I am not sure about whether I should use spatial regression for the proximity analysis I am trying to do.

I’ll just describe the scenario and goal:

People claim that convenience stores lower nearby home property values. I want to actually prove this. So I want to essentially prove: is there a statistically significant relationship between the value of your house and how close you are to a convenience store?

Here are the data layers I am working with for the city of focus:

vector polygons of houses in 2010 (contains property values of each house)

vector polygons of houses in 2015 (contains property values of each house)

vector polygons of convenience stores in 2015

Basically I am thinking I would need to first find out how much property values have changed between 2010 and 2015, by taking the 2015 houses layer and subtracting the 2010 houses layer. From this, I would produce a layer that would show the houses in either red (negative) or green (positive) with different shades. So in this new layer, if a house were red, it would mean the house went down in value and if it were green, it would mean the house went up in value. I would imagine these being in different shades, so pink would mean just went down a little bit in value, and dark red would mean went way down in value, and similarly for green, where light green means went up a little bit in value and dark green means went way up in value. Of course, I would first need to see if there is spatial autocorrelation associated with property values.

I would now have all of these red and green houses of various shades and convenience stores. My question is, how do I show if there is a relationship between proximity to convenience stores and the property values of the houses? Normally I would think this would just require regression, but since this is dealing with proximity, wouldn’t this require spatial regression or a geographically weighted regression? Does my approach sound reasonable? I am just trying to figure out what the output of this regression would look like and how to interpret it. Which tools and methods in QGIS would I need for this?

Thank you!

I am not sure about whether I should use spatial regression for the proximity analysis I am trying to do.

I’ll just describe the scenario and goal:

People claim that convenience stores lower nearby home property values. I want to actually prove this. So I want to essentially prove: is there a statistically significant relationship between the value of your house and how close you are to a convenience store?

Here are the data layers I am working with for the city of focus:

• vector polygons of houses in 2010 (contains property values of each house)
• vector polygons of houses in 2015 (contains property values of each house)
• vector polygons of convenience stores in 2015

Basically I am thinking I would need to first find out how much property values have changed between 2010 and 2015, by taking the 2015 houses layer and subtracting the 2010 houses layer. From this, I would produce a layer that would show the houses in either red (negative) or green (positive) with different shades. So in this new layer, if a house were red, it would mean the house went down in value and if it were green, it would mean the house went up in value. I would imagine these being in different shades, so pink would mean just went down a little bit in value, and dark red would mean went way down in value, and similarly for green, where light green means went up a little bit in value and dark green means went way up in value. Of course, I would first need to see if there is spatial autocorrelation associated with property values.

I would now have all of these red and green houses of various shades and convenience stores. My question is, how do I show if there is a relationship between proximity to convenience stores and the property values of the houses? Normally I would think this would just require regression, but since this is dealing with proximity, wouldn’t this require spatial regression or a geographically weighted regression? Does my approach sound reasonable? I am just trying to figure out what the output of this regression would look like and how to interpret it. Which tools and methods in QGIS would I need for this?