Tag Archives: governance

Final

 

Question

How are the spatial patterns of individuals’ perception of natural resource governance related to the spatial patterns of environmental restoration sites via distance and abundance of improved sites?

Datasets

 Puget Sound Partnership Social Data

My data came from a survey on subjective human wellbeing related to natural environments. The sample was a stratified random sample (28% response, n= 2323) of the general public from the Puget Sound in Washington from one time period. I am specifically examining questions related to perceptions of natural resource governance. Around 1730 individuals gave location data (cross street and zip code) which have been converted to GPS points. I also have demographic information for individuals, their overall life satisfaction, and how attached and connected they feel to the environment.

Environmental Data

I have three different forms of environmental data. (1) Point locations of restoration sites in the Puget Sound. (2) A shapefile of census tract level average environmental effects (the environmental condition ranked from 1 good to 10 very poor, and (3) Land cover raster files from 1992 and 2016.

  • Individual restoration sites numbered over 12,000. They spanned many years, and point locations overlapped significantly through time.
  • The environmental effects data comes from an online mapping tool that was created in a collaboration between the University of Washington, the Washington Department of Ecology, and the Washington Department of Health. Environmental effects are based on lead risk from housing, proximity to hazardous waste treatment storage and disposal facilities (TSDSs), proximity to national priorities list facilities (Superfund Sites). Proximity to Risk Management Plan (RMP) facilities, and wastewater discharge. The combination of these effects was aggregated within census tracks to produce an environmental effects ‘Rank’ from low effects (Rank of 1) to high effects (Rank of 10).
  • The land cover files contained 25 different types of land cover. I reclassified these land cover types by combining similar types of land cover (e.g. deciduous forest and conifer forest to ‘forest’).

 

Hypotheses

 1) Shorter distances between individuals and restoration sites, and greater number of restoration sites near individuals, would correlate positively with governance perceptions

2) Positive environmental conditions will correlate positively with governance perceptions

Richard Petty and John Cacioppo developed the elaboration likelihood model (1980) which asserts how individuals change their attitude based on persuasive stimuli. Perry and Cacioppo develop this idea by implementing the idea of two routes of persuasion—the central and peripheral paths—where determinants of routes are determined by motivation, ability, and nature of mental processing. The purpose of this theory is to help explain how individuals elaborate on ideas to form attitudes and how strong, or long lasting, those attitudes are. This theory may suggest that individuals will form stronger attitudes when reasons to form attitudes are stronger and more immediate. Stimuli of this kind tend to be nearer to individuals, as processing of issues far away is cognitively difficult. Research has shown that individuals have a difficult time thinking about problems on larger spatial scales, and there is an inverse relationship between how feelings of individual responsibility for environmental problems and spatial scale (as scale gets larger, feelings of responsibility go down (Uzzell, 2000).

Cognitive biases also suggest that people use heuristics to answer questions when they are unsure. One common heuristic is the saliency bias, where individuals overemphasize things that are emotionally more important, and ignore less interesting things (Kahneman, 2011). This may suggest that spatial patterns are more important than temporal patterns because what is happening now is more salient to individuals than what happening in the past. This may imply that even if environmental outcomes have improved over time, the immediacy of the current environment or things influencing the environment may have a greater effect on individual perceptions.

 

Approaches

Statistical Analyses

Spatial Autocorrelation Analyses

Moran’s I: To compute Moran’s I, I used the “ape” library in R. I also subset my data to examine spatial autocorrelation by demographics including area (urban, suburban, rural), political ideology, life satisfaction, income, and cluster (created by running a cluster analysis on the seven variables which comprise the governance index

Correlograms: I created correlograms for the variables that were significant (urban, conservative, and liberal) using the “ncf” library and the correlog() function. These figures give a better picture of spatial autocorrelation at various distances.

Semivariograms: To create semivariograms, I used the “gstat” and “lattice” libraries which contain a function called variogram. This function takes the variable of interest along with latitude and longitude locations. The object created can then be plotted. For this analysis I used the same subsets as in the Moran’s I analysis. These figures are excluded from the results as they do not provide information beyond what the correlograms show.

Nearest Neighbor analysis: A file of restoration sites was loaded into R. The sites were points. This analysis required four libraries as the points were from different files. The libraries were: nlem, rpart, spatstat, and sf. I first had to turn my points into point objects in R (.ppp objects). I used the convenxhull.xy() function to create the ‘window’ for my point object, then I used that to run the ppp() function. I then used the nncross() function. This function produced min, max, average, and quartile distances from individuals to the nearest restoration site. I added the ‘distance’ from the nearest neighbor as a variable in a linear regression to determine an association. I used these distances (1st quartile, median, and 3rd quartile) to produce buffers. I ran spatial joins between the buffers and the restoration sites. This produced an attribute table that had a join count—the number of restoration sites within the buffer. In R I ran a linear regression with join count as an independent variable.

Geographically weighted regression: I joined individual points to my rank data, a shapfile at census tract level. This gave individuals a rank for environmental effects at their location. Initially I used the GWR tool in Arc to run the regression, but wanted the flexibility of changing parameters and an easily readable output that R provides. In R, I used two different functions to run GWR, gwr(), and gwr.basic(). The gwr() required creating a band using gwr.sel, and the gwr.basic required creating a band using bw.gwr. The difference between these functions is that gwr.basic produces p-values for the betas. I ran gwr on both my entire data set and a subset based on perceptions. The subset was the ‘most agreeable’ and ‘least agreeable’ individuals who I defined as those one standard deviation above and below the mean perception.

Neighborhood analysis: I created a dataset of the upper and lower values of my governance perceptions (one standard deviation above and below the means). I then added buffers to these points at 1 and 5 miles. I joined the buffers to the rank values to get an average rank within those buffers. I exported the files for each buffer to R. I created a density plot of average rank for the low governance values at each buffer, and for the high governance values at each buffer.

T-test: 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.

Mapping

Kriging and IDW: I used the Spatial Analysis toolbox to preform Kriging and IDW to compare the outputs of the two techniques. I used my indexed variable of governance perceptions. The values of the variable vary from 1 to 7. I then also uploaded a shapefile bounding the sample area.

Hotspot Analysis: I used the Spatial Analysis toolbox to preform ‘hotspot analysis.’ I used my indexed variable of governance perceptions with values from 1 to 7.

Tabulate area: I used two land cover maps from 1992 and 2016 to approximate environmental condition. I loaded the raster layers into ArcGIS Pro, and 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.

 

Results

What are the spatial patterns of perceptions of natural resource governance?

 Moran’s I statistic for overall governance perceptions was insignificant, indicating a lack of spatial autocorrelation at the regional scale (I value; p = 0.51). Significant autocorrelation was detected when the data were subset by demographic variables. Individuals residing in urban areas exhibited spatial autocorrelation of perceptions while individuals in suburban and rural areas did not. Lastly, individuals classified as ‘conservative’ (political ideology < 0.5) exhibited significant spatial autocorrelation for governance perceptions (I statistic: -0.0062, p = 0.0018). The spatial autocorrelation for governance perceptions was mainly restricted to short distances as shown in the correlograms below. Significant correlations for the entire study population and individuals in urban areas were detected at near distances Individuals classified as conservative exhibited significant correlation across multiple distances. No significant correlations were found among liberal individuals, suggesting a non-spatial driver mechanism may be at play.

 

Local patches of significantly lower perceptions (cold spots) and significantly higher perceptions (hot spots) were identified as shown in the map below. Three ‘cold spots’ were located in the areas around Port Angeles, Shelton, and the greater Everett area. Two hot spots were identified surrounding Bainbridge Island, and the city of Tacoma, which is where the Puget Sound Partnership is located.

Blue points are “cold spots” at 99%, 95%, and 90% confidence that the points reside in a cold spot. Cold spots are areas where perception is significantly lower than the average compared to those around them. Red points are “hot spots” at 99%, 95%, and 90% confidence that the points resides in a hot spot. Hot spots are areas where perception is significantly higher than the average compared to those around them. Small grey points are insignificantly different. Bounding lines are counties in Northwest Washington.

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

The median distance for individuals from restoration site was 0.037 degrees, 1st quartile was 0.020 degrees, and 3rd quartile was 0.057 degrees.

The regression on whether distance correlates with individual perception was insignificant (p = 0.198), which means distance from the nearest restoration site does not influence perceptions. For each regression on the number of sites near an individual, all coefficients were negative, meaning the more sites near an individual, the more disagreeable their perception was. All produced significant results, but the effect size of number of sites near individuals was very minimal (Table 1).

Table 1. Regression results for ‘nearest neighbor’ of individuals to restoration sites.

Buffer Size b p-value Effect Size (rpb)
Buffer 1 (1st quartile) -0.003 0.0181 .003
Buffer 2 (median) -0.002 0.045 .002
Buffer 3 (3rd quartile) -0.001 0.002 .003

In a geographically weighted regression (GWR) model, rank, life satisfaction, years lived in the Puget Sound, and race are significant (Table 2). All other variables were insignificant. For rank (the combination score of environmental effects) the coefficient was positive. Higher rank values are worse environmental effects, so as agreeable perceptions increase, environmental condition decreases. Overall, the effect size of this model on governance perceptions was small, explaining about 10% of the variance in the data (Table 2).

A map of residuals from the GWR confirms the results from the hotspot analysis about where high and low responses congregate.

Table 2. Regression results for environmental effects at individuals’ locations.

Variable1 b p-value2
Rank 0.077 0.007**
Life Satisfaction 0.478 <0.001**
Years Lived in the Puget Sound -0.010 0.002**
Sex -0.156 0.289
Area -0.150 0.179
Education -0.002 0.922
Income -0.022 0.622
Race 0.006 0.034*
Ideology 0.103 0.533

1R2 = 0.094

2 ** = significant at the 0.01 level, * = significant at the 0.05 level

The plot of high and low governance values at the two buffers is presented below. The black and grey curves represent respondents from the survey that were at least one standard deviation lower than the mean (the mean was neutral). The green curves represent respondents from the survey that were at least one standard deviation higher than the mean. The solid curves are the average rank at the one mile buffer, and the dotted curve is the average rank at the five mile buffer.

This figure indicates there are two peaks in average rank, at low environmental effects (~1) and at mid-environmental effects (~4.75). Those with lower perceptions of environmental governance had higher peaks at low environmental effects for each buffer size. Those with higher perceptions of environmental governance had a bimodal distribution with peaks at low and mid-environmental effects.

 

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

I used land cover change as a proxy for environmental condition over time. 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 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 3). 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 3. 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

As one land cover type goes up, another land cover type must go down. While forest overall decreased in the region for both groups over time, areas with lower perceptions saw significantly less decrease. The percentage is small, but a lot of the area in the region is forested, so the actual amount of land is large. The two maps below show land cover in 2016 (top) and 1992 (bottom) around the area of Port Angeles. This area is one that had a significant cold spot (low perceptions). From these maps, you can see a spread of the dark green color (forest), and a decrease of the yellow (grassland). Port Angeles historically was a timber community (the same could be said for the regions of the other cold spots in the region. This change in forest and grassland could be the source of negative perceptions if these communities believe the governance structures are responsible for the change in forest cover.

Significance

Overall, in this analysis, poor environmental condition relates to positive perceptions, and land cover change may provide insight into reasons for poor perceptions. Areas with negative perceptions may not directly indicate poor natural resource governance. Elsewhere, trust in natural resource governance was primarily driven by individual value orientations (Manfredo et. al. (2017). The three areas identified as cold spots in this study could be areas where individuals do not feel their values are represented by the natural resource governing systems. Conversely, hot spot area A is located near the headquarters of the Puget Sound Partnership, potentially facilitating trust, representation, and access to information that may influence the perceptions of individuals located nearby. This urban, liberal, developed area contrasts the area to the west with a highly significant cold spot (Area 2). Individuals from area two, a more rural, conservative, historic logging site likely have different value orientations than those from Area A. Similar comparisons of the other cold spots could be made to areas near them. More research should be conducted in this area to determine if value orientations have a significant influence on perceptions of natural resource governance.

 

Your learning

 

During this course I significantly advanced my knowledge in ArcGIS Pro and in R studio. I learned how to run many new tools in Arc including hotspot, geographically weighted regression, and tabulate area. I also learned new skills in troubleshooting problems. For example, I was having difficulty getting the rank data to display on my map. Another student taught me that as I imported the data from a .csv and joined it to a shapefile, I would need to create a new feature class from it before it would be able to display. I also gained knowledge in how to run more spatial analyses in R. These included many spatial autocorrelation analyses, and geographically weighted regression (which involved creating a point object!).

 

What did you learn about statistics?

 

I first and foremost learned of the existence of many types of spatial statistics. These included statistics Hotspot (Getis-Ord Gi*), spatial autocorrelation (correlogram, semivariogram, Moran’s I), geographically weighted regression (GWR). For hotspot, I learned what the hotspot clusters mean, and the best ways to pick the fixed-distance band of the neighbors the tool looks at (either through selecting the number of minimal points near it, or by looking at the z-score at the distance that shows the highest amount of spatial autocorrelation). Speaking of spatial autocorrelation, I learned what that meant in terms of the correlation of perceptions of governance between individuals depending on their distance from each other. Finally, I learned a little about GWR, including that it is necessary to plot the output to examine patterns over the space in question, and I also learned that the reason Arc does not display p-values for independent variables is not something to due with the equation, but actually due to computing power of Arc.

Exercise 3: Perception of natural resource governance and changes in environment over time

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.

 

Exercise 2: Spatial Patterns of Perceptions of Natural Resource Governance and Environmental Condition

Questions

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

 

Environmental condition was represented as: (1) Environmental restoration sites, and (2) aggregate ‘environmental effects’ scores by census tract on a scale from (1) low to (10) high that include lead risk from housing, proximity to hazardous waste treatment storage and disposal facilities, proximity to nation priorities list facilities (Superfund Sites), proximity risk management plan facilities, and wastewater discharge.

 

Sub-questions related to the primary question are:

a1. Does being near a restoration site associate with individual perception? a2. Does being near more restoration sites associate with individual perception?

b1. Do the environmental effects where an individual lives associate with individual perception? b2. Do the environmental effects around an individual associate with individual perception?

Tools and Approaches

  1. Nearest Neighbor analysis in ArcGIS Pro and R studio
  2. Geographically weighted regression in R studio
  3. Neighborhood analysis in ArcGIS Pro

Analysis Steps

  1. Nearest neighbor analysis was used to examine questions a1 and a2. First, a file of restoration sites was loaded into R. The sites were points in the same projection as my participant location points. This analysis required four libraries (as the points were from different files. The libraries were: nlem, rpart, spatstat, and sf. To use the tool I needed, I first had to turn my points into point objects in R (.ppp objects). First I used the convenxhull.xy() function to create the ‘window’ for my point object, then I used that to run the ppp() function. After doing this for both sets of points, I was able to use the nncross() function. This function produced min, max, average, and quartile distances from individuals to the nearest restoration site. I added the ‘distance’ from the nearest neighbor as a variable in a linear regression to determine an association for a1.

 

To examine a2, I used these distances (1st quartile, median, and 3rd quartile) to produce buffers in ArGIS Pro. After creating the buffers, I ran spatial joins between them and the restoration sites. This produced an attribute table that had a join count—the number of restoration sites within the buffer. I exported the three attribute tables from the three buffer distances back to R. In R I ran a linear regression with join count as an independent variable.

 

  1. To test b1, I preformed geographically weighted regression. I used both ArcGIS Pro and R studio to run this analysis. Initially I used the GWR tool in Arc to run the regression, but wanted the flexibility of changing parameters and an easily readable output that R provides. First I joined individual points to my rank data, a shapfile at census tract level. This gave individuals a rank for environmental effects at their location. In R, I used two different functions to run GWR, gwr(), and gwr.basic(). The gwr() required creating a band using gwr.sel, and the gwr.basic required creating a band using bw.gwr. The difference between these functions is that gwr.basic produces p-values for the betas. I ran gwr on both my entire data set and a subset based on perceptions. The subset was the ‘most agreeable’ and ‘least agreeable’ individuals who I defined as those one standard deviation above and below the mean perception.

 

  1. I preformed neighborhood analysis to test the final question, b2. First, I created a dataset that was just the upper and lower values of my governance perceptions (one standard deviation above and below the means. I then added buffers to these points at 1 and 5 miles. I then joined the buffers in ArcGIS Pro to the rank values to get an average rank within those buffers. I exported the files for each buffer to R. I R I created a density plot of average rank for the low governance values at each buffer, and for the high governance values at each buffer.

Results

  1. The median distance for individuals from restoration site was 0.037 degrees, 1st quartile was 0.020 degrees, and 3rd quartile was 0.057 degrees.

The regression on whether distance correlates with individual perception was insignificant (p = 0.198). This led to the conclusion that distance from the nearest restoration site does not influence perceptions.

 

For each regression on the number of sites near an individual, all coefficients were negative. This implies that the more sites near an individual, the more disagreeable their perception was. All produced significant results, but the effect size of number of sites near individuals was very minimal (Table 1).

 

Table 1. Regression results for ‘nearest neighbor’ of individuals to restoration sites.

Buffer Size b p-value Effect Size (rpb)
Buffer 1 (1st quartile) -0.003 0.0181 .003
Buffer 2 (median) -0.002 0.045 .002
Buffer 3 (3rd quartile) -0.001 0.002 .003

 

 

 

 

  1. For the geographically weighted regression, I am presenting the results from the gwr.basic() model. For this model, I included demographic variables to control for these factors. In the model, rank, life satisfaction, years lived in the Puget Sound, and Race are significant (Table 2). All other variables were insignificant, so I will not discuss their trends. For rank (the main variable of interest), the coefficient was positive. In this case higher rank values are worse environmental effects, so as agreeable perceptions increase, environmental condition decreases. Life satisfaction is a variable of how satisfied individuals are with their life overall, which correlated positively with perception (Table 1). Years lived in the Puget Sound correlated negatively, or perception decreased the longer someone lives there (Table 1). Race, a dummy variable of white or not-white, indicated higher perceptions were held by white individuals.

 

Overall, the effect size of this model on governance perceptions was small, explaining about 10% of the variance in the data (Table 1).

 

Table 1. Regression results for environmental effects at individuals’ locations.

Variable1 b p-value2
Rank 0.077 0.007**
Life Satisfaction 0.478 <0.001**
Years Lived in the Puget Sound -0.010 0.002**
Sex -0.156 0.289
Area -0.150 0.179
Education -0.002 0.922
Income -0.022 0.622
Race 0.006 0.034*
Ideology 0.103 0.533

1R2 = 0.094

2 ** = significant at the 0.01 level, * = significant at the 0.05 level

 

  1. The plot of high and low governance values at the two buffers is presented below. The black and red curves represent respondents from the survey that were at least one standard deviation lower than the mean (the mean was neutral). The black curve is average rank with a one mile buffer, and the red curve is average rank at the five mile buffer. The green and blue curves represent respondents from the survey that were at least one standard deviation higher than the mean. The green curve is the average rank at the one mile buffer, and the blue curve is the average rank at the five mile buffer.

What this figure indicates is that there are two peaks in average rank at low environmental effects (~1) and at mid-environmental effects (~4.75). Those with lower perceptions of environmental governance had higher peaks at low environmental effects for each buffer size. Those with higher perceptions of environmental governance had a bimodal distribution with peaks at low and mid-environmental effects. The bimodal nature of these density functions leads me to believe there is some variable moderating governance perception related to environmental effects.

 

  1. Critique

The methods I used were all helpful in determining the spatial relation of environmental condition to perceptions of natural resource governance. However, switching between the two programs (ArcGIS and R) was a little bit of a hassle. R has greater flexibility when running analyses, as in running is easier. This meant it was ideal for the analyses that required me to tweak my models multiple times. I also ran into little problems with Arc in terms of loading the data which made everything run slower, but Arc is more intuitive in terms of finding and executing analyses/

Exercise 1: The Spatial Patterns of Natural Resource Governance Perceptions

Question

What are the spatial patterns of natural resource governance perceptions in the Puget Sound?

Tools and Approaches

  1. Moran’s I (with correlograms) and Semivariograms in R studio
  2. Kriging and IDW in ArcGIS Pro
  3. Hotspot Analysis in ArcGIS Pro

 

Analysis Steps

  1. To compute Moran’s I, I used the “ape” library in R which has a function called Moran.I(). This function takes the variable in question (governance perceptions), and a distance matrix to compute the observed and expected values of Moran’s I, as well as the standard deviation and a p-value. For this analysis, I also subset my data to examine spatial autocorrelation by demographics including area (urban, suburban, rural), political ideology, life satisfaction, income, and cluster (created by running a cluster analysis on the seven variables which comprise the governance index).  I created correlograms for the variables that were significant (urban, conservative, and liberal) using the “ncf” library and the correlog() function. These figures give a better picture of spatial autocorrelation at various distances.  To create semivariograms, I used the “gstat” and “lattice” libraries which contain a function called variogram. This function takes the variable of interest along with latitude and longitude locations. The object created can then be plotted. For this analysis I used the same subsets as in the Moran’s I analysis.
  2. To preform interpolation on my data, I loaded my point data into ArcGIS Pro. I then used the Spatial Analysis toolbox to preform Kriging  and IDW to compare the outputs of the two techniques. I used my indexed variable of governance perceptions. The values of the variable vary from 1 to 7. I then also uploaded a shapefile bounding the sample area, as well as a shapefile of shoreline, to delineate my study area better.
  3. To run a hotspot analysis I used my previously loaded point data inArcGIS Pro. I then used the Spatial Analysis toolbox to preform ‘hotspot analysis.’ I used my indexed variable of governance perceptions with values from 1 to 7. I used the shapefile of shoreline to delineate my study area better.

Results

  1. The Moran’s I calculation was insignificant for rural, suburban, cluster groups, life satisfaction, and income, suggesting no spatial autocorrelation of governance perceptions by these subsets.

 

The Moran’s I calculation was significant for urban:

Observed value: -0.014

P-value: 0.0002

 

The Moran’s I calculation was also significant for ideology:

Conservative

Observed value: -0.006

P-value: 0.002

Liberal

Observed value: -0.002

P-value: 0.05

 

This suggests that in these subsets there is spatial autocorrelation between individual governance perceptions.

The semivariograms for the subsets that are significantly spatially autocorrelated are presented below.

 

None of these plots suggest high degrees of spatial autocorrelation. The urban plot does so more than the ideology plots, but the y axis scale is still very small.

 

 

 

 

 

 

 

 

The plot (top Urban, bottom left Liberal, bottom right Conservative) help to confirm the findings from above. The Moran’s I fluctuates around zero without much variation. The large spike in variation that the graphs do show are only for non significant points. Significant points are filled in, where non-significant points are open circles.

2. Interpolation

The kriging (bottom left) with individual points and IDW (bottom right), do not look incredibly different in terms of general trends. The kirging with shoreline (top) gives possibly the most interesting visual of spatial patterns. In general, perceptions are better (more green) in the center, where there is greater shoreline. There are also two sections that appear much more negative. To examine these locations further, I preformed a hotspot analysis.

3.  Hotspot Analysis

This image confirms the two bright red spots from the interpolation to be “cold spots” or spots that the value of perception is significantly lower  than the average perception (neutral) at a 99% confidence. The orange dots are a 95% confidence. The green corridor appears to hold in the southern part of the Sound and is confirmed at a “hotspot” or a spot that the value of perception is significantly higher than the areas surrounding it at a 99% confidence level.

The three main areas of red or orange correspond to the cities of Shelton (bottom), Port Angeles (west), and Everett with a little of south Whidbey Island (east).

  1. Critique

I believe all methods are useful, but some are redundant. I think it would probably be sufficient to do only one of each type of method—spatial autocorrelation and interpolation—but it is interesting and more convincing to see the same type of analysis done in different ways. The p-values from the Moran’s I appear to agree with the shape of the curve’s in the semivariograms, where the smaller p-values have more defined shapes. The same goes for the interpolation methods, while they are interesting to see side-by-side, they essentially tell the same story. I think in this case, the hotspot analysis shows the most interesting interpretation of the data because it indicates areas of potential concern.

Natural Resource Governance Perceptions and Environmental Restoration

Research Question

How is the spatial pattern of individuals perception of natural resource governance related to the spatial pattern of environmental restoration sites via distance and abundance of improved sites?

  

My Datasets

Puget Sound Partnership Environmental Outputs Data

The Puget Sound Partnership—a governmental monitoring entity—keeps records of environmental restoration projects throughout the Sound. There are GPS points for restoration site locations across their governing boundaries. I have downloaded the points, but I am still working on figuring out this dataset. There are over 12,000 entries, and many appear duplicative.

Puget Sound Partnership Social Data

I stratified a random sample (28% response, n= 2323) of the general public from the Puget Sound in Washington from one time period. They data are from a survey of subjective wellbeing related to natural environments. I am specifically examining the first block of seven questions related to perceptions of natural resource governance. These questions have been indexed into one perception score. Around 1770 individuals gave location data (cross street and zip code) which have been converted to GPS points. I also have demographic information for individuals.

 

Hypotheses

Based on current research, there is a significant correlation between environmental metrics and subjective wellbeing such as green space and air pollution (Diener, Oishi, and Tay 2018). I hypothesize that 1) shorter distances between individuals and restoration sites, and greater number of restoration sites near individuals, will correlate positively with governance perceptions, and 2) positive environmental outcomes will correlate positively with governance perceptions.

 

Approaches

I would like to test the statistical significance of distance from individual to restoration sites on governance perceptions, and test whether the number of sites within a radius moderates that relationship. I have previously created a plot of perception versus distance from other individuals, and perceptions are not spatially autocorrelated. To expand on this work, I would like to use spatial relationship modeling approaches, such as geographically weighted regression.

  

Expected outcome

I would like to produce statistical relationships between my dependent and independent variables. My dependent variables are good governance and life satisfaction (collected with demographic information). My independent variables are age, sex, race, area (self-indicated urban, suburban, or rural), years lived in the Puget Sound, political ideology (a proxy from voting precincts), income, education, number of restoration sites, and environmental improvement score.

I expect my relationships to be correlational and produce betas, p-values, and r2 values, which I will display as tables. The large volume of points (n = 1770 individuals & n = 12,000 restoration sites) I do not believe maps would provide visually relevant images. I already have maps of both perception points, and restoration points.

 

Significance

Incorporating aspects of subjective wellbeing and general public perspectives about natural resources into scientific assessment and decision-making processes could help managers improve human wellbeing and environmental outcomes simultaneously. The links between metrics of subjective wellbeing related to natural environments and metrics of ecosystem health have not been studied holistically. There are gaps in knowledge around understanding the connections among these systems. Research suggests that good governance plays an important role in improving wellbeing because governing systems provide goods and services that make people better off (Landman 2003). Current research, around good governance perceptions, has shown links to support for environmental improvement measures, but also shows individuals care less about environmental effectiveness of measures (Bennett et al. 2017). Research lacks knowledge in whether positive perceptions are linked to environmental conditions. To understand the connections between natural systems and subjective wellbeing, further research is needed that includes case studies that can illuminate general trends, as well as analyses that can show connections spatially (Milner‐Gulland et al. 2014).

 

Level of preparation

  • Arc-Info

I have taken one class that used ArcPro; GEOG 560.

  • Modelbuilder and/or GIS programming in Python

In GOEG 560 we completed one exercise that used Modelbuilder.

  • R

I have taken one class on R (FW 599), and have been using it actively for my own analyses for a few months, as well as taken GEOG 561, which primarily used R.

  • image processing

I took three digital photo classes using adobe photoshop and am very proficient in its use. I often use it to amend maps I make in Arc.

  • other relevant software

I do not believe I have expertise in any other relevant software.

 

Literature Cited

Bennett, Nathan J., Robin Roth, Sarah C. Klain, Kai Chan, Patrick Christie, Douglas A. Clark, Georgina Cullman, et al. 2017. “Conservation Social Science: Understanding and Integrating Human Dimensions to Improve Conservation.” Biological Conservation 205 (January): 93–108. https://doi.org/10.1016/j.biocon.2016.10.006.

Diener, Ed, Shigehiro Oishi, and Louis Tay. 2018. “Advances in Subjective Well-Being Research.” Nature Human Behaviour 2 (4): 253. https://doi.org/10.1038/s41562-018-0307-6.

Landman, Todd. 2003. “Map-Making and Analysis of the Main International Initiatives on Developing Indicators on Democracy and Good Governance.” Human Rights Centre University of Essex.

Milner‐Gulland, E. J., J. A. Mcgregor, M. Agarwala, G. Atkinson, P. Bevan, T. Clements, T. Daw, et al. 2014. “Accounting for the Impact of Conservation on Human Well-Being.” Conservation Biology 28 (5): 1160–66. https://doi.org/10.1111/cobi.12277.