Category Archives: Geographic Information Systems

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.

Census 2020 – First Results

.. the first results of Census 2020, the apportionment data, were released on April 26, 2021.  Based on the decennial census, the United States total resident population increased from 308,745,538 (2010) to 331,449,281 (2020), a change of 22,703,743 (7.3%). For now, these data should be trusted and assumed accurate.  The apportionment data provide only total population counts at the state level.  More will be revealed about the accuracy of these data when the redistricting data are released in August 2021.

Apportionment of the U.S. House of Representatives
Congressional apportionment is the process of dividing the 435 members, or seats, in the House of Representatives among the 50 states based on the population data from the decennial census. See more about congressional districts and demographic-economic characteristics. See this related web section for detailed information on apportionment. Use the interactive table to view/analyze the Census 2010 and Census 2020 apportionment data. The following view shows patterns of congressional seats based on the decennial census. Labels show the number of seats based on the 2020 Census. Color patterns show the change in seats, 2010 to 2020.

Census 2020: the Process & Challenges
Counting the total population and selected population attributes in a pandemic is not only challenging but not possible.  During 2020, as the data were collected, it seemed good news that more than two-thirds of the potential respondents had completed the questionnaire.  But then the questions set in.  Bureau public announcements frequently made reference to the number or housing units and the number of households (occupied housing units) “accounted for” reaching 90 percent and progressively more.  By observation, using administrative record data, and other methods, housing units can be much more easily counted than the population and population attributes.  Likewise, determining the number households is  easier than determining the population count and characteristics.

The fact that the state population counts were unexpectedly different from the Bureau’s model based estimates is troubling.  We seek more assurance that the count of  population and population characteristics — by location — are as represented by the apportionment data.

Census Bureau 2020 Model-Based Estimates
New Census Bureau sourced U.S. by county model-based population estimates by age/gender/race-origin as of July 1, 2020 will be released by the Bureau in May 2021.  These estimates are independent of Census 2020 and make use of methods used annually throughout the 2010-2020 period.  An upcoming blog will report on ProximityOne’s analysis of these estimates in comparison with the Census 2020 data.

ProximityOne Estimates & Projections to 2060
ProximityOne annual demographic estimates and projections 2010-2060 by county will begin a new update cycle in May 2021.  The schedule is shown here.  

Starting with the May updates, two base projection series will be developed and progressively updated: one controlled to the Census 2020 data and one based on continued use of 2020 model-based estimates. As more information is released from Census 2020. Follow this blog for more information on evolving developments.

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.

Examine Neighborhood Demographics for any City

.. examine neighborhood demographics for any city (or county, school district ..) using the no fee, no registration new beta version of the Visual Data Analytics (VDA2) Web GIS. Nothing to install, access with any Web browser. Use this unique and powerful resource to make custom maps similar to the one shown below.

Patterns of Economic Prosperity by Neighborhood in Tampa, FL Area
Examine patterns of economic prosperity by neighborhood in context of a selected county, city or other geography. The following view shows patterns of median household income by census tract in Tampa, FL area. The bold black border shows the city boundary for Tampa; the thematic pattern shows colors associated with intervals of median household income ($MHI). The $MHI layer is set with a see through transparency enabling a view of the underlying topology. Create a view similar to this for any of the 19,500 cities in the U.S. See detailed steps to develop this view in the notes below the graphic.

Map Your Own Map View
Follow these stpe to create your own neighborhood by city map view.  Use other features of VDA2 to access demographic-economic data in a tabular or visual form.

More About VDA2 Web GIS
New in VDA2, not available in VDA1, are the table/query operations. View/analyze data for any layer in a spreadsheet/grid form.  Sort and perform queries on subject matter of interest. Click a button in the data grid to zoom to that geographic area in the map window.  The following graphic shows the VDA2 start-up view after clicking the Query/Table On/Off button. This button (shown below map window at right by pointer) toggles the table view on/off.

.. click graphic for larger view.

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook 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.

Housing Value Appreciation

.. U.S. housing prices rose nationwide in August, up 1.5% from the previous month, based on the FHFA Housing Price Index (HPI). Housing prices rose 8.0% from August 2019 to August 2020.

If you purchased a housing unit in 2019Q2 at $260,200 (the ACS 2019 median housing value), the value of the unit in 2020Q2 would be $271,000, an increase of 4.2%. A good deal in this era of low interest rates.

U.S. housing prices posted a strong increase in August .. the 1.5% increase is the largest one-month price increase observed since the start of the HPI measurement in 1991. This large month-over-month gain contributes to an already strong increase in prices over the summer. These price gains can be attributed to the historically low interest rates, rebounding housing demand and continued supply constraints.

The HPI has various limitations as a measure to assess the housing market. One important limitation is that it a measure in isolation; other related demographic-economic measures are not included. This is unlike the American Community Survey (ACS) estimates of the median housing value ($MHV), used as an annual, year-over-year measure of housing value appreciation.

Median Housing Value
The U.S. ACS 1-year estimate of median housing value ($MHV) increased from $229,700 in 2018 to $240,500 in 2019. The ACS 2020 estimate, which will be impacted by the pandemic, will not be available until September 2021. The ProximityOne 2020 estimate of $MHV is $270,500.

Click this API link to view a CSV-like file showing the 2019 median household income and median housing value by state. Join me in a Data Analytics Web Session (see below) to integrate these data into a map view like shown below. Add other data.

Patterns of Median Housing Value by State

– view developed using ProximityOne CV XE GIS
– click graphic for larger view

An advantage of using the ACS or ACS-like $MHV data is that this measure is synchronized with other related measures, like total population, total housing units, housing units by tenure and age built and so on. Though a popular measure to assess geographically comparable housing values, the $MHV has many limitations. A key limitation is that few survey responders really know the value of their home. Other limitations have to do with the definition itself and how the data are collected/developed. ACS $MHV measures value of only occupied housing units and excludes houses on 10 or more acres and housing units in multi-unit structures. See more. While there are other Federal sources of $MHV, it remains that the usabilty aspects of the ACS or ACS-like measures are second to none.

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook 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.

Tip of the Day – Examining Median Housing Value – 2020 Update

.. tip of the day .. a continuing weekly or more frequent tip on developing, integrating, accessing and using geographic, demographic, economic and statistical data. Join in .. tip of the day posts are added to the Data Analytics Blog on an irregular basis, normally weekly. Follow the blog to receive updates as they occur.

.. in this era of uncertainly, we ponder the risk and opportunity associated with changing housing value.  Median housing value by ZIP Code area is one metric of great interest to examine levels and change.  While only one measure useful to examine housing characteristics, it is part of a broader set of demographic-economic data that enable analysis of the housing infrastructure and change in a more wholistic manner. How is housing value trending at the neighborhood level in 2020 and beyond? See more about the Situation & Outlook.

.. 5 ways to access/analyze the most recent estimates of median housing value and other subject matter by ZIP Code area .. based on the American Community Survey (ACS) 5-year estimates. See related Web section.

Option 1. View the data as a thematic pattern map
Option 1 is presented as Option 1A (using CV XE GIS) and Option 1B (using Visual Data Analytics VDA Mapserver). See more about GIS.

Option 1A. View $MHV as a thematic pattern map; using CV XE GIS:
— Median Housing Value by ZIP Code Area; Los Angeles Area
Click graphic for larger view with more detail.

Click graphic for larger view.
Use the Mapping ZIP Code Demographics resources to develop similar views anywhere in U.S.

Option 1B. View $MHV (ACS 2018) as a thematic pattern map; using VDA Mapserver:
— Median Housing Value by ZIP Code Area; Phoenix/Scottsdale, AZ area
Click graphic for larger view with more detail.

Click graphic for larger view. Expand window to full screen for best quality view. View features:
– profile of ZIP 85258 (blue crosshatch highlight) shown in Attributes panel at left
– values-colors shown in Legend panel at left
– transparency setting allows “see through” to view ground topology below.
Use VDA Mapserver: to develop similar views anywhere in U.S. using only a browser. Nothing to install.

Option 2. Use the interactive table:
– go to http://proximityone.com/zip18dp4.htm (5-year estimates)
– median housing value is item H089; see item list above interactive table.
– scroll left on the table until H089 appears in the header column.
– that column shows the 2018 ACS H089 estimate for for all ZIP codes.
– click column header to sort; click again to sort other direction.
– see usage notes below table.

Option 3. Use the API operation:
– develop file containing $MHV for all ZIP code areas in U.S.
– load into Excel, other software; link with other data.
– median housing value ($MHV) is item B25077_001E.
click this link to get B25077_001E ($MHV) using the API tool.
– this API call retrieves U.S. national scope data.
– a new page displays showing a line/row for each ZIP code.
– median housing value appears on the left, then ZIP code.
– optionally save this file and import the data into a preferred program.
– more about API tools.
Extending option 3 … accessing race, origin and $MHV for each ZIP code …
click on these example APIs to access data for all ZIP codes
.. get extended subject matter for all ZIP codes
.. get extended subject matter for two selected ZIP codes (64112 and 65201)

Items used in these API calls:
.. B01003_001E – Total population
Age
.. B01001_011E — Male: 25 to 29 years (illustrating age cohort access)
.. B01001_035E — Female: 25 to 29 years (illustrating age cohort access)
Race/Origin
.. B02001_002E – White alone
.. B02001_003E – Black or African American alone
.. B02001_004E – American Indian and Alaska Native alone
.. B02001_005E – Asian alone
.. B02001_006E – Native Hawaiian and Other Pacific Islander alone
.. B02001_007E – Some other race alone
.. B02001_008E – Two or more races
.. B03001_003E – Hispanic (of any race)
Income
.. B19013_001E – Median household income ($)
.. B19113_001E – Median family income ($)
Housing & Households
.. B25001_001E – Total housing units
.. B25002_002E – Occupied housing units (households)
.. B19001_017E — Households with household income $200,000 or more
.. B25003_002E — Owner Occupied housing units
.. B25075_023E — Housing units value $500,000 to $749,999
.. B25075_024E — Housing units with value $750,000 to $999,999
.. B25075_025E — Housing units with value $1,000,000 or more
.. B25002_003E – Vacant housing units
.. B25077_001E – Median housing value ($) – owner occupied units
.. B25064_001E – Median gross rent ($) – renter occupied units

View additional subject matter options.

Option 4. View the $MHV in context of other attributes for a ZIP code.
Using – ACS demographic-economic profiles. Example for ZIP 85258:
General Demographics ACS 2018 .. ACS 2017
Social Characteristics ACS 2018 .. ACS 2017
Economic Characteristics ACS 2018 .. ACS 2017
Housing Characteristics ACS 2018 .. ACS 2017 .. $MHV shown in this profile.

Option 5. View 5- and 10-mile circular area profile from ZIP center.
– profile for ZIP 80204 dynamically made using SiteReport tool.
– with SiteReport running, enter the ZIP code, radii and click Run.
– comparative analysis report is generated in HTML and Excel structure.
Click this link to view resulting profile.
– from the profile, site 2 is 1.9 times the population of site 1.
– Site 1 $MHV is $296,998 compared to Site 2 $MHV $269,734.
– GIS view with integrated radius shown below.

This section is focused on median housing value and ZIP code areas. Many other subject matter items will be apparent when these methods are used. Optionally adjust above details to view different subject matter for ZIP codes.

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.

VDA Mapserver: Comparing Census Tracts & ZIP Codes

.. for small area demographic-economic analysis, census tracts and ZIP code areas both have their advantages and disadvantages.  While the same scope of subject matter data are available from the American Community Survey (ACS) and ProximityOne current estimates and projections for these geographies, it can be difficult to view how the geographic areas visually relate or intersect in a map.  A flexible solution, accessible by any Web browser, is the Virtual Data Analytics (VDA) Mapserver.  See details.  You can start using VDA immediately with nothing to install.

Visual Data Analytics Mapserver
The VDA Mapserver is a learning resource, a tool that you can use for interactive mapping and geospatial analysis using only Internet and a browser. The VDA Mapserver is set apart from related tools due to the scope and style of accessing data for wide-ranging geography and frequently updated demographic-economic subject matter data. Use the unique combination of Federal statistical data with proprietary current estimates and projections.

Other geographies and subject matter will be reviewed in subject posts.

An Illustration: ZIP Code Area 85258, Scottsdale, AZ

– click for larger view.

The above shows a zoom-in to ZIP code area 85258 in Scottsdale, AZ. A step-by-by description of how to develop this view is shown in this section of the VDA guide.

As shown in the graphic, ZIP Code area 85258 intersects with 8 census tracts. ZIP code areas and tracts are not coterminous. On average there are approximately 2.5 tracts per ZIP code area. But there are more than 150,000 intersecting combinations of ZIP Code areas and tracts. See intersecting areas and interactive table.

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook 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.

America’s Cities: Situation & Outlook

.. the path forward .. planning for the future .. in April 2019, the employment in Houston, TX was 1,111,283 with an unemployment rate of 3.2%. In April 2020, the employment in Houston, TX was 927,105 with an unemployment rate of 14.9%. What will the 2020 annual look like? 2021? There are many paths to get to 2021 and beyond. What policy and action measures might work best? What about your cities of interest? See the related Web section for more details.

Houston characteristics: Demographic .. Social .. Economic .. Housing
Get for any city/area .. e-mail your request

The pandemic impacts on America’s cities in different ways .. some experiencing little change, others with massive change. When, where and how will these disparate patterns change in cities and communities of interest? How might this change impact you and your community? A comprehensive plan needs to be developed and set in motion to achieve best outcomes. This section provides access to tools and data that stakeholders can use to examine America’s cities demographic-economic characteristics and trends. Examine cities of interest. Use ProximityOne data, tools, methods and advisory services to achieve improved results.

Of the nation’s 327.2 million people, an estimated 206.0 million (62.9%) live within an incorporated place. Of approximately 19,500 incorporated places, about 76 percent had fewer than 5,000 people and nearly 50 percent had fewer than 1,000 people. Examine characteristics of individual city population trends and compare cities in states, regions and peer groups using the interactive table below.

Patterns of Economic Prosperity; Cities 50,000 Population or More
The following view shows cities with 2019 population of 50,000 or more as markers .. mainly principal cities of metropolitan statistical areas (MSAs). Nationally, there are 69 cities with 2019 population of 5,000 or more (determine using interactive table below). The marker color shows the median household income; see inset legend. Click graphic for larger view; expand window to full screen.

– View developed using the ProximityOne CV XE GIS software.

Patterns of Economic Prosperity; Cities 5,000 Population or More
– zoom-in to Dallas Metro
The following view shows cities with 2019 population of 5,000 or more as polygons/city boundary-area in the Dallas metro area. There are 201 cities that intersect with the Dallas metro (code 19100); 96 of these cities have a population greater than 5,000 (determine using interactive table below). The color patterns show the median household income range; see inset legend. Click graphic for larger view; expand window to full screen.

Patterns of Economic Prosperity by Neighborhood & Adjacent Areas
The following view shows patterns of median household income by block group (sub-neighborhoods) within city (bold black boundary) in the Dallas County, TX area. In examining the situation & outlook for a city it is important to examine characteristics of drill-down geography and adjacent cities/areas. Inset legend shows median household income color intervals. Click graphic for larger view; expand window to full screen. In the larger view, a cross-hatch pattern is applied to Dallas city. It is easier to see how Dallas city is comprised of a core area as well as outlying areas and extends into adjacent counties.

Interactive Analysis of Cities: Demographic-Economic Patterns & Trends
Use the interactive table to view, rank, compare cities based on demographic-economic trends and characteristics. The following static graphics provide two examples.

 

Largest 15 U.S. Cities Ranked on 2019 Population

California Cities Ranked on Educational Attainment

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook 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.

Business Establishment Characteristics by County

.. what are the number and types of businesses underlying county economies of interest? What is the employment size by type of business establishment? What scope of wages, earnings do they contribute? Learn more here.

The pandemic impact on businesses remains in flux .. this post tools and data that can be used to examine pre-pandemic business establishments and employment pattern characteristics by county. By examining pre-pandemic conditions, we can better assess the impact of how and why business, demographic and economic change and impact as we move forward. The magnitude and duration of the impact on businesses will vary by community/area and become more measurable in the months ahead. The “How & Where of Business Establishment/Employment Change” will be updated later in 2020. See related, more detailed web section. See related section focused business establishments by ZIP code.

Where Things are Made by County
The following graphic shows patterns of the number of manufacturing establishments (NAICS 31) by county for the U.S. 48 contiguous states. Inset legend in map view shows number of establishments by interval/color. View/examine all U.S. states and areas using the related GIS project. Create custom maps similar to this view for your regions of interest depicting establishments, employment or payroll for your type of business selection(s). Click graphic for larger view with more detail; expand browser window for best quality view.

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

The above view shows patterns for only one type of business. Data are tabulated more than 2,000 NAICS/type of business codes. These data may be examined by county using the interactive table. Use the GIS tools and related GIS project to develop variations of the views shown here.

Using the Interactive Table
The 10 largest counties based on the number of manufacturing establishments are shown in the static graphic below. Click for larger view.

Use the interactive table to dynamically create similar rankings on employment size or payroll. Set a query for a county, metro or state of interest.

Updates
These data update in June 2020. Follow the blog (click button at upper right) to receive updates.

Learn more — Join us in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook 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 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.