GIS Applications Interpolation Report Assessment Item 1

Background

You have been employed by Snowy Wilderness Walkers as a GIS consultant to produce and document the best methodology to interpolate the surface of an area of Mt Jagungal Wilderness Area. The study area covers 168km2 (14km by 12km) about 30km north of Mt Kosciuszko (Figure 1).

Figure 1. Location of study area (source: Google Maps)

Snowy Wilderness Walkers are a bushwalking group who organise walks in Kosciuszko National Park and surrounding areas. They have requested your report so that they can create an accurate interpolated surface of the entire Mt Jagungal region in order to calculate hiking times across the terrain (see Assessment Item 2: Hiking times report).

You will investigate 18 different interpolation approaches, report on their strengths and weaknesses and recommend the best approach for the study area.

Interpolation approaches

You will choose three (3) interpolation techniques from the following:

• Second Order Polynomial Trend

• Natural Neighbour

• Inverse Distance Weighted

• Spline with Tension

• Ordinary Kriging

You will evaluate your three chosen interpolation techniques using the following six sampling strategies (for a total of 3*6 = 18 interpolation approaches):

1. High density regular sampling (29359 points, 75m between points)

2. Medium density regular sampling (7254 points, 150m between points)

3. Low density regular sampling (1794 points, 300m between points)

4. High density random sampling (29359 randomly selected points)

5. Medium density random sampling (7254 randomly selected points)

6. Low density random sampling (1794 randomly selected points)

Validation

You will validate your chosen interpolated surfaces by calculating Root Mean Square Error (RMSE) of your surface compared to two validation data sets. Each validation point set contains 500 points (not used in any of the sampling schemes) inside a 4km2 (2km x 2km) region. Validation point set 1 covers a low‐lying river bed, while validation point set 2 covers higher, more jagged terrain.

Subjective evaluation

In addition to the validation process, you will also subjectively evaluate the surface by visually assessing contour maps and 3D visualisations.

Recommendation

Based on your validation and subjective evaluation of the interpolated surfaces, you will make a recommendation in your report to Snowy Wilderness Walkers on which sampling strategy and which interpolation technique they should use to create a surface of the entire Mt Jagungal region.

Introduction

Aim of project, brief overview of Mt Jagungal area, including type of terrain, and an overview of the interpolation methods investigated.

Methods

Description of the 18 different interpolation methods (3 interpolation techniques x 6 sampling strategies), and the methods used to evaluate them (validation and subjective evaluation methods).

Results

Results of validation and subjective evaluation methods, including maps, graphs, tables and images as appropriate.

Discussion and conclusions

Critical discussion of results, including strengths and weaknesses of each method; recommendation on which method should be selected justified based on evidence from your results and from relevant literature.

Data provided

• Sampling

  • reghigh.shp Regular high density sample points

  • regmed.shp Regular medium density sample points

  • reglow.shp Regular low density sample points

  • randhigh.shp Random high density sample points

  • randmed.shp Random medium density sample points

  • randlow.shp Random low density sample points

• Validation

  • val1.shp Validation point set 1

  • val2.shp Validation point set 2

Interpolation technique descriptions

You need to create your chosen interpolated surfaces using the point files in the Sampling directory. You can choose how to create them and what settings to use, and you need to justify why you used the settings that you chose. If in doubt, a good rule of thumb is to keep it simple and use default settings.

• Trend surfaces can be created using Geoprocessing Toolboxes > Spatial Analyst Tools > Interpolation > Trend or Geoprocessing Toolboxes > Geostatistical Analyst Tool > Interpolation > Global Polynomial Interpolation.

• Natural neighbour surfaces can be created using Geoprocessing Toolboxes > Spatial Analyst Tools > Interpolation >Natural Neighbour.

• Inverse distance weighted surfaces can be created using Geoprocessing Toolboxes > Spatial Analyst Tools >Interpolation > IDW or Geostatistical Analyst Tool > Interpolation > IDW.

• Spline surfaces can be created using Geoprocessing Toolboxes > Spatial Analyst Tools > Interpolation > Spline or Geostatistical Analyst Tool > Interpolation > Radial Basis Functions.

• Kriging surfaces can be created using Geoprocessing Toolboxes > Spatial Analyst Tools > Interpolation > Kriging or Geostatistical Analyst Tool > Interpolation > Empirical Bayesian Kriging.

4. Compare contours of the interpolated surfaces

• Create contour maps for each of your interpolated surfaces – it is up to you to

decide the best contour interval, but be consistent. Decide whether you will be comparing the entire region, or a zoomed in portion. Make sure you use the same extent and colour scheme (symbology) for each contour map.

• Once you are happy with your contour map, select Insert > New Layout >choose

margins. A blank layout will open you can change its orientation from Landscape to Portrait. While on Insert, click dropdown arrow of Map Frame and choose extent. Please choose the second option with a scale. Now your mouse cursor is ready to add the maps. Drag it as a box (covering almost all of the white sheet). The map will take a while to appear. In contents pane you can choose which layers you want to add. Now you can see North arrow, scalebar, legend which should be added at appropriate place (for the sake of this figure, you do not need to add these elements). See layout figure example. Once you are happy with the current view of map, which you can change by going to Map, click Share and then Map Export. Now you can make figures of all the interpolations, respective contours, with specific samples. Please note that it’s your choice to add how many in the figure. One idea is the explained below

• Create a table in Word, and use the Insert Image option to insert the images into the

• Create a legend and scalebar for each group of images – they should all cover the

 same extent and have the same symbology, therefore you do not need a separate legend and scalebar on every map. There should be a legend and scalebar on every page however. It is up to you to decide the best way to lay out the table for meaningful comparison. Following is an example of layout for interpolated surfaces for IDW (left column) and Kriging (right column) using random, low density samples. A, D show 2D interpolated surfaces (Not required to be presented in your assessment). B,E show contours, while C,F are the 3D visualisations. Please note this is an EXAMPLE of how to layout your figures – Combining contours and 3D visualisations is probably not a great idea. Come up with a way of arranging images that facilitate your comparisons.

1. Compare 3D visualisations of the interpolated surfaces

• Click View then Convert (dropdown) and To Local Scene. •

It will take some time, so please be patient at this step. A 3 D map will open now you can see

control at the lower left cover. Click on up arrow to see the full control as shown below:

Move the arrow and middle ring to make a 3 D visualization. As shown below:

• In the Table of Contents, right click on the layer and select Properties. In the Layer

Properties window select the Elevation tab. Under Cartographic offset to 2. Mak sure the selected custom surface is the same as the surface you are setting the layer properties for. Click OK. It will take a while to appear as desired. It is up to you what verticalexaggeration you use (if any), just make sure it is the same for all your 3D visualisations.

• In the Layer Properties window you can also change the symbology. Change the colour ramp if desired.

• Use the Navigate button to navigate around the scene and select the view you wish to display. You can zoom into a portion of the surface if you wish. Make sure that you show the same view for each surface.

• You can consider adding contours to your 3D visualisations, if it works for the scale

you are displaying. You will need to use the Base Heights settings to float the contour layer on the surface, and you’ll need to experiment with the layer offset to make sure the contour lines are visible.

• When you are happy with your image, Click Insert > New Layout > Map Frame drop down and select Map 3D1 and choose the second option (not scale one) As shown below:

• Create a table in Word, and use the Insert Image option to insert the images into the table

• Create a legend for each group of images (note that a scale bar is not necessary, as it would only be approximate due to perspective).

1. Bringing it together

• You should now have a table of RMSE values, a table of contour images and a table of 3D visualisation images. Consider the best way to present the RMSE values graph or table? Consider also at this stage whether there is any extra analysis (e.g. surface statistics) or images (e.g. close ups of a particular feature) that could add to your story. Remember you need to bring all this evidence together to justify the best interpolation approach. Don’t forget to weigh up sampling effort as well.

• Refer back to the presentation guidelines and also the marking rubric. Remember you also need to use literature to add to your discussion on the strengths and weaknesses of each approach. End your report with a justified recommendation.

 Questions to consider:

• Do the results for each interpolation method fit with what you would expect?
• Which interpolation approach has the lowest RMSE in the two validation regions? Why?
• Which interpolation approach has the highest RMSE in the two validation regions? Why?
• Which interpolation approach results in the most aesthetically pleasing contours? Why?
• Which interpolation approach results in the least aesthetically pleasing contours? Why?
• Which interpolation approach results in the most aesthetically pleasing 3D visualisation Why?

• Which interpolation approach results in the least aesthetically pleasing 3D visualisation Why?

• Overall, which interpolation approach gives the best compromise between accuracy and ease of data collection?

Summary of Assessment Requirements

The GIS Applications assessment required the student to design and document the most effective methodology for interpolating the surface of the Mt Jagungal Wilderness Area. The study involved:

• Evaluating 18 different interpolation approaches by combining 3 techniques (e.g., Trend, Natural Neighbour, IDW, Spline, Kriging) with 6 sampling strategies (regular and random at high, medium, and low density).

• Validating results using Root Mean Square Error (RMSE) against two independent validation datasets (500 points each), representing both riverbed and rugged terrain.

• Conducting subjective evaluations through contour maps and 3D visualisations.

• Comparing the effectiveness, accuracy, and presentation quality of interpolation outputs.

• Recommending the best interpolation method and sampling strategy, backed by evidence and literature.

The final report was structured into:

• Introduction – Aim, study area, overview of methods.

• Methods – Description of 18 approaches, evaluation metrics, and validation.

• Results – RMSE tables, contour comparisons, 3D models.

• Discussion & Conclusion – Critical analysis, strengths/weaknesses, recommendation.

• References – APA 7th style.

Academic Mentor’s Step-by-Step Guidance

The Academic Mentor supported the student in a structured way, ensuring clarity and academic rigor:

Step 1: Understanding the Task

• The mentor began by explaining the objective of the assessment: finding the best interpolation strategy for hiking time calculations.

• Highlighted how the choice of method and sampling directly impacts accuracy and usability.

Step 2: Selection of Interpolation Techniques

• Guided the student to choose three techniques that differ in methodology (e.g., Kriging for geostatistical rigor, IDW for simplicity, and Spline for smoothness).

• Explained the theory and applications of each, referencing ArcGIS tools and default settings for reproducibility.

Step 3: Application of Sampling Strategies

• Helped the student generate surfaces using six sampling densities for each technique.

• Emphasized consistency in parameters (e.g., extent, cell size, symbology).

• Encouraged documenting why each setting was chosen.

Step 4: Validation Using RMSE

• Walked through the Extract Multi Values to Points tool in ArcGIS.

• Showed how to export attribute tables and calculate RMSE in Excel.

• Reinforced the importance of validating against both low-lying and rugged terrain datasets.

Step 5: Visual Evaluation

• Supported creation of contour maps with consistent scales and color ramps for fair comparison.

• Assisted in generating 3D visualisations, teaching navigation, vertical exaggeration, and offset adjustments for clarity.

• Encouraged critical observation—e.g., which method produced smoother, realistic contours vs. jagged, unrealistic ones.

Step 6: Bringing Results Together

• Helped structure tables and figures logically (e.g., RMSE summary tables, side-by-side contour/3D comparison layouts).

• Encouraged integrating objective (RMSE) and subjective (visual quality) findings.

• Guided writing the Discussion section using both results and supporting literature on interpolation methods.

Step 7: Final Recommendation

• Ensured the student justified their chosen method and sampling not only based on lowest RMSE but also on feasibility, data effort, and map usability.

• Reinforced that a strong conclusion balances accuracy, efficiency, and practical application.

Outcome and Learning Objectives Achieved

By the end of the process, the student had:

• Produced a well-structured GIS report with maps, visualisations, tables, and critical discussion.

• Learned how to apply multiple interpolation techniques in ArcGIS using varied sampling strategies.

• Understood the importance of validation through RMSE and how to compute it in practice.

• Developed skills in subjective map evaluation, including contours and 3D visualisation.

• Gained experience in balancing accuracy with data collection effort.

• Strengthened academic writing, presentation, and referencing skills.

The final outcome was a comprehensive, evidence-based recommendation for Snowy Wilderness Walkers, supported by quantitative and qualitative analysis.

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