Tag Archives: Mapping Patterns

Examining County Gross Domestic Product

.. what is the annual per capita real-valued output of counties of interest? How is this measure trending? Why is this important? This section reviews tools and data to examine county-level Gross Domestic Product (GDP) trends and patterns. The first ever county-level GDP estimates to be developed as a part of the official U.S. national scope GDP estimates were released in December 2018. The county GDP estimates join the county-level personal income by major source, both now part of the Regional Economic Information System (REIS). See more detail about topics reviewed in this post in the related County GDP web section.

Patterns of Real Per Capita GDP by County
The graphic below shows patterns of per capita real GDP, 2015, by county.

– View developed using CV XE GIS and related GIS project.
– create custom views; add your own data, using the GIS project.

Gross Domestic Product (GDP) by county is a measure of the value of production that occurs within the geographic boundaries of a county. It can be computed as the sum of the value added originating from each of the industries in a county.

Example … use this interactive table to see that 2015 Los Angeles County, CA total real GDP of $656 billion was just slightly larger that than of New York County, NY (Manhattan) at $630 billion. Yet, the total 2015 population of Los Angeles County of 10.1 million is 6 times larger than that of New York County of 1.6 million — see about steps. GDP provides very different size measures, and economic insights, compared to population.

In 2015, real (inflation adjusted) Gross Domestic Product (GDP) increased in 1,931 counties, decreased in 1,159, and was unchanged in 23. Real GDP ranged from $4.6 million in Loving County, TX to $656.0 billion in Los Angeles County, CA.

This post is focused on U.S. national scope county level estimates of Gross Domestic Product (GDP) annually 2012 through 2015. This marks the first time county level GDP estimates have been developed, a part of the Regional Economic Information System (REIS). Use the interactive table to rank, compare, query counties based on per capita GDP, current GDP, real GDP by type of industry. Use the related GIS project to develop thematic map views such as the one shown below. See more about these data.

Current Annual Estimates & Projections
ProximityOne uses these and related data to develop and analyze annual Situation & Outlook demographic-economic estimates and projections. GDP items included in the table below are included in the “annual 5-year” projections as shown in the schedule of release dates; next release April 18, 2019 and quarterly.

Examining County GDP Using GIS Tools
Use the County REIS GIS project. Make your own maps; select different item to map; modify colors, labels. Zoom in views of selected states shown below. Graphics open in a new page; expand browser window for best view. Patterns: see highlighted layer in legend to left of map; MSAs bold brown boundaries with white shortname label
counties labeled with name and 2015 per capita real GDP
.. Arizona .. Alabama .. California .. Colorado .. Iowa .. Georgia .. Kansas .. Missouri
.. New York .. Nevada .. North Carolina .. South Carolina .. Nevada .. Texas .. Utah .. Vermont

Using the County GDP Interactive Table
The graphic below illustrates use of the interactive table. Tools below the table have been used to view only per capita real GDP for all sectors (total sources) and for county with total population between 50,000 and 60,000. Counties were then ranked on 2015 per capita real GDP (rightmost column).

– click graphic for larger view.

Using County GDP: Data Analytics Web Sessions
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.

Assessing Why and How the Regional Economy is Changing

.. data, tools and insights .. which counties are experiencing the fastest economic growth? by what economic component? what does this look like on a per capita level? how might county economic change impact you? Use our county level annual estimates and projections to 2030 to get answers to these and related questions. Get started with the interactive table that contains a selection of these data for all counties and states.

Visual Analysis of Per Capita Personal Income Patterns
The following map shows changing patterns of economic prosperity, U.S. by county, based on percent change in per capita personal income, 2010 to 2017. Create variations of this view — this view uses a layer in the “US1.GIS” GIS project installed by default with all versions of the CV XE GIS software.
– click graphic for larger view.
– view developed with CV XE GIS software.

Measuring the economy and change. One important part of this is Personal Income and components of change. Personal income is the income available to persons for consumption expenditures, taxes, interest payments, transfer payments to governments and the rest of the world, or for saving. Use the interactive table to examine characteristics of counties and regions of interest; how they rank and compare. The table provides access to 31 personal income related summary measures — the interactive table shows data for one of eight related subject matter groups. See more about the scope of subject matter descriptions.

Assessing How the Economy is Changing and How it Compares
The U.S. Per Capita Personal income (PCPI) increased from $40,545 in 2010 to $51,640 in 2017 — a change of $11,095 (27.4%). Compare the U.S. PCPI (or for any area) to a state or county of interest using the table. For example, Harris County, TX (Houston) .. click the Find GeoID button below the table .. increased from $45,783 in 2010 to $53,188 in 2017 — a change of $7,405 (16.2%).

Economic Profile; 2010-2017 & Change — An Example
The following graphic shows and example of the economic profile for Harris County, TX (Houston). Access a similar profile for any county or state.

Data Analytics Web Sessions
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.

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.

Children in Households by Neighborhood

Between 1970 and 2012, the share of households that were married couples with children under 18 decreased from 40 percent to 20 percent. During this period, the average number of people per household declined from 3.1 to 2.6
(details). These trends vary by state and region. Patterns vary widely at the neighborhood level. What are the characteristics and patterns of households with children under 18 at the neighborhood level?

This section illustrates use of CV XE GIS with associated GIS project to examine patterns of children in households by age by block group. Data are based on Census 2010 Summary File 1 Table 32 as shown in the table presented below in this section. See more about the GIS project. See related Web section for more details.

San Francisco Area
  — Patterns of Children in Households by Block Group
Block groups with 400 or more children in households (item P0320001) appear blue. Block groups with 100-300 children in households appear with green fill pattern.

Click graphic for larger view

San Francisco Zoom-In
  — Patterns of Children in Households by Block Group
Block groups with 400 or more children in households (item P0320001) appear blue. Block groups with 100-300 children in households appear with green fill pattern.

Click graphic for larger view

San Francisco Zoom-In
  — Patterns of Children in Households, ages 6-17, by Block Group
Illustrating different colors, different ranges, population by age; other ages could be used. Block groups with 300 or more children in households (items p0320009 – p0320012 summed) appear red. Block groups with 100-300 children in households appear with orange fill pattern.

Click graphic for larger view

Cupertino Area Zoom-In
  — Patterns of Children in Households, ages 6-17, by Block Group
Illustrating further zoom-in, street detail, labels showing total population under 18 years; could be other item as label.
Block groups with 300 or more children in households (items P0320009 – P0320012 summed) appear red. Block groups with 100-300 children in households appear with orange fill pattern.

Click graphic for larger view — larger view use of identify tool to profile a selected block group.

Examining Study Areas Using Site Analysis Tool
Above view in site analysis mode; selecting three block groups as a study area using SiteAnalysis API tool. See summary profile in lower right section/table; export to HTML or XLS file. Use mouse to cherry pick areas to add and/or use circular area selection.

Click graphic for larger view.

Viewing Selected Records in Tabular Form
After selecting areas (block groups in this example) that comprise a study area, click the View File in the lower right panel above the grid. The dBrowse feature is started; the selected records dataset can be opened and used. The $$siterecs.dbf is overwritten with each new site selection. It can be optionally be saved to a CSV, TXT or dBase file for later use; possibly merger/comparison with other study area selections.

Click graphic for larger view.

GIS Project
The GIS project used to develop views shown in this section is focused on the state of California by block group. It can be used to examine patterns similar to those shown here for any area in California. A similar project could be developed for a specific county, another state or the U.S.

Data from Census 2010 SF1 Table P32 (see item list below) were merged into the California block group shapefile. Many of the items available in Table P32 were not used in the map views shown here but could be used to develop alternative views; e.g., specific age patterns or percentages.

A GIS project is itself a file that knits together a set of layers that have certain settings. In the GIS project used here, the layers mainly reflect attributes of a corresponding shapefile (U.S. by state, U.S. by county and California by block group).

Children in Households; Census 2010 Summary File 1; Table P32
P32.  HOUSEHOLD TYPE BY RELATIONSHIP BY AGE
FOR THE POPULATION UNDER 18 YEARS [45]
Universe: Population under 18 years
P0320001   Total
P0320002     In households
P0320003       Householder or spouse
P0320004       Related child:
P0320005         Own child:
P0320006           Under 3 years
P0320007           3 and 4 years
P0320008           5 years
P0320009           6 to 11 years
P0320010           12 and 13 years
P0320011           14 years
P0320012           15 to 17 years
See full table item list in related Web section.

About Households
A household contains one or more people. Everyone living in a housing unit makes up a household. One of the people who owns or rents the residence is designated as the householder. For the purposes of examining family and household composition, two types of households are defined: family and nonfamily.

A family household has at least two members related by birth, marriage, or adoption, one of whom is the householder. A nonfamily household can be either a person living alone or a householder who shares the housing unit only with nonrelatives; for example, boarders or roommates. The nonrelatives of the householder may be related to each other.

Family households are maintained by married couples or by a man or woman living with other relatives. Children may or may not be present. Nonfamily households are maintained only by men or women with no relatives at home.

Own children are a subset of all children; they are the biological, step, or adopted child of the householder or family reference person (in the case of subfamilies) for the universe being considered, whether household, family, or family group. Own children are also limited to children who have never been married, are under the age of 18 (unless otherwise specified), and are not themselves a family reference person. Foster children are not included as own children since they are not related to the householder.

Support & DMI Web Sessions
Learn more about using resources described in this section. Join us in a Decision-Making Information Web session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants. We can address your specific questions about tools to analyze patterns of children’s demographics.