Tag Archives: San Francisco

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.

2016 Presidential Election – Voting & Citizen Voting Age Population by County

In 2015, the U.S. citizen voting age population (CVAP) was 227,019,486 of the total U.S. resident population of 321,418,821 (70.6%). 2016 CVAP data are not yet available. In the 2016 presidential election, 128,298,470 votes were cast — approximately 56% of the citizen voting age population. For individual counties the 2016 presidential election vote ranged from 16% of the CVAP to near 100%. Use the interactive table in this section to examine characteristics of the 2016 presidential election vote and citizen voting age population by county.

This section reviews access to tools to view/analyze characteristics of the U.S. voting population (ages 18 and older and citizen) and participation in the 2016 presidential election. Data are based on Census Bureau annual population estimates, American Community Survey 2010-14 5 year (ACS 2014) Citizen Voting Age Population (CVAP) special tabulation and 2016 presidential election results.

Visual Analysis of 2016 Presidential Election Vote by County
The following graphic shows the 2016 presidential vote as a percent of the citizen voting age population.

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

U.S. Electorate Profile: Characteristics of the Citizen, 18 and Older Population

– based on 2015 American Community Survey 1-Year estimates
*Except where noted, “race” refers to people reporting only one race.
**Hispanic refers to the ethnicity category and may be of any race.
***Households with citizen householders.

U.S. by County Interactive Table Analysis 
Use the interactive table to examine characteristics of the 2016 presidential election vote and citizen voting age population by county. The following graphic illustrates how the table can be used to examine patterns in the Houston, TX metro by county. The Find in CBSA button is used below the table to select only counties in this CBSA/metro. The rightmost column header cell is clicked to rank counties on the voter participation rate for the 2016 presidential election.

– click graphic for larger view.

Try it yourself. Use the table to examine a set of counties in a metro or state of interest.

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.

Monthly Local Area Employment Situation; 2015-2016

.. this update on the monthly and over-the-year (August 2015-August 2016) change in the local area employment situation shows general improvement. Yet many areas continue to face challenges due to both oil prices, the energy situation and other factors.  This section provides access to interactive data and GIS/mapping tools that enable viewing and analysis of the monthly labor market characteristics and trends by county and metro for the U.S. See the related Web section for more detail. The civilian labor force, employment, unemployment and unemployment rate are estimated monthly with only a two month lag between the reference date and the data access date (e.g., August 2016 data are available in October 2016).

Unemployment Rate by County – August 2016
The following graphic shows the unemployment rate for each county.

— view created using CV XE GIS and associated LAES GIS Project
— click graphic for larger showing legend details.

As shown in the illustrative interactive table view below, seven of the ten MSAs having the highest August 2016 unemployment rate were in California. Use the table to examine characteristics of counties and metros in regions of interest. As apparent from the monthly patterns shown in the table, some areas are impacted by season factors, but others are not.

View Labor Market Characteristics section in the Metropolitan Area Situation & Outlook Reports, providing the same scope of data as in the table below integrated with other data. See example for the Dallas, TX MSA.

The LAES data and this section are updated monthly. The LAES data, and their their extension, are part of the ProximityOne Situation & Outlook database and information system. ProximityOne extends the LAES data in several ways including monthly update projections of the employment situation one year ahead.

Interactive Analysis
The following graphic shows an illustrative view of the interactive LAES table. Seven of the ten MSAs having the highest August 2016 unemployment rate were in California (ranked on far right column in descending order). Use the table to examine characteristics of counties and metros in regions of interest. Click graphic for larger view.

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

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

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.

Examining Neighborhood Change

Goto ProximityOne  How have neighborhoods of interest changed between 2000 and 2010?  Since 2010?  How will a neighborhood change going forward?  How does a neighborhood’s current and trending characteristics compare to adjacent of peer neighborhoods?  This section is focused on resources and methods to examine neighborhood geographic, demographic and economic characteristics and change. View related Web section, providing more detail.

The exact geographic definition of a neighborhood is elusive; neighborhood geography is not defined by a national standard.  Many areas have multiple neighborhood renderings.  While many counties/cities have well defined neighborhoods, most do not. To examine the demographic-economic characteristics of a neighborhood, the best options are to use a combination of census blocks, block groups and census tracts.

Nob Hill Neighborhood … San Francisco, CA
The Nob Hill neighborhood in San Francisco has changed in population from 20,142 in 2000 to 18,599 in 2010. The White alone population has grown a little while the Asian alone population has decreased from 11,532 (2000) to 9,705 (2010).  These population data are from Census 2000 and Census 2010 and determined by aggregating census block level data.  The Nob Hill census block geography did not change from 2000 to 2010 (75 census blocks).  Many neighborhoods and census block boundaries and codes change over time complicating longitudinal analysis of neighborhoods.  The Nob Hill neighborhood is shown in the following graphic with bold blue boundary; Census 2010 census blocks are shown with lighter blue boundaries.

nobhill1

The Nob Hill neighborhood in context of San Francisco.
nobhill2

Neighborhood Demographic-Economic Characteristics
Demographic-economic characteristics of neighborhoods play an important role as decision-making information.  The similarity, or dissimilarity, of these small area geographies are the basis for many local government planning operations ranging from law enforcement to transportation.  They help businesses determine where to locate — to serve markets where demand for their product or services is greatest.  Knowing about neighborhood geography and demographic-economic characteristics are critical to real estate businesses.

Census Blocks, Block Groups and Census Tracts
Census blocks, block groups and census tracts are the most useful geographies from which we can develop neighborhood demographic-economic characteristics.  These geographies are all defined by the Census Bureau and nest together.  All counties are comprised of a set of contiguous census tracts. Census tracts average 4,000 population and are comprised of block groups that average 1,200 population.  Block groups are comprised of a set of blocks that average 100 population.  In built-up urban areas, a census block is often the same as a city block bounded by streets.   These areas are defined for each decennial census and most boundaries do not change for the decade.  These features of known boundaries, covering the U.S. wall-to-wall, non-changing geography, nesting geographic hierarchy — and the availability of extensive demographic-economic data — make them the ideal choices to examine neighborhood characteristics and change.  There are advantages and disadvantages for each type of geography.

Census Blocks
The most appealing feature of census blocks is geographic detail.  There are more than 11 million Census 2010 census blocks; approximately 1/3 of these are water blocks and have no population.  These are the smallest geographic areas for which the Census Bureau tabulates demographic data.  The most limiting feature with using census block demographics is that only decennial census data are available by block; no richer demographics such as income or educational attainment.

Block Groups
The most appealing feature of block groups is geographic detail combined with availability of 1) decennial census data, 2) richer demographics from the American Community Survey (ACS) and 3)  annual updates from ACS.  Like blocks, there are no richer demographics for block groups from the decennial census.  There are 217,000 Census 2010 block groups. The most limiting features of block group demographics from ACS include 1) a relative high margin of error associated with ACS estimates, 2) a reduced scope of subject matter data compared to census tracts, and 3) data access and integration is challenging.

Census Tracts
Census tracts were originally designed by the Census Bureau as a pseudo-neighborhood areas averaging 4,000 population.  Approximately 73,000 Census 2010 census tracts cover the U.S. wall to wall.  Over time, the demographic-economic composition of many tracts change.  Tracts changing the most are often of most interest.  The advantages of using census tracts is similar to that for block groups.  Compared to block groups, tract estimates are more reliable and there is a broader set of subject matter available.  Tract data are easier to access and use that block or block group data.  One of the most limiting features of using census tracts to characterize neighborhoods is that tract geography often cuts through more than one neighborhood. 

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.