What are some best practices for validating and communicating spatial uncertainty?

1 Understand the sources and types of spatial uncertainty

The first step to validate and communicate spatial uncertainty is to understand where it comes from and how it affects your data and analysis. Spatial uncertainty can be classified into two main types: aleatory and epistemic. Aleatory uncertainty is the inherent variability or randomness of natural phenomena, such as soil properties, climate, or land use. Epistemic uncertainty is the lack of knowledge or information about the true state of the system, such as measurement errors, missing data, or model parameters. You can use various methods and tools to identify, quantify, and visualize the sources and types of spatial uncertainty, such as error propagation, sensitivity analysis, or spatial statistics.

2 Choose appropriate methods and models for spatial prediction and uncertainty

The next step is to choose the most suitable methods and models for spatial prediction and uncertainty estimation. Depending on your data, objectives, and assumptions, you can use different approaches, such as interpolation, regression, geostatistics, or machine learning. Each method and model has its own advantages and limitations, and may produce different levels and patterns of spatial uncertainty. You should consider the following factors when selecting a method or model: the spatial distribution and structure of your data, the spatial scale and resolution of your analysis, the complexity and nonlinearity of your relationships, and the availability and quality of your covariates.

3 Validate your spatial predictions and uncertainty estimates

The third step is to validate your spatial predictions and uncertainty estimates using independent or cross-validation data. Validation is the process of assessing how well your method or model performs in predicting the observed or expected values of your variable of interest. Validation can help you evaluate the accuracy, precision, reliability, and robustness of your spatial predictions and uncertainty estimates. You can use various metrics and indicators to measure the validation performance, such as mean error, root mean squared error, coefficient of determination, confidence intervals, or probability plots.

4 Communicate your spatial uncertainty clearly and honestly

The final step is to communicate your spatial uncertainty clearly and honestly to your intended audience. Communication is the process of conveying your results, methods, assumptions, and limitations to others, such as stakeholders, decision-makers, or the public. Communication can help you increase the transparency, credibility, and usefulness of your spatial analysis and prediction. You can use various formats and techniques to communicate your spatial uncertainty, such as maps, charts, tables, narratives, or interactive tools. You should consider the following principles when communicating your spatial uncertainty: use simple and consistent language, avoid jargon and technical terms, provide context and interpretation, acknowledge sources of error and uncertainty, and avoid overconfidence and false precision.

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?