Category Archives: Geographic Information Systems

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.

Census 2020 P.L. 94-171 Redistricting Data Updates

.. we are just two years away from the first census block level data from Census 2020.  The initial block level data will be the P.L. 94-171 redistricting data.  But before that, the initial Census 2020 TIGER/Line shapefiles/GIS files, the geography, will become available in November 2020, maybe earlier.  Stakeholders will be able to see how block and tract codes and geography have changed in many areas since 2010.  The prototype P.L. 94-171 data (see final file layout and subject matter items) are expected in the last week of March 2019 and will cover the Providence County, RI area. This post shows illustrative views and related details about the area. The Census 2020 P.L. 94-171 program and plans are reviewed in this Federal Register notice.

The applications/views shown below have been developed using the ProximityOne CV XE GIS software and related GIS project.

Census 2020 Data Access and Use Program
ProximityOne operates a comprehensive Census 2020 Data Access and Use Program providing tools to integrate and analyze these data with other data for redistricting, planning, evaluation, management, general analysis and policy-related applications. Contact us for more information; mention Census 2020 Data Access and Use Program in text section.

Providence, RI Census 2020 P.L. 94-171 Prototype
The Census 2020 P.L. 94-171 prototype covers Providence County, RI, part of the Providence-Warwick, RI-MA MSA (39300) — see Situation& Outlook report. Providence County is shown with cross-hatch pattern in the following graphic.

The next graphic shows a zoom-in to the county with cities/places shown with green fill pattern.

The next graphic shows patterns of economic prosperity for the county based on ACS 2017 median household income by census tract — blue, higher and red, lower.

The next graphic shows Census 2010 blocks for the county. Demographics described in the P.L. 94-171 file described about will be provided at the census block level.

Census block boundaries are primarily defined by roads. Providence County roads are shown in the next view.

The next view shows a zoom-in to the downtown Providence city area. Census blocks are shown with red boundaries and labeled with the 15-character U.S. national scope unique census block code. The pointer is located in census block 440070012001001, or 44-007-001200-1001, expressed as SS-CCC_TTTTTT-BBBB. Access these Census 2010 data (an example) using the Census FactFinder tool via this link. This is the “P1 RACE” table. The Census 2010 population of the block was 598. This census block is one of 13,597 Census 2010 census blocks comprising Providence County.

Rhode Island 116th Congressional Districts 01 and 02 (labeled) split Providence city (cross-hatch pattern) as shown in the graphic below. Pointer shows CD boundary.

Similar to above, the graphic below shows census blocks in context of Providence city (bold green boundary) and CDs 01 and 02.

Next Steps
This section provides a geographic orientation the Census 2020 P.L. 94-171 prototype area. A subsequent post (March 2019) will extend on this post with Census 2020 P.L. 94-171 data and related details. Use the downloadable project and software to examine geodemographics and redistricting operations.

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.

Housing Price Index 2017Q3-2018Q3

.. this past week the 3rd quarter 2018 (2018Q3) Housing Price Index (HPI) was released for the U.S., states and metros. As a leading economic indicator, the HPI often gives insights into how the housing market and economy might be changing in the months ahead. The fact that the HPI data are quarterly and become available with a short lag time makes the measure even more valuable. This section provides an update on the HPI 2018Q3 and quarterly data for the past year. See the related Web page for more detailed data and access to the HPI data via interactive table.

Visual Analysis of 2017Q3-2018Q3 HPI Patterns
The following graphic shows housing value appreciation 2017Q3-2018Q3 by metro based on the HPI.

Click graphic for larger view and details. This view developed using CV XE GIS and related GIS project. Members of the ProximityOne User Group (join now, no fee) may used the CV XE GIS software and GIS project to create similar views with different HPI measures. Zoom-in. Add labels. Add other geography/data. Create views/graphics for reports and stories.

The Larger Picture
The HPI is calculated using home sales price information from Fannie Mae- and Freddie Mac-acquired mortgages. By itself, the HPI provides limited insights into the broader picture of “the why” and “how otherwise” states and metros are changing. The Situation & Outlook Metro Profiles provide an integrated view of the HPI measure in combination with other economic, demographic and business activity measure. View the HPI integrated with other subject matter … choose a metro. Metro Profiles are updated continuously and are available for each of the metropolitan area.

HPI Interactive Table
Use the HPI interactive table to view/rank/compare the non-seasonally adjusted “all transactions” HPI for the most recent 5 quarters for all Metropolitan Statistical Areas (MSAs), states and the U.S. The graphic shown below illustrates use of the interactive table to rank all metros in descending order on the percent change over the past year.

Updates & Related Measures
Quarterly HPI measures are used to updated the interactive table, GIS project and Metro Profiles. HPI by county, ZIP Code and census tract are updated annually. The 2018 county, ZIP Code and census tract HPI data are scheduled for release in February 2019.

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.

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.