Tag Archives: natural resources

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.