Project Overview

Introduction to Neighborhood Change in Austin, Texas.

I have visited Austin, Texas many times and really enjoy it as a visitor. Each time I go though, a few new buildings have popped up, a couple more eateries have gone away, and those hidden free parking areas are either now metered or permit parking. It goes further than that though. Area which were traditionally depended on by artists and low income families are becoming gentrified and no longer affordable for those who lived there.

Furthermore, those areas of Austin remind me a lot of areas around the Phoenix Metro Area, which I call home. We are beginning to see a lot of rapid growth here, with similar changes in the areas around Downtown Phoenix and Arizona State University (Tempe, AZ). This brings me to my analysis of neighborhood changes in Austin, TX.

In this analysis we will be examining Census data to compare neighborhood trends between 2000 and 2010. We will do this through visually examining the data, regressions, neighborhood clustering, mapping these trends and examining change over time.

Data Dictionary

Introducing the Data

We are using a dataset that consist of 2000 and 2010 Census tract data, and combining it with shape files for the Austin Metropolitan Area to create spatial representations of these communities. By examining changes in median home value as a dependent variable of the demographic data listed in the data dictionary to the left, we are able to see how changes in these variables can correlate with change in home value. The independent variables that we will be using are Racial and Ethnic makeup, presence of poverty, occupation and immigrant traits.

Data Source

The Census data used was made available through the Diversity and Disparities Project. They have created a dataset with 170 variables that have been harmonized from 1970 onward for analysis of changes in tracts over time.

View Data

Changes in Variables

Temporal Change in Variables

To determine temporal change in variables, we examined 2010 data as a factor of 2000 data, to see how each variable preformed over the 10 year period.

Over the first few tracts we can see that median home values increase over those 10 years. Poverty and unemployment also increased in most areas.Populations of Black community members seemed to decrease while Hispanic membership increased. As far employment is concerned, most types of employment seemed to see a decrease in workforce, while female labor force saw an increase across all areas.

5-point summary

Summary of Temporal Change

When we look at the 5-point we see a more definitive picture of the trends for all of the Austin Metropolitan Area we were seeing in the separate areas.

We are still seeing an increase in most median home values, although not all. We do see an increase in immigrant traits for most areas, although some did see no immigration at all. Poverty and unemployment did see still see an increased in most areas. Populations of Black community members did decrease across in Austin, while Hispanic membership increased. Employment decreased in manufacturing by almost half, while there was a subtle increase in professionals and self-employed individuals. Finally, the increase in female labor force is much more subtle, when examining it at the metropolitan area level.

Histogram

Visual Representations of Temporal Change

For most histograms we are able to see a smaller and more abrupt distribution on the lower side of the bel curve than the higher. This tells us that most of the variation in these variables are above the average for each- The median values are most likely higher than the means.

The median home value increased by about 1.25 in most areas, although increases of 1.5 to 2 times 2000 prices were not all that rare. The number of manufacturing workers, veterans, and Black populations all saw a pretty similar decrease, being replaced predominately by unemployment and a female workforce, but we also see a pretty wide distribution in the increase of self-employed individuals.

Correlation Plot

Correlations in Variable Change

Correlations for the Austin Metro Area are pretty subtle in most cases, with the strongest correlations being positive.

Some of the strongest are the expected, with there being a strong correlation between recent immigration, being foreign born and poor English proficiency. We were able to see that the highest contributor to an increase in poverty was an increase in the poverty of white individuals, with Hispanics being second. We did see a pretty strong correlation between house values and the increase in professional workforce. Finally, we are able to see that a large number of foreign born community members are Hispanic, although not so much recently immigrated.

Identifying Communities

Neighborhood Clustering Determination

Clusters were determined by taking the 2010 Census data by using the ‘mclust()’ command to split the areas up into 4 significant groups based on trends in the independent variables (grouping into 4 groups while minimizing the amount of variation between observations). You can find a table of the raw statistics in the table to left. These stats were then turned into visual interpretations, for easier comparison- you will find these graphs on the tabs to follow.

Cluster 1

Labeling Group 1

Label: Predominately Hispanic community in earlier stage of gentrification

Description: Group 1 is predominately Hispanic with a moderate degree of poverty and immigrant community members. It also has a large number of females in the workforce and community members with professional employment.

Cluster 2

Labeling Group 2

Label: Predominately Hispanic community with high degrees of poverty, immigrant populations and female workforce.

Description: Group 2 is also predominately Hispanic with a high degree of poverty and immigrant community members. It also has a large number of females in the workforce. Unlike Group 1, immigrant and impoverished communities far outweigh the professionals.

Cluster 3

Labeling Group 3

Label: Diverse community in later stage of gentrification

Description: Group 3 is a diverse community in the later stages of gentrification, with a very large professional and female workforce. It also has moderate veteran and self-employed populations, and a low degree of poverty.

Cluster 4

Labeling Group 4

Label: Diverse professional community with little poverty and unemployment

Description: Group 4 is a diverse community with a high degree of professional workforce and (although lower) female workforce. The area has a moderate foreign born population and manufacturing workforce, with low levels of unemployment and poverty.

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[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"

Creating Transition Matrix

   
             1          2          3          4
  1 0.68686869 0.25252525 0.06060606 0.00000000
  2 0.18421053 0.81578947 0.00000000 0.00000000
  3 0.21374046 0.03053435 0.69465649 0.06106870
  4 0.32876712 0.05479452 0.23287671 0.38356164

Predicting the past (2000), using information from 2010

Cluster Group 1: Predominately Hispanic community in earlier stage of gentrification

I expected that group 1 would have been part of the same group (areas in beginning stages of gentrification), or Group 2 (areas of high poverty).

Cluster Group 2: Predominately Hispanic community with high degrees of poverty, immigrant populations and female workforce.

I expected that group 2 would have been part of the same group (areas of high poverty), or Group 1(areas in beginning stages of gentrification).

Cluster Group 3: Diverse community in later stage of gentrification

I expected that group 3 would have been part of the same group (areas in later stages of gentrification), or Group 1 (areas in beginning stages of gentrification).

Cluster Group 4: Diverse professional community with little poverty and unemployment

I expected that group 4 would have been part of the same group (areas with low poverty and large professional workforce), or Group 3 (areas in later stages of gentrification) or Group 1 (areas in beginning stages of gentrification).

Acutal 2000 Distributions: In all cases, the transitions between clusters were as expected, with minor discrepancies (6% or less). Group 1 was made up of 69% Group 1, 25% Group 2 and 6% Group 3. Group 2 was made up of 82% Group 2, and 18% Group 1. Group 3 was made up of 69% Group 3, 33% Group 1 and 6% Group 4 and 3% Group 2. Group 4 was made up of 38% Group 4, 33% Group 1, 23% Group 3 and 5% Group 2.

Neighborhood Transitions

Transitions Visualized:

The adjacent Sankey transition plot is a visual representation of the matrix that we just discussed. As we can see, Group 1 was predominately made up of itself or Group 2. Group 2 predominately was made up of itself. Group 3 was predominately made up of itself or Group 1. And Group 4 was predominately made up of itself, Group 1 or Group 3. What I didn’t expect to see were areas that digressed, although this might be due to the Great recession of 2008. I also didn’t expect to see so much of group 2 (high poverty transition to group 3 or 4. Although, I could definitely see how cheap real estate could encourage that transition. There are definitely signs of gentrification, as we see significant proportions of each cluster transition over time.

About the Developer

Introducing Ricky Duran

Ricky Duran (Not pictured to the left) is a Master of Public Policy Student (class of 2021) in in the Arizona State University School of Public Affairs. He has an interest in data sciences as they relate to public service, and hopes to find a position where he can use data to inform decision making processes and policy.

Currently Ricky is working with an initiative in ASU Watts College of Public Service and Community Solutions, Opportunities for Youth, where is the Marketing and Data Specialist. In this initiative he works with over 80 partner organizations to bring resources and opportunities to opportunity youth (Youth 16-24 who are not in school and/or not working) to help them find their “Pathway to a Brighter Future”.

When he is not working or going to school, Ricky enjoys the hiking, gardening and the outdoors in general. He also enjoys working with local non-profits to help them become better aware of how their programs are preforming, to help them find ways to increase efficiency.

Ricky Duran

602-206-6337

ricky.duran@asu.edu

Documentation

# R libraries used for this project, with descriptions (right).
library( tidycensus )
# using `census_api_key(“your key here”)`
library( plyr )
library( DT )
library( ggplot2 )
library( cartogram )
library( maptools )
library( tmap )
library( tidyverse )
library( stargazer )
library( corrplot )
library( purrr )
library( mclust )
library( flexdashboard )
library( leaflet )

Data Collection and Oranizing

library( tidycensus ) allows us pull cencus data from a shared Census Data Bank.

census_api_key(“your key here”) using a personal key, we can access and pull data through tidycensus. If you do not yet have a Cencus AIP Key, you can request on at aip.census.gov.

library( plyr ) allows us to format our data so that it better fits into our analyses.

Data Tables

library( DT ) allows us to make data tables, as in this project.

Data Graphing

library( ggplot2 ) allows us to create more impressive graphs, such as the cluster plots.

Data Mapping

library( cartogram ) allows is to create spatial maps w/ tract size bias reduction from shapfiles.

library( maptools ) allows us to manipulate spatial object, to better represent our data.

library( tmap ) allows us to create more customised maps, as seen in the map of our cluster data.

library( tidyverse ) tidyverse allows us to use multiple packages (that play well together) to create more customize development.

Data Analysis

library( stargazer ) allows us to more effectively share our analyses in a formatted presentation, such as with our regression table.

library( corrplot ) allows us to present data in matirces.

library( purrr ) allows us to better work with functions and vectors.

library( mclust ) allows us to develop and analyze cluster groups from a dataset.

Data Presentation

library( flexdashboard ) allows us to place our content in an appliction format like this one.

library( leaflet ) allows us to display inteactive maps, as in this project.