Measuring & Analyzing Households by Social Class by PUMA

.. a social class is a population or household group typically referred to as a lower, middle and upper class. The size of the population or households in a social class is often determined in relationship to an interval related to the median household income of an area — from two-thirds of median household income to twice the median household income (MHI). Subsequent blog posts will address a broader definition for class determination. By better understanding composition and determinants of social class for an area, we might better understand and improve on income inequality and create new opportunities. This is a multi-part blog post on social class analytics. Click Follow at right to receive updates.

Percent Population in Households by Social Class by PUMA
.. Los Angeles area .. click for larger view.

Los Angeles metro area by social class .. PUMAs shown with black boundaries; pointer at Los Angeles-Orange County line

Percent of Households by Social Class by PUMA
.. Los Angeles area .. click for larger view.

Los Angeles metro area by social class .. PUMAs shown with black boundaries; pointer at Los Angeles-Orange County line

Using American Community Survey Microdata
We use of the American Community Survey microdata or “public use microdata samples” (PUMS) http://proximityone.com/pums.htm to develop estimates of population and households by middle class, lower class and upper class by “public use microdata area” (PUMA) http://proximityone.com/puma.htm. Microdata files are comprised of anonymized individual respondent data within PUMAs. The approximate 2,800 PUMAs cover the U.S. wall-to-wall and must have 100,000 population or more. 2010 and 2020 vintages PUMAs may be examined and compared with other geography using the VDA Web GIS http://proximityone.com/vda.htm with the MetroDynamics Project.

Social Class Participation by PUMA
Using custom software, the PUMA (ACS 2021 1 year data in this case), individual housing records are summarized for each PUMA. An estimate is developed for the lower, middle and upper class based on an algorithm.

Examine patterns of social class stratification using VDA Web GIS anywhere in U.S.
The estimates are then integrated into a PUMA shapefile. The PUMA shapefile is added to a Geographic Information System (GIS). Access this shapefile/layers using VDA Web GIS to examine patterns of social class, such as the graphics shown above, or in combination with other geography and subject matter.

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).

Data Analytics Web Sessions
Join us in the every Tuesday, Thursday Data Analytics Web Sessions. See how you can use VDA Web GIS and access different subject matter for related geography. Get your geographic, demographic, data access & use questions answered. Discuss applications with others.

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.

Patterns of Income in America’s Largest Cities

The retreat in personal and household income resulting from the pandemic will be historic and substantial. How long term? Which cities of what size and location will be affected the most? We start to study patterns and trends as new data become available in the next several weeks.

America’s largest 629 cities accounted for a group population of 121,228,560, or 37.1%, of the total U.S. population (327,167,434) in 2018. All of these cities are in Metropolitan Statistical Areas (MSAs). With contiguous cities and places, these urban areas account for more than 80% of the U.S. population. These cities, each with 65,000 population or more, are shown as markers in the thematic pattern view below. See more about cities/places and city/place 2010-2018 demographic trends.

Patterns of Economic Prosperity: America’s Largest Cities
– cities with 2018 population 65,000+ shown as markers
– markers show level of 2018 median household income
– data used to develop this veiw were extracted using GeoFinder.
– click map for larger view; expand browser to full screen for best quality view.

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

Top 25 Largest Cities based on Median Household Income

About America’s Largest Cities & Economic Characteristics
The set of the 629 America’s largest cities is based on data from the 2018 American Community Survey 1-year estimates (ACS 2018). ACS 2018 1-year estimates, by design, provide data only for areas 65,000 population or more. The ACS 2018 data are the only source of income and related economic data for national scope each/all cities/places (29,853) on an annual and more recent basis. These data will update with 2019 estimates in September 2020. ACS-based data reflecting the impact of the pandemic will not be available until September 2021.

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.

Neighborhood Median Family Income: Measuring Economic Well-Being

.. Median Family Income ($MFI) and Median Household Income ($MHI) are two measures of economic well-being. Based on the 2018 American Community Survey 1-year (ACS) data, the U.S. 2018 $MFI was estimated to be $76,401 while the $MHI was estimated to be $61,937 .. both in 2018/current dollars. Create insights into patterns of well-being by neighborhood using geospatial analysis. $MFI patterns are illustrated by the following thematic pattern map.

Patterns of Economic Prosperity by Neighborhood/Census Tract
The following view shows patterns of $MFI by census tract for the inner beltway area of Houston/Harris County, TX. Income interval color patterns are shown in the inset legend. Tracts are labeled with $MFI. Click graphic for larger view. Expand browser window for best quality view. Larger view shows tracts labeled with tract code. It is easy to see how west Houston and east Houston areas differ.

– view developed with ProximityOne CV XE GIS software and related GIS project.
– these $MFI data are based on the 2018 ACS 5-year estimates.

This section focuses on $MFI but could just as well focus on $MHI and yet other related income measures. $MFI will almost always be greater that $MHI, generally by a large margin. See the U.S. 2018 $MFI and $MHI in context of related demographic-economic measure here. See more about the distinctions/definitions of families and and households below.

The ACS data are a unique source of income and related data at the neighborhood or sub-county level. View more about accessing and using the 2018 ACS 5-year estimates.

Family Definition
A family is a group of two people or more (one of whom is the householder) related by birth, marriage, or adoption and residing together; all such people (including related subfamily members) are considered as members of one family. The number of families is equal to the number of family households. However, the count of family members differs from the count of family household members because family household members include any non-relatives living in the household.

Related … an unmarried partner, also known as a domestic partner, is specifically defined as a person who shares a close personal relationship with the reference person. … Same-sex unmarried-partner families or households – reference person and unmarried partner are both male or female.

Household Definition
A household consists of all the people who occupy a housing unit. A house, an apartment or other group of rooms, or a single room, is regarded as a housing unit when it is occupied or intended for occupancy as separate living quarters; that is, when the occupants do not live with any other persons in the structure and there is direct access from the outside or through a common hall.

A household includes the related family members and all the unrelated people, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. There are two major categories of households, “family” and “nonfamily”.

Situation & Outlook Weekly Web Sessions
Join me in a Situation & Outlook web session to discuss more details about demographic-economic estimates and projections.

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.

116th Congressional Districts & Patterns of Economic Prosperity

.. Congressional District Analysis and Insights .. tools to examine patterns of median household income .. median household income is one measure of economic prosperity. This section reviews patterns of median household income (MHI) by 116th Congressional Districts based on the 2018 American Community Survey 1-year estimates (ACS 2018). View, rank, compare the MHI by congressional district, among related demographic attributes using the interactive table on the main Congressional Districts page.

116th Congressional District Analysis & Insights
.. patterns of household income & economic prosperity:
Based on the ACS 2018 median household income (MHI):
• the MHI among all districts was $60,291
• the U.S. overall MHI was $61,937
As of November 2019:
• the 19 districts with highest MHI have Democrat incumbents
• the 10 districts with the highest Gini Index have Democrat incumbents
• there are 69 Republican incumbent districts above the all districts MHI
• there are 149 Democrat incumbent districts above the all districts MHI
• the MHI of the 236 Democrat incumbent districts is $66,829
• the MHI of the 199 Republican incumbent districts is $56,505
Median household income is only one measure of economic prosperity.
See more at http://proximityone.com/cd.htm.

Patterns of Economic Prosperity 116th Congressional District
The following graphic shows patterns of 2018 median household income by 116th Congressional District. Use GIS tools/data to generate similar views for any state and/or drill-down. Click graphic for larger view with more detail. Expand browser window for best quality view.

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

Using the Interactive Table –
— view, rank, compare districts based on your criteria.
— example,which districts have the highest median household income?
Use the interactive table to examine incumbency and and demographic characteristics of the 116th Congressional Districts (CDs). The following view illustrates use of the table. This view shows use a query to show the ten CDs having highest 2018 median household income.

Try using the interactive table to existing districts and categories of interest.

Congressional District/State Legislative District Group
Join in .. be a part of the Congressional Districts/State Legislative District (CDSLD) group. Access analytical tools and data. Learn about CDSLD analytics, patterns and trends. Share insights with like-minded stakeholders.

Demographic-Economic Analytics Web Sessions
Join me in a Demographics 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 HMDA/CRA Census Tract Demographics

.. the ability to effectively analyze low, moderate, middle, and upper income population and households by small area geography is important to housing market stakeholders, lenders, investors, cities/neighborhoods and others. Low and moderate income data by block group and census tract are used for compliance, eligibility determination and program performance in many Federal programs and agencies. See the main Web page for more detail.

This section reviews the scope and use of the FFIEC 2019 HMDA/CRA census tract data (released September 2019). Use the interactive table to view, rank, compare selected items from these updated data for any/all tracts. Use GIS tools with these data to map and geospatially analyze these data as illustrated and further described as illustrated here. See more about banking, CRA and LMI tracts and more about these data.

Visual Analysis of Banks in Context Census Tract Demographics
Click graphic for larger view; expand browser window for best quality view.

– view developed using CV XE GIS and related GIS project.
– install this GIS tool and related GIS project on your computer to examines patterns, market share and more.

Low & Moderate Income Population by Census Tract
Low, moderate, middle, upper income classification by census tract is based on the median family income of a specific census tract relative to the metropolitan statistical area (MSA) or non-MSA area in which the tract is located. The FFIEC data include a “low and moderate income indicator”:
1 – Low — MFI is less than 50% of the MSA/parent area MFI
2 – Moderate — MFI is from 50% to 80% of the MSA/parent area MFI
3 – Middle — MFI is from 80% to 120% of the MSA/parent area MFI
4 – Upper — MFI is 120% or more of the MSA/parent area MFI
0 – NA — MFI is 0 or not available
where MFI is the Median Family Income

Low and moderate income designation is closely associated with implementation of the Home Mortgage Disclosure Act (HMDA) and the Community Reinvestment Act (CRA) and is a widely used in many other applications as a measure of economic prosperity.

Using the Interactive Table
Use the interactive table to examine individual tracts or sets of tracts as to their low and moderate income status and related demographics. The following view illustrates use of the table. Clicking buttons below table, this sequence of steps was used to obtain this view:
– click ShowAll
– click “Find CBSA; Low & Mod Tracts”
  >this selects tract in CBSA 26420 (Houston) that are low or mod
– click “Status Cols”
The table refreshes to show 470 tracts that are low/mod in this metro.
Finally, click the column header “Tract MFI %Region” to sort in descending order.

View your areas of interest. Start the steps over and use your CBSA code for a metro of interest.

Bankers Analytics Tools Web Sessions
Join me in a Bankers Analytic Tools Lab session (every Wednesday 3:00 pm ET) 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.
Topics:
• mapping and geospatially analyzing your data with FFIEC data
• tract demographic vintages and trends
• issues regarding MSA/MD vintage, change; about the 2018 vintage CBSAs
• defining and using assessment area geography
• examining the community & neighborhoods in context of assessment areas
• using the FDIC bank location/deposits data with FFIEC/ACS demographics
• using the FFIEC/ACS interactive table below
• alternative methods of accessing census tract ACS data

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 Economic Well-Being by Metro

.. examining the state of economic well-being: 2008-2017 .. expanding insights through data analytics .. tools and data to examine how and where the U.S. by metropolitan area real personal income is changing.  Real per capita personal income (RPCPI) is the best single measure of personal economic-well being. By examining RPCPI trends over a period, stakeholders can better determine if the area in improving, stable or declining. Still, RPCPI is but one important measure among many others, such as the Regional Price Parities (RPP). Use the interactive table, and related GIS tools, to examine these and other real personal income measures for the Nation’s 383 Metropolitan Statistical Areas (MSAs). See related Web section for more detail.

Based on data released by the Bureau of Economic Analysis in May 2019, see about in statistical release dates, real state personal income grew 2.6 percent in 2017, after increasing 1.5 percent in 2016. Real state personal income is a state’s current-dollar personal income adjusted by the state’s regional price parity and the national personal consumption expenditures price index. Across metropolitan areas, the percent change ranged from 14.8 percent in Midland, MI to -5.9 percent in Enid, OK

Visually Examining Patterns of Economic Well-Being
Geographic Information Systems (GIS) tools provide a powerful to explore these data. The following graphic shows a view of real per capita personal income (RPCPI), 2017, for Texas MSAs. Color patterns for levels of RPCPI are shown in the inset legend. MSAs are labeled with the 2017 RPCPI rank among all U.S. MSAs. Click graphic for larger view; expand browser window for best quality view. The larger view shows MSAs labeled with short name and a mini-profile for Harris County (pointer).

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

Using the Interactive table
The interactive table (click link to view/use) includes a row for the U.S., U.S. nonmetro area and each Metropolitan Statistical Area (383). Each row provides a 2008-17 annual time series for four items (and derived items): real per capita personal income, regional prices parities-all items, total real personal income, implicit regional price deflator.

The following graphic shows these areas ranked on RPCPI in 2017. See rightmost column rank. Click graphic for larger view. Midland, TX MSA has the 4th highest 2017 RPCPI (shown in blue; also shown in blue in the map graphic above). But … that metro experienced a decrease of 12.1 percent over the period 2010-2017 .. and down from a high RPCPI of $115,069 in 2014. The dynamics of the oil industry!

Data Analytics Web Sessions
See these applications live/demoed. Run the applications on your own computer.
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.

Block Group Demographic Data Analytics

.. use tools described here to access block group data from ACS 2016 (or ACS2017 in December 2018) using a no cost, menu driven tool accessing the data via API. Select from any of the summary statistic data. Save results as an Excel file or shapefile. Add the shapefile to a GIS project and create unlimited thematic pattern views. Add your own data. Join us in a Data Analytics Web session where use of the tool with the ACS 2017 data is reviewed.

See related Web section for more details.
– examine neighborhoods, market areas and sales territories.
– assess demographics of health service areas.
– create maps for visual/geospatial analysis of locations & demographics.

Illustration of Block Group Thematic Pattern Map – make for any area

– click graphic to view larger view
– pointer (top right) shows location of Amazon HQ2

Topics in this how-to guide (links open new sections/pages)
• 01 Objective Thematic Pattern Map View
• 02 Install the CV XE GIS software
• 03 Access/Download the Block Group Demographic-Economic Data
• 04 Download the State by Block Group Shapefile
• 05 Merge Extracted Data (from 03) into Shapefile (04)
• 06 Add Shapefile to the GIS Project; Set Intervals
• 07 Viewing Profile for Selected Block Group
• 08 BG Demographics Spreadsheet
• 09 Block Group Demographics GIS Project
• 10 Why Block Group Demographics are Important

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.

Low & Moderate Income Census Tracts; 2017 Update

..  data and tools to analyze characteristics and patterns of census tract geography with a focus on low and moderate income.   See related Web page for more detail.

Of the total 75,883 census tracts for which low and moderate income data were tabulated in the HMDA 2017 data, 6,023 (8.7%) were low income, 16,873 (24.5%) were moderate income, 32,509 (47.1%) were middle income and 19,159 (27.8%) were upper income. See more about these classifications. Find out about your tracts/neighborhoods of interest and how they compare to others using data and tools provided in this section.

Analysis of the low, moderate, middle, and upper income of the population and households by small area geography is important to housing market stakeholders, lenders, investors, cities/neighborhoods and others. Low and moderate income data by block group and census tract are used for compliance, eligibility determination and program performance in many Federal programs and agencies.

• Use the interactive table below to view, query, compare, sort census tracts.
• Use tract estimates & projections to examine changing characteristics.
– extended demographic-economic measures, annual 2010-2022

Low & Moderate Income by Census Tract
The following view shows census tracts designated as low and moderate income (orange fill pattern) in the the Houston, TX MSA (bold brown boundary) area. These are tracts having income level with codes 1 and 2 in the interactive table. A wide range of market insights can be created zoom-in views for counties, cities and neighborhoods and linking these with other data. Make variations of this view using ProximityOne data and tools described in this section.

– View developed using CV XE GIS and related GIS project.

View similar maps for these areas:
.. Atlanta metro
.. Chicago, IL metro
.. Dallas, TX metro
.. Knoxville, TN metro
.. with drill-down views for Knoxville city
.. Los Angeles, CA metro
.. San Francisco, CA metro

Using the Interactive Table
  – Examining LMI Tracts in Your Metro
Use the interactive table to view, query, sort compare tracts based on various demographic and LMI characteristitcs. The following graphic illustrates how the table can be used to view low and moderate income tracts for the Charlotte, NC-SC metro.
– click ShowAll button below table.
– enter a CBSA code in the edit box at right of Find CBSA LMI>.
– click the Find CBSA LMI button.
Resulting display of Charlotte metro LMI tracts only.

– 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.

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.

Tools to Analyze County Demographic-Economic Characteristics

.. demographic-economic characteristics of counties are essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine these data for all counties in the U.S. This annual update includes geographic area characteristics based on ACS 2015 data.  The tools/data are organized into four related sections summarized below.

1. General Demographics
View interactive table at http://proximityone.com/us155dp1.htm
Patterns of School Age Population by County
Use GIS tools to visually examine county general demographics as illustrated below. The following view shows patterns of percent population ages 5 to 17 years of age by county — item D001-D004-D018 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

2. Social Characteristics
View interactive table at http://proximityone.com/us155dp2.htm 
Patterns of Educational Attainment by County
– percent college graduate
Use GIS tools to visually examine county social characteristics as illustrated below. The following view shows patterns of percent college graduate by county — item S067 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

3. Economic Characteristics
View interactive table at http://proximityone.com/us155dp3.htm 
Patterns of Median Household Income by County
Use GIS tools to visually examine county economic characteristics as illustrated below. The following view shows patterns median household income by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

4. Housing Characteristics
View interactive table at http://proximityone.com/us155dp4.htm 
Patterns of Median Housing Value by County
Use GIS tools to visually examine county housing characteristics as illustrated below. The following view shows patterns median housing value by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

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.