## 3 Results  
### Preprocessing Results  
During the preprocessing phase, we encountered and resolved several data quality issues across our source datasets. These challenges required specific handling strategies to ensure data integrity and analytical validity.  
In the socioeconomic dataset, we encountered challenges related to non-unique administrative unit names. Specifically, 10 county pairs, such as \"powiat bielski,\" shared identical names despite being located in different ovivodeships. To prevent erroneous data merging, a compound key, comprising both the county and ovivodeships names, was systematically employed. This approach was necessary for approximately 5% of the records to ensure unambiguous joins.  
A further complication arose from inconsistent naming conventions for administrative units across different data sources. We found that 17% of county names required prefix standardization. This was primarily addressed by removing prefixes such as \"m.\" and \"m. st.\" from urban county designations, for instance, converting \"powiat m. Bielsko-Biala\" to \"powiat Bielsko-Biala\". Additionally, temporal discontinuities required explicit handling. The urban county of Walbrzych, for example, had distinct entries for different time periods (\"powiat m. Walbrzych od 2013\" and \"powiat m. Walbrzych do 2002\"), which were unified into a single record. A similar issue was observed with karkonoski county, which appeared under its former name, \"powiat jeleniogorski\", in datasets predating 2021. This discrepancy, affecting approximately 0.2% of records, was resolved by standardizing to the current official name. These comprehensive standardization procedures collectively impacted 18% of all records in the socioeconomic dataset, ensuring consistency in subsequent preprocessing steps.  
For the weather parameters, outlier detection was applied to identify and remove physically implausible or erroneous values. Temperature data falling outside the historically observed range for Poland (\(-41\)\({}^{\circ}\)C to \(50\)\({}^{\circ}\)C) were excluded. Relative humidity values were constrained to the physically meaningful range of 0-100% through clipping. For precipitation, a statistical approach was employed: the histogram of recorded values was analyzed, and based on both the distribution and typical maximum values observed in Poland, outliers falling outside a plausible range were removed. Values below 0.01 mm, representing the minimum detectable precipitation threshold in the IMGW dataset, were set to zero to account for measurement precision limitations, affecting approximately 10.5% of records. These quality control procedures resulted in missing values in the final dataset for temperature (0.11%), precipitation (0.19%), and 24 h precipitation (0.17%), primarily due to outlier removal and occasional algorithm estimation failures.  
Overall, preprocessing operations improved data quality while preserving most records, with less than 1% of the original dataset being excluded or substantially modified. Following standardization, socioeconomic indicators exhibited no significant outliers, and the visual inspection of the distributions before and after preprocessing confirmed that the transformations preserved the expected regional patterns across Poland. Similarly, weather-related variables demonstrated consistency with anticipated trends after outlier removal, further supported by visual validation of distributions' integrity.