Contextualizing Reports of Illegal Dumping in the City of L.A.
Geographically Weighted Regression Results (GWR) and Significant Explanatory Variables, log transformed (81% explained)This dashboard is created using HTML, JavaScript, CSS and remote web servers. Variable selection options appear in order of significance (p < 0.05).
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In this map, Geographically Weighted Regression (GWR) is modeling (explaining) 81% of the variance in the dependent variable: Reported Illegal Dumping Counts per 2022 census block group. The GWR Deviance Residual explains where the regression model is accurately explaining or under-explaining the dependent variable. Areas with values between -0.5 and 0.5 are being accurately explained while negative values outside this range are unexplained lower-occurences of illegal dumping and positive values outside this range are unexplained higher-occurrences of illegal dumping. The same is true for the OLS Standard Residuals except OLS works to identify unexplained positive or negative clustering in regression. The remaining variable selection options are available in the dropdown by order of significance (p < 0.05) in the GWR model. Each variable has been log transformed to follow a normal distribution.
Dashboard Actions
Hover your mouse over a census block group to view its GWR Residuals, OLS Residuals and the log transformed value for reported illegal dumping occurences.
Filter the map by selecting a variable in the "Select a Variable" dropdown.
Choose a color scheme with the "Choose a Color Ramp" drodown.
Other Dashboard Plans
I would like to add dropdown options to show either raw data values, log-transformed values, or coefficients for the explanatory variables as well as create a function to auto-apply color schemes and legend updates based on the selected variable.