Category: Labs

Background

Over the past 3 weeks we have been looking at different environmental data in different locations around the world in order to analyze the Capitalocene. In general, the Capitalocene analyzes how as countries become more economically advanced the worse their effect on the environment is. In our first lab we use EPI and World bank data to compare income level with region their environmental impact. Graphing this data showed us that less developed and poorer countries tended to have a worse EPI level. Our second lab we wanted to look at more factors that contribute to the Capitalocene to potentially see further correlation, and then map these findings on to ARCGIS. From this lab I actually found that more developed countries, especially in Europe, actually had a worse pollution score compared to less developed countries. Look at this specific data mapped it was clear we needed to further look at Capitalocene data to make a clear connection. So, for our third lab we analyzed world values survey v.s income level. We specifically looked at citizens responses to Economic development v.s Environmental protection, by finding a hypothesis and a p value based on our data, we proved that the lower the countries income the higher they’ll prioritize economic growth over environmental protection. All these labs helped us analyze the ideas of the Capitalocene and how the world’s current environmental data correlates.

For this final Capitalocene lab we shifted our focus to EJ, environmental justice. The U.S EPA defines environmental justice as ““the fair treatment and meaningful involvement [emph. added] of all people regardless of race, color, national origin, or income with respect to the development, implementation and enforcement of environmental laws, regulations and policies”. We analyzed this data while relating it to the Capitalocene by looking at race v.s class, specifically we wanted to look at environmental injustice having to do with toxicities. We also utilized multiple Portland Air Toxicity Reports (PATS) in order to look at pollution that affects the Portland area. We specifically focused on polycyclic aromatic hydrocarbons, which is pollution from wood combustion. We chose this because based on PATS report, this is the pollutant they were most worried about. We did this in order to see spacial coincidences with waste and race/class. While it may connect to the Capitalocene in some ways, some issues with analyzing this type of data is that spatial correlation may not imply causation. We will further talk about factors that might affect our data when we discuss our maps.

Procedure

  We first began this lab by importing data from ACS and PATS. The PATS data, like i mentioned above, is from the Portland Air Toxics Solutions, a database that gathers information on toxic air pollutants in Portland. The ACS data is a census community survey that regularly gathers information on different demographics. We utilized the information about the toxin PAH15, or Poly Aromatic Hydrocarbons. This toxin is an outcome from wood combustion and we used it because it is the pollutant PATS is most concerned about. The ACS data gave us information on income level and race demographics from citizens in Portland. We were able to manipulate this data by combining income over $100,000 to make a high income group and combining low income (under $50,000) groups. Additionally we combined the black and hispanic groups to create a minority race group to compare to the residence location of white citizens.

Results:

Toxicity level of wood combustion based on race –

Toxicity v.s Income ( x < 49,000)

Toxicity v.s Income (100,000 ≤ x ≤ 200,000)

Heat Map of Toxicity levels for wood combustion pollutant

From our maps we can start to see some correlation between class and race v.s toxicity levels in an area. EJ focuses more on race when looking at environmental justice and injustices. We chose to look at it from multiple angles as to really flush out a true correlation or conclusion. As you can see from our two income level maps, lower income areas and residents are more central to these toxicities compared to higher income. This same thing is true if you compare our white residents map to our black and hispanic map, there are more concentrated areas of black and hispanic residents where the toxins are more concentrated. While this might point out environmental injustices in Portland as well as the Capitalocene, which I will discuss below,  it is important to recognize other factors. Some of these being geographic influences as where more concentrated populations live are going to have more toxins as well as non-human used areas. Overall, it is important not to generalize when looking at smaller pieces of data as you don’t always have to come to an immediate and clear solution.

Discussion:

Based on our mapping, the high income group seems to reside farther outside the city, which is farther away from these toxins. We found one exception on the map which is  a cluster in the center of the city which also happens to be an expensive place to live. A lot of the low income group resides not in the center of the city but also not on the far out cleaner suburban areas. The minority residents seemed to be living in closer proximity to the city in higher toxin areas. Compared to white residents who are settled on the outside of the city in lower population areas illustrating an even larger divide between white residents and black and hispanic ones.

     We only have demographics on these residents and not where they are employed. This factor could affect the ACS groups as proximity to work is an important factor to where you live. This is a limitation of what our data allows us to look at. Our results seem to indicate that minority and low income groups are more exposed to air pollutants and toxins. I believe it would be interesting to look at low income minority groups and low income white groups to see if race is important or if it depends mostly on the income level. This would be especially interesting having to do with EJ because it would allow us to really look at environmental injustices specifically with race. This further showed us how the Capitolscene affects the environmental as we look at specific environmental factors and how different groups are affected by industrialization. I think it would also be  interesting to see if minorities make up the majority of the low income sections. 

Featured Image: Courtesy Ricardo Levins Morales

Background:

In order to look at the Capitalocene we have been analyzing different world data the past two weeks. The Capitalocene looks at how as countries become more economically advanced, their impact on the environment worsens. We’ve been analyzing EPI and World country data in order to look at how different countries environmental standards compare to their level of development. For this lab, we used the World Values Survey to look at how the Capitalocene can explain significant environmental values and beliefs among people from country to country. These views we assume are related to each countries correlating environmental practices, which was measured through EPI. This survey gives us an entirely new perspective on different countries environmental impact because it gives individual citizens views on how they value the environment as well as how they feel their government should deal with the environment.

Procedure:

We first selected three countries we wanted to look at in the World values survey, one of each income group. We chose Haiti (low income), Algeria (Middle income) and Argentina (High income). We extracted each of these countries data from the world values survey and picked a variable, or specific survey question, we wanted to look at. We chose protecting the Environment v.s Economic growth, with a 1 representing a priority for protecting the environment even if it causes slower economic growth and some loss of jobs, and a 2 representing a priority on economic growth and creating jobs even if the environment suffers. We then graphed each countries response to this question into a histogram in order to look at the number of responses compared to whether the majority answered 1 or 2. From there, we calculated the average and standard deviation for all three countries in order to map the varying degree of responses from each country. From this information we came up with a hypothesis in order to later find a null hypothesis and do a t-test to test our hypothesis. Our hypothesis stated, the lower the countries income the higher they’ll prioritize economic growth over environmental protection. In order to find a p variable to compare the data we had to do a t-test for each country, i.e Haiti v.s Algeria, Haiti v.s Argentina and Argentina v.s Algeria. Our null hypothesis stated, the higher the countries income the lower they’ll prioritize environmental protection. We calculated the p-value for  each set of countries, and found they were very small, as you can see in the analysis. These very small P-values make sense though, because of our huge sample size. After we found this, we compared our values in order to decide if we should reject or discard our null hypothesis.

Results:

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These three histograms are showing the responses (either 1 or 2) of the citizens of each country. These responses correlate with our hypothesis because for the lower income country (Haiti), the majority of responses fall towards 2 which is economic growth as a priority. Where as the higher income country (Argentina), mostly falls to 1 which puts environmental protection as more important.

This graph is our mean for each countries response, which we calculated from the world values survey data. The bars in the middle represent the standard deviation for each countries data.  This helped us look at the different information country to country and see how much they differed.

Discussion:

These findings further back up our hypothesis we made stating, the lower the countries income the higher they’ll prioritize economic growth over environmental protection. From our results you can see that Argentina was the most concerned about their environmental impact, followed by Algeria and the Haiti. From my first Capitalocene lab I found similar results, which suggested that higher income regions also had higher EPI scores. Without further research from lab 1, I wasn’t confident to make a strong link between the Capitalocene due to the EPI being based off of performance goals. In lab 2 I compared Exports and Trade % of GDP to air pollution and overall emissions, which actually showed the opposite findings from my first lab. The data showed that more developed countries in Europe and some parts of Asia actually had a worse EPI indicator for pollution and emissions compared to less developed countries. From that lab I found that the data you map and find is very dependent on the data you pick, so it has the possibility to not give you a good overall look at the Capitalocene country to country. This lab, although, had the ability to give a more broad look at countries view on the environment which I believe is more accurate in terms of the Capitalocene. From this lab, I found that there may be a correlation to attitudes about the environment and protection based off of income level. The higher income countries have a larger emphasis on the environment compared to lower income countries who prioritize economic development. This suggests, there is a trend which shows that the better and more stable the economy may mean that people can focus on other issues such as the environment and their impact. 

Over the past 4 weeks we’ve been doing research and having discussions in order to situate land cover change in our local environment. We researched land use cover change in three different areas, Lewis and Clark College campus, Collins view neighborhood and Riverview national area. From our data collection and comparison we were able to make connections and find unique differences in all three areas. From this research we were able to develop some thoughts and hypotheses about broader land cover changes. In order to synthesize our thoughts and findings, you can find our story map here where we situate our data in terms of our findings, as well as the broader context of land use cover change.

 While continuing our study of land use cover change in the Portland area, we have started to map our data from our other labs in order to analyze change which may have occurred since 1931. Our last labs, posted here, were used to collect data from our specific site, a residential home in Collins View, as well as gain data from 11 other sites that our peers took, including Collins View, Riverview and Lewis and Clark, 4 groups in each area respectively.      

    For this specific lab, we took all our data which we organized in our last lab, and began adding it to ARCGIS. This is a program that can give us an aerial map view of the sites separated by color, and the field points indicated, as you can see in the base map below in figure 1. The pink indicates the RVNA area that 4 groups went to, showing one outlier as one group went slightly farther than the designated area. The orange indicating the Collins view boundary, which had 4 more groups and the red indicates the Lewis and Clark boundary which also contains all 4 groups. Once we added the three separate locations of study, we entered all the collected data we comprised last week. This data consisted of humidity, temperature, canopy cover, ground cover, and more data from the past labs. We looked at many different versions of this map by applying different layers as well as adding 3 overlaying pictures. These 3 pictures showed the same area in 1939, 1961 and 1982 respectively. This allowed us to overlay different data points found by our teams of current data, over the older maps to analyze the land cover change, as you can see below.

 Starting by mapping temperature compared to canopy cover (figure 2). It’s important to note that percent canopy cover and average temperature, as illustrated in figure  2, seem to be inversely correlated as the canopy cover goes down, the average temperature rises. We suspected this in our last lab but now that we can see the data on top of each other, it is clear that the amount of tree canopy removed over the years has affected the areas temperature. We chose to focus on two data points that seemed to have visually changed the most over the years. From the archives,  we were able to overlay 4 different aerial photos from the area, a 1939 photo, 61’ photo, 82’ photo and the 2018 google maps photo. We used these different photos and the 12 data points to observe the change in land use since 1939. We used the 1939 map as a base year for what we would compare all the other maps to, as thats the oldest we were able to compare to. For out site in collins view, (45.46, -122.68), land use is visibly changed from the 39 photo (figure 4)and 61(figure 5). The most significant change is the agriculture around the area, and some canopy cover, but not too significantly. The time period between 39 and 61 we see the whole area shifting into a more suburban setting. The Lewis and Clark campus also shifts a lot during this time period, as after 1934 the red area was bought in order to create Albany college. By 1939, you see can see this shift in the Lewis and Clark area.  By 1961 Collins view has shifted into a larger residential area, similar to today, and Lewis and Clark has formed into a college.

 RVNA, on the other hand, stays pretty consistent if you compare the 1939 photograph to the current google maps photo, there is not much canopy cover change in the area. In comparison, the two other sites canopy cover was vastly altered in the 80 year period. We noted this in our last lab as well, as the large shift in canopy cover was a larger data point for those two groups. Looking at figure 2 again, it seems like the shift in canopy cover has affected the temperature range of the areas. As you can see that the less canopy cover percentage, the larger the temperature max.

   The change in canopy and ground cover could also be a concern for biodiversity in the area. From these three areas we can see how we’ve used the land and affected biodiversity potentially. As Collins view and Lewis and Clark we saw from our data, share many of the same attributes, compared to Riverview which has stayed relatively consistent. Because of this, Riverview represents how the land used to act like. As far as biodiversity is concerned, I am curious as to how these canopy and ground cover changes may be advantageous or possibly disadvantageous. To better understand the full impact on the area over the years we would need more data from additional areas to compare the effect on the environment. As well as accounts from people living in the area for a long time might aid in our research.