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Creating Statistical Surfaces Using Thiessen Polygons

Introduction: A Deep Dive into Interpolation and the Magic of Thiessen Polygons

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In the realm of spatial analysis, the ability to estimate values within two known values is more than just connecting dots—it’s an art and a science. This magical process is known as interpolation. At its core, interpolation provides a bridge between known data points, allowing us to make educated guesses about areas or points where we don’t have specific data. Think of it as solving a puzzle; while you might be missing some pieces, by closely observing the surrounding pieces, you can make a fairly good guess about what the missing piece might look like.

While there are various methods of interpolation, one stands out due to its unique approach and broad applicability: the method using Thiessen polygons, often synonymous with Voronoi diagrams. These are not just fancy terms thrown around in the geospatial world; they are foundational constructs that have facilitated countless spatial studies.

Thiessen polygons operate on a straightforward principle: any location inside a particular Thiessen polygon is closer to its central sample point (or “source point”) than to any other sample point outside the polygon. This means that when estimating values inside a Thiessen polygon, we use the value of its central point. It’s like having a protective sphere around a known data point, ensuring that its influence is felt strongest within its domain, without interference from neighboring points.

2: Understanding the Mechanics of Thiessen Polygons

2.1 What are Thiessen Polygons?

At its simplest, a Thiessen polygon is the area defined around a specific sample point in which any location inside that polygon is closer to that specific point than any other. If you imagine a set of sample points scattered on a plane, Thiessen polygons can be thought of as the territories these points would claim if they were competing for space.

2.2 Constructing Thiessen Polygons

  1. Starting Point: Begin with a set of discrete sample points on a plane. These could be locations of weather stations, archaeological sites, or any other point data.
  2. Drawing Perpendicular Bisectors: For every pair of neighboring points, draw the perpendicular bisector (a line that cuts in half the shortest distance between them). This bisector represents a boundary where locations are equidistant to the two points.
  3. Meeting of Bisectors: As more bisectors are drawn, they’ll intersect, forming the vertices of polygons.
  4. Completing the Polygon: Once all necessary bisectors have been drawn, the polygons are formed, each surrounding its respective sample point.

2.3 Key Features and Assumptions:

  • Regions of Influence: Each polygon represents the region of influence of its central sample point.
  • Equal Distance Boundaries: The edges of a polygon represent areas equidistant between two neighboring sample points.
  • Value Assignment: When interpolating values using Thiessen polygons, the value of the entire polygon is assumed to be the same as its central sample point. For example, if you’re working with rainfall data, and a specific point received 20mm of rain, every location within its corresponding Thiessen polygon would be assigned that same 20mm value.

2.4 Practical Implications:


  • Simplicity: Thiessen polygons offer an easy and straightforward way to visualize and interpret spatial data.
  • Exact Interpolator: This method doesn’t generalize or average out values; the estimated value for each polygon is precisely the value of its sample point.


  • Abrupt Transitions: Since the entire polygon takes on the value of its central point, this can lead to sudden changes in value at the boundaries, which might not always reflect real-world scenarios.
  • Lack of Complexity: In cases where data is more nuanced, Thiessen polygons might oversimplify, missing out on capturing intricate spatial patterns.

By now, you should have a foundational understanding of Thiessen polygons and their role in nearest neighbor interpolation. As we move forward, we’ll delve deeper into their applications and nuances, allowing you to harness their full potential in spatial analysis.

Section 3: Applying Thiessen Polygons in Spatial Analysis

3.1 Preparing Your Data

Before diving into creating Thiessen polygons, ensure your data is:

  1. Clean: Remove any anomalies or outliers that might distort the analysis.
  2. Georeferenced: Ensure each data point has accurate spatial coordinates.
  3. Relevant: Include only those points that are pertinent to your study or analysis.

3.2 Tools and Software

Various GIS (Geographical Information Systems) tools offer functionalities to create Thiessen polygons, such as:

  • ArcGIS: Uses the Create Thiessen Polygons tool.
  • QGIS: Offers the Voronoi Polygons tool under the Vector geometry menu.
  • GRASS GIS: Uses the v.voronoi function.

Choose the software that best fits your familiarity and the requirements of your project.

3.3 Step-by-step Guide to Creating Thiessen Polygons

  1. Import Your Data: Load your point data into your chosen GIS software.
  2. Access the Relevant Tool: Navigate to the function or tool that generates Thiessen polygons.
  3. Specify Parameters: Some tools may allow you to set specific parameters, such as an extent for the polygons.
  4. Run the Tool: Generate the Thiessen polygons.
  5. Review and Refine: Check the polygons for accuracy. Adjust any parameters if needed and re-run the tool.

3.4 Interpreting Results

  • Visual Analysis: Look at the patterns, sizes, and shapes of the polygons. Larger polygons might indicate a lack of data points in that region.
  • Comparative Analysis: Overlay the Thiessen polygons with other spatial data layers to derive insights. For instance, overlaying with elevation data can show how sampled points correspond to high or low elevations.
  • Identifying Gaps: Areas where polygons are notably vast might indicate regions where more sample points are needed.

3.5 Advanced Applications

  • Combining with Other Methods: Thiessen polygons can be used in tandem with other interpolation methods to create more sophisticated spatial analyses.
  • Temporal Analysis: If your data points are time-stamped, you can generate Thiessen polygons for different time intervals, allowing for a dynamic view of how spatial influences change over time.

In this section, we’ve walked you through the process of creating and interpreting Thiessen polygons using GIS software. As with any analytical method, practice and experience will deepen your understanding and enhance your ability to derive meaningful insights from your data.

4: Advantages and Limitations of Thiessen Polygons

4.1 Advantages of Thiessen Polygons

  1. Simplicity: One of the most straightforward methods to implement in GIS software.
  2. Definitiveness: Every point within a polygon relates unambiguously to its nearest data point.
  3. Visual Clarity: Allows for easy visualization of zones of influence for each data point.
  4. Efficiency: Due to its simplicity, it requires less computational power compared to more complex interpolation methods.

4.2 Limitations of Thiessen Polygons

  1. Assumption of Uniformity: Assumes the data within each polygon is homogeneous, which might not always be accurate in real-world scenarios.
  2. Sensitivity to Outliers: A single outlier can significantly distort the shape and size of polygons.
  3. Lack of Smooth Transitions: Can produce abrupt boundaries between polygons, which may not represent real-world gradual transitions.
  4. Dependence on Sample Density: Areas with sparse data can result in large polygons that may not accurately represent the spatial variability of the phenomenon being studied.

4.3 Considerations for Use

  • Purpose of Analysis: Thiessen polygons are ideal for specific applications (e.g., determining service areas for facilities) but may not be suitable for others requiring a smooth surface (e.g., predicting temperature variations).
  • Data Quality: Ensure the integrity of your data points. Given the sensitivity of Thiessen polygons to outliers, it’s vital to clean and preprocess your data.
  • Combining Methods: For a more nuanced analysis, consider using Thiessen polygons in conjunction with other interpolation techniques. This hybrid approach can capitalize on the strengths of multiple methods while mitigating their individual limitations.

4.4 Case Studies: Successful Uses of Thiessen Polygons

  1. Hydrology: Determining watersheds based on river gauging stations.
  2. Retail Analysis: Identifying the service area of a store or facility based on its location and the location of competitors.
  3. Ecology: Establishing territories of animal sightings to study habitat use.

In this section, we’ve delved into the strengths and weaknesses of Thiessen polygons as an interpolation method. By understanding its advantages and limitations, researchers and professionals can make informed decisions about when and how to use this technique effectively in their spatial analyses.

5: Practical Tips and Best Practices for Implementing Thiessen Polygons

5.1 Data Collection and Preprocessing

  1. Sampling Strategy: Ensure that data points are collected strategically to avoid over-representing or under-representing specific areas. Consider the spatial distribution and the phenomenon you’re studying.
  2. Outlier Detection: Use statistical methods or visualization tools to identify and address outliers that can distort the Thiessen polygon results.

5.2 Software Choices

  1. Popular GIS Platforms: Tools like ArcGIS, QGIS, and GRASS GIS offer straightforward options to create Thiessen polygons.
  2. Custom Scripts: For more tailored needs, programming languages such as Python, with libraries like scipy and geopandas, can be employed to generate Voronoi diagrams.

5.3 Enhancing Visualizations

  1. Use Color Strategically: Choose color schemes that enhance the differences between polygons and make the map easy to interpret.
  2. Consider 3D Visualizations: For datasets like elevation or population density, 3D visualizations can provide more depth and context.
  3. Overlay Other Layers: Integrating other spatial datasets can give additional insights into the relationships and patterns present in your data.

5.4 Interpretation and Analysis

  1. Cross-check with Other Methods: Always validate the results of Thiessen polygon interpolations with other interpolation methods for a comprehensive analysis.
  2. Spatial Context: Ensure you are contextualizing your results within the broader spatial patterns and trends.
  3. Temporal Considerations: If you have time-series data, consider how the patterns may change over time and adjust your polygons accordingly.

5.5 Post-Processing and Use Cases

  1. Refining Boundaries: Sometimes, natural or man-made barriers (e.g., rivers or highways) might make certain polygon boundaries unrealistic. Manually adjust these based on the context.
  2. Combining with Other Datasets: Incorporate datasets like population density or land use to give more depth to your analysis.
  3. Feedback Loop: After implementing and analyzing the results, seek feedback from stakeholders or field experts. This iterative process can guide the refinement of your analysis.

5.6 Case Study: A Real-world Application

  1. Scenario Setup: Briefly outline a practical scenario where Thiessen polygons were applied.
  2. Implementation Steps: Describe the step-by-step process undertaken, emphasizing the best practices employed.
  3. Results and Takeaways: Discuss the outcomes and the insights gleaned from the analysis.

6: Pitfalls and Limitations of Using Thiessen Polygons

6.1 Understanding Assumptions

  1. Uniformity: Thiessen polygons assume that any location within a polygon is more closely related to its generating point than any other point. This isn’t always the case in real-world scenarios.
  2. Exact Interpolation: The method assumes that values within the polygon are exactly the same as the generating point, which can oversimplify spatial variation.

6.2 Spatial Biases

  1. Sampling Bias: If data points are not uniformly and strategically collected, the resulting polygons can produce misleading interpretations.
  2. Large Polygons: In areas with sparse data, polygons can become excessively large, leading to overgeneralization.

6.3 Practical Limitations

  1. Irregular Boundaries: Natural barriers like rivers, mountains, or man-made infrastructures can disrupt the ideal Voronoi cell structure, leading to unrealistic representations.
  2. Lack of Gradient: Unlike other interpolation methods, Thiessen polygons don’t show gradients or transitional zones between data points.

6.4 Comparisons with Other Interpolation Methods

  1. Kriging, IDW, and Splines: These methods can sometimes offer more nuanced representations of spatial phenomena by accounting for spatial correlations and gradients.
  2. Context Specificity: While Thiessen polygons are beneficial for some applications, other interpolation methods might be more suitable depending on the specific context and nature of the data.

6.5 Overcoming Limitations

  1. Hybrid Approaches: Combining Thiessen polygons with other interpolation methods can sometimes offset the limitations of each approach.
  2. Incorporating Additional Datasets: Using supplementary spatial data can help in contextualizing and refining the polygons.
  3. Continuous Refinement: Periodic re-evaluation and adjustment of the polygons as more data becomes available can improve accuracy.

6.6 Takeaway Message

  1. Mindful Usage: It’s crucial to be aware of the pitfalls and limitations of Thiessen polygons to use them effectively. Ensure that the method fits the specific goals and nature of your project.
  2. Continuous Learning: Stay updated with advances in GIS and spatial analysis. Regularly evaluate your techniques against new methods and findings to ensure you’re using the best tools for your needs.

frequently asked questions (FAQs) about Thiessen polygons and nearest neighbor interpolation:

What is the difference between Thiessen polygons and Voronoi diagrams?

  • Answer: Essentially, Thiessen polygons and Voronoi diagrams refer to the same concept. Both methods partition a space based on a set of points such that any location within a given polygon (or cell) is closer to its associated point than to any other point. The terms are used interchangeably, with “Thiessen polygons” being more common in meteorology and geosciences, and “Voronoi diagrams” being more prevalent in computational geometry and computer science.

Why are Thiessen polygons used in spatial analysis?

  • Answer: Thiessen polygons are used in spatial analysis because they offer a simple yet effective way to estimate or interpolate values across a geographic space based solely on known sample points. They represent areas of influence around each point, allowing for the assignment of values to unsampled locations based on the nearest known point.

How are Thiessen polygons constructed from a set of points?

  • Answer: Thiessen polygons are constructed by taking a set of points and drawing perpendicular bisectors (lines) between each pair of neighboring points. The intersections of these bisectors create polygons where any point within a polygon is closest to its associated sample point.

What kind of data is suitable for Thiessen polygon interpolation?

  • Answer: Thiessen polygons are most suitable for data where the phenomenon being studied changes abruptly at unknown locations, or where the assumption is that a value remains constant until the midpoint between sample points. Examples include rainfall measurement from weather stations or cellular signal strengths from towers.

Are there any key limitations or challenges when using Thiessen polygons for spatial analysis?

  • Answer: Yes. The method assumes abrupt changes between polygons, which might not represent gradual transitions in reality. It doesn’t account for possible barriers or influences that might cause spatial variations within the polygons. Also, if sample points are unevenly distributed, it might produce large polygons that oversimplify the space.

How do Thiessen polygons differ from other interpolation methods, such as Kriging or Inverse Distance Weighting (IDW)?

  • Answer: While Thiessen polygons assign a uniform value within each polygon based on the nearest point, Kriging and IDW consider multiple points and their distances to predict values at unsampled locations. These methods can produce smoother surfaces and consider more nuanced spatial relationships.

In which fields or industries are Thiessen polygons commonly used?

  • Answer: Thiessen polygons find applications in meteorology (for rainfall mapping), hydrology, agriculture (soil samples), ecology, cellular network design, and urban planning, among others.

Can Thiessen polygons be used with non-uniform or irregularly spaced data points?

  • Answer: Yes, Thiessen polygons can be constructed from any set of points, regardless of their spacing. However, irregular spacing may result in polygons of widely varying sizes.

What software or tools can I use to generate Thiessen polygons?

  • Answer: Popular Geographic Information System (GIS) software like ArcGIS, QGIS, and GRASS GIS have built-in tools for generating Thiessen polygons. There are also computational geometry libraries in programming languages like Python that can generate Voronoi diagrams.

Are there any ways to refine or improve the results of Thiessen polygon interpolation?

  • Answer: While the basic concept of Thiessen polygons is straightforward, refinements can be made by incorporating additional spatial information, such as barriers or influences that might affect spatial relationships, or by combining with other interpolation techniques.

How does the nearest neighbor principle relate to the construction of Thiessen polygons?

  • Answer: The nearest neighbor principle is fundamental to Thiessen polygons. Each polygon represents the area where locations are closer to its associated sample point (the “nearest neighbor”) than to any other.

Can Thiessen polygons be used in 3D space or only in 2D?

  • Answer: While commonly used in 2D, the concept can be extended to 3D, resulting in Voronoi polyhedra. These define regions in three-dimensional space closest to a set of sample points.

What are the computational demands of creating Thiessen polygons, especially with large datasets?

  • Answer: The computational complexity of constructing a Voronoi diagram (Thiessen polygons) for n points in a plane is O(n log n). However, with very large datasets, the process might become computationally intensive and require optimized algorithms or software.

How do I interpret the boundaries of the polygons in relation to the source data points?

  • Answer: The boundaries of the polygons represent locations equidistant from two or more sample points. They delineate where one sample point becomes closer than another.

Are there specific geographic or topographic scenarios where Thiessen polygons might not be the best interpolation choice?

  • Answer: Yes, in areas with gradual transitions or where spatial relationships are influenced by factors other than just distance (e.g., elevation affecting temperature), other interpolation methods that account for these influences might be more appropriate.
About the Author
I'm Daniel O'Donohue, the voice and creator behind The MapScaping Podcast ( A podcast for the geospatial community ). With a professional background as a geospatial specialist, I've spent years harnessing the power of spatial to unravel the complexities of our world, one layer at a time.

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