Category Archives: GIS

Population Living Alone & Age 65 Years and Over

.. how many people are living alone in your community, neighborhood? How does this population impact the community? What are their special needs? How does this population vary by area and population group? There were 37.9 million one-person households, 29% of all U.S. households in 2022. In 1960, single-person households represented only 13% of all households. These estimates are based on the 2022 Current Population Survey (CPS). Moving forward, the number of one-person households, people living alone, will increase at the rate of one million or more per year. People in households exclude people living in group quarters. This post examines patterns of people living alone with focus on people living alone age 65 year and over and distribution by small area geography.

While the CPS data provide a current snapshot of the number of people living alone, we have to use data from the American Community Survey to obtain data for smaller area geography like counties and census tracts.

Population Living Alone by Census Tract –Visual Data Analytics
The four graphics below show patterns of the population living alone by census tract. These views have been developed using the Visual Data Analytics (VDA GIS) tools with integrated demographics. Develop variations on these views using the VDA Web GIS using only a web browser.

Patterns of Population Living Alone by Tract

.. click graphic for larger view.

Patterns of Population 65 and Over Living Alone by Tract

.. click graphic for larger view.

Patterns of Population Living Alone by Tract — Houston Metro Area

Patterns of Population 65 and Over Living Alone by Tract — Houston Metro Area

Examine the Data in More Detail
As noted in this related New York Times story, nearly 26 million Americans 50 or older now live alone, up from 15 million in 2000. Older people have always been more likely than others to live by themselves makes up a bigger share of the population than at any time in the nation’s history. The trend has also been driven by deep changes in attitudes surrounding gender and marriage. People 50-plus today are more likely than earlier generations to be divorced, separated or never married. Similar ACS data as used to develop the graphics shown above are available by race/origin. These data are based on the ACS 2020 data; the same scope of data will be available from ACS 2021 to be released in December 2022.

About VDA GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. Use VDA Web GIS with only a Web browser; nothing to install; GIS experience not required. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Personal Economic Well-Being

.. examining characteristics, patterns and change in personal economic well-being; learning about what per capita personal income by county tells us. Per capita personal income (PCPI) is the best single measure of personal economic well-being. PCPI differs American Community Survey (ACS) measure of per capita income, median household income and similar income measures as PCPI includes non-monetary income .. PCPI provides a more comprehensive measure. This post provides an update focused on new data released November 2022, county level personal income time series data starting in 1969.

Patterns of 2021 Per Capita Personal Income by County

.. click graphic for larger view
.. use VDA Web GIS for Web-based interactive viewing/analytics.
.. see this more detailed analytical framework for analytics using VDA Desktop.

Importance of these Data
How is the regional economy doing? How is it trending? What policies might be changed to improve personal economic well-being? Answers to these and similar questions are why knowing about personal income and its derivation, components is important — to residents, businesses and governments. While median household income is often considered the best measure of buying power for an area, it is not the best measure of personal or household economic well-being. PCPI and the Regional Economic Information System provides insights and answers to these questions.

U.S. Change in PCPI
In U.S. metropolitan areas, PCPI increased 7.3 percent in 2021, up from 6.0 percent in 2020. In U.S. nonmetropolitan areas, PCPI increased 7.5 percent, down from 7.9 percent.

Regional Economic Information System
PCPI is a small part of the broader Regional Economic Information System (REIS). The following links show examples of detailed tables for Harris County, TX comparing 2019 and 2021 developed using the ProximityOne REIS package. Develop these profiles for any county for your selected year 1970 through 2021.
  • Personal Income by Major Source
  • Earnings by Source & Sector
  • Employment by Type & Sector
  • Transfer Payments
  • Economic Profile
  • Farm Income & Expenditures

About VDA GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. Use VDA Web GIS with only a Web browser; nothing to install; GIS experience not required. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Naturally Occurring Retirement Communities in Path of Hurricane Ian

.. 25.8 million U.S. households had a head of household age 65 years or over in 2010; 22.1% of total households. 3.1 million households with head of household 65 years or over were located in 10,201 “naturally occurring retirement communities” (NORCs) — areas where the percent of head of household age 65 or over is 40 percent or more. Has the number of NORCs tripled by now? Where and how much .. a next blog.

Here, we review Naturally Occurring Retirement Communities in the Path of Hurricane Ian by block group (BGs) in Lee County, FL … using VDA Web GIS. The same process can be applied to any area.

The following view shows a map view of the BGs meeting the criteria. A partial list of the BGs are shown in the table below the map. In the graphic, BGs meeting the criteria show with blue cross-hatch. Zoom in, label, get a profile if viewing real-time. In the graphic, for example, BG 120710019151 (state 12, county 171, tract 001951, BG code 1) has a total Census 2020 population of 2,061.

vda_lee_norc_bgs

Step-by-Step Guide
See this VDA Web GIS tutorial showing how to develop a similar view for areas of interest.

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Examining Majority-Minority Population by Census Tract

.. a geographic area, such as a census tract or county, has a majority-minority (MM) population when the percent of the population less White, NonHispanic population is more than 50-percent or more of the total population. This section examines patterns of MM by 2020 census tract in the Houston, TX area using the VDA Web GIS (VDA). You can use VDA to examine patterns of MM by tract for areas of interest. All you need is a Web browser and Internet.

MM Tracts in the Houston Area
The following graphic shows patterns of the MM by tract in the Houston metro developed with VDA. The map shows tracts having more than 50% MM population with a red/salmon color. Tracts having 50% or less MM population are shown in blue/green/yellow. Harris County, TX has 1,115 2020 census tracts; 854 of these tracts have a MM population. See the partial list of those tracts by tract code and %MM in the table below the map. A query was used to determine the number of tracts meeting a criteria and sorted in descending order by %MM.

See this VDA Guide section for a step-by-step description
of how you can develop this or a similar view/analysis.

Percent Majority-Minority is a demographic attribute/measure for a geographic area. Like population density or percent population of a certain age, %MM can provide insights for stakeholders or analysts.

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Housing Price Index by ZIP Code – How Housing Markets are Trending

.. housing prices can impact residential investment and affect economic growth, business opportunities and the housing market. The Housing Price Index (HPI) is one measure of how housing prices are changing. The HPI by ZIP code, as reviewed here, is an index based on the year 2000=100. Changing trends in the HPI can used used the determine the relative costs of housing and change in housing valuation. Hosing prices, and the HPI, are only one part of determining how housing markets are trending. Other measures important to examine include building permits and new construction.

Examining Housing Price Trends
Use the VDA Web GIS tool to examine the Housing Price Index for ZIP Codes of interest. Create maps and tabular profiles such as the one shown below.

Video of Steps to Explore HPI by ZIP Code
Click graphic to view video showing how to use VDA Web GIS to access a ZIP Code profile.

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Housing Price Index
The Housing Price Index used here is developed by the Federal Housing Fiance Agency (FHFA). The FHFA House Price Index is the nation’s only public, freely available house price indexes that measure changes in single-family home values based on data from all states that extend back to the mid-1970s.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

New Authorized Monthly Residential Construction by City 

.. access/analyze monthly building permits data for any/all of 7,700 cities in a few minutes via a web browser. Building permits data, a leading economic indicator, are one of the few current indicators available at the city level to examine characteristics and trends. These data can provide insights into the type and value of housing that is being added. Use the VDA Web GIS to examine how the housing situation is changing for cities of interest.

The VDA Web GIS MetroDynamics city layer/dataset is updated with the most recent 6 months of the number of housing units by type and value. The current data provide monthly aggregated data for January 2022 through June 2022. The layer/dataset includes many other attributes (see details). Of the ~19,000 U.S. cities, ~7,700 issue monthly building permits and accessible via this resource.

Examining City Housing Characteristics & Trends
An example: cities in the Phoenix, AZ area labeled with the total building permits.

.. table under map view shows cities with largest number of building permits.
.. see steps to develop the above map and table.

Illustrative Profile for Surprise, AZ
.. selected items from MetroDynamics City layer/dataset
.. integrated subject matter from several sources
.. building permits most current

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About Building Permits Data
Building permits data are collected monthly by the Census Bureau from permit issuing agencies. Monthly data are reported for access within one month of the reporting date. Data are collected on the number of new housing units authorized by type of units in building by value.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Real Quarterly GDP Trends: State of the States

.. real gross domestic product (RGDP) decreased in 46 states and DC in 2022Q1, as real GDP for the nation decreased at an annual rate of 1.6 percent. The percent change in real GDP in 2022Q1 ranged from 1.2 percent in New Hampshire to 9.7 percent in Wyoming. Updated quarterly GDP estimates have only a 3-month lag from the reference date (e.g., 2022Q1) to access date (e.g. June 2022). Used as a time series, these data provide insights into recent economic change and prospective future change. The RGDP estimates are developed by sector, enabling an assessment of which and how sectors are contributing to change. While we look at all states in this post, California is used as an example. Examine your state(s) of interest using the tools and data described here.

See this companion video illustrating some aspects of using VDA Web GIS with the state quarterly GDP.

Analyzing Real Quarterly GDP by State Using VDA MetroDynamics
Percent Change in Real GDP, 2021Q1 to 2022Q1, U.S. by State

.. create variations of this view using VDA Web GIS .. nothing to install.
.. about MetroDynamics GIS Project
.. click here to start VDA Web GIS

Examine Real Quarterly GDP by State by Sector
GDP is estimated quarterly for 21 sectors. the following graphic shows quarterly RGDP by sector for California. Use VDA to examine these data for state(s) of interest. Step-by-step how-to details below.

View the above data as an Excel file (save to file and open with Excel).

Using VDA Web GIS to Examine GDP by State – Mapping Patterns
Follow these steps:
Start VDA Web GIS
– Select MetroDynamics Project in next form.
.. VDA user interface appears
– select States in upper left dropdown
– in legend panel, uncheck “Metros Base ..”
– in legend panel, check the top 4 States layers on
– in legend panel, check “+” by “St RGDP%Ch 21q1-22q1” layer name
.. view now shows similar to view presented above.

– click a state to view a profile of the demographic-economic attributes.
.. the profile appears in lower left panel.
.. click the HTML button, lower left, to view the attributes in HTML format.

Using VDA Web GIS to Examine GDP by State — Table/Query
Complete the above steps, then click Query/Table button (below map at right)
– a table/grid now shows below the map window
.. the table has a row for each state; columns show subject matter items
– use Select Field button to select only a few items:
  .. state name
  .. RGDP2021Q1
  .. RGDP2022Q1
  .. RGDP2021Q1-2022Q1 change
  .. RGDP2021Q1-2022Q1 % change
.. table refreshes with view shown below (sorted on RGDP2021Q1-2022Q1 % change).

Looking Ahead; Updates
New quarterly GDP by state estimates for 2022Q2 and revised annual GDP by state estimates for 2017 to 2021 and will be released on September 30, 2022.

About GDP and Measurement
State GDP estimates are updated quarterly by the U.S. Bureau of Economic Analysis (BEA). The following terminology may be a useful reference.
Gross domestic product (GDP) by state is the market value of goods and services produced by the labor and property located in a state. GDP by state is the state counterpart of the nation’s gross domestic product, the most comprehensive measure of U.S. economic activity.
Current-dollar statistics are valued in the prices of the period when the transactions occurred—that is, at “market value.” Also referred to as “nominal GDP” or “current-price GDP.”
Real GDP values are inflation-adjusted statistics, these values exclude the effects of price changes.
State GDP is the lowest geographic level for which quarterly estimates of GDP are produced by BEA.
Seasonal adjustment and annual rates. Quarterly values are expressed at seasonally adjusted annual rates.
Quantities and prices. Quantities, or “real” measures, are expressed as index numbers with a specified reference year equal to 100 (currently 2012). Quantity indexes are calculated using a Fisher-chained weighted formula that incorporates weights from two adjacent periods (quarters for quarterly data and annuals for annual data). “Real” dollar series are calculated by multiplying the quantity index by the current dollar value in the reference year and then dividing by 100. Percent changes calculated from chained-dollar levels and quantity indexes are conceptually the same. Chained-dollar values are not additive, because the relative weights for a given period differ from those of the reference year.
Chained-dollar values of GDP by state are derived by applying national chain-type price indexes to the current dollar values of GDP by state for the 21 North American Industry Classification System-based industry sectors. The chain-type index formula that is used in the national accounts is then used to calculate the values of total real GDP by state and real GDP by state at more aggregated industry levels. Real GDP by state may reflect a substantial volume of output that is sold to other states and countries. To the extent that a state’s output is produced and sold in national markets at relatively uniform prices (or sold locally at national prices), real GDP by state captures the differences across states that reflect the relative differences in the mix of goods and services that the states produce. However, real GDP by state does not capture geographic differences in the prices of goods and services that are produced and sold locally.

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Examining Ukraine and Region using VDA

Ukraine is a country that is now on many people’s minds. It is easy to examine the geography of Ukraine in context of the region using VDA Web GIS.

The Visual Data Analytics (VDA) Web GIS supports worldwide applications. Explore characteristics of Ukraine and the surrounding area with only a browser. There is no cost. Follow the steps described below to get started.

• Start VDA Web GIS using a browser .. link to start VDA Web GIS.
– you will need to sign-up, then you can easily revisit.
• Open the Situation & Outlook Project.
– next you will see a form asking you to select a project.
– select the Situation & Outlook project.
– the opening view is the U.S. by county.
– note that for the VDA Level 1 free version, the session closes in 10 minutes.
– just start over … you are now more familiar with operations.
– if you are new to VDA, please review getting started.
• Check on “OpenStreetMaps” in Legend Panel; left of Map Window toward bottom:

• Navigate to Kyiv, Ukraine
– Key in the name Kyiv in Find Address edit box and click Find Address button
– the view refreshes; a marker (for Kyiv location) shows in the Map Window:

• In the legend panel, click on the “Country Projections to 2050” layer
• Zoom-out to Ukraine country view
– click zoom-out button below map window 6 times (or use zoom window).
– navigate in the map window to obtain this view of the region:

• To get the above view, with Ukraine the selected country, proceed as follows:
– select “Country Projections to 2050” in the “Select Layer” dropdown (upper left)
– click anywhere in map window on Ukraine
– the crosshatching appears; a profile of Ukraine appears in lower left Profile Panel.
– click the HTML button (lower left) to view this profile in HTML format.
– browser must be enabled to show popups.
– in the HTML profile, see that the 2022 Ukraine population is 43.5 million.
• Examining Ukraine provinces — Luhansk Oblast (eastern Ukraine):
– select “Country by State” in the “Select Layer” dropdown (upper left)
– navigate to eastern Ukraine
– click anywhere on Luhansk Oblast map and the following view appears:

– the crosshatching appears showing this Oblast.
– a profile of Luhansk Oblast appears in lower left Profile Panel.
– click the HTML button (lower left) to view this profile in HTML format.
– in the HTML profile, see the Oblast VDA computed area is 26,310,276,552 sqmt
… or 10,158 square miles (26310276552/2589988).

About VDA Web GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Majority-Minority Population by Census Tract

.. based on the Census Bureau’s assessment of Census 2020 results, the most prevalent racial or ethnic group for the United States was the White alone non-Hispanic population at 57.8%. This decreased from 63.7% in 2010. This trend will continue into the future. Over time, interest has grown in majority-minority areas ranging from congressional districts to neighborhoods. Of the 85,190 Census 2020 census tracts, 28,317 tracts were majority-minority based on Census 2020 demographics. Decision-making information using VDA Web GIS .. examining lending institution assessment areas .. see below.
.. of the 1,113 Census 2020 census tracts in Harris County, TX (Houston), 549 tracts are majority-minority tracts based on Census 2020 data. Minority-majority areas are those areas where the resident population is less than 50% non-Hispanic whites. Use tools and methods described in this section to analyze the majority-minority patterns in your census tracts, neigborhoods, areas of interest.
Patterns of Majority-Minority Population in Harris County, TX
The following view shows patterns of Census 2020 percent majority-minority population by Census 2020 census tract in the Houston area.
This view was developed using the VDA Web GIS. You can create a similar for areas of interest. Only a Web browser is needed. In this view, the tracts layer is selected as the “active layer” and a census tract is clicked in the map window. The tract highlights with a blue hatch pattern and a profile is shown in the lower left panel. See that this tract has a Census 2020 total population of 5,380 (TotPop) and White alone, non-Hispanic population (White1NH) of 131. Use the VDA Table/Query feature to examine tracts in a spreadsheet/grid.
Majority-Minority Population by Tract Interactive Table
The following view illustrates use of the VDA Table/Query feature to examine and query the majority-minority population by tract.

This SQL statement is used to select, compute and show the data in a tabular form by tract.
select uid, geoid, totpop, white1nh, 100*(totpop-white1nh)/totpop where totpop>0 and geoid like ‘48201%’
The percent majority-minority is computed “on the fly”.
The table is ranked by on this sort instruction:
100*(totpop-white1nh)/totpop desc
The resulting table shows tracts as rows with the highest percent majort-minority at the top. A larger population tract is selected by clicking on it in the grid. The tract 48-201-331700 is selected in the table; that tract is zoomed-to in the map, and the demographics are shown in the profile (TotPop 4,045, White1NH 29).
Examining Majority-Minority Tract Patterns in Context of Lending/Mortgages
Majority-minority census tracts relates to banks/lenders and the Community Reinvestment Act (CRA). The following graphic developed using VDA Web GIS, shows locations of a California bank in context of patterns of percent majority-minority tracts. It is easy to see how bank locations relate to patterns of majority-minority tracts. Lenders and stakeholders are enabled to analyze patterns and gain insights. See more about adding/using the national FDIC bank locations data to VDA Web GIS to perform more in-depth analysis.

Using VDA GeoSelect Tool to Examine Assessment/Service Areas
The following graphic illustrates use of the VDA GeoSelect tool to select tracts around a bank location to evaluate demographic characteristics of an area .. an assessment area or service area. As tracts are selected they are shown with a hatch pattern. The Profile panel, lower left, is dynamically updated to show aggregated demographics for the set of tracts selected including total population and race/origin details.

Using the Visual Data Analytics (VDA) Web GIS
Learn more about VDA.
Sign-in to to VDA using browser, nothing to install.
Select the “Base — Majority-Minority Tracts” GIS Project.
The opening view shows majority-minority tract patterns, similar to the above graphic/view.

Learn more — Join me in the Data Analytics Web Sessions
Join me in a Accessing & Using GeoDemographics Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Warren Glimpse/ProximityOne/Alexandria, VA USA and Takashi Hamilton/Tsukasa/Osaka, Japan are co-developers of VDA. Contact Warren. Join Warren on LinkedIn.

Arizona’s Shifting Demographics

.. part of a state-by-state series .. these periodic posts examine how and why the state and its counties changed bwteen 2010 and 2020. Later posts will provide more of a drill-down look at change. Click the Follow link at right to receive new and updated information.

Census 2020 Arizona Demographics
The Arizona July 1, 2020 Census model-based population estimate of 7,421,401 compares to the Census 2020 population count of 7,151,502 people. The difference of -269,899 between the 2020 estimate and the 2020 count can be explained by several factors. First, the estimate is for a point in time that is three months later that the Census. There will be a tendency of the Census Bureau to adjust the Joly 1, 2020 population estimate to conform to the Census 2020 value. The July 1, 2020 estimate will likely be adjusted to reflect this change when the July 1, 2021 estimates are released April/May of 2022.

The 2020 population estimate is determined using a component method. The 2020 population estimate is the sum of the 2019 population estimate (7,291,843 for Arizona) and each of the following for the period July 1, 2019 through June 30, 2020 …
plus births (AZ 81,451)
less deaths (AZ 66,385)
plus international migration (AZ 9,272)
plus domestic migration (AZ 105,435)
plus an estimation residual (AZ -214)

Any one or a combination of these 6 estimate based values could be wrong, or the Census 2020 value could be wrong. It is likely a combination of all of these factors.

The remainder of this section is based on Census Bureau model-based estimates, released April 26, 2021. See more about these data for all U.S. counties in the Demographics 2060 section where Arizona demographic projections can be examined.

Visualizing Arizona Demographic Change
The following graphic illustrates how Arizona county demographics have changed from 2010 to 2020. The labels show the actual percent change; the color patterns, as shown in the legend, provide a visual thematic pattern view.

Examining the How and Why of Demographic Change
The following table shows a row for the state and each county, providing more detail as to the where, what/how much, how and why demographic change has occurred from 2010 to 2020.


Click graphic for larger view.

Looking Ahead
More geographically detailed data (counties for example) based Census 2020 (August 2021) will reveal much starker percentage differences between the 2020 estimates versus Census results. The ProximityOne annual estimates and projections to 2060 are developed using two basic series (and variation among those (low, base, high): Census 2020 based series and 2020 estimates series. See http://proximityone.com/demographics2060 for details.

Learn more — Join me in the Data Analytics Web Sessions
Join me in a Accessing & Using GeoDemographics Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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.