How do you validate and evaluate QGIS results and outputs for spatial statistics and machine learning?

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QGIS is a powerful and versatile tool for spatial analysis and visualization, but how do you know if your results and outputs are reliable and meaningful? Whether you are using spatial statistics or machine learning methods, you need to validate and evaluate your QGIS workflows and outcomes to ensure quality and accuracy. In this article, we will show you some tips and techniques for doing so, using QGIS features and plugins.

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1 Data Quality and Preparation

Before you apply any spatial statistics or machine learning algorithm, you need to make sure that your data is of high quality and suitable for the analysis. This means checking and correcting for errors, outliers, missing values, duplicates, inconsistencies, and spatial autocorrelation. You can use QGIS tools such as the Geometry Checker, the Field Calculator, the Vector Layer Properties, and the Processing Toolbox to inspect and modify your data. You can also use QGIS plugins such as the Data Plotly, the Geostatistical Analyst, and the Hotspot Analysis to explore and visualize your data.

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Jake Sales

Residential Data Analyst at PriceHubble | GIS Analyst | Chartered Geographer | Real Estate Insights and Data | Qualified Teacher/Lecturer | Open Data Enthusiast | London Geographer | RStudio | Python
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I usually examine the data in R before doing any Kriging or anything else. Helps to understand outliers and the distribution of the values.

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2 Model Selection and Parameterization

Once you have prepared your data, you need to choose the appropriate spatial statistics or machine learning model and parameters for your research question and hypothesis. You can use QGIS plugins such as the QGIS Network Analysis Toolbox, the QGIS Machine Learning Tools, the QGIS Spatial Regression Analysis, and the QGIS Processing R Provider to access a variety of models and methods. You can also use QGIS tools such as the Model Designer, the Batch Processing, and the Graphical Modeler to create and run custom models and workflows. You should compare different models and parameters using criteria such as accuracy, performance, complexity, and interpretability.

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Jake Sales

Residential Data Analyst at PriceHubble | GIS Analyst | Chartered Geographer | Real Estate Insights and Data | Qualified Teacher/Lecturer | Open Data Enthusiast | London Geographer | RStudio | Python
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SAGA is my go to for most of my analysis currently. There are some great tools. I’m happy using several separate programs too.

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3 Model Validation and Evaluation

After you have run your model, you need to validate and evaluate its results and outputs. This means assessing how well your model fits the data, predicts new data, and explains the spatial patterns and relationships. You can use QGIS tools such as the Vector Layer Properties, the Raster Layer Properties, the Print Layout, and the Atlas to display and export your results and outputs. You can also use QGIS plugins such as the QGIS Zonal Statistics, the QGIS Point Sampling Tool, the QGIS Accuracy Assessment, and the QGIS LecoS to calculate and visualize various metrics and indicators of model validation and evaluation.

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Swaroop Kallakuri

CTO - Piren Technology | Follow me to get daily insights about A.I & Quantum computing
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Validation Metrics: Use relevant metrics to evaluate the performance of your models, such as accuracy, precision, recall, F1-score, ROC-AUC, or spatial autocorrelation measures like Moran's I. Confusion Matrix: Generate and analyze confusion matrices to understand the distribution of true positives, false positives, true negatives, and false negatives. Visual Inspection: Visualize the results of your spatial statistics or machine learning models using maps, graphs, or other visualizations to gain insights into spatial patterns and relationships.

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4 Model Sensitivity and Uncertainty

Finally, you need to consider the sensitivity and uncertainty of your model results and outputs. This means examining how your model responds to changes in the data, the parameters, the assumptions, and the environment. You can use QGIS tools such as the Processing Toolbox, the Model Designer, and the Graphical Modeler to perform sensitivity analysis and uncertainty analysis. You can also use QGIS plugins such as the QGIS Monte Carlo Simulation, the QGIS Geostatistics for Python, the QGIS Geospatial Uncertainty, and the QGIS Geostatistical Realizations to generate and visualize scenarios and probabilities of model outcomes.

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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?

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Swaroop Kallakuri

CTO - Piren Technology | Follow me to get daily insights about A.I & Quantum computing
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Spatial Autocorrelation: Account for spatial autocorrelation in your data by using appropriate statistical tests or incorporating spatial lag terms into your models. Feature Importance: Investigate the importance of different input variables/features in your models to understand their contribution to model performance. Model Interpretability: Balance model complexity with interpretability, especially if stakeholders require transparent decision-making processes.

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How do you validate and evaluate QGIS results and outputs for spatial statistics and machine learning?

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1 Data Quality and Preparation

2 Model Selection and Parameterization

3 Model Validation and Evaluation

4 Model Sensitivity and Uncertainty

5 Here’s what else to consider

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