How can you avoid the implications of using inaccurate spatial data in analysis?

3 Assess and communicate uncertainty

The third step to avoid the implications of using inaccurate spatial data in analysis is to assess and communicate the uncertainty that is inherent in your data and your results. You should quantify and qualify the sources and levels of uncertainty in your data, such as measurement errors, sampling errors, classification errors, or modeling errors. You should also evaluate and report the uncertainty in your results, such as confidence intervals, error propagation, or sensitivity analysis. You should communicate the uncertainty in your data and your results clearly and transparently, using appropriate visualizations, indicators, or annotations.

4 Validate and verify your results

The fourth step to avoid the implications of using inaccurate spatial data in analysis is to validate and verify your results against independent and reliable sources. You should compare your results with other data, methods, or studies that can confirm or contradict your findings. You should also test your results for logical consistency, accuracy, and reliability. You should identify and correct any errors, anomalies, or inconsistencies in your results, and explain any discrepancies or deviations from your expectations or assumptions.

5 Document and share your data and methods

The fifth step to avoid the implications of using inaccurate spatial data in analysis is to document and share your data and methods with others. You should record and describe the details of your data sources, methods, tools, results, and uncertainty in a clear and comprehensive way. You should also share your data and methods with other users, researchers, or stakeholders who can benefit from or contribute to your work. You should follow the best practices and standards for data management, documentation, and sharing, and respect the ethical and legal aspects of your data and methods.

6 Learn and improve from feedback

The sixth and final step to avoid the implications of using inaccurate spatial data in analysis is to learn and improve from feedback. You should seek and receive feedback from other users, researchers, or stakeholders who can review, evaluate, or use your data and results. You should also provide feedback to your data sources, methods, or tools that can help them improve their quality and reliability. You should use the feedback to refine, revise, or update your data and results, and to enhance your skills and knowledge in spatial data analysis.

7 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?