How do you address common GIS data quality issues?

1 Identify data quality requirements

Before you start working with any GIS data, you need to define your data quality requirements based on your project objectives, scope, and specifications. Data quality requirements can include criteria such as accuracy, completeness, consistency, currency, and validity. You also need to identify the sources, formats, and standards of your data, and how they relate to your requirements. For example, if you need high-resolution imagery for a detailed land cover analysis, you might not want to use low-resolution satellite data from a different time period.

2 Assess data quality

Once you have your data quality requirements, you need to assess the actual quality of your data and identify any issues or gaps. You can use various methods and tools to assess data quality, such as metadata, data dictionaries, data models, data validation rules, data quality indicators, and data quality reports. You can also perform visual inspections, statistical analyses, and spatial queries to check for errors and anomalies. For example, you can use a GIS software to compare the spatial extent, projection, scale, and attributes of different data layers and look for any discrepancies or mismatches.

3 Resolve data quality issues

After you have assessed your data quality, you need to resolve any issues or gaps that affect your data quality requirements. You can use various techniques and tools to resolve data quality issues, such as data cleaning, data conversion, data integration, data editing, data interpolation, and data enhancement. You can also use data quality control and assurance procedures to verify and document your data quality improvements. For example, you can use a GIS software to correct any geometric or attribute errors, convert any incompatible data formats, integrate any disparate data sources, edit any missing or outdated data, interpolate any sparse or incomplete data, and enhance any low-quality or noisy data.

4 Maintain data quality

Finally, you need to maintain your data quality throughout your project lifecycle and beyond. You can use various strategies and tools to maintain data quality, such as data governance, data standards, data documentation, data security, data backup, and data update. You can also use data quality monitoring and evaluation methods to track and measure your data quality performance and outcomes. For example, you can use a GIS software to establish and enforce data governance policies and standards, document and share your data metadata and quality reports, secure and backup your data files and databases, and update your data regularly and consistently.

GIS data quality is not a one-time task, but an ongoing process that requires careful planning, execution, and maintenance. By following these tips and best practices, you can address common GIS data quality issues and ensure that your data is reliable, accurate, and useful for your GIS projects.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?