County 5-Year Trends: Income & Income Inequality

.. tools and data to examine how the U.S. by county household income and income inequality are changing … how is household income changing in counties of interest? What are the trends; what is causing the change? What are the characteristics of income inequality and how is it changing? How might this change impact your living environment and business?

This section provides access to tools and data to examine U.S. by county measures of household income and income inequality between two 5-year periods (2006-10 and 2011-2015). These data can provide insights into how household income and income inequality are changing for one county, a group of counties and the U.S. overall. Use the interactive table to view median household income and measures income inequality for all counties. See more detail about these topics here. Measures of income inequality can be estimates/examined using the Gini Index.

The Gini Index & Measuring Income Inequality
The Gini Index is a dimensionless statistic that can be used as a measure of income inequality. The Gini index varies from 0 to 1, with a 0 indicating perfect equality, where there is a proportional distribution of income. A Gini index of 1 indicates perfect inequality, where one household has all the income and all others have no income.

At the national level, the 2015 Gini index for U.S. was 0.482 (based on 2015 ACS 1-year estimates) was significantly higher than in the 2014 ACS Index of 0.480 (based on 2014 ACS 1-year estimates). This increase suggests that income inequality increased across the country.

Examining Household Income & Income Inequality Patterns & Change
The following two graphics show patterns of the GIni Index by county. The first view is based on the American Community Survey (ACS) 2010 5-year estimates and the second is based on the ACS 2015 5-year estimates. The ACS 2010 estimates are based on survey respondents during the period 2006 through 2010. The ACS 2015 estimates are based on survey respondents during the period 2011 through 2015. One view compared with the other show how patterns of income inequality has changed at the county/regional level between these two 5-year periods.

Following these Income Inequality views are two corresponding views of median household income; using data from ACS 2010 and ACS 2015. Use CV XE GIS software with the GIS project to create and examine alternative views.

Patterns of Income Inequality by County; ACS 2010
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Income Inequality by County; ACS 2015
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2010
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2015
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

K-12 Data Analytics: Dallas ISD, Texas

.. using tools and data analytics methods for analysis of K-12 schools located in Dallas ISD school district, Texas .. use the interactive table in related section to examine demographic-economic characteristics of Dallas County by block group. Apply these tools and methods to your schools, school districts and related areas of interest. Contact me for details. Tools and methods described here can help leadership, staff and stakeholders answer key questions and facilitate strategic planning. See the related main Web section for more details and tools access.

Examining the Current Situation
While most schools and school districts know a lot about the students, often less is known about children’s living environment and the broader school district community. See the demographic-economic profile for Dallas ISD total population (compare to Dallas city). These profiles tell a story. It helps stakeholders know “where we are now.”

Use the annually updated School District Special Tabulation that provides similar data but for the grade relevant school age population by type of enrollment universe. See Dallas ISD Children’s Demographic-Economic Profile by Universe of Enrollment.

Dallas ISD School District in Context
Dallas ISD (bold brown boundary) shown in regional context.

  — view created using CV XE GIS and associated GIS Project

School Locations in Context
Zoom-in to Dallas ISD. Pointer at county boundary; Dallas ISD is located in Dallas County. Schools are shown by different types of markers. See markers/style in legend at left. Using the query feature, it is possible to identify charter schools as one type of marker, irrespective of grade range.

Use the national scope interactive tables to examine characteristics of individual public schools and individual private schools. Rank, query, compare and contrast schools within a state or on a national basis.

  — view created using CV XE GIS and associated GIS Project

Zoom-in View of Schools; School Characteristics
Further zoom-in shows schools labeled with name.  The identify tool is used to show a mini-profile of a school by clicking on the Edna Rowe school marker.
The partial view of the profile shows free and reduced lunch participation and enrollment by grade.

  — view created using CV XE GIS and associated GIS Project

School Attendance Zones
Elementary school attendance (black boundaries) are shown in the next graphic. Dallas ISD has three types of attendance zones (elementary, middle, high school). The query feature is used to show only elementary zones. Click graphic for larger view showing more detail.

  — view created using CV XE GIS and associated GIS Project

Cities/Places & School District
Cities/places are shown in the next graphic (cross-hatch pattern) in context with the school district. Places are non-incorporated areas of population concentration. Click graphic for larger view showing more detail; adjacent city/places shown with yellow diagonal cross-hatch pattern.

  — view created using CV XE GIS and associated GIS Project

Road Infrastructure
Roads/streets are added to the view as shown in the next graphic. Vital to student transportation planning and management, the street density view also helps view the scope of build-out. Click graphic for larger view showing more detail; mini-profile shows attributes of street segment by school/pointer.

  — view created using CV XE GIS and associated GIS Project

Schools in Context of Urban/Rural Geography
Census blocks are categorized as urban or rural based on Census 2010. The graphic below shows urban census blocks with an orange fill pattern. It is easy see that most of Dallas ISD is urban; but a large area in the southeast part of the district is rural. See related K-12 Schools by Urban/Rural Status
.

  — view created using CV XE GIS and associated GIS Project

Percent Population in Poverty by Census Tract
Census tracts are statistically defined geographic areas covering the U.S. wall-to-wall (73,057 areas). The view below shows patterns of percent population in poverty by census tract. Click on graphic to view larger view. Choose from hundreds of demographic-economic measures to assess patterns of well-being, age distribution, housing structure and age, educational attainment, housing value, race/origin, employment, language spoken at home among many others.

  — view created using CV XE GIS and associated GIS Project

See zoom-in view of Edna Rowe school vicinity with tract and %poverty labels

Patterns of Economic Prosperity by Block Group
Block groups are statistically defined geographic areas covering the U.S. wall-to-wall (217,740 areas) and nest within census tracts (see above). Block groups (BGs) provide a finer geographic granularity compared to census tracts. The view below shows patterns of economic prosperity as measured by the median household income (MHI). MHI interval/color patterns are shown in the highlighted layer at left of the map. Click on graphic to view larger version that uses the MHI as a label for each BG. Choose from hundreds of demographic-economic measures to assess patterns of well-being. This view illustrates use of transparency setting to “see through” the pattern layer to view the topology/road infrastructure.

  — view created using CV XE GIS and associated GIS Project

Dallas County Block Group Demographic-Economic Characteristics
Use the interactive table to view, rank compare Dallas County block group demographic-economic characteristics. See about Dallas County demographic trends. All block groups for the county are included in the table. Optionally key in an address using the location-based demographics tool to determine the block group code of interest. You can then use the Find button below the table to locate that block group. See about using block group codes — a 12 character code uniquely identifying that area.

Block Groups in Vicinity of School — Interactive Table
The graphic below illustrates use is the Dallas County block group interactive table. Block groups in census tract “48113012206” are shown in the table. This tract was selected as it contains the Edna Rowe school (location used above in maps). The school’s address was used in the location-based lookup tool. Do this for any school or address in Dallas County. It is easy to see that the Gini Index is low indicating high degree of income equality. It is easy to see and compare the number and type of housing units, median income, housing values, and rent. Try this process yourself:
1 – enter an address using this tool to obtain a census tract code see this example.
2 – below the interactive table click ShowAll button, enter your 11 character tract code, click Find.
All block groups in this tract will show as rows in the table.

Data Analytics Lab Session
Join me in a Data Analytics Lab session. There is no fee. Discuss how tools and methods reviewed in this section can be applied to your situation.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data.

Patterns of Block Group Income Inequality

Income inequality exists in an area where there is a mix of households that have very high incomes coexisting with a set of households with very low income. The high heterogeneity of income inequality among households typically extends to other demographic, social, economic and housing attributes. A neighborhood with high income inequality is unlikely to be homogeneous in most respects.

This section reviews data and methods to examine/analyze income inequality at the block group level of geography. See more detail in related Web section. Block groups (BGs) average 1,200 population and cover the U.S. wall-to-wall. Using higher level geography, even census tracts, may tend to mask the existence of income equality. By using BGs, we are able to examine income inequality patterns for other geography such as cities, counties and school districts (by looking at BGs that intersect with these areas). The next map graphic illustrates how these patterns can be examined using GIS resources.

Patterns of Income Inequality by Neighborhood & School District
The following view shows patterns of income inequality by block group within school districts in the Pelham, NY vicinity just north of New York City. K-12 public schools are shown as yellow markers. The Gini Index, based on ACS 2013, is used as the measure of income inequality. Colors/values of the Gini Index are shown in the legend as the left of the map. See more about the Gini Index below.

View created with CV XE GIS. Click graphic for larger view.
The larger view shows BGs labeled with the Gini Index value.

The following related view shows patterns of median household income (MHI) by census tract for the same area as above. This view shows the high median income for the census tracts in the southern section of Pelham school district. Compare patterns in the MHI by tract view with the Gini Index by BG view above.

View created with CV XE GIS. Click graphic for larger view.
Tracts labeled with percent population 25 years and over who are high school graduates.

Patterns of Income Inequality by Block Group; New York City area

View created with CV XE GIS. Click graphic for larger view.

Block Group Income Measures
Block group income measures are only available from the American Community Survey (ACS). Block groups are the smallest geographic level for which data are tabulated from the annually updated ACS. In the applications reviewed here, the ACS 2013 5-year estimates are used.

Using the block group income inequality measures, enables us to examine characteristics in the vicinity of schools and how neighborhood inequality might exist across school districts. Neighborhods with high inequality might directly impact on K-12 student opportunities and educational outcomes.

Gini Index of Income Concentration
The Gini Index can be used as a measure of income concentration/inequality. The Gini Index is based on the Lorenz curve The Gini Index varies from 0 to 1, with a value of 0 indicating perfect equality, where there is a proportional distribution of income across all households. A value of 1 indicates perfect inequality, where one household has all the income and all others have no income.

In the graphic shown below, the Gini Index represents the area (A) between the diagonal, or line of perfect equality, and the Lorenz curve, as a percentage of the total area lying beneath the diagonal (A + B). When income inequality rises, the Lorenz curve bows further downward and the area (A) between it and the diagonal increases in size. The result is that the Gini Index increases.

The Gini Index for the U.S. in the 2013 ACS (0.481) was significantly higher than in the 2012 ACS (0.476). This increase suggests that income inequality increased nationally. Examine state-by-state patterns of income inequality using the interactive table in this related section.

The annually updated ACS 5-year estimates can tell us how income inequality is changing at the block group level. Appealing reasons for using the ACS data include the availability of related subject matter, such as educational attainment, that are relevant to extended analyses.

Analyzing Patterns for Areas of Interest
Use the existing state K-12 schools GIS projects to examine income inequality based on block groups. Project datasets include a block group layer/shapefile that contains the Gini Index and several related income attributes. More about the state K-12 GIS projects.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data.

State Patterns of Income & Income Inequality

The growth of income inequality has been widely reported. A recent New York Times story summarizes the wealth gap, a result of income inequality growth. The Pew Research Center report finds that in 2013, the wealth gap between upper-income and middle-income families was larger than at any point in the last 30 years for which we have data. Income inequality is a social issue and more.  As the income inequality grows, market patterns and opportunities change and differ ways in different areas.  Understanding the how, where and when of income inequality change is important for many reasons.  See the related Web section for more data access and more options for analyzing income and income inequality.

There are many ways to measure how income inequality is growing. This section uses data from the American Community Survey (ACS). Appealing reasons for using the ACS data include the availability of the same subject matter reviewed here for states is also available down to the census tract and block groupgeographic levels. The ACS data also provide many related subject matter items, such as educational attainment, that are relevant to extended analyses.

State Income & Income Inequality — Interactive Table
Use the interactive table to view, rank, and compare national and state level median household income based on the 2012 ACS and 2013 ACS. Interactively examine how the Gini Index, a measure of income inequality, varies by state. These data are from the ACS 1-year estimates and compare respondent data for calendar year 2012 data with calendar year 2013. Estimates from the 2013 ACS compared to the 2012 ACS estimates show a significant increase in median household income at the national level and for many states.

The following graphic view of the interactive table shows states ranked on the 2013 Gini Index (more about the Index below). Nine states have a Gini Index higher than the U.S. overall.

Visual Analysis of State Patterns of Income Inequality
The following graphic shows state patterns of income inequality based on the 2013 Gini Index. See more about the Gini Index below and examine patterns using the interactive table below.

View created with CV XE GIS. Click graphic for larger view.
Use the MHI/Gini Index GIS project for further analysis and alternative map views; see details in Web section.

Median Household Income
An analysis by the Census Bureau indicates that real median household income in the U.S experienced a statistically significant increase between the 2012 ACS and the 2013 ACS. The 2012 U.S. median household income was $51,915, and the 2013 U.S. median household income was $52,250. Use the interactive table to examine patterns median household income among states.

Income Inequality
The Gini Index is a summary measure of income inequality. The Gini Index varies from 0 to 1, with a value of 0 indicating perfect equality, where there is a proportional distribution of income across all households. A value of 1 indicates perfect inequality, where one household has all the income and all others have no income.

The Gini Index for the U.S. in the 2013 ACS (0.481) was significantly higher than in the 2012 ACS (0.476). This increase suggests that income inequality increased nationally. The Gini Index for the 2013 ACS increased in 15 states. Examine patterns of income inequality using the interactive table.

In the near future, a new Web section and blog post will examine patterns of income and income inequality change on a national scope at the block group/census tract geographic levels.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more many years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data.