Questions

What are the spatial patterns of natural resource governance perceptions in relation to environmental condition over time?

Tools and Approaches

1. Tabulate area in ArcGIS Prop
2. T-test in R

Analysis Steps

1. To begin to answer this question, I first needed to find spatial layers of the environmental condition in the Puget Sound over time. I could not use the same ‘environmental effects’ data I used for Exercise 2, as the data was only completed 5 months prior. Instead, I substituted two land cover maps from 1992 and 2016 to approximate environmental condition.

I first loaded the raster layers into ArcGIS Pro. I then reclassified the land types (of which there were 255, but only 25 present in the region) down to 14. I then created buffers of 5km for my points of individuals with governance scores one standard deviation above and below the average. I then tabulated the land cover area within the buffers for each time period.

1. To test whether land cover change differed between those with high and low perceptions, I exported the tables and calculated the change in land cover for the two samples. I then ran two-sample t-tests for each land cover type change between the two groups.

Results

Land cover change differed significantly for four different land cover types between the two groups—grassland, forest, shrub/scrub, and bare land cover. Grassland cover decreased for both groups, but decreased by 5% more in in the areas with individuals with below average governance perceptions. Forest cover also decreased for both group, but decrease by 1% more individuals with below average governance perceptions (the amount of forest cover in the region is very large which accounts for why a 1% difference is significant). Shrub and scrub cover increased for both groups, but increased by 3% more in areas with individuals with below average governance perceptions. Lastly, bare land decreased for both, but decreased by 5% more for individuals in areas with individuals with below average governance perceptions (Table 1).

The effect size of land cover change on perception was small for each of the significant variables with biserial correlations between .07 and .09 (Table 1).

Table 1. Differences in land cover change between individuals with above average perceptions of natural resource governance perceptions and individuals with below average governance perceptions

	Governance Perception1
	Above Average	Below Average	t-value	p-value	Effect size (rpb)
High Development	17	16	0.80	.426	.02
Medium Development	19	19	0.67	.500	.02
Low Development	11	11	0.22	.829	.00
Developed Open Space	12	13	0.21	.837	.00
Agriculture	-1	0	0.74	.461	.02
Grassland	-13	-18	3.03	.002	.09
Forest	-10	-9	2.61	.009	.08
Shrub/Scrub	40	43	3.04	.002	.09
Wetlands	0	0	1.67	.095	.05
Unconsolidated Shore	2	2	0.10	.918	.00
Bare Land	-21	-25	2.43	.015	.07
Water	0	0	0.90	.368	.03
Aquatic Beds	-1	-5	1.19	.233	.03
Snow/Tundra	0	0	0.00	.997	.00

¹Cell entries are percent changes of land cover from 1992 to 2016

 

1. Critique

The biggest problem I had with this method was figuring out the most appropriate way to test my question. I struggled with classifying ‘good’ or ‘bad’ land cover change, and ultimately decided it was inappropriate. I also don’t think land cover is necessarily the best way to test environmental condition. I think it would be more appropriate if I were able to use environmental effects over time. I think it would also be best if I did different sized buffers around individuals.

Additionally, I believe I should have controlled for how long they lived in the region. The average number of years individuals have lived in the region was 34, which is less than the land cover change of 23 years. However, as years lived in the Puget Sound is a significant determinant of perception, it may have more to add in this case. The problem here is that this method is clunky. The way in which I differentiated high/low perception was by having different csv files. Having to then split these by years in the region would create an additional step, and more files, that could ultimately increase personal error because of the difficulty in keeping track of which buffer is which type of individual.