I’m going to be performing an analysis on Tax Abatement data for the city of Cincinnati, provided by the City of Cincinnati data portal. This data shows information for various properties in the City of Cincinnati.
library(tidyverse)
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Rows: 4744 Columns: 17
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chr (10): CRA_ID, ADDRESS, APPLICATION_DATE, CONSTRUCTION_TYPE, LEED_CLASS, ...
dbl (6): HCA_ABATED_VALUE, ABATEMENT_QUALIFYING_TERM, INCENTIVE_AMOUNT, LON...
lgl (1): DCED_NEIGHBORHOOD
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Question
Do better LEED ratings increase tax abatement? The LEED (Leadership in Energy and Environmental Design) rating is assigned to properties to asses their energy and environmental impact. The rating is given by either Silver, Gold, or Platinum depending on the different aspects of the property such as water, gas, electric, or HAVC system within the property. My hypothesis is that a better LEED rating would give the property owner a better Tax Abatement.
Method
Answering this question would involve the Tax Abatement data from the City of Cincinnati’s website. I would need to filter the (LEED_CLASS) variable to only show “Silver, Gold, and Platinum” instead of the other categories like “Not Certified and Certified”. Then, the filtered data would be piped into a ggplot with (LEED_CLASS) on the x-axis and (HCA_ABATED_VALUE) on the y-axis. The visualization used would be a boxplot or “geom_boxplot” to show the distribution of the different LEED ratings with the tax abatement they received. I will also use “scale_y_log10” because the data has outliers that pull the visual down and format to dollars.
Results
taxdata %>%filter(LEED_CLASS %in%c("SILVER", "GOLD", "PLATINUM")) %>%ggplot(aes(x = LEED_CLASS, y = HCA_ABATED_VALUE)) +geom_boxplot() +labs(title ="Abatement Values for Different LEED Ratings",x ="LEED Rating",y ="Abatement Value ($)")
Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Interpretation
The results provided evidence to support the idea that better LEED ratings do improve the abatement values given. The platinum rating provided the highest abatement values, with Gold being the lowest and Silver being the lowest.
Source Code
---title: "Cincinnati Tax Abatement Analysis"author: "Logan"editor: visualtoc: true format: html: code-tools: TRUE embed-resources: TRUE ---## IntroductionI'm going to be performing an analysis on Tax Abatement data for the city of Cincinnati, provided by the City of Cincinnati data portal. This data shows information for various properties in the City of Cincinnati.```{r}#| label: Setuplibrary(tidyverse) library(skimr) ``````{r}#| label: data_importtaxdata <-read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/scalesl_xavier_edu/EcTCPv86iudIgDCGBViy5uQBcmdWYT3XZ_cznoUMsyEPJg?download=1")```## QuestionDo better LEED ratings increase tax abatement? The LEED (Leadership in Energy and Environmental Design) rating is assigned to properties to asses their energy and environmental impact. The rating is given by either Silver, Gold, or Platinum depending on the different aspects of the property such as water, gas, electric, or HAVC system within the property. My hypothesis is that a better LEED rating would give the property owner a better Tax Abatement.## MethodAnswering this question would involve the Tax Abatement data from the City of Cincinnati's website. I would need to filter the (LEED_CLASS) variable to only show "Silver, Gold, and Platinum" instead of the other categories like "Not Certified and Certified". Then, the filtered data would be piped into a ggplot with (LEED_CLASS) on the x-axis and (HCA_ABATED_VALUE) on the y-axis. The visualization used would be a boxplot or "geom_boxplot" to show the distribution of the different LEED ratings with the tax abatement they received. I will also use "scale_y_log10" because the data has outliers that pull the visual down and format to dollars.## Results```{r}#| label: Boxplottaxdata %>%filter(LEED_CLASS %in%c("SILVER", "GOLD", "PLATINUM")) %>%ggplot(aes(x = LEED_CLASS, y = HCA_ABATED_VALUE)) +geom_boxplot() +labs(title ="Abatement Values for Different LEED Ratings",x ="LEED Rating",y ="Abatement Value ($)")```## InterpretationThe results provided evidence to support the idea that better LEED ratings do improve the abatement values given. The platinum rating provided the highest abatement values, with Gold being the lowest and Silver being the lowest.
