Category Archives: data analytics

Employment by Occupation by Census Tract; 5-Year Trends

.. data and tools to examine patterns of employment by occupation by census tract and 5-year change .. the U.S. civilian employed population increased from 142.9 million in 2012 to 155.1 million in 2017, an increase of 12.1 million (8.5%) based on the American Community Survey (ACS) 1-year estimates. See this table to see how the employed population were distributed by occupation in 2012, 2017 and the 5-year change. How did your neighborhoods or market/service areas of interest change over the past 5 years? How will occupational employment patterns by tract/neighborhood change between now and 2023?

Patterns of Percent Employed in Health Occupations by Census Tract
The following graphic shows patterns of the employed population in health occupations as a percent of total civilian employed population ages 16 and over in the Minneapolis-St. Paul metro. This view uses the occupational category MBSA40 Healthcare practitioners and technical listed in scroll section below. Tracts with blue or green pattern exceed the national average as shown in national table. Click graphic for larger view, more detail (shows schools layer) and legend color/data intervals. This map illustrates the geographic level of detail available using census tract demographics and the relative ease to gain insights using geospatial data analytics tools. View related graphic showing tract with the largest employment in the “Healthcare practitioners and technical” occupational group among all tracts.

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

Drill-down to Census Tract Level
Examining patterns of employment by occupation, for the same scope of subject matter, at the sub-county level can provide more insights. What is the size of the employment for a selected occupation in a neighborhood or market/service area of interest? How has the size of an occupational group by census tract changed over the past five years? How do these patterns rank/compare by tract in a particular state, metro or county? Data on employment by occupational category from the Federal statistical system on a U.S. national scale for counties, cities and census tracts are only available from the American Community Survey (ACS).

Use tools, resources and methods described here to access, integrate and analyze employment by occupation for the U.S. by census tract. Use the interactive table to view, query, rank, compare census tract occupational characteristics, patterns and trends. Data are based on the American Community Survey (ACS) 2017 5-year estimates.

Related sections with census tract interactive tables:
– General Demographics .. Social .. Economic .. Housing 

Current Estimates & Projections
ACS tract/small area estimates lag by four years or more between the current year and reference year. ACS does not produce current year annual estimates but estimates based on a 5-year period. The 2017 ACS estimates are centric to 2015. Use the ProximityOne annual tract estimates and projections 2010 through 2023 for current year (e.g., characteristics as of 2018) estimates and anticipated change 5 years ahead.

Using the Interactive Table
An example of using the interactive table to view, query, rank, compare census tract occupational characteristics, patterns and trends is shown by the graphic presented below. The table shows 6 columns of employment data for all tracts in Harris County, TX. The table is ranked on the ACS 2017 health occupations employment (MBSA40) column. Tract 48-201-312600 had largest ACS 2017 health employment of 1,078 among all tracts in the county. Compare to 2012 patterns. Use settings below table to develop a similar view your geography and occupations of interest.

Occupational Categories
The interactive table includes occupational categories listed below.
Total population
Total Civilian employed population 16 years and over
MBSA00 . Management, business, science, and arts
MBSA10 . . Management, business, and financial
MBSA11 . . . Management
MNSA12 . . . Business and financial operations
MBSA20 . . Computer, engineering, and science
MBSA21 . . . Computer and mathematical
MBSA22 . . . Architecture and engineering
MBSA23 . . . Life, physical, and social science
MBSA30 .. Education, legal, community service, arts, and media
MBSA31 … Community and social service
MBSA32 … Legal
MBSA34 … Education, training, and library
MBSA35 … Arts, design, entertainment, sports, and media
MBSA40 .. Healthcare practitioners and technical
MBSA41 … Health diagnosing & treating practitioners & other tech
MBSA42 … Health technologists and technicians
SVC00 . Service
SVC10 . . Healthcare support
SVC20 . . Protective service
SVC21 . . . Fire fighting/prevention & other protective services
SVC22 . . . Law enforcement workers including supervisors
SVC30 . . Food preparation and serving related
SVC40 . . Building and grounds cleaning and maintenance
SVC50 . . Personal care and service
SOF00 . Sales and office
SOF10 . . Sales and related
SOF20 . . Office and administrative support
NRC00 . Natural resources, construction, and maintenance
NRC10 . . Farming, fishing, and forestry
NRC20 . . Construction and extraction
NRC30 . . Installation, maintenance, and repair
PTM00 . Production, transportation, and material moving
PTM10 . . Transportation
PTM20 . . Material moving

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.

Examining County Gross Domestic Product

.. what is the annual per capita real-valued output of counties of interest? How is this measure trending? Why is this important? This section reviews tools and data to examine county-level Gross Domestic Product (GDP) trends and patterns. The first ever county-level GDP estimates to be developed as a part of the official U.S. national scope GDP estimates were released in December 2018. The county GDP estimates join the county-level personal income by major source, both now part of the Regional Economic Information System (REIS). See more detail about topics reviewed in this post in the related County GDP web section.

Patterns of Real Per Capita GDP by County
The graphic below shows patterns of per capita real GDP, 2015, by county.

– View developed using CV XE GIS and related GIS project.
– create custom views; add your own data, using the GIS project.

Gross Domestic Product (GDP) by county is a measure of the value of production that occurs within the geographic boundaries of a county. It can be computed as the sum of the value added originating from each of the industries in a county.

Example … use this interactive table to see that 2015 Los Angeles County, CA total real GDP of $656 billion was just slightly larger that than of New York County, NY (Manhattan) at $630 billion. Yet, the total 2015 population of Los Angeles County of 10.1 million is 6 times larger than that of New York County of 1.6 million — see about steps. GDP provides very different size measures, and economic insights, compared to population.

In 2015, real (inflation adjusted) Gross Domestic Product (GDP) increased in 1,931 counties, decreased in 1,159, and was unchanged in 23. Real GDP ranged from $4.6 million in Loving County, TX to $656.0 billion in Los Angeles County, CA.

This post is focused on U.S. national scope county level estimates of Gross Domestic Product (GDP) annually 2012 through 2015. This marks the first time county level GDP estimates have been developed, a part of the Regional Economic Information System (REIS). Use the interactive table to rank, compare, query counties based on per capita GDP, current GDP, real GDP by type of industry. Use the related GIS project to develop thematic map views such as the one shown below. See more about these data.

Current Annual Estimates & Projections
ProximityOne uses these and related data to develop and analyze annual Situation & Outlook demographic-economic estimates and projections. GDP items included in the table below are included in the “annual 5-year” projections as shown in the schedule of release dates; next release April 18, 2019 and quarterly.

Examining County GDP Using GIS Tools
Use the County REIS GIS project. Make your own maps; select different item to map; modify colors, labels. Zoom in views of selected states shown below. Graphics open in a new page; expand browser window for best view. Patterns: see highlighted layer in legend to left of map; MSAs bold brown boundaries with white shortname label
counties labeled with name and 2015 per capita real GDP
.. Arizona .. Alabama .. California .. Colorado .. Iowa .. Georgia .. Kansas .. Missouri
.. New York .. Nevada .. North Carolina .. South Carolina .. Nevada .. Texas .. Utah .. Vermont

Using the County GDP Interactive Table
The graphic below illustrates use of the interactive table. Tools below the table have been used to view only per capita real GDP for all sectors (total sources) and for county with total population between 50,000 and 60,000. Counties were then ranked on 2015 per capita real GDP (rightmost column).

– click graphic for larger view.

Using County GDP: 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.

State of the States: 2018 Population & Components of Change

.. Welcome to 2019 .. how the U.S., states and world population are changing … the Census Bureau estimates the U.S. population is 328,231,337 as of January 1, 2019. This represents an increase of 2,013,241, or 0.62 percent, from New Year’s Day 2018 (326,218,096). The population as of Census Day (April 1) 2010, was 308,745,538 and has grown by 19,485,799, or 6.31 percent.

This section updates January 2020, with corresponding 2019 updates and additional details. Follow (click follow button at upper right) to receive updates on this and geographic, demographic and economic change with drill-down to the street intersection level.

In January 2019, the U.S. is expected to experience one birth every 8 seconds and one death every 11 seconds. Meanwhile, net international migration is expected to add one person to the U.S. population every 29 seconds. The combination of births, deaths and net international migration will increase the U.S. population by one person every 19 seconds … one net international migrant every 34 seconds.

The world population on January 1, 2019 is estimated to be 7,541,221,651. The world has experienced a population increase of 96,777,770, or 1.3 percent, from New Year’s Day 2018 (population 7,444,443,881). During January 2019, 4.8 births and 1.9 deaths are expected worldwide every second.

Patterns of Population Change by State, 2010-2018
The following graphic shows patterns of percent population change from 2010 to 2018. Use the associated GIS project to examine different years or subject matter items. Click graphic for larger view; expand browser window for best quality view.

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

How the U.S. Population is Changing
The following graphic shows how the population of the U.S. has changed from 2010 to 2018 and how the population might change 2019 through 2020. Click graphic for larger view; opens in new page. The population is as of July 1 for each year. The components of change (birth, deaths and migration) are for the period July 1 through June 30 for that year.

Population for each year is computed by the population identity equation:
  P[t]=P[t-1] + B[t,t-1] -D[t,t-1] + M[t,t-1]
Viewing the larger image, see how each of the components of change are impacting the total population and population change.
… see more detail about these data for the U.S. and by state at http://proximityone.com/states2018.htm.

More About Population Trends, Patterns and Characteristics
See more about how population dynamics; use the interactive tables in these sections:
  • School Districts — http://proximityone.com/sdtrends.htm
  • Cities — http://proximityone.com/places2017.htm
  • Counties — http://proximityone.com/countytrends2017.htm
  • Metros — http://proximityone.com/metros.htm
  • States — http://proximityone.com/states2018.htm

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.

Assessing Why and How the Regional Economy is Changing

.. data, tools and insights .. which counties are experiencing the fastest economic growth? by what economic component? what does this look like on a per capita level? how might county economic change impact you? Use our county level annual estimates and projections to 2030 to get answers to these and related questions. Get started with the interactive table that contains a selection of these data for all counties and states.

Visual Analysis of Per Capita Personal Income Patterns
The following map shows changing patterns of economic prosperity, U.S. by county, based on percent change in per capita personal income, 2010 to 2017. Create variations of this view — this view uses a layer in the “US1.GIS” GIS project installed by default with all versions of the CV XE GIS software.
– click graphic for larger view.
– view developed with CV XE GIS software.

Measuring the economy and change. One important part of this is Personal Income and components of change. 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; how they rank and compare. The table provides access to 31 personal income related summary measures — the interactive table shows data for one of eight related subject matter groups. See more about the scope of subject matter descriptions.

Assessing How the Economy is Changing and How it Compares
The U.S. Per Capita Personal income (PCPI) increased from $40,545 in 2010 to $51,640 in 2017 — a change of $11,095 (27.4%). Compare the U.S. PCPI (or for any area) to a state or county of interest using the table. For example, Harris County, TX (Houston) .. click the Find GeoID button below the table .. increased from $45,783 in 2010 to $53,188 in 2017 — a change of $7,405 (16.2%).

Economic Profile; 2010-2017 & Change — An Example
The following graphic shows and example of the economic profile for Harris County, TX (Houston). Access a similar profile for any county or state.

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.

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.

How the New York Metro is Changing

.. or more precisely, how the New York Metropolitan Statistical Area (MSA) is changing. As of Census 2010 the New York MSA (officially the New York-Newark-Jersey City, NY-NJ-PA MSA) consisted of 20 counties. With the new OMB metropolitan statistical areas defined as of February 2013, the New York MSA became 22 counties, absorbing the Poughkeepsie, NY MSA two counties (Dutchess and Orange). The Poughkeepie MSA was removed from the official MSAs. The delineation remained that way until the new September 2018 delineations when the Census 2010 delineation was restored. Now, the Poughkeepsie, NY MSA exists as a 2 county area and the New York MSA exists as a 20 county area (both as they existed geographically in Census 2010).

These metro-county relationships are shown in the graphic presented below. The Poughkeepsie, NY MSA is shown with the blue cross-hatch to the north and the New York MSA is shown with the salmon color pattern.

– view developed using the CV XE GIS software and related GIS project.
– see the related New York Metro Situation & Outlook report.

What Difference Does it Make?
A lot! First, during the interim period 2013-2018, the Poughkeepsie, NY MSA lost the metropolitan area identity/status as conferred by the OMB delineations. It might have been omitted from size class market development and research analyses. Related, that metro was not included as a tabulation or estimation area of MSAs by Federal statistical agencies. An example of the impact is that the official demographic estimates for the Poughkeepsie, NY MSA developed by the Census Bureau were not tabulated as such and omitted from various statistical reports. Also, the removal of designation and now adding the designation back, creates a hiccup in the time series — affecting both the Poughkeepsie NY MSA and the New York MSA.

Detailed Demographic Profiles for New York MSA and Poughkeepsie, NY MSA
.. click link to view profile.

New York-Newark-Jersey City, NY-NJ-PA MSA
  Bergen County, NJ (34003)
  Essex County, NJ (34013)
  Hudson County, NJ (34017)
  Hunterdon County, NJ (34019)
  Middlesex County, NJ (34023)
  Monmouth County, NJ (34025)
  Morris County, NJ (34027)
  Ocean County, NJ (34029)
  Passaic County, NJ (34031)
  Somerset County, NJ (34035)
  Sussex County, NJ (34037)
  Union County, NJ (34039)
  Bronx County, NY (36005)
  Kings County, NY (36047)
  Nassau County, NY (36059)
  New York County, NY (36061)
  Putnam County, NY (36079)
  Queens County, NY (36081)
  Richmond County, NY (36085)
  Rockland County, NY (36087)
  Suffolk County, NY (36103)
  Westchester County, NY (36119)
  Pike County, PA (42103)

Poughkeepsie-Newburgh-Middletown, NY (CBSA 39100)
  Dutchess County, NY (36027)
  Orange County, NY (36071)

Looking Forward
The September 2018 CBSA delineations define counties that will be used for Census 2020 (likely, there could be yet further changes) — 384 MSAs in the U.S. In the cases of the New York MSA and the Poughkeepsie, NY MSA, it appears that the geography (component counties) used for Census 2010 will be the same as for Census 2020. Going forward, ProximityOne estimates and projections will use the most current vintage of CBSAs.

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.

U.S. House of Representatives 2020 Apportionment

.. Congressional Apportionment by State .. 2010 & projected 2020 state by state congressional seats.

What will the results of Census 2020 tell us us about how the House of Representatives will be reapportioned, state by state? This section examines scenarios which might occur based on state population projections. See related Web section http://proximityone.com/apportionment.htm for more detail and interactive table.

Use the GIS tools and project to make your own map views … see details
.. use in classroom .. research .. reference .. collaboration.

This section has been developed using
– 2020 apportionment population projections
.. part of the ProximityOne Situation & Outlook (S&O)
– the reapportionment/redistricting feature of the CV XE GIS software
The 2020 population projections reflect anticipated change under one scenario. Those values are then used in the CV XE GIS reapportionment operation to compute the number of House seats shown in the related table.

Apportionment of the U.S. House of Representatives
— based on the 2010 Census

– view created with CV XE GIS. Click graphic for larger view with more detail.

Apportionment of the U.S. House of Representatives
— based on ProximityOne 2020 Population Projections

– view created with CV XE GIS. Click graphic for larger view with more detail.

Congressional apportionment is the process of dividing the 435 memberships, or seats, in the House of Representatives among the 50 states based on the population figures collected during the decennial census. The number of seats in the House has grown with the country. Congress sets the number in law and increased the number to 435 in 1913. The Constitution set the number of representatives at 65 from 1787 until the first Census of 1790, when it was increased to 105 members. More about apportionment.

Initial Census 2020 demographic data, the apportionment data, will be released by December 31, 2020. See related Census 2010 Apportionments.

Apportionment totals were calculated by a congressionally defined formula, in accordance with Title 2 of the U.S. Code, to divide among the states the 435 seats in the U.S. House of Representatives. The apportionment population consists of the resident population of the 50 states, plus the overseas military and federal civilian employees and their dependents living with them who could be allocated to a state. Each member of the House represents, on average, about 710,767 people for Census 2010.

Using the Interactive table
The following graphic illustrates use of the 2010 & 2020 apportionment by state and historical apportionment 1910 to 2010. Sort on any column; compare apportionment patterns over time. Click graphic for larger view.
Use the interactive table at http://proximityone.com/apportionment.htm#table.

Congressional District/State Legislative District Group
Join the CDSLD Group (http://proximityone.com/cdsld.htm), a forum intended for individuals interested in accessing and using geodemographic data and analytical tools relating to voting districts, congressional districts & state legislative districts and related geography with drill-down to intersection/street segment and census block level. Receive updates on topics like that of this section.

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.