I have cleaned disaggregate travel data down to a household level (of about 75% of households) and socio economic data aggregated into 100-200 household blocks. What I'm wanting to do is display a map of the household vehicle travel to visualize the geographical influence of urban form on transport energy consumption. Using an IDW interpolation of the vector data, I have done this already to produce the following map:
However this doesn't take into account the spatial distribution of the demography/income characteristics of residents. I need to account for other explainable factors including household size and income to isolate the unexplained influence of the built environment. The end result I am wanting is a map similar to above of the amount of travel a 'mean resident' would carry out if they moved to that area. I realize such an inference is subject to the ecological fallacy but I'm fine with a crude analysis.
My experience with statistics is rather beginner level (a few 100/200 level papers) as I'm coming at this problem with a background as a mechanical engineer, but I'm happy to dedicate a bit of time into getting this done.
Where I am at
As a first step I have aggregated all data to a larger block size (See images 2,3 and 4). I'll eventually start working down to a lower level of aggregation when I sort out I understand that to control for income and HH size, I can use an OLS, obtain the coefficients and use average values for the controlled variables. However if I am not explaining the built environment variables in the equation, how do I account for that in the equation. I have tried this already using the above data to produce a map of residuals i.e how much more or less the a resident of the area travels than explained by socioeconomic variables(see image 5), but this isn't quite what I want.
A bit blunt but: what is the correct way of doing this?
- Median VKT
- Median Income
- Mean HH size