Category Archives: Chcago, IL

Important Upcoming Data Releases: September 2017

.. monthly updates on recent & upcoming data analytics tools & resources .. this section provides a monthly update on important new data developments and applications/developments to further their use in data analytics. A focus of this section is on new or revised geographic, demographic and economic data. Most of these data are used to develop and update ProximityOne census tract-level up demographic-economic projections to 2022 and county-level up population by single year of age projections to 2060. See about September projection updates below on this page. This section is organized into recent past data updates and upcoming (month ahead) data releases and may be updated to reflect new or extended details. See related news and updates:
• What’s New daily updates
• Situation & Outlook Calendar

See related Web section.

Recent Past Data Releases/Access

U.S. by Census Tract 2017 HMDA Low & Moderate Income (FFIEC)
• Release date — 8/17; next update — mid 2018
• 2017 annual HMDA data — covers all income levels not only LMI
• New 2017 HMDA data
• See more information – access data.

U.S. by County Population by Single Year of Age (NCHS)
• Release date — 8/22/17; next update — mid 2018
• 2010 through 2016 annual population by single year of age
• New 2016 data extending annual series 2010 forward
• See more information – access updates.

Housing Price Index (FHFA)
• Release date — 8/22/17; next update — 11/28/17
• Quarterly HPI
• New 2017Q2 data extending quarterly time series.
• See more information – access updates.

Quarterly Gross Domestic Product by State (BEA)
• Release date — 9/20/17; next update — 11/21/17
• Quarterly GDP by Industry
• New 2017Q1 data extending quarterly time series.
• See more information – access data.

Upcoming Data Releases/Access 

2017 TIGER Digital Map Database (Census)
• Expected ~ 9/7/17
• Topologically Integrated Geographic Encoding & Referencing (TIGER) data.
• Geographic data; predominately shapefiles.
.. intersection to intersection road segment geography and attributes.
• New 2017 GIS/mapping shapefiles for use with wide-ranging data
.. including with Census 2010, ACS 2016 & other subject matter.
• See more information – updates to access summarized in that section.

Census of Employment and Wages (BLS/CEW)
• Release date — 9/6/17; next update — 12/5/17
• AKA ES-202 data — establishments, employment & wages by NAICS code/type of business
• U.S. by county.
• New 2017Q1 data extending quarterly time series.
• See more information.

2016 American Community Survey 1-year estimates (Census/ACS)
• Release date — 9/14/17
• Wide-ranging demographic-economic data for areas having population 65,000+
.. all states, CDs, PUMAs, MSAs and larger cities/CBSAs/school districts/counties (817 of 3142)
• New 2016 estimates.
• See more information – updates to access summarized in that section.

SY 2015-16 Annual School & School District Characteristics (NCES)
• Expected ~ 9/14/17
• National school school & school district characteristics.
• New 2015-16 school year administratively reported data.
• Schools … see more information – access updates.
• School District … see more information – access updates.

2016 Annual Gross Domestic Product by Metro (BEA)
• Release date — 9/20/17
• GDP by Industry by Metro
• New 2016 data extending time series
• See more information – access updates.

Census Tract Estimates and Projections to 2022 — ProximityOne
• Release data ~ 9/27/17
• National census tract and higher level geography demographic-economic updates
• Annual estimates & projections; 2010 through 2022
• Updated to reflect/integrate data released through 9/2017 as summarized above   • See more information.

County Population by Single Year of Age Projections to 2060 — ProximityOne
• Release data ~ 9/27/17
• National county and higher level geography demographic updates
• Annual estimates & projections; 2010 through 2060
• Updated to reflect/integrate data released through 9/2017 as summarized above.   • See more information.

Notes [goto top]
– BEA – Bureau of Economic Analysis
– BLS – Bureau of Labor Statistics
– Census – Census Bureau
– FFIEC – Federal Financial Institutions Examination Council
– FHFA – Federal Housing Finance Agency
– NCES – National Center for Education Statistics
– NCHS – National Center for Health Statistics

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.

TractWatch — Examining Small Area Change

Understanding the demographic-economic landscape for small area geography and how it is changing is vital for many stakeholders. Businesses and other organizations need to know how their market/service areas are changing … getting answers to questions like knowing about recent trends, where we are now and the how/where/how much things might change in the future.

Examining Tract Change
The following view shows census tracts (black boundary) located in the northeast Houston, TX area. Tracts are labeled with 2017 population estimates and percent population change from 2010 to 2017. Tract geography and characteristics are shown in context of three cities/places — Houston (orange cross-hatch), Humble (blue) and Atascocita CDP (green). It is easy to see what census tracts intersect with what cities and where. The pointer/hand is located in census tract 48-201-240902, partly intersecting with Humble city. The tract 2017 population of 12,984 reflects an increase of 10.4% since 2010. The dark brown bold boundary at the top of this tract is the Harris County, TX boundary.

.. view developed with ProximityOne CV XE GIS and related GIS project.
.. create views like this for any area in the U.S.; add your own data.

TractWatch tells us which tracts in a region of interest changed during the past year based on quarterly observable data with only a one quarter lag.

Census Tracts & TractWatch
TractWatch is a new tool/service focused on examining recent demographic-business change for each census tract. These approximate 74,000 geographic areas cover the U.S. wall-to-wall and averaged 4,000 population as of Census 2010. Tracts have a generally stable geography between decennial censuses and are coterminous with county boundaries. Tracts cover the U.S. with more than a 2-to-1 ratio compared to ZIP code areas (see tract-ZIP relationship table).

Integrated with Situation & Outlook
TractWatch insights are developed through the use of the ProximityOne Situation & Outlook (S&O) database and information system — a part of S&O demographic-economic estimates and projections developed and updated annually. The 2017 vintage tract estimates and projections (annual data) cover the period 2010 through 2022 (5-year projection).

TractWatch – Monitoring Change
As a part of the S&O annual estimates and projections development, a range of measures are updated quarterly at the census tract level. Quarterly data are developed that include population, housing units, vacant units, households and business establishments.

There is only a one-quarter lag in the availability of observable census tract data. For example, observable 2017Q1 data can be added to the S&O database in July 2017. Data are analyzed and converted into a TractWatch national dataset.

Situation & Outlook Reports
The Situation & Outlook Reports (S&O Reports) are updated weekly, for the U.S. and each county, metro and state. TractWatch is a part of the “Recent Change and Outlook” S&O Report section and updated quarterly. See schedule of updates the shows when TractWatch is updated.

The S&O Reports (metro and county) Recent Change and Outlook section includes a list of census tracts which have shown significant change over the past year for that geography. A table of typically 10-to-25 key tracts are listed in a table with selected demographic-business change attributes.

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.

Life Expectancy Change by County, 1980-2014

.. data and tools to examine changing life expectancy by county. Use the interactive table to examine life expectancy characteristics and related demographics for counties and regions of interest. Use the related GIS project and datasets to examine life expectancy contextually with other geography & subject matter. See details below. These data and tools are part of the ProximityOne health data analytics resources.

Life expectancy is rising overall in the United States, but in some areas, death rates are going in the other direction. These geographic disparities are widening.

Life Expectancy Change by County, 1980-2014
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Life expectancy is greatest in the high country of central Colorado, but in many pockets of the U.S., life expectancy is more than 20 years lower. These data are based on research and analysis by the University of Washington Institute for Health Metrics and Evaluation.

Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policymakers, clinicians, and researchers seeking to reduce disparities and increase longevity.

Life Expectancy Change by County, 1980-2014 — drill-down view
— South Central Region
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view. The larger graphic shows counties labeled with change in life expectancy from 1980-2014.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Additional Views — use the GIS project to create your own views
.. click link to view
Alaska
Hawaii
Minneapolis metro

Using the Interactive Table
Use the interactive table to view, rank, compare life expectancy characteristics. This graphic shows California counties ranked on life expectancy change 1980-2014 in descending order. Select states or metros 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.

115th Congressional Districts: Analysis and Insights

.. interpretative data analytics; tools, data & methods ..  this section is focused on 115th Congressional District geographic, demographic and economic patterns and characteristics. Use tools and data reviewed here to examine/analyze characteristics of one congressional district (CD) or a group of CDs based on state, party or other attribute. Use the GIS resources described here for general CD reference/pattern/analytical views, to examine current demographics and demographic change and for redistricting applications. See this related Web section for more details.

Examining the 115th Congressional Districts
• the 115th Congress runs from January 2017 through December 2018.
• FL, MN, NC, VA have redistricted since the 114th CD vintage;
  .. some 115th CDs have new boundaries compared the 114th CDs.
• view, rank, compare CDs using the interactive table.
  .. table uses ACS 2015 data for 115th CDs & include incumbent attributes.
  .. examine districts by party affiliation.
• use these more detailed 114th CD interactive tables
  .. data based on 2015 American Community Survey – ACS 2015.
  .. corresponding data for the 115th CDs from ACS 2016 available Sept 2017.
• use the new GIS project including 114th & 115th CDs described below.
  .. create CD thematic and reference maps;
  .. examine CDs in context of other geography & subject matter.
• join us in the April 25 Data Analytics Lab session

Visual Analysis of Congressional Districts
The following views 1) provide insights into patterns among the 115th CDs and 2) illustrate how 114th to 115th geographic change can be examined. Use CV XE GIS software with the GIS project to create and examine alternative views.

Patterns of Household Income by 115th Congressional District
The following graphic shows the patterns of the median household income by 115th Congressional District based on the American Community Survey 2015 1-year estimates (ACS2015). The legend in the lower left shows data intervals and color/pattern assignment

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

Charlotte NC-SC Metro Area
  – with 114th/115th Congressional District 12

The following graphic shows North Carolina CD 12 with 114th boundary (blue) and 115th boundary (pale yellow) and Charlotte metro bold brown boundary. Click graphic for larger view with more detail. Expand browser window for best view.

.. view developed using the CVGIS software.

• View zoom-in to Charlotte city & Mecklenburg County.

115th Congressional District Interactive Table
Use the interactive table to examine characteristics of one congressional district (CD) or a group of CDs. The following graphic illustrates use of the interactive table. First, the party type was selected, Democratic incumbents in this example. Next, the income and educational attainment columns were selected. Third, the set of districts were sorted on median household income. It is quick and easy to determine that CA18 has the highest median household income and that the MHI is $1,139,900. Try using the table to examine districts 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.

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.

Creating Custom School District Maps

…tools & data to map & geospatially analyze school districts. Ready-to-use state-by-state GIS projects may be downloaded enabling you to view and create custom maps almost instantly. Benefit from the power of using GIS software to perform tasks not available on Web-based mapping options. Use the latest school district and related shapefiles. See more information about using these resources in this related Web section.

Federal Revenue per Student by School District
Create views similar to the one shown below. Optionally combine layers as illstrated here by showing four Texas metros.

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

Extending Reference and Analytical Possibilities

Texas by School District
Examine reference maps at the state, regional or local level. Optionally combine with roads/streets and other layers.

Patterns of Economic Prosperity by School District
Select from many ready-to-use demographic-economic subject matter items to create custom pattern views.

Drill-down — Houston Metro Area by School District
Zoom-in to a school district of interest. Set attributes of district as shown here.

County/School District
Visually examine the boundaries or school districts and counties. This view shows Harris County, TX area; select a county of interest.

Drill-down to Street Level
Add road/street and other layers. Drill-down within Fort Bend ISD, Houston metro, showing general earth surface features with streets layers. Mouse used to click on street (see pointer) and display mini-profile of street segment attributes.

Use for Analysis, Reference or in the Classroom
Schools and teachers: consider using these resources for classroom use. Familiarize students about how GIS resources can be used with a minimum of learning time and no cost. Enable students to use their own geography and adapt that learning to more general geography. See related Mapping Statistical Data ready-to-use GIS projects.

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.