thu, 16-jul-2009, 15:30

Yesterday I looked at how wind might be affecting my bicycling to and from work. Today I’ll examine the idea that Miller Hill is confounding the effect of wind on average speed by excluding this portion of the trip from the analysis. To do this, I include a bounding box comparison in the SQL statement that extracts the wind factors for track points. The additional WHERE condition looks like this:

ST_Within(point_utm, ST_SetSRID(ST_MakeBox2D(ST_Point(454861,7193973), ST_Point(458232,7199159)), 32606))

The same ST_Within test is used in the calculation of average speed for each of the trips from work to home. After compiling the wind factors and average speeds, we compare the two using R. Here are the updated results:

lm(formula = mph ~ wind, data = data)

     Min       1Q   Median       3Q      Max
-1.87808 -0.55299  0.04038  0.62790  1.19076

            Estimate Std. Error t value Pr(>|t|)
(Intercept)  16.8544     0.2176  77.442   <2e-16 ***
wind          0.3896     0.2002   1.946   0.0683 .
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9445 on 17 degrees of freedom
Multiple R-squared: 0.1822,     Adjusted R-squared: 0.1341
F-statistic: 3.788 on 1 and 17 DF,  p-value: 0.06834

This time around the model and both coefficients are statistically significant (finally!), and “wind factor” is positively correlated with my average speed over the part of the route that doesn’t include Miller Hill and Railroad drive. It’s not a major contributor, but it does explain approximately 18% of the variation in average speed.

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