Daytime Population Patterns

  The daytime population in cities, counties and other areas changes due to commuting.  The daytime population in Manhattan, New York County, increases by 95% during the day; the daytime population of Redmond, WA (Microsoft) increases by 111%; details below.   Daytime population change impacts on decision-making relating to business opportunities, security, transportation management and wide-ranging issues.  This section is focused on using daytime-related population to facilitate decision-making.

The concept of daytime population refers to the number of people who are present in an area during normal business hours, including workers. This is in contrast to the resident population present during the evening and nighttime hours. Information on the expansion or contraction experienced by different communities/areas between nighttime and daytime populations is important for many planning purposes, including those dealing with market size, trade/service areas, transportation, disaster, and relief operations.

The following tables/graphics show the ten counties and ten cities with 50,000 population or more that experience the largest percent change due to commuting.   See 9th column %DayPop.

Counties with Highest %Population Increase Due to Commuting

Cities with Highest %Population Increase Due to Commuting

Use this interactive table to examine characteristics of counties and cities/places for the U.S. overall, by selected state and/or by population size class.   The above graphics were developed using that table.  Column headers in the above graphics are defined in that section.  These data are based on 2010 ACS 5-year estimates and will update in December 2013.  These data are the first commuter-adjusted population estimates based on the American Community Survey (ACS) and the first commuter-adjusted population estimates since Census 2000.

In the interactive table section, go through the step-by-step example for Texas substituting your own state of interest.  For example, Shawnee has the highest percent population decrease among Kansas cities 50,000 and over while Overland Park has the highest percent population increase.  These two Kansas cities are both in Johnson County in Kansas City metro.

Net Importers of Labor — the Employment-Residence Ratio
The Employment-Residence (E-R) Ratio is a measure of the total number of workers working in an area relative to the total number of workers living in the area. The E-R Ratio is a rough indicator of the jobs-workers balance in an area, although it does not take into account whether the resident workers possess the skills needed for the jobs that are available. E-R Ratios greater than 1.00 occur when there are more workers working in the area than living there. These areas can be considered as net importers of labor.  View the E-R Ratio for any city or county in the interactive table.  The E-R Ratios are shown in the rightmost columns in the above graphics.

Visual Analysis of Daytime Population Patterns
The following graphic shows the Kansas City MSA (brown boundary) with county labels. Cities are shown with E-R colors:  orange: <0.8 E-R; yellow: 0.8 to -1.2 E-R; green: 1.2 or more E-R.  Cities with an E-R ratio of 1.2 or higher (green fill pattern) means that there are 20 percent more workers working in the city than living in the city.  The CV XE GIS software and the daytime demographics dataset can be used to visually examine areas of interest. 

What’s Ahead
An upcoming post will cover updated, more recent data.  An ACS 2011 update is planned for December 2013; an ACS 2012 update is planned for spring 2014. These estimates are now being prepared annually and will evolve into an annual time-series.  The time series will facilitate longitudinal analysis of the daytime population and commuting patterns.

School Location Analysis

  Location-location-location … this section illustrates use of GIS tools to develop custom maps showing location of points of interest, based on addresses, in context of other geography and patterns. Anyone with Internet access and a Windows-based computer can develop location analyses shown in this section for locations of interest anywhere in the U.S.

Starting at the end of this post, the objective is to display/examine the address in the context of related geography as shown in the graphic below.  The location, address, is shown by the red marker in context with street (and mini-profile of the street segment) and associated census block (red boundary, yellow census block code). The Koko Head Elementary School  address (189 Lunalilo Home Road, Honolulu, HI 96825) is used in this example. It could be any school in the U.S. or any address, school or otherwise. The view and details are reviewed in more detail below.

Examining Location/School in Context of Neighborhood
While there are many Web-based resources to obtain a map view of the location based on the address, and/or attributes of the school based on name, resources reviewed here provide a more flexible and comprehensive way to examine a location/school in context of the neighborhood and surrounding area demographics.  This process requires the types of resources reviewed here.

Study Area
The study area is in the vicinity of Hanauma Bay located west of Waikiki Beach in Honolulu, HI. The steps illustrated here can be similarly applied to anywhere in the U.S.

The next graphic shows a zoom-in view of the study area.  The coastline edge is shown with a line-shapefile that depicts perennial water features.

Geocoding: Assign Latitude-Longitude & Census Block Code
The address is entering into the CV XE GIS Find Address tool input box:

Clicking OK, a point shapefile is automatically created and added to the active GIS project. The next graphic shows the address location as a red marker.  The Identify tool is used to show the results of the geocoding.  Clicking on the marker displays the mini-profile.  The census block code is 150030001141014 or 15-003-000114-1014.  The address is located in FIPS state code 15, FIPS county code 003, Census 2010 census tract code 000114, and Census 2010 census block code 1014.  For other potential geo-referencing purposes, and as shown in the mini-profile, the latitude-longitude values are also retained in the shapefile dbf record.

A zoom-in view shows the study area in more detail. Roads are shown as black lines; census blocks are shown with red boundaries.

Zoom-in Detail; Location in Context
The view shown below shows the location (red marker) in context of street location and in context of associated census blocks.  Census blocks are shown with red boundaries and show yellow census block codes as labels.  The school is located within census block 1014.  Selected attributes of the street segment are shown in the profile.  The left- and right-side from address and to address ranges are shown as well as the left-side and right-side ZIP code.  These left- and right-side attributes are available for any intersection-to-intersection street segment in the U.S.

Economic Prosperity by Neighborhood
The next graphic shows patterns of economic prosperity in the broader area.  Koko Head ES is shown by the red marker (see pointer).  The color-coded areas show patterns of median household income (MHI) based on block group geography.  Intervals and color codes are shown in legend at left of map.  Labels show the MHI by block group.  The gray area has population of 39; MHI estimate is not available.  The MHI estimates are based on 2011 ACS 5-year data.  A more comprehensive analysis could select from approximately 250 subject matter items by block group (view available items).

Resources and Using the Tools
A GIS project was developed using the CV XE GIS software (no cost, details below.   The GIS project is comprised of two public domain shapefiles: the TIGER/Line 2013 edges/roads and the TIGER/Line 2013 census blocks.  The address point location was determined by entering the address into the CV XE Tools>Find Address feature.  The Find Address feature determines the latitude-longitude and census block code for the location.  A point shapefile is dynamically created.  The project is then saved for subsequent re-opening and use.

The GIS project and tools used to develop views shown in this section are available to ProximityOne User Group members. Join now, no fee. Members may download the GIS project and dynamically create related view.  This application can serve as an example to develop similar applications for locations of interest.

Related Sections
• Schools and School Districts Main Page.
• Hawaii Demographics.

Next Steps
An upcoming post will delve deeper into using these smaller area geodemographics to develop and analyze neighborhood patterns and trends.  Join us in the upcoming December 17, 2013 one hour web session where we talk about using the ACS 2012 5-year demographics for small area analysis.  Those new data are scheduled to be released that day.

Metro Housing Price Index: 2013Q3

  Examining housing value appreciation … the Housing Price Index (HPI) provides a measure to examine/analyze housing price levels and variations among metros and states.  The HPI, calculated using home sales price information from Fannie Mae- and Freddie Mac-acquired mortgages, continued upward momentum in U.S.  Housing prices remained strong in the third quarter 2013, as prices rose 2.0 percent from the previous quarter. This is the ninth consecutive quarterly price increase in the purchase-only, seasonally adjusted index.  It marks the first time since 2009 that the national house price level is higher than it was five years ago.  This section reviews use of HPI data and analytical tools to examine housing price patterns and trends among metros and states.

HPI Percent Change 2012Q3-2013Q3 by Metro

The HPI alone provides only partial insights — based on this one measure. Evaluation of housing markets, and the regional economy, trends and patterns need to use the HPI in combination with many other measures. Situation & Outlook reports integrate HPI data with other demographic-economic measures.

Interactive Trend Analysis: Metros & States
Use this interactive table to examine the HPI quarter to quarter over the past year by state and metro. The ranking table provides an easy way to rank/compare housing prices for a single metro area or a group of metros.  The graphic presented below shows the top-ranked metros based on HPI percent change from 2012Q3 to 2013Q3.  Optionally rank on any of the quarter-to-quarter changes.  Note that seven of the top ten metros (rate of appreciation over past year) are in California. Using the  interactive table, scroll down using the right scroll bar to view how other metros rank.  Or, double-click the rightmost column to rank in descending order to view metros experiencing the highest rates of decline.

HPI by Quarter; Annual/Quarter-to-Quarter Appreciation Percent Change

The above graphic illustrates selecting all metros.  Using tools below the interactive table, alternatively select all metros in only one state, all states and no metros or other combinations.

Thematic Pattern Maps
ProximityOne User Group members (join now, no fee) may download the all U.S. metro shapefile with all items shown in the interactive table integrated into the shapefile, ready to use.  Create thematic maps using HPI items with pattern settings of interest.  The map view presented above was developed using this shapefile with integrated HPI data.

Alternative Methods of Estimating Housing Value Appreciation
The HPI is one indicator of measuring home value appreciation at the metro or state level.  See the FHFA home value calculator that illustrates how the HPI can be used to compute estimated value now or at a different point in time.  This tool illustrates the concept but is fraught with problems of real usability.  Using differently sourced data, ProximityOne computes housing value appreciation indexes at the ZIP Code area, census tract and higher level geography.

Watching HPI Patterns
This post will be updated in late February 2014, with release of the 2013 fourth quarter HPI.

Examining Neighborhood Change

  How have neighborhoods of interest changed between 2000 and 2010?  Since 2010?  How will a neighborhood change going forward?  How does a neighborhood’s current and trending characteristics compare to adjacent of peer neighborhoods?  This section is focused on resources and methods to examine neighborhood geographic, demographic and economic characteristics and change. View related Web section, providing more detail.

The exact geographic definition of a neighborhood is elusive; neighborhood geography is not defined by a national standard.  Many areas have multiple neighborhood renderings.  While many counties/cities have well defined neighborhoods, most do not. To examine the demographic-economic characteristics of a neighborhood, the best options are to use a combination of census blocks, block groups and census tracts.

Nob Hill Neighborhood … San Francisco, CA
The Nob Hill neighborhood in San Francisco has changed in population from 20,142 in 2000 to 18,599 in 2010. The White alone population has grown a little while the Asian alone population has decreased from 11,532 (2000) to 9,705 (2010).  These population data are from Census 2000 and Census 2010 and determined by aggregating census block level data.  The Nob Hill census block geography did not change from 2000 to 2010 (75 census blocks).  Many neighborhoods and census block boundaries and codes change over time complicating longitudinal analysis of neighborhoods.  The Nob Hill neighborhood is shown in the following graphic with bold blue boundary; Census 2010 census blocks are shown with lighter blue boundaries.

The Nob Hill neighborhood in context of San Francisco.

Neighborhood Demographic-Economic Characteristics
Demographic-economic characteristics of neighborhoods play an important role as decision-making information.  The similarity, or dissimilarity, of these small area geographies are the basis for many local government planning operations ranging from law enforcement to transportation.  They help businesses determine where to locate — to serve markets where demand for their product or services is greatest.  Knowing about neighborhood geography and demographic-economic characteristics are critical to real estate businesses.

Census Blocks, Block Groups and Census Tracts
Census blocks, block groups and census tracts are the most useful geographies from which we can develop neighborhood demographic-economic characteristics.  These geographies are all defined by the Census Bureau and nest together.  All counties are comprised of a set of contiguous census tracts. Census tracts average 4,000 population and are comprised of block groups that average 1,200 population.  Block groups are comprised of a set of blocks that average 100 population.  In built-up urban areas, a census block is often the same as a city block bounded by streets.   These areas are defined for each decennial census and most boundaries do not change for the decade.  These features of known boundaries, covering the U.S. wall-to-wall, non-changing geography, nesting geographic hierarchy — and the availability of extensive demographic-economic data — make them the ideal choices to examine neighborhood characteristics and change.  There are advantages and disadvantages for each type of geography.

Census Blocks
The most appealing feature of census blocks is geographic detail.  There are more than 11 million Census 2010 census blocks; approximately 1/3 of these are water blocks and have no population.  These are the smallest geographic areas for which the Census Bureau tabulates demographic data.  The most limiting feature with using census block demographics is that only decennial census data are available by block; no richer demographics such as income or educational attainment.

Block Groups
The most appealing feature of block groups is geographic detail combined with availability of 1) decennial census data, 2) richer demographics from the American Community Survey (ACS) and 3)  annual updates from ACS.  Like blocks, there are no richer demographics for block groups from the decennial census.  There are 217,000 Census 2010 block groups. The most limiting features of block group demographics from ACS include 1) a relative high margin of error associated with ACS estimates, 2) a reduced scope of subject matter data compared to census tracts, and 3) data access and integration is challenging.

Census Tracts
Census tracts were originally designed by the Census Bureau as a pseudo-neighborhood areas averaging 4,000 population.  Approximately 73,000 Census 2010 census tracts cover the U.S. wall to wall.  Over time, the demographic-economic composition of many tracts change.  Tracts changing the most are often of most interest.  The advantages of using census tracts is similar to that for block groups.  Compared to block groups, tract estimates are more reliable and there is a broader set of subject matter available.  Tract data are easier to access and use that block or block group data.  One of the most limiting features of using census tracts to characterize neighborhoods is that tract geography often cuts through more than one neighborhood. 

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.

2013 Metros: Houston, TX

  94% of the U.S. population live in metropolitan areas.  Metropolitan areas are comprised of one or more contiguous counties having a high degree of economic and social integration. This section is one in a continuing series of posts focused on a specific metropolitan area — this one on the Houston-The Woodlands-Sugar Land, TX MSA.   This section illustrates how relevant Decision-Making Information (DMI) resources can be brought together to examine patterns and change and develop insights.  The data, tools and methods can be applied to any metro. About metros.

Focus on Houston-The Woodlands-Sugar Land, TX MSA
A thumbnail … in 2012, the Houston-The Woodlands-Sugar Land, TX MSA had a per capita personal income (PCPI) of $51,004. This PCPI ranked 23rd in the United States and was 117 percent of the national average, $43,735. The 2012 PCPI reflected an increase of 4.5 percent from 2011. The 2011-2012 national change was 3.4 percent. In 2002 the PCPI of the Houston MSA was $34,696 and ranked 37th in the United States. The 2002-2012 compound annual growth rate of PCPI was 3.9 percent. The compound annual growth rate for the nation was 3.2 percent.  These data are based in part on the Regional Economic Information System (REIS).  More detail from REIS for the Houston metro at the end of this section.

Geography of the Houston MSA
The geography of the Houston-The Woodlands-Sugar Land, TX MSA is shown in the graphic below.  The green boundary shows the 2013 vintage metro, black boundary/hatch pattern shows the 2010 vintage boundary, counties labeled. San Jacinto County is no longer a part of the metro.

Changing Metro Structures Reflect Demographic Dynamics
Click here to view a profile for the 2013 vintage Houston metro. Use this interactive table to view demographic attributes of these counties and rank/compare with other counties.

The Census 2010 population of the 2013 vintage metro is 5,920,416 (6th largest MSA) compared to the 2012 estimate of 6,177,035 (5th largest MSA). See interactive table to examine other metros in a similar manner.

Demographic-Economic Characteristics
View selected ACS 2012 demographic-economic characteristics for the Houston metro (2010 vintage) in this interactive table.  Examine this metro in context of peer metros; e.g., similarly sized metros.  In 2012, the Houston metro had a median household income of $55,910, percent high school graduates 81.1%, percent college graduates 29.6% and 16.4% in poverty.

Houston Demographic-Economic Profiles
Use the APIGateway to access detailed ACS 2012 demographic-economic profiles.  A partial view of the Houston 2010 metro DE-3 economic characteristics profile is shown below.  Install the no fee CV XE tools on your PC to view extended profiles for Houston or any metro. See U.S. ACS 2012 demographic-economic profiles.  Viewing graphic with gesture/zoom enabled device suggested.  

Houston 2010 vintage MSA Economic Characteristics

Houston Metro Gross Domestic Product
View selected Houston 2013 vintage metro Gross Domestic Product (GDP) patterns in this interactive table.  The Houston metro 2012 real per capita GDP is estimated to be $62,438 ($385,683M real GDP/6,177,035 population).

Examining Longer-Term Demographic Historical Change
— Use this interactive table to view, rank, compare Census 2000 and Census 2010 population for Census 2010 vintage metros (all metros).
— Use this interactive table to view, rank, compare 2013 vintage metros (all metros) — Census 2000, Census 2010, 2012 estimates population and related data.

Houston Metro by County Population Projections to 2060
The graphic presented below shows county population projections to 2060 for the 2013 vintage metro.  Use this interactive table to view similar projections for all counties.  The metro population is projected to increase to 2.8 million by 2030 and to 3.4 million by 2060 based based on current trends and model assumptions. Viewing graphic with gesture/zoom enabled device suggested.

Houston Metro Population Projections by County to 2060

Thematic Maps & Visual Analysis
The graphic below shows the 2013 vintage metro (bold boundary) counties labeled with county name and county per capita personal income (PCPI).  The legend shows the change in PCPI from 2008 to 2012.

The above graphic illustrates the power of using visual analysis tools (CV XE GIS).  These data are from the  Regional Economic Information System (REIS) introduced earlier in this section.  Use the links shown below to examine much more detail from REIS at the metro and county level.  A thematic pattern map could be developed for any one of these items.  The REIS data are annual time series starting in 1970 and continue to 2012.  Click a link to view a sample profile spreadsheet for Harris County, TX and the Houston MSA for 2011 and 2012.
• Personal income, per capita personal income, and population (CA1-3)
• Personal income summary (CA04)
• Personal income and earnings by industry (CA05, CA05N)
• Compensation of employees by industry (CA06, CA06N)
• Economic profiles (CA30)
• Gross flow of earnings (CA91)

Join us in an Upcoming Decision-Making Information Webinar
We will review topics and data used in this section in the upcoming webinar “Metropolitan Area Geographic-Demographic-Economic Characteristics & Trends” on January 9, 2014.  This is one of many topics covered in the DMI Webinars (see more).  Register here (one hour, no fee).

About Metropolitan Areas
By definition, metropolitan areas are comprised of one or more contiguous counties. Metropolitan areas are not single cities and typically include many cities. Metropolitan areas contain urban and rural areas and often have large expanses of rural territory. A business and demographic-economic synergy exists within each metro; metros often interact with adjacent metros. The demographic-economic makeup of metros vary widely and change often.

2013 vintage metropolitan areas include approximately 94 percent of the U.S. population — 85 percent in metropolitan statistical areas (MSAs) and 9 percent in micropolitan statistical areas (MISAs). Of 3,143 counties in the United States, 1,167 are in the 381 MSAs in the U.S. and 641 counties are in the 536 MISAs (1,335 counties are in non-metro areas).

Regional Economic Information System Updates

  Are we better off? … How is our per capita measure of economic prosperity trending?  Answers to these questions, helping us determine where we are and how things might change in the future are partly provided by the REIS economic data updated in November 2013. This section provides an overview of the latest  Regional Economic Information System (REIS) data and how these data compare to other data in related analyses and decision-making applications.  The REIS county-level data are annually updated by the U.S. Bureau of Economic Analysis.

The change in per capita personal income, 2008-2012, by county is shown in the map below (second map shows percent change, same period).  Per capita personal income (PCPI) is the most comprehensive measure of economic activity available at the county level for all counties in the U.S.

PCPI Percent Change 2008-12 almost mirrors dollar change shown above.

Our work with the REIS data dates to the 1970s where these data were used in state forecasting models by the State of Missouri.  Today, we integrate the REIS data into Situation&Outlook.  The REIS annual time series is an indispensable part of any continuing county and regional comprehensive modeling and analysis.

Relation to ACS
REIS and the Census Bureau American Community Survey (ACS) 5-year data provide an annual, but different, characterization of the county level economy.  The two sources should be used in combination; one is not better than the other.  Some selected comparisons … REIS most current data are for 2012; the ACS 2012 5-year data (December 2013) are centric to 2010.  REIS data are developed from employer reported data supplemented with other wide-ranging measures for one year; ACS 5-year estimates are based on a sample survey over a 5 year period. REIS data provide a 20+ year annual time series; ACS 5-year data are available for four years on a comparable basis.  REIS data are available for counties, metros and states; ACS 5 year data go down to block group level and cover many other types of geography.  REIS is predominantly economic; ACS data are predominately demographic. REIS metro data use the 2013 vintage metros; all available ACS 5-year data are based on the 2009/Census 2010 vintage metros.

Relation to CEW
REIS data are based substantially on employment and payroll data from the Bureau of Labor Statistics Census of Employment and Wages (CEW).  Analyses should make use of both sources.  Some comparisons … REIS data are annual only; CEW data are quarterly and annual.  REIS most current data are for 2012; CEW data are available for 1st quarter 2013; CEW will be almost two years more recent when the REIS data are released in 2014.  REIS data go to the 2-digit NAICS level; CEW data go to the 6-digit NAICS level. County is the low level geography for REIS and CEW.

Scope of REIS Data
As a generalization, the REIS data are annual time series starting in 1970 and continue to 2012.  The series are developed for many types of subject matter by county, metro and state.  The series are organized as shown below.  Click a link to view a sample profile for Harris County, TX for 2011 and 2012.

• Personal income, per capita personal income, and population (CA1-3)
• Personal income summary (CA04)
• Personal income and earnings by industry (CA05, CA05N)
• Compensation of employees by industry (CA06, CA06N)
• Economic profiles (CA30)
• Gross flow of earnings (CA91)

Access and Interactive Analysis
Use the interactive table at http://proximityone.com/reis13.htm to view, rank compare annual estimates, and change, of per capita personal income for selected years by county, metro and state.  See related maps and about mapping tools using the REIS data in that section.  The more detailed data shown in above examples are available via Situation & Outlook integrated with multi-sourced data.

Powerful School District Maps

AASA Partners with ProximityOne to Share Powerful School District Maps

This post is a partial summary of this AASA blog … AASA, American Association of School Administrators, was pleased to partner with ProximityOne for our latest economic impact report, Unequal Pain: Federal Public Education Revenues, Federal Education Cuts and the Impact on Public Schools.

  Using ProximityOne’s custom mapping tools, AASA’s report includes a national map showing the role that federal dollars play in schools district operating budgets. The national map is detailed, shading the various shares for school districts, also outlined at the state and congressional district level. The map is a huge asset to AASA’s report. Absent the map, the report is very data heavy. The map is a clear, concise representation of a very wonky discussion.

The map is also a very powerful tool: It shows, at the most local level, how any cut to federal education funding–regardless of how it may be described as ‘across the board’ or ‘uniform’–is anything but. Even the most modest of cuts (less than 2 percent) will be felt very differently in a district where federal dollars are upwards of 60 or 70 percent of their operating budget than in a district where less than 4 percent of the operating budget is federal dollars. The map is a very clear illustration of the unequal pain that stems from federal education cuts.

AASA first partnered with ProximityOne a year ago. ProximityOne uses geographic-demographic-economic data and analytical tools to inform discussions and explain current situation/area characteristics. They work with a wide variety of clients, including both private and public sector organizations.

This type of analysis may prove valuable to AASA members. As such, we will be partnering with ProximityOne to host a webinar on this map, the ProximityOne mapping tools, and what it can mean for schools.

See more about ProximityOne School District Decision-Making Information
… more about ProximityOne School District Finances

Using TIGER/Line Shapefiles

  Most of us use a derivative of the TIGER/Line shapefiles every day. And in multiple ways. The TIGER/line shapefiles lie at the foundation of GPS navigation systems used in the U.S. and Web-based mapping applications such as Google maps.  The following map view, showing Census 2000 and Census 2010 census tracts, was developed entirely using TIGER/Line shapefiles and CV XE GIS.  More about this map below.

TIGER (Topologically Integrated Geographic Encoding and Referencing) is a vector-oriented digital map database developed and maintained by the Census Bureau. For Census, it is the backbone for management of address-oriented data collection and tabulation. Increasingly it is used by Census to develop reference and thematic pattern maps.  Census develops public-use shapefiles from the internal TIGER system.

The origin of TIGER dates to the 1970s with development of GBF/DIME files for metro areas developed by Census. With the TIGER modification and update program in the 2000s, TIGER has evolved in into a U.S. wall-to-wall accurate digital map database including most road segments.

TIGER and Decision-Making Information
“Turn right now.”  Navigation systems in vehicles and on mobile devices used in the U.S. are based in large part on a map database derived from TIGER.  Most such systems now benefit from annual updates (new/different roads) to TIGER. “Deliver the package to the third house on the right.” These automated basic navigation decisions are made possible through TIGER and its variations.

TIGER contains the digital geography that enables us to tie together demographic-economic data through the use of GIS and other application software. By using a TIGER-based Census 2010 census tract boundary file, maps can be developed  showing thematic patterns.  From the thematic patterns, insights can be developed that facilitate planning and decision-making.  Decisions relating to the development of the 113th Congressional Districts boundaries, and state legislative district boundaries, were all made through the use of TIGER-based applications.

TIGER roads are comprised of line segments from intersection to intersection with left- and right-side low- and high-address ranges.  TIGER road shapefiles may be used to geocode addresses and assign geographic codes, such as census block or tract codes, to address data — such as sales data.  Businesses can then use the geocoded (latitude-longitude assigned) address-oriented data in GIS applications to analyze customer distribution and how the sales mix is changing.

Vintages of TIGER
Until the early 2000s, TIGER/Line files were just that.  The files were sets of lines and points organized on a county by county basis.  TIGER/Line files were updated periodically but not annually.  Since 2009 TIGER/Line has evolved into predominately a shapefile-based architecture, though other types of geographic renderings also exist.  Thankfully, TIGER/Line updates are, for now, being released annually.  These decade-long and annual update vintages have many important differences that can impact on uses of the shapefiles.

Just two type of vintages are reviewed here.  Others will be covered in subsequent sections.

Census to Census Changes
Census tabulation blocks, block groups and census tracts often change from census to census (e.g., between Census 2000 and Census 2010).  Within the decade, these geographic areas and codes are relatively stable making them attractive geographic areas for small area analyses.  These areas are defined by different vintages of the TIGER shapefiles.  A challenging problem is relating Census 2000 census tract demographics to Census 2010 census tract demographics to analyze how and where demographic change has occurred. See more on this topic: Census 2010 Demographics for Census 2000 Geography (a topic for an upcoming post).

A simple example of Census 2000 to Census 2010 census tract boundary change/relationship is illustrated by the graphic below. This example shows an area in Honolulu County, Hawaii. Census 2000 tracts are shown with a dark blue boundary. Census 2010 tracts are shown with a red boundary. Census 2000 tract 001902 is split into Census 2010 tracts 001903 and 001904.  This view is created in the GIS by showing one layer (Census 2010 census tracts shapefile) “above” another layer (Census 2000 census tracts shapefile) both “over” another layer ([select vintage] roads/edges shapefile).  Most applications would want to use the most current roads shapefile vintage even though many are available.

Annual Changes and Census Blocks
For Census 2010, tabulation blocks are uniquely defined by a geocode comprised of 15 characters: state FIPS code (2), county FIPS code (3), Census 2010 census tract code (6) and Census 2010 census tabulation block code (4).  Accordingly, the Census 2010 census block shapefiles depict census block areas corresponding to the 15 character code.

Following Census 2010, many types of geographic changes occur that are reflected in annual updates and vintages of TIGER/Line census block and other shapefiles.  Geographic changes reflected at the census block level include some road changes and other boundary changes.

To accommodate “split blocks,” the post Census 2010 census blocks are uniquely defined by a geocode comprised of 16 characters: state FIPS code (2), county FIPS code (3), Census 2010 census tract code (6), Census 2010 census tabulation block code (4) and a suffix code.  For example, what was census block code 1051 is now the set of 1051A and 1051B and so on. In the post Census 2010 block shapefile vintages, each of the census block parts has its own identity, centroid and boundary.  If block level demographic data are merged with post Census 2010 vintages, unintended results can occur – affecting an analysis/decision-making.

The following view shows Census 2010 census block 11-001-000100-1000 (15 character Census 2010 2010-vintage) with orange boundary, yellow code.  The block is split into three parts with 16 character codes in the 2013-vintage census block shapefile (black boundary, black code).  The 2010-vintage shapefile has 1 record for block 11-001-000100-1000 and the 2013-vintage shapefile has three records for this block. Merging demographic data into the 2013 vintage can result in an unintended “triple counting” of the data in some geospatial applications.

Using TIGER/Line Shapefiles & Visual Data Analysis
Join in at an upcoming one hour Web session on Using TIGER/Line Shapefiles & Visual Data Analysis.  Topics include how to access TIGER/Line shapefiles, integrate demographic-economic data, creating and interpreting thematic maps and related topics on geospatial analysis. See details.

The Road Ahead
Using TIGER/Line shapefiles will be revisited in future posts.  These topics will include integrating subject matter data … making and using thematic maps … how use of different TIGER/Line vintages impact geographies, and hence decision-making, including roads/street, metros and county combinations, congressional and state legislative districts, cities/places and school districts.

Homeownership Patterns by Census Block

The homeownership rate peaked in America in 2004 at approximately 69.2 percent.  Homeownership is defined as the percent of occupied housing units (households) that are owner-occupied.  The homeownership rate in 2013 is roughly the same as in 1995. The gradual decline continues.

        Homeownership Rate 1970Q1-2013Q3 not seasonally adjusted; based on CPS/HVS

The focus of this section is on creating thematic pattern map views depicting homeownership by census block for the Washington, DC area.  This section builds on the previous post Mapping Demographic Patterns: Census Blocks.

The starting place is where Mapping Demographic Patterns: Census Blocks left off — the required software and GIS project are installed on the computer.  The next step is to start the CV XE GIS software, open the DC GIS project and set the intervals for the thematic pattern views.

To compute homeownership, the minimum required data items/fields are the number of owner-occupied housing units and the number of housing units. By examining the Census 2010 SF1 table shells (xls), these items are found in Table H4. Tenure — owner/renter occupancy of occupied units (line 9259 in the xls file).  Looking at the  SF1 technical documentation (pdf) matrix section (sequential page 483, numbered page 6-321), it is determined that the items field needed are (H0040002 + H0040003) — owner occupied housing units and H0040001 (occupied housing units).  These items are already loaded into the DC block shapefile dbf.

Intervals are defined for the map view with queries that set blue to blocks with homeownership rate of 65% or more, orange to blocks with rate 50%-65% and red to population blocks with a rate below 50%.  Zero population blocks are set to gray.  The initial view shown below tells the visual story that more of Washington, DC has a homeownership rate below the national average than above the national average — and how these homeownership rate patterns are distributed by block.

Homeownership Rate by Census Block — Washington, DC

Zoom-in to area east of U.S. Capitol complex
— transparency set to 60% enabling “see through” of color patterns
— mini profile of blue block at pointer
— h0040001: 79 occupied housing units and 54 owner occupied housing units (h0040002+h0040003)

Similar thematic maps showing patterns of homeownership rate by census block may be created for any area in the U.S.  Procedures to access and use these no cost resources for Census 2010 Summary File 1 census block applications are summarized in the the APIGateway Guide.

In a future post, homeownership rates will be reviewed for all states and metros, 2008 through 2012, using annual American Community Survey (ACS) data.

Healthcare Sector Demographic-Economic Characteristics

As of the first quarter 2013, private sector healthcare and social assistance sector (NAICS 62) accounted for approximately 13.3 percent of total U.S. payroll and 15.8 percent of total U.S. employment.  The average weekly wages were $836 in this sector compared to $995 for all industries (based on BLS/CEW data). About NAICS. About NAICS 62.

Much of the healthcare marketplace is now dominated by confusion with exceptional uncertainty and risk.  Businesses and stakeholders seeking to understand the what, when and where of change might impact their markets and affect them, their clientele and the marketplace.  This section reviews selected geographic, demographic and economic data, mainly from  the Federal statistical system, that can help reduce uncertainty and risk through data-driven decision-making.

Selected U.S. National Scope Data Resources

Quarterly Census of Employment and Wages (CEW)
Data about healthcare sector business activity by NAICS
Strengths
… quarterly time series; most recent data available; county level data;
… 6-digit NAICS detail; based on employer reported data
Limitations
… difficult to access and use; extensive suppression
… suppression: data withheld to avoid release of confidential information
Access
… Bureau of Labor Statistics

County Business Patterns (CBP)
Data about healthcare sector business activity by NAICS
Strengths
… annual; county and ZIP code data; based on employer reported data
Limitations
… most recent data are too dated; suppression
… suppression: data withheld to avoid release of confidential information
Access
… Census County Business Patterns

Metro Gross Domestic Product (GDP)
Metro data on the characteristics of healthcare sector as GDP component
Strengths
… annual; per capita real GDP time series; data for each metro;
Limitations
… suppression
… suppression: data withheld to avoid release of confidential information
Access
… Metro GDP

Area Health Resources Files (AHRF)
HRSA integrated source of health-related data
Strengths
… annual; county; multi-sourced; wide ranging subject matter
Limitations
… limitations on use and redistribution
Access
… HRSA ARF

American Community Survey (ACS) Summary Data
Data about the population: employment by industry; disabilities; insurance
Strengths
… annual; county to block group data;
Limitations
… smaller areas: data are dated
Access
… American Community Survey … ACS 2012

ACS Public Use Microdata Samples
Data about the population: user develops custom estimates
Strengths
… annual; enables custom demographic estimates
Limitations
… smallest area of estimation: PUMA; 100,000 population or more
Access
… Public Use Microdata Samples

Health Insurance
Health insurance data based on
Current Population Surveys Annual Social and Economic Supplement (CPS ASEC)
Survey of Income and Program Participation (SIPP).
Strengths
… annual; enables custom demographic estimates
Limitations
… larger geographic areas
Access
… Census Bureau Health Insurance Data

Demographic-Economic Projections
Projected data about the population and businesses
Insights into how age-gender-race groups will change in future
Strengths
… annual projections of population by single year of age to 2060
… county level data
Limitations
… estimates based on assumptions and models
Access
… 5 year estimates & projections … 2060 projections

Clinic and Hospital Facilities
Establishments/locations of healthcare provider facilities
Strengths
… establishment location data
Limitations
… incomplete universe; limited subject matter
Access
… HHS Medicare

TIGER/Line Geographic Shapefiles
Shapefiles to link/associate many subject matter data listed here.
Strengths
… most national scope statistical and political geography
… enables visual and geospatial analysis
Limitations
… no subject matter data
Access
… 2013 TIGER/Line Shapefiles

About NAICS

The North American Industry Classification System (NAICS) is the statistical standard used in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. NAICS was developed under the auspices of the Office of Management and Budget (OMB), and adopted in 1997 to replace the Standard Industrial Classification (SIC) system.

About the Healthcare and Social Assistance Sector

The Health Care and Social Assistance sector includes both health care and social assistance because it is sometimes difficult to distinguish between the boundaries of these two activities. Industries in this sector are on a continuum starting with those establishments providing medical care exclusively, those providing health care and social assistance, and those providing only social assistance. Services provided by establishments in this sector are delivered by trained professionals. Industries in the sector share commonality of labor inputs of health practitioners or social workers with requisite expertise. Many industries in the sector are defined based on the educational degree held by the practitioners included in the industry.
Ambulatory Health Care Services: NAICS 621
– Offices of Physicians: NAICS 6211
– Offices of Dentists: NAICS 6212
– Offices of Other Health Practitioners: NAICS 6213
– Outpatient Care Centers: NAICS 6214
– Medical and Diagnostic Laboratories: NAICS 6215
– Home Health Care Services: NAICS 6216
– Other Ambulatory Health Care Services: NAICS 6219Hospitals: NAICS 622
– General Medical and Surgical Hospitals: NAICS 6221
– Psychiatric and Substance Abuse Hospitals: NAICS 6222
– Specialty (except Psychiatric and Substance Abuse) Hospitals: NAICS 6223
Nursing and Residential Care Facilities: NAICS 623
– Nursing Care Facilities: NAICS 6231
– Residential Mental Retardation, Mental Health/Substance Abuse Facilities: NAICS 6232
– Community Care Facilities for the Elderly: NAICS 6233
– Other Residential Care Facilities: NAICS 6239Social Assistance: NAICS 624
– Individual and Family Services: NAICS 6241
– Community Food/Housing, and Emergency/Other Relief Services: NAICS 6242
– Vocational Rehabilitation Services: NAICS 6243
– Child Day Care Services: NAICS 6244

An upcoming section will review tools and methods to integrate and use these data.