation in the data. The medians of the boxes are all above the first quartile, which indicates that the data is positively skewed. There are a few outliers in the data, as indicated by the whiskers of the boxplots. The overall trend of the data is increasing, as indicated by the upward slope of the center lines of the boxes. Based on these observations, somebody can conclude that the data is likely to represent a large number of citizen reports, with a significant amount of variation in the number of reports received each day. The data is also positively skewed, which means that there are more days with a high number of reports than days with a low number of reports. There are a few outliers in the data, which could be due to unusual events or data entry errors. Finally, the overall trend of the data is increasing, which suggests that the number of citizen reports is increasing over time. Figure 8 shows a scatterplot for \"Latitude\" versus \"Longitude\", with \"Areas_int\" as hue and \"issue_int\" as size. The points are concentrated in the city of Patras, with a few outlying suburbs scattered around the city. The largest concentrations of reports are in the city center and in the areas to the north and south of the city. The size of the points in the scatter plot suggests that there is a significant variation in the number of reports received at different locations in the city.