Tag Archives: economic prosperity

Monthly Local Area Employment Situation: 2017

.. tools & data to examine the local area employment situation .. this update on the monthly and over-the-year (Jan 2016-Jan 2017) 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., March 2017 data are available in May 2017).

Use our new tools to develop your own LAES U.S. by county time series datasets. Link your data with LAES data. Run the application monthly extending/updating your datasets. Optionally use our 6-month ahead employment situation projection feature. See details

Unemployment Rate by County – January 2017
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.

New with this post are the monthly 2016 monthly data on the labor force, employment, unemployment and unemployment rate. Use the interactive table to view/analyze these data; compare annual over the year change, January 2016 to January 2017.

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.

Interactive Analysis
The following graphic shows an illustrative view of the interactive LAES table. In January 2017, 149 counties experienced an unemployment rate of 10% or more. The graphic shows counties experienced highest unemployment rates. Use the table to examine characteristics of counties and metros in regions of interest. Click graphic for larger view.

Metro by County; Integrating Total Population
The following graphic shows an illustrative view of the interactive LAES table focused on the Chicago MSA. By using the query tools, view characteristics of metro component counties for any metro. This view shows Chicago metro counties ranked on January 2017 unemployment rate (only 10 of the 14 metro counties shown in this view). Click graphic for larger view.

The above view shows the total population (latest official estimates) as well as employment characteristics.

More About Population Patterns & Trends
U.S. by county population interactive tables & datasets:
  • Population & Components of Change 2010-2016 – new March 2017.
  • Population Projections to 2060 2010-2060 – updated March 2017.

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.

ZIP Codes with Highest & Lowest Economic Prosperity

.. the latest data for ZIP Code Areas show that eleven had a median household income of $250,000 or more during the period 2011-15. More than 20 ZIP code areas had a median housing value of $2,000,000 or more. Contrast these ZIP code areas with higher economic prosperity with the more than 150 ZIP codes that had a median housing value of less than $30,000.  Use the interactive table in this related Web section to see which ZIPs meet these and other criteria.

ZIP Codes with MHI $100,000 or More; Dallas, TX Metro
Analyzing economic prosperity patterns using combined types of small area geography … the following graphic shows ZIP code areas a red markers with the median household income or $100,000 or more in context of median household income by census tract thematic pattern. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view for your areas of interest. .

– view developed with CV XE GIS software.

This section reviews measures of economic prosperity for all ZIP code areas. These data were released in December 2016. This section updates with new data December 2017. See the list of all ZIP ccdes showing population, housing and economic characteristics in the interactive table shown below. Use the interactive table to view, rank, compare and query ZIP code attributes.

Examining demographic-economic characteristics by ZIP code is important for several reasons. We are familiar with our own ZIP codes as a geographic location. We tend to be interested in our area compared to other areas. ZIP codes provide an easy way to do that. Also, many secondary data resources are tabulated by ZIP code area; some important data are only available by ZIP code. See more about ZIP Code areas.

Resources & Methods to Examine Small Area Demographics
• See related ZIP Code Demographic-Economic Interactive Tables
  .. extended subject matter
• See related Census Tract Code Demographic-Economic Interactive Tables
• Examine ZIP Code Urban/Rural Characteristics
• Examine ZIP Code Business Establishment patterns
• Examine ZIP Code Housing Price Index patterns
• Join us in the weekly Data Analytics Lab Sessions
  .. reviewing applications using these and related data.

ZIP Code Areas with $MHI $100,000 or More
The following graphic shows ZIP code areas as red markers having median household income or $100,000 or more. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view; integrate other data; select alternative ACS 2015 subject matter.

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

ZIP Code Areas with $MHV Less than $30,000
The following graphic shows ZIP code areas as orange markers having median housing value of less than $30,000. Click graphic for larger view with more detail. Expand browser window for best quality view. Use CV XE GIS software and associated GIS project to develop variations of this view; integrate other data; select alternative ACS 2015 subject matter.

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

ZIP Code Areas: Population & Economic Prosperity
  — Interactive Table –
Use the interactive table to view, rank, compare, query ZIP codes based on a selection of demographic-economic measures. The following graphic illustrates how the table can be used to examine patterns of the three digit ZIP code area (San Diego) by 5-digit ZIP code. Table operations are used to select ZIP codes in the 921 3-digit area (containing 39 5-digit ZIP codes). These 39 ZIP code are then ranked in descending order on median household income. See results in the table shown below. ZIP code 92145 has the highest $MHI in this group with $228.036.

– click graphic for larger view.

Try it yourself. Use the table to examine a set of ZIP codes on your selected criteria in for a state/area 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.

Demographic-Economic Patterns: Composite & Related Geography

.. we often need data for study areas that do not conform to conventional political/statistical geography. The geography for a market, sales territory, impact zone or other type of study area often do not align with political or statistical geographic areas for which relevant demographic-economic data are available. While the interest might be in demographic-economic characteristics for a particular county, patterns and trends within a county cab vary widely for sub-county geography such as ZIP code areas, census tracts, cities, school districts and other types of geography. It is important to be able to examine the composite, or drill-down, geography for a larger area. Related geography are equally important. Even though primary interest might be in three ZIP code areas, knowing about patterns in related, contiguous ZIP codes is also important. This section illustrates how to examine semi-comprehensive demographic-economic characteristics and trends using organized profiles for alternative geography.

Patterns of Economic Prosperity by Neighborhood
ZIP Code Area 60565
in Naperville, IL area — bold black boundary

– note this ZIP code area intersects with many census tracts;
    … in many cases tract boundaries are not coterminous with ZIP code.
– colors show patterns of median household income by census tract
– view developed with CV XE GIS … view full profile

More information — get for your areas anywhere in U.S.

Illustrative set of different types of geography; Naperville, IL; Chicago metro.
Click links to view full profile.
ZIP Code Area 60565 — Naperville, IL area — see graphic above
Census Tract 17197880119 — Naperville, IL area — see graphic below
Naperville city, IL — see graphic below
Naperville School District, IL — see graphic below
DuPage County, IL — see graphic below

Patterns of Economic Prosperity by Neighborhood
Census tract 17197880119
in Naperville, IL area — bold black boundary

– ZIP code 60565 shown with red boundary.
– colors show patterns of median household income by census tract
– view developed with CV XE GIS … view full profile

Patterns of Economic Prosperity by Neighborhood
Naperville, IL city
— cross-hatched pattern, bold black boundary

– colors show patterns of median household income by census tract
– view developed with CV XE GIS … view full profile

Patterns of Economic Prosperity by Neighborhood
Naperville School District, IL
— cross-hatched pattern, bold black boundary

– Naperville city shown with semi-transparent cross-hatch pattern.
– colors show patterns of median household income by census tract
– view developed with CV XE GIS … view full profile

Patterns of Economic Prosperity by Neighborhood
DuPage County, IL
— cross-hatched pattern, bold black boundary

– Naperville city shown with semi-transparent cross-hatch pattern.
– colors show patterns of median household income by census tract
– view developed with CV XE GIS … view full profile

There are many other ways to use composite and related geography in data analytics. GIS tools enable wide-ranging geospatial analysis not covered in detail here. See more about this topic in the data analytics program.

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.

Financial Institutions & Neighborhood Characteristics

Examining patterns of financial institutions, neighborhoods and geographic-demographic-economic relationships … this section is one of several related sections focused on data and resources useful to analyze America’s banks and savings institutions. This section is focused on use of Geographic Information System (GIS) tools to examine the nation’s 96,000 FDIC-insured institutions/branch offices in context of neighborhoods and economic prosperity. This is not intended as a study to draw conclusions, but rather to illustrate how these data and tools can be used to perform more detailed analyses for any metro, county or city in the U.S. See related more detailed Web section.

This section illustrates use of FDIC Deposit Market Share (DMS) data by institution. Subsequent sections will integrate other related data into the GIS applications including the FFIEC “Census 2014/2015” dataset (data by state, metro, county and census tract).

Deposit Market Share
The Deposit Market Share (DMS) is the percentage of deposits an FDIC-insuredinstitution has within a defined geographic market. We use these data in GIS applications reviewed below. See the example of the DMS Report in the related Web section. These data are based on the annual Summary of Deposits survey of FDIC-insured institutions. The DMS data provide information for each/all FDIC-insured institutions by address and a range of related attributes. Market presence and growth rate analyses can be examined annually by institution or bank holding company.

The 2014 annual DMS address-based data were geocoded and converted into a shapefile for GIS analysis. The DMS shapefile was integrated into a GIS project. The GIS project also includes a U.S. national scope census tracts shapefile with demographic-economic data from the 2013 American Community Survey 5-year estimates (ACS2013). GIS tools can be used to examine a single institution, institutions within a geographic area or aggregated within a geographic area. Optionally examine these institutional locations in context with patterns of neighborhood or regional economic prosperity (or choose many other types of subject matter).

Branch Locations by Size; Houston, Texas
The map presented below shows banks as markers in the Houston, TX area. Harris County appears with bold blank boundary. Bank markers shown by 2014 deposit size class. See size class/color patterns in legend at left of map.

Click graphic for larger view; view developed with CV XE GIS.
Map view developed using Banks2015 GIS Project.

Branch Locations in Context of Neighborhood Economic Prosperity
Similar to the map above, the map below shows banks as markers in the Houston, TX area. Patterns of economic prosperity (based on median household income – MHI) are shown by census tract/neighborhood. See MHI intervals/color patterns in legend at left of map. It is easy to see where concentrations of banks in more affluent neighborhoods.

Click graphic for larger view; view developed with CV XE GIS.

Selecting Specific Institutions — using site analysis tool
The map presented below shows financial institution locations as markers with a zoom-in to neighborhood level. The site analysis tool is used to select a set of institution locations within a census tract (red boundary, yellow label) — tract code 4115.01 or 411501, located in Harris County, TX. See more about census tracts. Eight locations are selected (hatched markers) using the circle selection method (any location intersecting with circle is selected). Alternatively select only one institution, visually cherry-pick certain institutions or apply a select-from-list query. One variable is summarized, sum of deposits 2014 ($2.2 billion for sum of these 8 locations).

Click graphic for larger view; view developed with CV XE GIS.

Tabular View of Selected Institutions
The view presented below shows the data grid populated with attributes of the eight selected locations (see above). This view is displayed by using the View File button — see at right of map view in above graphic. The table/grid shows the institution/branch name, sum of deposits for that location, and other attributes. Optionally save this selection of locations an a dbase/CSV/Excel/text file for further analysis.

Click graphic for larger view; view developed with CV XE GIS.

Deposit Market Share Report
The view presented below shows the Deposit Market Share Report for ZIP code 77027. This report is for the ZIP code area that includes the selected locations shown above. See the full interactive DMS report.

Click graphic for larger view.

Top 50 Commercial Banks & Savings Institutions Interactive Table
The following graphic shows the largest Commercial Banks & Savings Institutions among all FDIC-Insured Institutions based domestic deposits as of June 30, 2014. See full interactive table.

Click graphic for larger view.

Upcoming Blog Posts on Related Topics
Upcoming blog topics will include using the following data resources integrated into the GIS and related applications focused on financial institution and market research and analysis.
  • FFIEC tract level estimates (2010, 2013, 2014)
  • FFIEC 2015 tract estimates (not yet released)
  • ProximityOne tract demographic-economic estimates (2015) and projections (2020).
  • FFIEC “Census2014” dataset, containing 1,200+ subject matter items
  • Quarterly CEW county time series data on financial services sector establishments.
  • Other FDIC institutional characteristics by address/location.

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 is developer of the CV XE GIS software used to develop the GIS project and views shown in this section. 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.