The Center for Disease Control and Prevention (CDC) uses the social vulnerability index (SVI) to evaluate the impact of disasters on communities, weighting the damage with social factors in the states of South Dakota and Indiana (CDC, 2018a; CDC 2018b).
Each community requires independent evaluation to identify the vulnerability of the area. Two of the contributing factors for this analysis are minority status and language limitations. The data may lack credibility due to the fear of reprisal for persons that fall into these categories. Exploring the social and physical characteristics, excluding these metrics can provide insight on the overall impact on the SVI.
What impact does the exclusion of the metrics that represent minorities and language limited individuals have on the predictability of the CDC’s SVI, based on the 2018 data (CDC, 2018a; CDC, 2018b)?
Does the CDC’s SVI have key characteristics that impact the preclude potential exclusion without limiting the overall predictability of the SVI, based on the 2018 data (CDC, 2018a; CDC, 2018b)?
The data and data dictionaries are online.
o Center for Disease Control and Prevention. (2018a). Social Vulnerability Index [data set].
o Center for Disease Control and Prevention. (2018b). Social Vulnerability Index [code book]. https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf
o Note: Your raw data must be this report in its original form. Use the data dictionary to understand the data.
Create a subset of the data. Consider the metrics that are used in creating the SVI. Use the data dictionary to identify the state variable field, along with the appropriate fields that represent the SVI metrics, considering the research questions. The SVI index’s variable name is RPL_THEMES, in column 99.
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▪ Persons below the poverty estimate ▪ Civilian unemployed estimate
▪ Per capita income estimate
▪ Persons with no high school diploma
Household and composition disability features
▪ Ages 65 and older
▪ Ages 17 and under
▪ Persons with a disability, over the age of 5 ▪ Single-parent households
Minority status and language limitations
▪ Persons with minority status
▪ Persons with no or minimal use of the English language
▪ Multi-unit dwellings (10 or more units)
▪ Mobile homes
▪ Homes with more residents than a home is designed for ▪ Homes with no vehicle
▪ Group quarters or institutionalized quarters
Note: Do not use the columns that are follow-on calculations of these columns. Variable names preceded with “E_” are actual measures, while “M_” represents the margin of error estimates. Do not include the margin of error estimates at this time. Considering the research questions, after subsetting and excluding the variable that houses the STATE field, there will be 13 columns with relevant information for analysis. The state field can be utilized in data exploration.
o Define your plan for all analyses.
training and testing evaluations can be conducted; additionally, you will exclude missing values
from the model. • Prepare
o Carry out actions on the data that are necessary to prepare the data for the analyses. • Apply (or Analyze)
o When carrying out the random forest modeling, it may be imperative to understand how much time your analyses may take.
o Ensure that you analyze the results and understand what the different outcomes of the model represent. Provide interpretations of the overall model that is trained, how the trained model performs with the test data, and what features are most important for the explained variance.
Results, Impact of the Results:
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o Additionally,anopportunityforfutureresearchisexplorationmodelingtodeterminewhatother variables, when eliminated, have little or no impact on the ability to predict the SVI based on the supporting characteristics in the data.
• You will base your recommendations on your findings in the analysis you conduct.
Create a random forest model for each state you were assigned. Is there a difference in the models’ results? What does that result mean in terms of the data? Make sure that you do not speculate! Use evidence to support any assertions that you make.
Required files to submit:
Good to know:
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