Just to name a few, here are some practices you can use in order to have better precision with your data. 1. Data cleaning and quality control. 2. Cross-validation. 3. Resampling and refining data resolution.

To improve geocoding accuracy in GIS, start by ensuring high-quality, consistent input data, as inaccuracies often stem from incomplete or improperly formatted addresses. Regularly update your software and use the latest reference layers or address databases to avoid errors from outdated information. Adjust geocoding parameters, such as tolerance levels, within your GIS software to fine-tune precision. Additionally, integrating external geocoding services or multiple data sources can help cross-verify locations and enhance accuracy. These combined strategies can significantly boost the reliability of your geocoding results, allowing for more precise spatial analysis and decision-making.

I start by selecting a high-quality, up-to-date address dataset and choosing geocoding services that match the area’s specificity, such as local or premium geocoders. Then I refine the coordinate reference systems to align with the region, and adjust geocoding parameters to account for local address formats and potential anomalies.

Use High-Quality Data Sources: Rely on updated, high-quality datasets, such as commercial geocoding services, and supplement with local sources for better accuracy. Standardize Address Data: Clean and standardize address formats to avoid mismatches, using consistent abbreviations and formats. Optimize Geocoding Algorithms: Fine-tune algorithm settings, prioritize interpolated data, and adjust matching thresholds to improve precision. Implement Reverse Geocoding Verification: Use reverse geocoding to validate initial results, ensuring address-to-coordinate accuracy. Incorporate Quality Control and Error Reporting: Regularly check accuracy with automated quality control and log errors for improvement Regularly Update and Calibrate System