4.1.2 VIF
To ensure that our predictive model does not have multicollinearity, or multiple values telling the same story about permit counts, we use the VIF test. The table below lists each variables’s VIF score. Variables that have over a 5 are considered to potentially have some multicollinearity, and those over 10 certainly need to be flagged. Generally the council district and zoning overlays such as historic districts may be conflicting.
+Based on VIF, we drop:
+hist_dist_na 33.120644 district8 32.900255 district4 32.164688 district9 30.834357 district5 29.393431 district3 29.092806 district2 28.580678 district1 27.199582 district7 26.741373 hist_dist_historic_street_paving_thematic_district 26.735386 district6 19.912382 overlay_fne 15.601603 overlay_ne 11.179327 overlay_nis 7.717070 overlay_ndo 6.867713 overlay_fdo 6.574022 overlay_edo 5.595256 overlay_vdo 5.400210
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+
@@ -3620,7 +9768,7 @@ To to identify spatial clusters, or hotspots, in geographic data, we performed a Local Moran’s I test. It assesses the degree of spatial autocorrelation, which is the extent to which the permit counts in a block group tend to be similar to neighboring block group. We used a p-value of 0.1 as our hotspot threshold.
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Emergeging hotspots…? If I can get it to work.
@@ -3635,19 +9783,19 @@
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Our OLS model exhibits a Mean Absolute Error (MAE) of 2.66, a decent performance for a model of its simplicity. However, its efficacy is notably diminished in critical domains where optimization is imperative. Consequently, we intend to enhance the predictive capacity by incorporating more pertinent variables and employing a more sophisticated modeling approach.
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+
-We find that our OLS model has an MAE of only MAE: 2.66–not bad for such a simple model! Still, it struggles most in the areas where we most need it to succeed, so we will try to introduce better variables and apply a more complex model to improve our predictions.
+We find that our OLS model has an MAE of only MAE: 2.68–not bad for such a simple model! Still, it struggles most in the areas where we most need it to succeed, so we will try to introduce better variables and apply a more complex model to improve our predictions.
4.3.2 Random Forest
@@ -3655,16 +9803,16 @@ Compared to the OLS model, the relationship between predicted vs actual permits…
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Compared to the OLS Model, the Random Forest Model has a similar error distribution however, it exhibits a MAE of….
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@@ -3679,16 +9827,16 @@ Having settled on our model features and tuning, we now validate on 2023 data.
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-We return an MAE of MAE: 2.2.
+We return an MAE of MAE: 2.19.
6 Discussion
@@ -3697,7 +9845,7 @@
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To to identify spatial clusters, or hotspots, in geographic data, we performed a Local Moran’s I test. It assesses the degree of spatial autocorrelation, which is the extent to which the permit counts in a block group tend to be similar to neighboring block group. We used a p-value of 0.1 as our hotspot threshold.
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+
Emergeging hotspots…? If I can get it to work.
@@ -3635,19 +9783,19 @@
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+
4.3.2 Random Forest
@@ -3655,16 +9803,16 @@Compared to the OLS model, the relationship between predicted vs actual permits…
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Compared to the OLS Model, the Random Forest Model has a similar error distribution however, it exhibits a MAE of….
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We return an MAE of MAE: 2.2.
+We return an MAE of MAE: 2.19.
6 Discussion
@@ -3697,7 +9845,7 @@
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The constructed boxplot, categorizing observations based on racial composition, indicates that the random forest model generalizes effectively, showcasing consistent and relatively low absolute errors across majority non-white and majority white categories. The discernible similarity in error distributions suggests that the model’s predictive performance remains robust across diverse racial compositions, affirming its ability to generalize successfully.
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We find that error is not related to affordability and actually trends downward with percent nonwhite. (This is probably because there is less total development happening there in majority-minority neighborhoods to begin with, so the magnitude of error is less, even though proportionally it might be more.) Error increases slightly with total pop. This makes sense–more people –> more development.
Our analysis reveals that the error is not correlated with affordability and demonstrates a downward trend in conjunction with the percentage of the nonwhite population. This observed pattern may be attributed to the likelihood that majority-minority neighborhoods experience a comparatively lower volume of overall development, thereby diminishing the absolute magnitude of error, despite potential proportional increases. Additionally, there is a slight increase in error with the total population, aligning with the intuitive expectation that higher population figures correspond to more extensive development activities.
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-How does this generalize across council districts? Don’t forget to refactor
+How does this generalize across council districts?
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@@ -3730,15 +9878,15 @@
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We can extract development predictions at the block level to these parcels and then visualize them by highest need.
Furthermore, we can identify properties with high potential for assemblage, which suggests the ability to accomodate high-density, multi-unit housing.
@@ -3759,377 +9907,336 @@
