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.