Tag Archives: demographic trends

U.S. House of Representatives 2020 Apportionment

.. Congressional Apportionment by State .. 2010 & projected 2020 state by state congressional seats.

What will the results of Census 2020 tell us us about how the House of Representatives will be reapportioned, state by state? This section examines scenarios which might occur based on state population projections. See related Web section http://proximityone.com/apportionment.htm for more detail and interactive table.

Use the GIS tools and project to make your own map views … see details
.. use in classroom .. research .. reference .. collaboration.

This section has been developed using
– 2020 apportionment population projections
.. part of the ProximityOne Situation & Outlook (S&O)
– the reapportionment/redistricting feature of the CV XE GIS software
The 2020 population projections reflect anticipated change under one scenario. Those values are then used in the CV XE GIS reapportionment operation to compute the number of House seats shown in the related table.

Apportionment of the U.S. House of Representatives
— based on the 2010 Census

– view created with CV XE GIS. Click graphic for larger view with more detail.

Apportionment of the U.S. House of Representatives
— based on ProximityOne 2020 Population Projections

– view created with CV XE GIS. Click graphic for larger view with more detail.

Congressional apportionment is the process of dividing the 435 memberships, or seats, in the House of Representatives among the 50 states based on the population figures collected during the decennial census. The number of seats in the House has grown with the country. Congress sets the number in law and increased the number to 435 in 1913. The Constitution set the number of representatives at 65 from 1787 until the first Census of 1790, when it was increased to 105 members. More about apportionment.

Initial Census 2020 demographic data, the apportionment data, will be released by December 31, 2020. See related Census 2010 Apportionments.

Apportionment totals were calculated by a congressionally defined formula, in accordance with Title 2 of the U.S. Code, to divide among the states the 435 seats in the U.S. House of Representatives. The apportionment population consists of the resident population of the 50 states, plus the overseas military and federal civilian employees and their dependents living with them who could be allocated to a state. Each member of the House represents, on average, about 710,767 people for Census 2010.

Using the Interactive table
The following graphic illustrates use of the 2010 & 2020 apportionment by state and historical apportionment 1910 to 2010. Sort on any column; compare apportionment patterns over time. Click graphic for larger view.
Use the interactive table at http://proximityone.com/apportionment.htm#table.

Congressional District/State Legislative District Group
Join the CDSLD Group (http://proximityone.com/cdsld.htm), a forum intended for individuals interested in accessing and using geodemographic data and analytical tools relating to voting districts, congressional districts & state legislative districts and related geography with drill-down to intersection/street segment and census block level. Receive updates on topics like that of this section.

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.

How & Why State Demographics Are Changing

.. to examine how and why state demographics are changing, we look at the state as the sum of its parts — counties. Here we review tools and data to examine how and why state/county population is changing … is the population moving away or into your areas of interest? What are the trends; what is causing the change? what are the characteristics of the population moving in and out? How might this impact your living environment and business? See related Web section for more detail on topics covered here and access interactive table.

Patterns of Population Change by County, 2010-2017
The following graphic shows how counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2017. Click graphic for larger view; expand browser window for best quality view.

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

The above graphic provides a visual summary of how and why demographics are changing from 2010 to 2017 in terms of components of change: births, deaths and migration. See the underlying data in this interactive table.

Change in the population from births and deaths is often combined and referred to as natural increase/change. The other way an area population changes is through migration (net international, net domestic, net migration). Examining an area’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

Examine States of Interest
Click a state link to view details about specific states …
Alabama .. Alaska .. Arizona .. Arkansas .. California .. Colorado .. Connecticut .. Delaware .. 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

Situation & Outlook Briefing Sessions
Join me in a Situation & Outlook Briefing Session, every Tuesday, where we review the where, what, how, and when of demographic-economic-business change – and how change might impact you.  We review current topical issues and data — and how you can access/use tools/data to meet your needs/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.

 

Life Expectancy Change by County, 1980-2014

.. data and tools to examine changing life expectancy by county. Use the interactive table to examine life expectancy characteristics and related demographics for counties and regions of interest. Use the related GIS project and datasets to examine life expectancy contextually with other geography & subject matter. See details below. These data and tools are part of the ProximityOne health data analytics resources.

Life expectancy is rising overall in the United States, but in some areas, death rates are going in the other direction. These geographic disparities are widening.

Life Expectancy Change by County, 1980-2014
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Life expectancy is greatest in the high country of central Colorado, but in many pockets of the U.S., life expectancy is more than 20 years lower. These data are based on research and analysis by the University of Washington Institute for Health Metrics and Evaluation.

Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policymakers, clinicians, and researchers seeking to reduce disparities and increase longevity.

Life Expectancy Change by County, 1980-2014 — drill-down view
— South Central Region
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view. The larger graphic shows counties labeled with change in life expectancy from 1980-2014.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Additional Views — use the GIS project to create your own views
.. click link to view
Alaska
Hawaii
Minneapolis metro

Using the Interactive Table
Use the interactive table to view, rank, compare life expectancy characteristics. This graphic shows California counties ranked on life expectancy change 1980-2014 in descending order. Select states or metros of interest. 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.

Examining Houston Metro Demographic-Economic Characteristics

.. tools & data to examine metro demographic-economic characteristics .. this Houston, TX metro focused section is one of several similar metro sections that will be covered in weeks ahead.  Each metro-focused section provides a summary of tools and data that can be used to view, rank, compare, analyze conditions and trends within the metro and this metro relative to other metros, regions and the Nation.  The ready-to-use GIS project/datasets provide the basis for extended data/geographic views and analysis immediately.  See more detail about topics covered in this related Web section.

Relating your data to demographic-economic characteristics and trends in a region involves more than information provided by a report or set of statistical tables. It is important to use your data to be able to identify areas of missed opportunity and competitive position. It is important to have a “10,000 foot” view as well as understanding individual neighborhoods and market/service areas. Geographic Information System (GIS) tools, with the right set of geographic, demographic and economic data can facilitate decision-making through the use of visual and tabular data analytics.

This section provides information on installing and using the Houston Metro Demographic-Economic GIS software and project/datasets. This same scope of data, tools and operation is available for any metro, state or combination.

10,000 Foot View
The following graphic shows patterns of median household income by census tract for the Houston metro area. This is the start-up view when using the GIS tools and data described below. The color patterns/intervals are shown in the highlighted layer in legend at left of map window. Use the GIS tools described below to develop thematic pattern maps for a range of data and criteria.

.. view developed using the CVGIS software.

See more about census tracts; see tracts main page.

Several additional views follow, developed using this same GIS project. These views illustrate different levels of geographic granularity and patterns of different subject matter.

Median Household Value by Block Group
See more about block groups; see block groups main page.

.. view developed using the CVGIS software.

Population/Housing Unit by Block
See more about census blocks; see census block main page.

.. view developed using the CVGIS software.

Zoom-in to Sugarland/Fort Bend County
See more about cities/places; see cities/places main page.
Access data for any city using interactive table.

.. view developed using the CVGIS software.

Further Zoom-in Showing Street/Road Detail
See more about streets.

.. view developed using the CVGIS software.

Additional Information
See the related Houston metro Situation & Outlook Report.

Using the GIS Software and Project/Datasets
(requires Windows computer with Internet connection)
1. Install the ProximityOne CV XE GIS
… run the CV XE GIS installer
… requires UserID; take all defaults during installation
2. Download the Houston Metro GIS project fileset
… requires UserID; unzip Houston Metro GIS project files to local new folder c:\p1data
3. Open the c:\p1data\us1_metros_houston.gis project
… after completing the above steps, click File>Open>Dialog
… open the file named c:\p1data\us1_metros_houston.gis
4. Done. The start-up view is shown above.

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.

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.

Examining County Migration: 2010-2016

.. tools and data to examine U.S. by county migration 2010 to 2016 … is the population moving away or into your counties of interest? What are the trends; what is causing the change? What are the characteristics of the population moving in and out? How might this impact your living environment and business?

The total net international migration among all counties 7/1/2010 – 7/1/2016 was 5,641,260, an annual average of 940,432. The sum of net domestic migration among counties is zero by definition, but domestic migration among counties varies radically by size and direction. This section is focused on U.S. by county migration from 2010 to 2016. Migration is one component of change used to develop population estimates. See more about county population estimates and components of change in this related Web section.

Largest 10 Counties Based on 2016 Population
This table shows how domestic migration varies widely among the most populated counties. Use this interactive table to develop your own custom views for counties of interest.

Patterns of Population Change by County, 2010-2016
– the role and impact of migration
The following graphic shows how counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2016. Click graphic for larger view; expand browser window for best quality view.

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

Examining Population Components of Change
– net migration and natural change
Population change can be examined in terms of components of change. There are three components of change: births, deaths, and migration. The change in the population from births and deaths is often combined and referred to as natural increase or natural change. Populations grow or shrink depending on if they gain people faster than they lose them. Examining a county’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

Using the Interactive Table
– examining migration by county
Use the interactive table to examine characters of counties by states, metro or peer group. The following graphic illustrates use of the interactive table to view net migration for the Houston metro by county. The net migration button was used to select only the net migration columns, FindCBSA button used to show only counties in this metro and the final step was to sort the resulting table on 2016 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.

New ACS 2015 1-Year Demographic-Economic Data

.. essential data to assess where we are, how things have changed and how things might change in the future down to the sub-neighborhood level. The American Community Survey (ACS) is a nationwide survey designed to provide annually updated demographic-economic data for national and sub-national geography. ACS provides a wide range of important data about people and housing for every community across the nation. The results are used by everyone from planners to retailers to homebuilders and issue stakeholders like you. ACS is a primary source of local data for most of the 40 topics it covers, such as income, education, occupation, language and housing. ProximityOne uses ACS to develop current estimates on these topics and 5-year projections. This section is focused on ACS 2015 data access, integration and use and is progressively updated.

New ACS 2015 1-year estimates are available as of September 15, 2016.

Importance of ACS: Assessing Demographic-Economic Change
Oil prices plummeted in late 2014. How has this affected people and households in areas hardest hit? Find out for wide-ranging geographies using the ACS 2015 1-year estimates. Compare to ACS 2014 1-year estimates. Use the ACS 2016 1-year estimates (September 2017) to see how the impact has continued. Demographic-economic conditions change for many reasons; oil price changes are just one.

Keep informed about ACS developments and related tools and applications:
• Updates are sent to ProximityOne User Group members (join here).
… access special extract files and GIS projects available to members.
• ACS updates and applications are covered in the Data Analytics Blog.
• ACS data access, integration & use … join us in a Data Analytics Lab session.

In the weeks ahead, the following ProximityOne information resources will be updated with new ACS 2015 1-year data:
U.S.-State-Metro Interactive Tables
• Demographic component section of Metro Situation & Outlook Reports .. example for Dallas metro
• Housing characteristics component section of Metro Situation & Outlook Reports .. example for Dallas metro
Demographic-Economic Trend Profiles
• Special study reports.

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.