Category Archives: Metropolitan Statistical Area

Metro Population & Components of Change Trends 2010-2016

.. tools and data to examine how the U.S. by metro population is changing. Is the population moving away or into metros of interest? What are the trends; what is causing the change? What are the characteristics of the population moving in and out? How might this impact your living environment and business?

This section provides information on how and why the population is changing by metro from 2010 to 2016 in terms of components of change: births, deaths and migration. It provides a summary of tools, interactive table and GIS project, to analyze population change by metro using latest Census Bureau estimates through 2016. These data are used by ProximityOne to develop/update annual demographic-economic projections.  See related Web page to access full interactive table and more detail.

Patterns of Population Change by Metro, 2010-2016
The following graphic shows how metros (MSAs – Metropolitan Statisticsl Areas) changed from 2010 to 2016 based on percent population change. Click graphic for larger view; expand browser window for best quality view.

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

Narrative Analysis of Metro Demographic Change in Context
A narrative summary and analysis of metro demographic characteristics and change, contextually with other data and geography, is provided for each metro in the Situation & Outlook Reports. See more about the wide-ranging subject matter that are knitted together in the schedule of updates. Examine metro dynamics in context of the U.S. overall and related states and counties.

The nation’s 382 Metropolitan Statistical Areas (MSAs) had a population of 277.1 million in 2016 (86% of the total population). MSAs increased by 2.3 million people from 2015. The nation’s 551 Micropolitan Statistical Areas (MISAs) had a population of 27.7 million in 2016 (9% of the total population). MISAs increased by 16,000 people from 2015. See more highlights below

MSAs and MISAs together, or metro areas, comprised the set of Core-Based Statistical Areas (CBSAs). Each metro/CBSA is defined as a set of one or more contiguous counties.

Related Sections
• Metros Main
• Situation & Outlook Reports
• City/Place Population Trends
• County Population Trends
• County Population Projections to 2060
• ProximityOne Data Service

Examining Population Components of Change
Population change can be examined in terms of components of change. There are three components of change: births, deaths, and migration. The change in the population from births and deaths is often combined and referred to as natural increase or natural change. Populations grow or shrink depending on if they gain people faster than they lose them. Examining a county’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

See more about these topics below:
• Natural Increase/Change; birth & deaths
• Migration; net international, net domestic, net migration

Using the Interactive Table – Peer Group Analysis
Use the full interactive table to examine U.S. national scope metros by population and components of change. Consider an application where you want to study metros having a 2016 population between 250,000 and 300,000. Use the tools below the interactive table to select these metros as illustrated in the graphic shown below. The graphic shows these metros ranked on the overall U.S. metro rank (percent population change 2010-2016). As shown in the graphic, the Greeley, CO metro was ranked 11th among all metros and the fastest growing metro in this group. Use the tools/buttons below the table to create custom views.

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.

Examining Houston Metro Demographic-Economic Characteristics

.. tools & data to examine metro demographic-economic characteristics .. this Houston, TX metro focused section is one of several similar metro sections that will be covered in weeks ahead.  Each metro-focused section provides a summary of tools and data that can be used to view, rank, compare, analyze conditions and trends within the metro and this metro relative to other metros, regions and the Nation.  The ready-to-use GIS project/datasets provide the basis for extended data/geographic views and analysis immediately.  See more detail about topics covered in this related Web section.

Relating your data to demographic-economic characteristics and trends in a region involves more than information provided by a report or set of statistical tables. It is important to use your data to be able to identify areas of missed opportunity and competitive position. It is important to have a “10,000 foot” view as well as understanding individual neighborhoods and market/service areas. Geographic Information System (GIS) tools, with the right set of geographic, demographic and economic data can facilitate decision-making through the use of visual and tabular data analytics.

This section provides information on installing and using the Houston Metro Demographic-Economic GIS software and project/datasets. This same scope of data, tools and operation is available for any metro, state or combination.

10,000 Foot View
The following graphic shows patterns of median household income by census tract for the Houston metro area. This is the start-up view when using the GIS tools and data described below. The color patterns/intervals are shown in the highlighted layer in legend at left of map window. Use the GIS tools described below to develop thematic pattern maps for a range of data and criteria.

.. view developed using the CVGIS software.

See more about census tracts; see tracts main page.

Several additional views follow, developed using this same GIS project. These views illustrate different levels of geographic granularity and patterns of different subject matter.

Median Household Value by Block Group
See more about block groups; see block groups main page.

.. view developed using the CVGIS software.

Population/Housing Unit by Block
See more about census blocks; see census block main page.

.. view developed using the CVGIS software.

Zoom-in to Sugarland/Fort Bend County
See more about cities/places; see cities/places main page.
Access data for any city using interactive table.

.. view developed using the CVGIS software.

Further Zoom-in Showing Street/Road Detail
See more about streets.

.. view developed using the CVGIS software.

Additional Information
See the related Houston metro Situation & Outlook Report.

Using the GIS Software and Project/Datasets
(requires Windows computer with Internet connection)
1. Install the ProximityOne CV XE GIS
… run the CV XE GIS installer
… requires UserID; take all defaults during installation
2. Download the Houston Metro GIS project fileset
… requires UserID; unzip Houston Metro GIS project files to local new folder c:\p1data
3. Open the c:\p1data\us1_metros_houston.gis project
… after completing the above steps, click File>Open>Dialog
… open the file named c:\p1data\us1_metros_houston.gis
4. Done. The start-up view is shown above.

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.

Tools to Analyze County Demographic-Economic Characteristics

.. demographic-economic characteristics of counties are essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine these data for all counties in the U.S. This annual update includes geographic area characteristics based on ACS 2015 data.  The tools/data are organized into four related sections summarized below.

1. General Demographics
View interactive table at http://proximityone.com/us155dp1.htm
Patterns of School Age Population by County
Use GIS tools to visually examine county general demographics as illustrated below. The following view shows patterns of percent population ages 5 to 17 years of age by county — item D001-D004-D018 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

2. Social Characteristics
View interactive table at http://proximityone.com/us155dp2.htm 
Patterns of Educational Attainment by County
– percent college graduate
Use GIS tools to visually examine county social characteristics as illustrated below. The following view shows patterns of percent college graduate by county — item S067 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

3. Economic Characteristics
View interactive table at http://proximityone.com/us155dp3.htm 
Patterns of Median Household Income by County
Use GIS tools to visually examine county economic characteristics as illustrated below. The following view shows patterns median household income by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

4. Housing Characteristics
View interactive table at http://proximityone.com/us155dp4.htm 
Patterns of Median Housing Value by County
Use GIS tools to visually examine county housing characteristics as illustrated below. The following view shows patterns median housing value by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

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

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

Developing Geographic Relationship Data

.. tools and methods to build and use geographic relationship files … which census blocks or block groups intersect with one or a set of school attendance zones (SAZ)? How to determine which counties are touched by a metropolitan area? Which are contained within a metropolitan area? Which pipelines having selected attributes pass through water in a designated geographic extent? This section reviews use of the Shp2Shp tool and methods to develop a geographic relationship file by relating any two separate otherwise unrelated shapefiles. See relasted Web page for a more detiled review of using Shp2Shp.

As an example, use Shp2Shp to view/determine block groups intersecting with custom defined study/market/service area(s) … the only practical method of obtaining these codes for demographic-economic analysis.

– the custom defined polygon was created using the CV XE GIS AddShapes tool.

Many geodemographic analyses require knowing how geometries geospatially relate to other geometries. Examples include congressional/legislative redistricting, sales/service territory management and school district attendance zones.

The CV XE GIS Shape-to-Shape (Shp2Shp) relational analysis feature provides many geospatial processing operations useful to meet these needs. Shp2Shp determines geographic/spatial relationships of shapes in two shapefiles and provides information to the user about these relationships. Shp2Shp uses the DE-9IM topological model and provides an extended array of geographic and subject matter for the spatially related geometries. Sh2Shp helps users extend visual analysis of geographically based subject matter. Examples:
• county(s) that touch (are adjacent to) a specified county.
• block groups(s) that touch (are adjacent to) a specified block group.
• census blocks correspond to a specified school attendance zone.
• attributes of block groups crossed by a delivery route.

Block Groups that Touch a Selected Block Group
The following graphic illustrates the results of using the Shp2Shp tool to determine which block groups touch block group 48-85-030530-2 — a block group located within McKinney, TX. Shp2Shp determines which block groups touch this block group, then selects/depicts (crosshatch pattern) these block groups in the corresponding GIS map view.

Geographic Reference File
In the process, Shp2Shp creates a geographic relationship file as illustrated below. There are six block groups touching the specified block group. As shown in the above view, one of these block groups touches only at one point. The table below (derived from the XLS file output by Shp2Shp) shows six rows corresponding to the six touching block groups. The table contains two columns; column one corresponds to the field GEOID from Layer 1 (the output field as specified in edit box 1.2 in above graphic) and column 2 corresponds to the field GEOID from Layer 2 (the output field as specified in edit box 2.2 in above graphic). The Layer 1 column has a constant value because a query was set (geoid=’480850305302′) as shown in edit box 1.3. in the above graphic. Any field in the layer dataset could have been chosen. The GEOID may be used more often for subsequent steps using the GRF and further described below. It is coincidental that both layers/shapefiles have the field named “GEOID”.

Layer 1 Layer 2
480850305302 480850305272
480850305302 480850305281
480850305302 480850305301
480850305302 480850305311
480850305302 480850305271
480850305302 480850305312

Note that in the above example, only the geocodes are output for each geography/shape meeting the type of geospatial relationship. Any filed within either shapefile may be selected for output (e.g., name, demographic-economic field value, etc.)

How it Works — Shp2Shp Operations
The following graphic shows the settings used to develop the map view shown above.

See related section providing details on using the Shp2Shp tool.

Geographic Relationships Supported
The Select Relationships dropdown shown in the above graphic is used to determine what type of spatial relationship is to be used. Options include:
• Equality
• Disjoint
• Intersect
• Touch
• Overlap
• Cross
• Within
• Contains
See more about the DE-9IM topological model used by Shp2Shp.

Try it Yourself

See full details on how you can use any version, including the no fee versin, of CV XE GIS to use the Shp2Shp tools. Here are two examples what you can d. Use any of the geospatial relatoinships. Apply your own queries.

Using Touch Operation
Select the type of geographic operation as Touch. Click Find Matches button. The map view now shows as:

Using Contains Operation
Click RevertAll button. Select the type of geographic operation as Contains. Click Find Matches button. The map view now shows as:

Relating Census Block and School Attendance Zones
The graphic shown below illustrates census blocks intersecting with Joyner Elementary School attendance zone located in Guilford County Schools, NC (see district profile). The attendance zone is shown with bold blue boundary. Joyner ES SAZ intersecting blocks are shown with black boundaries and labeled with Census 2010 total population (item P0010001 as described in table below graphic). Joyner ES is shown with red marker in lower right.


– view developed using CV XE GIS and related GIS project; click graphic for larger view

See more about this application in this related Web section.

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 LUCA Program and You

.. what would be the financial impact of a one-percent understatement in the Census 2020 population count? Many political districts are drawn based upon population change and shifts, and allocations of government funding and services are made based upon official population data. Consider this one specific example. For each one-percent of the Atlanta MSA population missed in Census 2020, potentially due to less than fully accurate address and location data, the financial impact could be on the order of $414 million per year. How and why? At margin, each person not counted in the decennial census results in a per capita disposable income loss for the area in the magnitude of $5,494 in 2000, and $6,770 per person in 2020. 61,100 people undercounted times $6,770 yields $414 million.

This section is about the Censue 2020 Local Update of Census Addresses (LUCA) program and how it might impact the reduction in undercount .. and make the data more accurate for wide-ranging needs and uses. Read on for details about the LUCA program.

Atlanta-Sandy Springs-Roswell, GA MSA
The Atlanta metro shown with black bold boundary. More about this metro.

– View developed with CV XE GIS software.
– Click graphic to view patterns of neighborhood economic prosperity.

Financial Impact Details … the 2015 per capita current transfer payments (PCTP) in the Atlanta-Sandy Springs-Marietta MSA were $6,132, up from $5,494 in 2010. The PCTP figure in 2020 may be $6,770. For each one-percent of the Atlanta MSA population (61,100 people) missed in Census 2020, potentially due to less than fully accurate address and location data, the financial impact could be in the order of $414 million (61,100 x $6,770) per year as of Census 2020.  $414 million per year based on the 2020 population and PCTP.

Financial Impact in Your Areas of Interest
Estimate the financial impact in your areas of interest. Get the 2010 and 2015 population and PCTP data from the REIS Interactive Table for any county or state.  Compute the 2020 population and PCTP values, potential undercount to determine the financial impact on an area of interest

Census 2020 LUCA Overview
The Census 2020 LUCA program is an initiative of the Census Bureau, partnering with thousands of state and local governments across the U.S. At the core of this program, Census provides address list data to communities; those communities compare those data with their own data and provide address/geographic updates back to the Census Bureau.  The updated address and geographic data are integrated into the TIGER/Line files  — geographic backbone for collecting and tabulating the Census results. This important MAF/TIGER address-plus update program will help insure improved accuracy for Census 2020. LUCA is a geographic data development program engaging local communities across the U.S.

ProximityOne works with local areas to improve the TIGER/Line files leading up to Census 2020. Using the CV XE GIS software and specialized expertise, we helped hundreds of governmental units, including all of the State of Georgia, improve the coverage and content of the TIGER/Line files and thus the accuracy and completeness of Census 2010.

The Census 2020 LUCA program is starting now in 2016.  See the full schedule and related details in the LUCA Web section.

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.

Metropolitan Area Gross Domestic Product: Trends & Updates

… data and analytical tools to examine Metro GDP patterns and trends.  As a policy-maker, investor, business, advisor or stakeholder, it is important to know how and where the metro economy is changing … and how one or selected metros relate to the U.S. and other metros. Is metro X changing in a different direction than metro Y? By how much, why and is there a pattern? What does the healthcare sector, for example, contribute to a metro’s gross domestic product (GDP)? How does it compare to peer metros? How is the healthcare industry trending? Metro GDP data can provide insights and answers to these important questions. Developing insights using metro GDP data — an example. See related Web section for more detail.

Change in Per Capita Real GDP by Metro, 2010-2015
The following graphic shows patterns of change in per capita real GDP by metro from 2010 to 2015. The orange and red fill patterns show metros experiencing a decrease in per capita real GDP over the period. Click graphic for larger view that shows the 2015 rank of the metro among all 382 MSAs based on 2015 per capita real GDP.

— view created using CV XE GIS and associated MetroGDP GIS Project

282 metropolitan statistical areas, of the total 382, experienced an increase in real Gross Domestic Product (GDP) between 2010 and 2015. Growth was led by growth in professional and business services; wholesale and retail trade; and finance, insurance, real estate, rental and leasing, Collectively, real GDP for U. S. metropolitan areas increased 2.5 percent in 2015 after increasing 2.3 percent in 2014. Use the interactive table and GIS project/datasets described in this section to view/analyze patterns and characteristics in metros of interest.

Illustrative GDP by Sector Trend Profiles
Real GDP by sector profiles are available for the U.S. and each state and MSA. The Metro GDP data are part of the State & Regional Income & Product Accounts (SRIPA). The following profiles illustrate these data for metros, states and the U.S.

Atlanta, GA MSA
Charlotte, NC-SC MSA
Chicago, IL MSA
Columbia, MO MSA
Houston, TX MSA
Phoenix, AZ MSA
United States
Missouri
Texas

Metro Situation & Outlook Reports
View Metro GDP 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. GDP tells an important but small part of the broader metro demographic-economic characteristics. Most metros have sub-county areas experiencing growth or activity sometimes masked when looking at the entire metro. Click a metro (metro GDP estimated for MSAs only) link in the table at upper right to view the GDP estimate in context of related subject matter.

Interactive Analysis
The following graphic shows an illustrative view of the interactive MetroGDP table. This view shows California MSAs ranked in descending order on percent change in per capita real GDP from 2010 to 2015 (ranked on far right column). Use the table to examine characteristics of metros in regions of interest. Click graphic for larger view.

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

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

New Monthly Residential Construction by Metro Updates

.. tools, data & methods to assess the housing situation, examine housing supply and demand market conditions, and how metros of interest are changing.  New July 2016 building permits (new housing units authorized) and over-the year monthly data are now available for each metropolitan statistical area (MSA).

Use the interactive table to view, query, rank, compare data by metro. Map and geospatially analyze construction patterns with the CV XE GIS software and ready-to-use GIS project/datasets – see details.

Updated Resources to Examine Residential Construction Patterns
Metro Situation & Outlook Reports
.. metro by metro … examples: Houston, Los Angeles, Chicago, Atlanta.
County Annual U.S. by county
County & City/Place Monthly

Patterns of New Authorized Residential Units by Metropolitan Area
The following graphic shows value of single unit structures units authorized  by metro. Larger view shows more details including a mini-profile of housing units authorized detail. Create similar views for preferred time periods and different residential unit attributes using the GIS project.  Zoom-in to areas of interest.  Label the geography as desired.  Add your own data.

View created with CV XE GIS. Click graphic for larger view.

The time lag from reference date to access date of these data is one month, contributing to both the freshness of the data and importance of the data as a leading economic indicator. The importance of these data transcends issues concerning housing market conditions alone.  These data are one part of a mix of demographic-economic factors required to understand housing market conditions and the local/regional economy. These data are a part of the process to develop the ProximityOne county and sub-county demographic-economic estimates and projections.

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.