Data Quality

The guidelines outline procedures for data validation, error checking, and data accuracy assessments. Data of high quality is essential for making reliable decisions and producing accurate results. Below are guidelines on data quality.

• Implement data validation processes to identify and correct errors, inconsistencies, and outliers in the spatial data. This involves cross-checking data against predefined rules, range checks, and logical validations to maintain data integrity
  
• Thoroughly document metadata, providing essential information about the spatial data, including its source, collection methodology, accuracy, and update frequency 
  
• Follow established spatial data standards and protocols for data collection, storage, and dissemination. These standards ensure compatibility and interoperability across different systems and applications, enabling data sharing and integration