Category Archives: Congressional Districts

115th Congressional Districts: Analysis and Insights

.. interpretative data analytics; tools, data & methods ..  this section is focused on 115th Congressional District geographic, demographic and economic patterns and characteristics. Use tools and data reviewed here to examine/analyze characteristics of one congressional district (CD) or a group of CDs based on state, party or other attribute. Use the GIS resources described here for general CD reference/pattern/analytical views, to examine current demographics and demographic change and for redistricting applications. See this related Web section for more details.

Examining the 115th Congressional Districts
• the 115th Congress runs from January 2017 through December 2018.
• FL, MN, NC, VA have redistricted since the 114th CD vintage;
  .. some 115th CDs have new boundaries compared the 114th CDs.
• view, rank, compare CDs using the interactive table.
  .. table uses ACS 2015 data for 115th CDs & include incumbent attributes.
  .. examine districts by party affiliation.
• use these more detailed 114th CD interactive tables
  .. data based on 2015 American Community Survey – ACS 2015.
  .. corresponding data for the 115th CDs from ACS 2016 available Sept 2017.
• use the new GIS project including 114th & 115th CDs described below.
  .. create CD thematic and reference maps;
  .. examine CDs in context of other geography & subject matter.
• join us in the April 25 Data Analytics Lab session

Visual Analysis of Congressional Districts
The following views 1) provide insights into patterns among the 115th CDs and 2) illustrate how 114th to 115th geographic change can be examined. Use CV XE GIS software with the GIS project to create and examine alternative views.

Patterns of Household Income by 115th Congressional District
The following graphic shows the patterns of the median household income by 115th Congressional District based on the American Community Survey 2015 1-year estimates (ACS2015). The legend in the lower left shows data intervals and color/pattern assignment

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

Charlotte NC-SC Metro Area
  – with 114th/115th Congressional District 12

The following graphic shows North Carolina CD 12 with 114th boundary (blue) and 115th boundary (pale yellow) and Charlotte metro bold brown boundary. Click graphic for larger view with more detail. Expand browser window for best view.

.. view developed using the CVGIS software.

• View zoom-in to Charlotte city & Mecklenburg County.

115th Congressional District Interactive Table
Use the interactive table to examine characteristics of one congressional district (CD) or a group of CDs. The following graphic illustrates use of the interactive table. First, the party type was selected, Democratic incumbents in this example. Next, the income and educational attainment columns were selected. Third, the set of districts were sorted on median household income. It is quick and easy to determine that CA18 has the highest median household income and that the MHI is $1,139,900. Try using the table to examine districts of interest.

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.

Congressional District 2015 Demographic-Economic Characteristics

.. congressional districts vary widely in demographic-economic characteristics.  We have new data for 2015 providing insights to characteristics of the 114th Congressional Districts.  This section summarize a few of these characteristics and provides access to a wide range of data that you can use to view, sort, rank, and compare congressional districts using interactive tables.

Patterns of 2015 Educational Attainment
The following graphic shows patterns of educational attainment (percent college graduate) by congressional district in the Los Angeles area. White label shows the congressional district code; yellow label shows percent college graduate. Legend shows color patterns associated with percent college graduate intervals.

– View developed using CV XE GIS software and associated GIS project.

How Congressional Districts Compare
Reference items refer to items/columns shown in tables described below.

.. general demographics: congressional district UT03 has the smallest median age (27.5 years — item D017) and FL11 has the highest median age (53.5 years).

.. social characteristics: congressional district KY05 has the fewest number of people who speak English less than “very well” (2,676 — item S113) and FL27 has the largest number (281,053).

.. economic characteristics: congressional district ND00 has the lowest unemployment rate (2.6% — item E009) and MI13 has the highest unemployment rate (14.6%).

.. housing characteristics: congressional district MI13 has the lowest median housing value ($63,100 — item H089) and CA18 has the highest median housing value ($1,139,900).

Access the Detailed Interactive Tables
Click a link to view more thematic pattern maps and use the interactive tables.
.. General Demographics
.. Social Characteristics
.. Economic Characteristics
.. Housing Characteristics

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.

Analyzing ACS 2014 1-Year Supplemental Data

.. examining 2014 characteristics of areas with population 20,000 and over  .. this section summarizes how to use the America Community Survey (ACS2014) “supplemental” data (ACS2014S) to access more current estimates than otherwise available. The America Community Survey “supplemental” data are just that, a supplemental set of ACS 2014 1-year estimates — for areas 20,000 population and over. See the related Web section providing more detail.

The importance of the ACS 2014S data are two fold.
1 – 2014 1-year estimates for a larger number of areas than available from the ACS 2014 1-year (ACS2014) estimates.
2 – more current (2014) data for those areas only available from the 5-year estimates (centric to 2012) that are between 20,000 and 65,000 population.

The ten cities/places with the highest 2014 median family income based on 1-year estimates were all under 65,000 population. These cities were not included in the ACS 2014 1-year standard estimates but were included in the ACS 2014 1-year supplemental estimates. See list below.

This section provides an overview of the ACS 2014 supplemental data and provides a summary of tools, interactive table and GIS project, to analyze characteristics of these areas. These data are used by ProximityOne to develop/update annual county demographic-economic projections. See schedule of related 2016 updates.

Scope of Expanded Geography Available
As shown in the table below, 2014 1-year “supplemental” estimates are available for more than twice as many counties from the ACS2014S compared to the ACS2014 “standard” 1-year estimates. However, there area a more limited set of subject matter data available from the ACS2014S data compared to both the ACS 2014 1-year and 5-year estimates.

MSA/MISA: Metropolitan Statistical Areas/Micropolitan Statistical Areas Counties: county and county equivalent

ACS 2014S Data Availability by County
The following graphic shows the additional counties for which ACS 2014 1-year estimates are available using the “supplemental” data.
• ACS 2014 1-year “standard” estimate counties — blue fill pattern
• ACS 2014 1-year “supplemental” estimate counties — orange fill pattern
• Only ACS 2014 5-year estimates available for remaining counties
Click graphic for larger view; expand browser window for best quality view. The larger view shows metropolitan area (MSA) boundaries. Note that for example, ACS 2014 1 year data are available for all counties in the Austin and San Antonio metros (see pointer) — previously unavailable..

.. view developed with ProximityOne CV XE GIS and related GIS project.
.. any CV XE GIS user can create this view using the default US1.GIS project

ACS2014S Tables — scroll section
The ACS 2014 supplemental data include 42 tables and a total of 229 data items. Br> The table number and descriptions are summarized below.

View full table/item detail in tables shells: ACS 2014S Table shells (xls)

ACS 2014 Selected Supplemental Items for Selected Geography
  — interactive table
The interactive table contains all geography for which the ACS2014S data have been tabulated for these geographies: U.S., state, county, city/place, 114th Congressional District, MSA/MISA, PUMA, urban area and school district. The table provides access to key selected items.

The following graphic illustrates use of the interactive table. First cities/places were selected using the Type drop-down below the table. Next, the table is ranked in descending order on median family income. As shown in the graphic the largest 10 cities/places were under 65,000 population. Click graphic for larger view.

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.

Metro Situation & Outlook Reports Updated

Regional Demographic-Economic Modeling System (RDEMS) county table links are now embedded in Metro Situation & Outlook (S&O) Reports. Easily access the RDEMS county demographic-economic tables for metros of interest.

Use this link to access the Metro S&O Reports:
http://proximityone.com/metro_reports.htm
… click link in the “Code” column to access a specific metro.

… selected metros …
Atlanta .. Boston .. Charlotte .. Chicago .. Dallas .. Denver .. Los Angeles .. Honolulu .. Houston .. Miami .. Minneapolis .. New York .. Philadelphia .. Phoenix .. San Diego .. San Francisco .. Seattle .. Washington

All metros are available.

Join in … join us in the Data Analytics Lab sessions 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.

State and Regional Decision-Making Information

Organized on a state-by-state basis, use tools and geographic, demographic and economic data resources in these sections to facilitate planning and analysis. Updated frequently, these sections provide a unique means to access to multi-sourced data to develop insights into patterns, characteristics and trends on wide-ranging issues. Bookmark the related main Web page; keep up-to-date.

Using these Resources
Knowing “where we are” and “how things have changed” are key factors in knowing about the where, when and how of future change — and how that change might impact you. There are many sources of this knowledge. Often the required data do not knit together in an ideal manner. Key data are available for different types of geography, become available at different points in time and are often not the perfect subject matter. These sections provide access to relevant data and a means to consume the data more effectively than might otherwise be possible. Use these data, tools and resources in combination with other data to perform wide-ranging data analytics. See examples.

Select a State/Area

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C.
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Topics for each State — with drill-down to census block
Visual pattern analysis tools … using GIS resources
Digital Map Database
Situation & Outlook
Metropolitan Areas
Congressional Districts
Counties
Cities/Places
Census Tracts
ZIP Code Areas
K-12 Education, Schools & School Districts
Block Groups
Census Blocks

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.

Health Insurance Coverage by Census Tract

.. the overall percent civilian non-institutionalized population with health insurance coverage changed from 85.2% in 2012 to 88.3% in 2014. Health insurance coverage is one measure among many others that are important in Healthcare Data Analytics. This section uses healthcare data analytics tools to view/analyze healthcare coverage by census tract and other geographies. See more about using health insurance coverage data in context with other health-related data in this related section.

Percent Civilian Non-institutionalized Population
    with Health Insurance by Census Tract


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

Health Insurance Coverage Data & Interactive Table Access
Health insurance coverage data are one of several types of health-related data available in the 2014 ACS 5-year estimates. At the national level, the overall population with health insurance coverage changed from 85.2% in 2012 to 88.3% in 2014. The upper two intervals shown in the health insurance coverage by census tract map above are for the percent population with health insurance coverage at or above the national 85.2% level in 2012 (census tract data are only available from the 5-year estimates, the ACS 2014 5 year estimates are centric to 2012).

While health insurance coverage data are available in a range of demographic combinations, 25 health insurance coverage items (see table below) are available from the economic characteristics dataset for selected types of geography in these interactive tables:

ACS 2014 1-Year Estimates – data centric to mid-2014
U.S., State, CBSA/Metro
114th Congressional Districts

ACS 2014 5-Year Estimates – data are centric to mid-2012
Census Tracts
ZIP Code Areas
School Districts
State Legislative Districts

Join me in a Data Analytics Lab session to discuss more details about analyzing health and healthcare characteristics and patterns and use of data analytics to develop further detail related to your interests.

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.

Congressional District Citizen Voting Age Population

.. congressional districts are very diverse in terms of the overall percent and geographic distribution of citizen voting age population. The size and distribution of the citizen voting age population are important as this population group determines election outcomes. Among the 114th Congressional Districts, the citizen voting age population (CVAP) ranged from 43.2% of the total population (CA40) to 81.2% (FL11) in 2014. Nationally, the citizen voting age population (CVAP) was 70.5% of the total population

% Citizen Voting Age Population by Congressional District

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

Use the interactive table discussed in this section to view/rank/compare/query national scope citizen and CVAP estimates by 114th Congressional District, state and the U.S.  Access the interactive table in this related Web page.

Examining patterns of the citizen voting age population … the Voting Rights Act prohibits development of voting districts that discriminate against potential voters on the basis of race and/or language minority status. To examine how voting districts comply with the Voting Rights Act requires data on the citizen voting age population (CVAP) by race/origin for many types of geographic areas. This section provides data analytics tools to examine ACS 2014 1-year CVAP estimates for 114th Congressional Districts. See related section for information CVAP demographics at the census tract level.

The CVAP data can be used to develop insights into alternative interpretations of “one person one vote.” The Supreme Court on May 26, 2015, agreed to hear a case that will answer a long-contested question about a principle of the American political system — the meaning of “one person one vote.” The court has never resolved whether that means that voting districts should have the same number of people, or the same number of eligible voters. The difference matters in places with large numbers of people who cannot vote legally, including immigrants who are here legally but are not citizens; unauthorized immigrants; children; and prisoners.

The CVAP estimates provide only one part of the required data. Voting district and other boundaries and data are also needed to be used in combination with the CVAP estimates. Using GIS tools, the CVAP estimates can be used in mapping applications, such as those reviewed in this section, in combination with voting district boundaries to reveal potential non-compliance in the structure of voting districts.

Interactive Table
Use this interactive table to view/rank/compare/query national scope citizen and CVAP estimates by 114th Congressional District, state and the U.S. These data are based on ACS 2014 1-year estimates found in Table B05003. See more information about computing CVAP and accessing/integrating related data.

The graphic shown below illustrates using the interactive table to rank California congressional districts in ascending order on percent citizen voting age population. Use the interactive table to examine congressional districts of interest.

Join me in a Data Analytics Lab session to discuss more details about analyzing citizen voting age population and use of data analytics to develop further detail related to your interests.

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.