Geocoding-Feature_Selection

The growing popularity of location-based services has resulted in the enhancement of resources for producing geographical data and the development of the amount of data kept in geographic databases. The big data stored in databases is valuable for advanced geospatial analysis in several fields, including emergency responses, crime and traffic management, disease surveillance, and more. Geocoding, a crucial preprocessing step in geospatial data analysis, involves retrieving textual descriptions of locations into geographic identifiers. Nevertheless, geocoding outcomes delivered by worldwide service providers neglect various constraints related to textual data, including misspellings, abbreviations, and non-standard names. To overcome this issue, we propose a new approach for enhancing the quality of online geocoding services through the utilization of feature selection techniques. The proposed method is based on text similarity algorithms that are utilized to match the retrieved addresses. Compared to conventional geocoding outcomes, there is potential for an improvement of approximately 10% to 25% in the address-matching procedures employed in online geocoding services. The improvement was accomplished through the utilization of two feature selection methods, specifically mutual information feature selection and minimum redundancy maximum relevance, out of a total of fourteen approaches. Furthermore, the findings indicate that it is appropriate to prioritize character-based text similarity algorithms when comparing addresses retrieved from online geocoding services.