Continuing to understand the spatial and temporal correlation between census variables and their relation to gentrification

For this exercise I was interested in continuing to test out different variable combinations with in the Geographically Weighted Regression and further exploring the meaning of the relationship between these variables. This framed exercise three with the following research questions:

  • What are the individual relationships between change in race, education, and income across both decades?
  • How does each change variable relate to itself across the two decades?
  • Do these results produce information that provide information on where there might be gentrification?
  • How do these results compare to where others have identified gentrification and how they have calculated it?

Geographically Weighted Regression

I continued using the GWR tool to make more maps of how the variables are correlated. I expected some similarity in the patterns between variables, and I hypothesized that they would be in the northern central part of the city. When I compare each decade’s variables to the same variable in the second decade, I expect some correlation in areas that have steady change, but lower correlation in areas that change rapidly in one decade and not the next.

In order to do this, I used the GWR tool in ArcGIS and used the number of neighbors as the neighborhood type. I ran the tool with change in race from 1990-2000 as the dependent variable and change in education from the same decade as the explanatory variable. I repeated this with race and income, and then all of it with the second decade. I then ran it with education as the dependent variable and income as the explanatory variable for each decade.

Comparison to Other Gentrification Maps

I have begun to look at other maps of gentrification in Portland, OR to help make sense of the information that I am getting out of the GWR tool, compare results, and compare methods. Bureau of Planning and Sustainability for the City of Portland has produced maps of gentrification, also by census tract throughout the city. They created a taxonomy of gentrification from susceptible to late gentrification (and even continued loss after gentrification). They also have maps of the variables that went into their maps, which consisted of percentages of populations that were: renters, of color, without a bachelor’s degree, and how many households were below 80% of the median family income. These were all combined into a vulnerability map. The changes in renters, race, education, and income were combined to create a map of demographic change. The change in median home value and appreciation from 1990-2010 maps were combined to create a housing market conditions map. All of these were combined to create the final gentrification map.


The results of the GWR tool produced maps comparing two variables for one decade. As mentioned above, I ran the tool for all variable combinations for both decades. These results show that each combination of variables changes together in a different part of the city. Change in race and education occurs in the northwest part of the city in the first decade and north central/west part in the second decade. Race and education change in the eastern part of the city in the first decade, and some in the east and some in the northwest in the second decade. Education and income change in the southern part of the city in the first decade and southeast in the second decade. It is interesting that all of the combinations are slightly different but have some patterns.

The regression coefficients from the model runs when comparing each variable across the two decades show interesting patterns too. The changes in race each decade was relatively similar across the city but had the highest coefficients in the southern part of the city. Changes in education both was the same in the eastern part of the city, and changes in income both decades were similar in the north/central part of the city.

Figure 1: This figure shows the coefficients from the GWR between two variables. a) Coefficients of change in race and education. b) Coefficients of change in race and income. c) Coefficients of change in education and income.

When compared with the Bureau of Planning and Sustainability gentrification maps, there are some patterns of similarity. They find that the northwest part of the city there is gentrification currently happening, the central part of the city has a late stage of gentrification, the south part has some early stage gentrification, and the eastern side is susceptible.

Figure 2: Bureau of Planning and Sustainability for the City of Portland: Gentrification Neighborhood Typology map

The correlation between the changes in race and education seem to have similar patterns to current gentrification and areas that are susceptible. Change in race and income seem to follow a similar pattern to susceptible areas. Change in education and income seem to have the highest correlation in areas that are susceptible or are in early stages of gentrification.

I think the biggest difference in their calculations are using the opposite side of the data. For race they used percentage of populations that were communities of color, whereas I used percentage that is white. They used percentage without a bachelor’s degree, whereas I used percentage with a bachelor’s degree. They also used the percentage of the population that are renters. These differences tell a slightly different story since there are multiple patterns of change in the city of Portland.


I think one downfall of the GWR tool is that if you include multiple explanatory variables, you cannot get a coefficient value for how they all change together, you get separate coefficients for each explanatory variable. Granted, I’m not sure how this would be possible, but it would be useful to see if the three variables have any patterns together. This makes it difficult to compare three variables at the same time.

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1 thought on “Continuing to understand the spatial and temporal correlation between census variables and their relation to gentrification

  1. jonesju

    Elise, you are learning a lot from this!

    Two suggestions:
    1) it would be helpful for you to develop a conceptual model of how gentrification happens over time in a place: does education level and income increase first, followed by changes in racial composition? Can you test this?

    2) Regarding GWR – it sounds like GWR does not permit you to build a model with interactions among your independent variables. But since you can export the data you used for the GWRs, you could move these data into R and run models that permit 2 and 3-way interactions.

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