EXERCISE 1 – Managing and Describing Census Data

Komal Macwan

Course - PUP 571 Planning Methods I

Professor – Deirdre Pfeiffer

DES Report on the contemplation of subsidy in Arizona(For evidence-based analysis of the relationship between low-income households and lack of access to cars.)

Introduction:

The Arizona Department of Economic Security (DES) is a governmental organization whose mission is to advance the security and well-being of Arizonans. Its efforts are concentrated on supporting individuals, families, and communities at all levels of society. Recent research by Professor King of Arizona State University correlating household access to autos over the past

50 years and household income was discovered by DES authorities. The agency is interested in learning more about this connection and how it affects communities in Arizona. I reviewed American Community Survey (ACS) data from census tracts on automobile accessibility and income to see whether there was any relationship or correlation between those factors and households in the state of Arizona. More research is required to draw firmer conclusions regarding this relationship, even if there is a moderate correlation between the availability of cars and rising wealth.

Aim of the DES report and survey:

Making defensible choices about state and municipal policies requires an understanding of King's study's results and their potential effects on Arizonan communities. This study's goal is to determine whether there is a connection between lower income and a lack of access to vehicles in Arizonan areas. The findings of this study will enable DES to develop policies that encourage and facilitate low-income households' access to cars through subsidy programs in a more informed manner.

The method utilized in comprehending data:

The ACS 5-year estimates database from 2017 provided the secondary data used in this investigation. The information gathered includes household income, household access to vehicles, and median familyincome. Census tracts are the unit of analysis for the data gathered from the ACS 2017. I made a few decisions that helped the data analysis process. I started by excluding the tracts from the study for which there was no data for any of the variables. This made sure that the statistical analysis was limited to tracts with numerical variables. Additionally, I eliminated from the study the tracts where the estimates of the number of families without access to vehicles had coefficients of variation (CV) that were higher than 40. 1526 distinct tracts were included in the original data.

For the state of Arizona, the original data includes 1526 distinct tracts. There were 602 tracts left for the study after tracts lacking estimates and having a high CV were eliminated.

Findings from analysis:

Using the ACS 2017 5-year estimates, I conducted a statistical study of the relationship between the proportion of households without access to cars and the proportion of low-income households (those making less than $50,000 annually). An R-value is an outcome in Arizona census tracts is 0.60, indicating a moderately positive association between families' access to autos and their income.

Another statistical analysis I did was to see if neighborhoods with a high percentage of lowincome households (more than 75% of households in a tract were low-income) were also neighborhoods with a high percentage of households without access to vehicles (more than 10% of households in a tract lacked access). The result was that while tracts with a higher proportion of households lacking access to cars tend to also have a higher proportion of lowincome households, there is only a medium association between these two variables. This is based on Cramer’s V value. In the end, both statistical analysis of the variables gathered for this study concluded that there is a relationship between lack of access to vehicles and household income. However, in both statistical analyses only a moderate relationship or correlation can be drawn from the numbers in the data. All the data used for the analyses; the chart developed for the correlation between households' access to vehicles and their income for census tracts in Arizona; and the calculations can be found on the accompanying Excel spreadsheet.

The statistical analysis only found moderate correlation between lack of access to vehicles and presence of low-income households. This may possibly be caused by the above mentioned limited scope of the study, since so many tracts were removed from the analyses.

Recommendations:

I divide my recommendation into two possible routes for exploration by the DES. First, study the relationship between the variables through different time periods, this will include a larger dataset that could include data for the past 50 years since that is the timeframe Professor King points out there is a relationship. Lastly, the study could focus on the effects either on rural areas, or urban/suburban areas of the states. Since these two types or built environments create different demands when it comes to transportation needs. There might be a stronger case for creating policies focused only on rural communities, or only on urban/suburban communities