Census 2020 Residential Address Counts by Block

.. using Census 2020 residential address count data to examine change since 2010 .. the Census Bureau has released preliminary Census 2020 residential address counts by Census 2010 census block. These data, count of residential addresses and group quarters addresses, reflect updates as of October 2019 and do not represent final Census 2020 counts. The data will continue to be updated to support Census 2020. See related Web section with more detail and updates.

Importance and Use
These data are of immediate value in determining and analyzing how the number of housing units have changed, 2010 to 2019. Since the data are at the census block level, they may be aggregated to any other Census-defined summary level/type of geographic area such as block group, tract, ZIP code, city, county, school district, etc. These data are also important as they give us a “year in advance look” at how small area demographics are changing since 2010. Before this, the most recent census block data were from Census 2010. A lot has happened in many areas. These data provide insights into that change. The Census 2020 block level data will be released in early 2021 for Census 2020 census block geography. So, another important feature of these data is that they are summarized for Census 2010 census block boundaries. Census 2010 and 2020 block boundaries may differ, particularly in areas experiencing larger demographic growth/change. An important limitation is that they are counts, subject to change as the Census data are collected/tabulated.

Comparing Census 2010 Housing Units with Census 2020 Address Counts
The following graphic shows patterns of Census 2010 housing counts with the Census 2020 (late 2019 vintage) residential address counts by census block. This view is focused on census tract 3608100700 (tract 000700) in Queens County, NY (code shown near center of graphic). Individual blocks are labeled with block code (4 digits) with the Census 2010 housing units (yellow label) and Census 2020 residential address count (green label) shown below the block code. As an example, the block located at the pointer has block code 3006 (or full national scope unique block code 36-081-00700-3006) with a Census 2010 count 44 housing units and a Census 2020 (late 2019 count) of 232 residential addresses. Click graphic for larger view. Expand browser window to full screen for best quality view.

.. view created with ProximityOne CV XE GIS software and related GIS project.

More About Using these Data
We have summarized these data at the census tract level and are evaluating their use, in combination with other data, to develop current estimates and projections to 2025.

Data 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.

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.

America’s Cities: Demographic-Economic Characteristics Annual Update

.. tools and data to interactively examine demographic-economic characteristics of America’s 29,321 cities/places .. understanding demographic-economic characteristics of cities and places is 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 characteristics of all cities/places in the U.S. This annual update includes data for 29,321 cities/places based on ACS 2015 data.

Accessing the Data; Using Interactive Tables
Each of the four links below opens a new page providing access to U.S. by city/place interactive tables — by type of subject matter. Use tools and usage notes below table to select operations to perform queries, sort and select columns.
• General Demographics
• Social Characteristics
• Economic Characteristics
• Housing Characteristics

How the the Tables/Data Can be Used
The following table shows data derived from the Economic Characteristics table. The top 10 cities/places having the highest median household income ($MHI) are shown. The table also shows population, median family income ($MFI) and per capita income ($PCI). The $250,000 value is a cap; the actual value is $250,000 or higher. Use the interactive tables to create similar views for states of interest. Use the button below the table to select/view cities within a selected metro. Compare attributes of cities of interest to a peer group based on population size.

Visual Analysis of City/Place Population Patterns
Use GIS resources to visually examine city/place demographic-economic patterns. The following view shows patterns of population percent change by city in the Charlotte, NC-SC metro area.

… view developed using the CV XE GIS software.
… click map for larger view and details.

Related Data
• Cities/Places Main Section
• Citie Population Estimates & Trends, 2010-15

More About Using These Data
• Using ACS 1-year and 5-year data

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.

Regional Economic Information System: Annual Updates

.. which counties are experiencing the fastest economic growth? by what economic component? what does this look like on a per capita level?

.. access & analyze economic characteristics and patterns by county and state .. annual time series 1969 through 2015 with projections.  Personal income is the income available to persons for consumption expenditures, taxes, interest payments, transfer payments to governments and the rest of the world, or for saving. Use the interactive table to examine characteristics of counties and regions of interest. The table provides access to 31 personal income related summary measures. These data are a selection of a broader set of annual time series data from the Regional Economic Information System (REIS). REIS is a part of the ProximityOne State & Regional Income & Product Accounts (SRIPA) and Situation & Outlook (S&O) featuring current (2016) estimates and demographic-economic projections. Go to table.

Visual Analysis of Per Capita Personal Income Patterns
The following map shows the Houston metro (view profile) with bold brown boundary. Counties are labeled with county name and 2014 per capita personal income.

Click graphic for larger view. View developed with CV XE GIS software.

Per Capita Personal Income Change 2008-2014 by County
.. relative to U.S 2008-2014 change

Click graphic for larger view. View developed with CV XE GIS software.

Interactive Analysis – County or State Profiles
The following graphic illustrate use of the interactive table to view an economic profile for Harris County, TX. Use the table to examine characteristics of any county or state. Click graphic for larger view.

Interactive Analysis
– comparing per capita personal income across counties
The next graphics illustrates use of the interactive table to rank/compare per capita personal income across counties. Rank/compare states. Choose any of the economic profile items. 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.

Congressional District 2015 Demographic-Economic Characteristics

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

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

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

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

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

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

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

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

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

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Pieds-à-Terres & Interpreting the Vacancy Rate

… the October 26, 2014, New York Times story Pied-à-Neighborhood states “In a three-block stretch of Midtown, from East 56th Street to East 59th Street, between Fifth Avenue and Park Avenue, 57 percent, or 285 of 496 apartments, including co-ops and condos, are vacant at least 10 months a year. From East 59th Street to East 63rd Street, 628 of 1,261 homes, or almost 50 percent, are vacant the majority of the time, according to data from the Census Bureau’s 2012 American Community Survey.” The story reviews use of these data and draws conclusions about homeowner vacancy based on these data. Read the story to get the complete picture.

There are a few issues here that may be worth considering. These issues generalize to other situations involving interpretation of vacancy rates; issues at the core of using decision-making information.

The homeowner vacancy rate is the proportion of the homeowner housing inventory which is vacant for sale. It is computed by dividing the number of vacant units for sale only by the sum of owner-occupied units and vacant units that are for sale only, and then multiplying by 100.

The area “East 56th Street to East 59th Street” between “between Fifth Avenue and Park Avenue” is also Census 2010 block group 360610112021. The area “East 59th Street to East 63rd Street” between “between Fifth Avenue and Park Avenue” is also Census 2010 block group 360610114011 and block group 360610114012. These three contiguous block groups are shown in the map graphic below — highlighted with red hatch pattern.

Three Block Group Study Area

Click graphic for larger view. View created using CV XE GIS.

Interpretation, Conclusions and Alternatives
The statement “The Census Bureau tracks vacancy rates to find out who lives in Manhattan full time.” is only loosely accurate. But accepting it, here are some other issues with the inferences made …

1. Time dimension — the 2012 American Community Survey 5-year estimates (ACS 2012) are estimates centric to 2010, not 2012, and thus 4 years old. Some respondents responded as long ago as 2008. The statistical program reference/title is 2012, but the estimates are not.

2. Estimate Margin of Error — the block group level estimates have a relatively high margin of error. The actual values might be higher or lower by a noteworthy margin.

3. More accurate vacancy rate data from Census 2010? … the Census 2010 vacancy rate data are known to relate to 2010 whereas the ACS 2012 5-year estimates are centric to 2010 but are actually for the survey period from 2008 to 2012. In addition, the Census 2010 data are available at the census block level. Finally, there is no estimation error with the Census 2010 data (since those data are not estimates).

All said and done, using Census 2010 might bolster the argument rather than diminishing it. We would need to dive into an examination of the numbers to find out more.

Hispanic Population by Specific Origin by County

The Hispanic or Latino population group is comprised of many specific origin subgroups. The specific origin subgroups are often associated with different regions of the world. Many specific origin subgroups tend to cluster by location within the U.S. both as a result of the geography of emigration (e.g., Mexico to Texas) and association with where same subgroup emigrants have settled (e.g., Cuba to New York).  Census 2010 identifies approximately 70 Hispanic specific origin subgroups.

Use the interactive table at http://proximityone.com/hispanic_origin.htm  to gain insights into how the Hispanic specific origin subgroups are distributed across the U.S. by county. Tools are provided to analyze geographic and subgroup patterns of interest.