Exercise 3: Nearest Neighbor Analysis
How is the spatial presence of postfire woodpecker nests related to the spatial presence of salvage-logged forest stands?
- How are woodpecker nests clustered within survey units? (Exercise 1 and 3)
- How does this clustering relate to salvage treatment units within the survey units? (Exercise 2 and 3)
Average Nearest Neighbor in ArcMap
The Average Nearest Neighbor tool measures the distance between each feature centroid and its nearest neighbor’s centroid location and averages the distances for a nearest neighbor ratio. If the ratio is less than the average for a hypothetical random distribution, the feature distribution is clustered. If the ratio is greater than a hypothetical random distribution average, the features are dispersed.
Near Distance in ArcMap
The Near tool measures near distances between one set of target features and another set of target features. It produces additional columns in the original shapefile containing distance measurements in units of the user’s choosing. The user can enable a location option displaying X and Y distances individually.
For this analysis, I used 2016 and 2017 woodpecker nest point datasets clipped to each RMRS survey unit. I also used a polygon shapefile of 35 salvage harvest units within RMRS woodpecker survey units. I used another polgyon shapefile of the woodpecker survey units and a WorldView-3 1 m raster for supplementary data.
Nearest Neighbor Analysis Steps
- Export 2016 and 2017 nest points as one nest shapefile per RMRS survey unit for inputs into the Average Nearest Neighbor tool.
- Export salvage treatment polygons into three shapefiles for treatments 1, 2, and 3.
- Run the Average Nearest Neighbor tool in ArcMap with multiple inputs. I ran the tool on each 2016 and 2017 nest shapefile per survey unit, all 2016 and 2017 nests, all salvage units, and each salvage treatment type shapefile (see table below).
- Run the Near tool to produce near distances in meters for the distance of each nest to a salvage unit. I ran this tool for 2016 and 2017 nests using salvage unit centroids and salvage unit polygons, which produced different results as expected.
- Use Excel to create an Average Nearest Neighbor table for comparing 2016 and 2017 results.
- Use ggplot in R to plot near distance graphs for 2016 and 2017. These graphs display near distance distributions for nest points to salvage unit centroids and near distances for nest points to salvage polygon boundaries.
I produced boxplots displaying near distance distributions for nest points. Sets of graphs are featured with and without the control nests for different visual interpretations.
Near Distances for Nest Points to Salvage Centroids (With Control Nests)
Near Distances for Nest Points to Salvage Centroids (Without Control Nests)
Near Distances for Nest Points to Salvage Polygons (With Control Nests)
Near Distances for Nest Points to Salvage Polygons (Without Control Nests)
Nearest Neighbor Results Table
Above: Nearest neighbor results for woodpecker nests in each survey unit in 2016 and 2017. The NN Ratio is a threshold for expected clustering or dispersion. NN Ratio values less than 1 indicate clustering and NN Ratio values greater than 1 indicate dispersion. In 2017, green cells indicate an increase in value and red cells indicate a decrease in value. NN Ratio cells with an increased value in 2017 indicate units where nests increased dispersion. NN Ratio cells with a decreased value in 2017 indicate units where nests increased clustering. Alder Gulch (Treatment 3), Lower Fawn Creek (Treatment 2), and Upper Fawn Creek (Treatment 2) demonstrated increased clustering in 2017. Big Canyon (Treatment 1), Crazy Creek (Treatment 1), and Sloan Gulch (Treatment 3) experienced increased nest dispersion in 2017. All control units experienced increased or present dispersion in 2017. The “All Nests” and salvage shapefile results were produced for comparisons and to evaluate how clustering/dispersion within datasets may affect clustering/dispersion of other datasets.
With these two techniques creating near distance values and a nearest neighbor table, we can implement a refined nearest neighbor analysis. This analysis examines nearest neighbor distances in combination with distances to the study boundary. Refined nearest neighbor uses standardized formulas integrating the distribution of observed nearest neighbor distances and the distribution of expected (random) nearest neighbor distances while factoring in distance to unit boundaries. In the table above, the NN Expected and NN Observed values will aid refined nearest neighbor results.
Problems and Critique
Did this nearest neighbor analysis account for the target area size and shape? In the ArcMap Average Nearest Neighbor help documents, these assumptions are made:
- “The equations used to calculate the average nearest neighbor distance index (1) and z-score (4) are based on the assumption that the points being measured are free to locate anywhere within the study area (for example, there are no barriers, and all cases or features are located independently of one another).”
- “The average nearest neighbor method is very sensitive to the Area value (small changes in the Area parameter value can result in considerable changes in the z-score and p-value results). Consequently, the Average Nearest Neighbor tool is most effective for comparing different features in a fixed study area.”
Because of this, I’m not sure whether I can interpret these results properly based on the study design. We are surveying specially designated study units, not the entire burn area. Therefore, our sample calculations should not assume the entire area is available for clustering or dispersion. There is an option in ArcMap to input “Area” as an integer but not as a polygon shapefile (for survey and salvage units). To control for this, I calculated Average Nearest Neighbor on nest shapefiles for each survey unit instead of all nests together. However, as shown in the table above I also calculated Average Nearest Neighbor for all nests and different combinations of salvage units. These results may prove less reliable.
I would like to extend this analysis to a nearest neighbor technique adjusting for both the clustering/dispersion of the nests and the clustering/dispersion of the salvage units and survey units at the same time. The predetermined study design continues to require special consideration.