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We seek a collaborative focus on learning about and knowing:
• where we are now (in a geodemographic and economic sense)
• what will change when, where, and how
• why that change is happening
• knowing more about the future situation, as result of above
• assessing the impact that change will have
• identifying factors and actions that can result in alternative future outcomes
Events often impact geodemographic-economic sectors/systems and result in change.
These events often coalesce into patterns.
This section provides commentary on such events, happenings, their coalescence and resulting patterns
... and how they might impact you.
We welcome your feedback and comments.
Warren Glimpse (about)... wglimpse at proximityone.com.
Measuring Green Jobs
What is the trend for green jobs in America?
Until there is a definition for green jobs that can enable job measurement, it is only conjecture how many
green jobs there are, might be in the future and their prospective impact.
The Bureau of Labor Statistics (BLS), responsible for developing and implementing the collection of new data on green jobs,
is soliciting comments on definitions to be used in measuring green jobs, the industry list and associated matters.
See http://edocket.access.gpo.gov/2010/pdf/2010-5705.pdf.
Comments are being solicited now and must be submitted before April 30, 2010.
BLS hopes the resulting information will assist policymakers in planning policy initiatives and understanding
their impact on the labor market, and will facilitate the monitoring of labor market developments related to
protecting the environment and conserving natural resources.
The solitication itself reveals interesting information that has been developed by BLS in their study of green jobs and "green goods and services."
Broadly, green jobs are jobs involved in economic activities that help protect or restore the environment or conserve natural resources:
1. Renewable energy
2. Energy efficiency
3. Greenhouse gas reduction
4. Pollution reduction and cleanup
5. Recycling and waste reduction
6. Agricultural and natural resources conservation
7. Education, compliance, public awareness and training
Quarterly Housing Prices by Metro (030110a)
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Quarterly Housing Prices by Metro
Housing Price Index (HPI) estimates are prepared quarterly for all metros by the
U.S. Federal Housing Finance Agency based on Fannie Mae and Freddie Mac housing sale transaction data.
The most recent metro HPI estimates are for 4th quarter 2009; the estimates availability generally lag the reference quarter by two months.
The HPI is a measure designed to capture changes in the value of single-family homes in the U.S. by state and metropolitan area.
The HPI equals 100 for all MSAs in the first quarter of 1995.
To more easily assess housing price patterns by metro, we regularly update a quarterly time series dataset organized by metro.
The HPI ranking table
(http://proximityone.com/hpi.htm)
is then updated quarterly using this dataset. These data also feed into a GIS/mapping project, integrated with other data.
for geographic/visual pattern analysis.
The ranking table shows quarter-to-quarter change using data for the most recent 5 quarters.
The 5 metros with the largest percent decrease in HPI change 2008q4 to 2009q4 include
Bend, OR; Las Vegas-Paradise, NV; Reno-Sparks, NV; Lakeland-Winter Haven, FL; and Phoenix-Mesa-Glendale, AZ (ranging from -16 to -20%).
The 5 metros with the largest percent increase in HPI change 2008q4 to 2009q4 include
Steubenville-Weirton, WV-OH; Elmira, NY; Odessa, TX; Anniston-Oxford, AL; and Goldsboro, NC (ranging from 3.5 to 6.8).
Examine metros of interest using the ranking table.
Join us in a no fee Web meeting where we review use of the HPI data and related estimates of property value in ranking tables,
GIS applications and other analyses. See web meetings for more information --
http://proximityone.com/webmeetings.htm
Accessing Census 2000 Data
Most everyone with the need to access decennial census data faces the daunting question of where do I start?
Or, I have found the Census Bureau FactFinder
(http://factfinder.census.gov)
Web tool, but how do I navigate it to get just the data I need?
And in this lifetime?
This section provides a brief tutorial on accessing the Census 2000 "demographic profile" (DP) datasets. The DP datasets, also referred to as
"Quick Tables" in the FactFinder data access options, provide access to the most widely used Census 2000 subject matter.
A list of the subject matter fields is available in the four Web pages http://proximityone.com/dp1.htm through dp4.htm.
The no fee DMIGateway
(http://proximityone.com/dmigateway/guide)
tool simplifies the process for many applications, but not all.
DMIGateway also supports access to the American Community Survey data, non-Census sourced data and will support access to Census 2010 data as those
data become available.
America's Changing Birth Patterns
For the U.S. overall, births decreased in the two successive years July 2007 to July 2008 and July 2008 to July 2009.
This follows a set of long-term continuous annual increases in the number of U.S. births.
Do these decreases in the number of annual births reflect an aberration or an evolving pattern?
How might these changing patterns affect you?
A combination of factors accounts for this change.
While the economic downturn may play some role, other factors at work are more dominant including
the size of the childbearing cohort and
childbearing propensity for some age groups in some areas.
The Population Identity. Any good model used to estimate the total population of an area must include a variation of the
basic population identity/equation, P(t)=P(t-1)+B(t,t-1)-D(t,t-1)+M(t,t-1), where P population, B Births, D deaths, M Migration,
t point in time.
Natural Increase Decreases. One result of the declining births is that the annual Natural Increase
(births less deaths without considering migration) is now decreasing for many areas where it had been increasing.
Many of these same areas have been experiencing out-migration,
though migration has been flat more recently due to the economic downturn.
Who/Where is Doing Better or Worse?
See the CountyTrends web page
http://proximityone.com/countytrends.htm
and MetroTrends web page
http://proximityone.com/metros.htm
to view patterns at the sub-state level. These data will be updated with 2009 estimates and projections to 2020 in March 2010.
The ranking tables enable you to examine geography of interest.
Judge for yourself the extent to which the changing birth data are reflecting a long-term pattern and where.
Charter School Patterns
Do charter schools foster productive competition with traditional K-12 schools resulting in improved educational outcomes and opportunities?
What is the size and impact of charter schools in your areas of interest?
The U.S. Department of Education is now launching the Race to the Top program.
One facet of this program is intended to strengthen and expand charter schools by providing funds to state departments of education.
Charter schools are publicly funded elementary or secondary schools that have reduced regulations relative to other public schools.
Charter schools are chartered by individual states, though some states have no charter school program.
This Patterns topic examines the characteristics of charter schools by state and geographic distribution of charter schools based on preliminary 2008-09 state reported data.
School by School Detail & Rankings. The new, related state-by-state K-12 schools interactive ranking tables show all K-12 schools in each state based on data
reported by state education agencies for the 2008-09 school year.
See Arizona schools at http://proximityone.com/k12schools/azschools.htm;
click on left column links to view a ranking table for any state.
By sorting ascending on the charter school column, you can see the set of charter schools for the state.
Charter schools have a value/code of 1 in the charter column.
National & State by State Summary Data.
As of the 2008-09 school year, approximately 2.5% of America's public elementary and secondary regular school students were enrolled in charter schools.
Eleven states had no charter schools. States with percent enrolled in charter schools over 5 percent included Arizona (9.1%), Colorado (6.9%) and Michigan (6.2%).
The District of Columbia had 33.4 percent students enrolled in charter schools.
State-by-State Detail. The table presented below has been developed using the national scope school level database.
Each row corresponds to a state and shows the number of charter schools, students and teacher FTE (full-time equivalence) for the 2008-09 school year.
Charter Schools, Students & Teacher FTE by State, 2008-09
State
Schools
Students
Teachers (FTE)
Alabama
0
0
0.0
Alaska
20
3,601
191.3
Arizona
435
96,213
35.6
Arkansas
20
5,301
474.0
California
655
265,935
14,621.7
Colorado
123
53,791
3,066.5
Connecticut
16
4,146
324.8
Delaware
0
0
0.0
District of Columbia
71
20,350
1,078.6
Florida
290
101,152
4,694.0
Georgia
26
14,039
708.3
Hawaii
28
7,016
432.7
Idaho
29
11,461
490.4
Illinois
35
30,536
58.6
Indiana
40
13,230
744.6
Iowa
9
655
65.6
Kansas
30
3,600
202.3
Kentucky
0
0
0.0
Louisiana
4
2,581
169.8
Maine
0
0
0.0
Maryland
31
9,079
601.3
Massachusetts
60
25,832
2,059.9
Michigan
241
96,557
5,132.2
Minnesota
157
27,477
1,948.3
Mississippi
1
371
22.2
Missouri
38
16,254
1,109.0
Montana
0
0
0.0
Nebraska
0
0
0.0
Nevada
26
8,247
366.0
New Hampshire
12
568
68.2
New Jersey
56
18,176
1,745.8
New Mexico
59
9,783
684.4
New York
94
32,988
2,537.4
North Carolina
11
3,416
164.0
North Dakota
0
0
0.0
Ohio
272
80,255
4,004.8
Oklahoma
14
4,929
279.6
Oregon
76
13,084
597.1
Pennsylvania
122
70,215
4,130.0
Rhode Island
7
1,957
155.9
South Carolina
28
5,888
375.5
South Dakota
0
0
0.0
Tennessee
12
3,086
146.5
Texas
202
74,975
4,354.7
Utah
53
22,769
1,303.8
Vermont
0
0
0.0
Virginia
3
225
23.9
Washington
0
0
0.0
West Virginia
0
0
0.0
Wisconsin
184
33,567
1,903.6
Wyoming
2
243
36.9
Geographic Distribution within State.
While the table provides useful state by state summary data, it reveals nothing about the geographic distribution of charter schools within a state.
For example, Missouri's 38 charter schools are all in the Kansas City or St. Louis area.
The following map shows the distribution/location of charter schools in the 48 contiguous states.
A CommunityViewer GIS project, with national scope K-12 schools layer/shapefile,
has been used to develop this view. A query was applied to show only charter schools. Use this project as a starting
point for further anlayses. Add your own data ... create alternative thematic maps and perform geospatial analyses.
These pattern views lay the groundwork for a myriad of further questions. Such as, how are the number of charter schools,
students, and teachers changing? What will be the geographic distribution or productive impact of charter schools?
We will seek to examine some of these issues in a future Patterns topic.
Macro View of Educational Attainment in America (021510a)
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Macro View of Educational Attainment in America
Getting a drill-down view of educational attainment by city, school district, county and other smaller area geography was reviewed in a
previous Patterns topic (021210a). The same data and ranking table reviewed there provides insights into a more macro view
of educational "geodiversity" in America.
The start-up view of the ACS 2006-08 interactive ranking table (http://proximityone.com/acs.htm)
shows data for the U.S. by geographic component (summary level 010), the main focus of this topic.
A future Patterns topic will examine patterns for summary level 020 which
shows data for the U.S. by region
and division.
The percent population ages 25 years and older who are high school graduates (%HSG) accelerated most rapidly from the early 1950's to mid 1970's.
The %HSG in the U.S. overall is now 84.5% based on the ACS 2006-08 as shown in the table presented below.
This table shows U.S. data for selected geographic components derived from the
ranking table. The components are sorted on %HSG in ascending order.
%HSG ranges from 80.3% to 86.8% depending on the geographic component --
rural versus urban population and metro versus non-metro population.
Component Name
Code
Population
% H.S. Graduate+
% Bachelor Degree+
United States; Not in MSA or MISA
55
19,210,078
80.34
14.99
United States; Not in MSA
59
49,669,550
81.74
17.13
United States; In MISA -- in principal city
61
10,054,679
81.99
20.94
United States; In MSA -- in principal city
57
97,358,255
82.27
30.14
United States; In MISA
60
30,459,472
82.64
18.52
United States; In MISA -- not in principal city
62
20,404,793
82.95
17.42
United States; Urban
01
232,328,160
84.46
29.37
United States
00
301,237,703
84.53
27.41
United States; Rural
43
68,909,543
84.75
21.07
United States; In MSA or MISA
52
282,027,625
84.83
28.30
United States; In MSA
56
251,568,153
85.09
29.50
United States; In MSA -- not in principal city
58
154,209,898
86.83
29.11
Bachelor Degree or Higher Educational Attainment.
The percent educational attainment spread by geographic component is much wider for the population with bachelor degree or higher educational attainment
(rightmost column in above table). The U.S. overall is 27.4%, but ranges from 15% to 30.1% when viewed by geographic component.
Higher education programs have been least effective in reaching the population in non-metro areas.
The situation reflects an opportunity for higher education providers.
Regional, multi-county, non-metro areas acting in a collaborative manner have the opportunity to increase educational attainment
and potentially enhance economic prosperity.
Economic Distress and Educational Attainment (021210a)
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Economic Distress and Educational Attainment
Areas with higher educational attainment have a better chance of minimizing economic distress.
A more highly educated labor force has more opportunities to build a wider range of products
and provide a wider range of services.
How to examine these patterns for your areas of interest?
What are the most current measures for educational attainment for the largest number of sub-national areas across America?
Annual estimates of educational attainment are produced by the Census Bureau American Community Survey (ACS).
The ACS 2006-08 provides the most recent estimates of educational attainment for the largest number of areas.
Data are provided for areas with population of 20,000 or more including cities, counties, metros, school districts, and larger areas.
We have developed an interactive ranking table that provides access to educational attainment measures from the ACS 2006-08
for all approximate 15,000 ACS tabulation areas. Select state or type of geography of interest and rank on an educational attainment measure.
See the ranking table at http://proximityone.com/acs.htm.
An example. First click ShowAll button below the ranking table (more table usage information is provided in the expanding text section below the table).
Select the filter "SumLev" as "050 State-County". The table refreshes. Click on the far right column header, %Bchlr Deg+.
This column shows the percent population ages 25 and older with bachelor degree or higher educational attainment.
Click again on the column header so the counties are ranked in descending order.
The top-ranked five counties are also counties experiencing the lowest unemployment rates and relative higher earnings
among all counties in America.
See the monthly county employment situation ranking table to examine counties of interest and the unemployment rates for counties in the ACS
ranking table example --
http://proximityone.com/county_employment.htm. The employment situation ranking table is updated monthly and
shows very recent labor force characteristics.
The above example shows counties. Click ShowAll in the ACS ranking table and then apply a state and/or type of geography filter to examine your areas of interest.
Investing now, in all ways possible, to improve educational attainment is one of the best strategies to buffer against adverse impact from economic
downturns/distress.
Comparative Analysis of City Demographics (020910a)
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Comparative Analysis of City Demographics
How does Santa Fe, NM compare to Albuquerque, NM? Other cities in New Mexico? "Peer Group" cities across the U.S?
Generalizing, how do your cities of interest compare to others? This section describes tools/resources to view/analyze selected
characteristics, patterns and trends. While the examples make use of cities in New Mexico, similar steps can be followed to examine
patterns in any set of U.S. cities.
Comparisons Based on Selected Demographics
We have developed an interactive ranking table that includes selected demographics from the
Census Bureau American Community Survey (ACS) 2006-08 "3-year" estimates.
See the ranking table at http://proximityone.com/places2.htm.
The ranking table includes all U.S. cities/places with population 20,000 or larger (tabulation threshold established by ACS).
To illustrate use of this table, and compare Santa Fe, NM to other cities in New Mexico, first click on the State dropdown below
the table and select NM. As you can see in the State column, now only New Mexico cities are shown. To "get back" to all cities,
click the "Show All" button or refresh the page. Expand the width of the "places2" Web page so that you can see the scroll bar to the far right
in the table. Rank the cities on any one of the columns/items by clicking the header column. Click that same header column again
to sort in reverse order. For example, click on $MHI to rank on Median Household Income.
South Valley CDP (Census Designated Place) has the lowest CDP. Click $MHI header again; the ranking shows that Rio Rancho has the highest $MHI
and Santa Fe is ranked second. Again, this table includes only cities/places with 20,000 population or more.
Rank the cities on any column/selected characteristic by using steps similar to those described for $MHI.
View the abbreviations for column headers by clicking the "View" link below the table to see item descriptions
(Ranking Table Item Descriptions & Usage Notes View). For example, the column LFPR is an abbreviation for Labor Force Participation Rate.
Comparisons Based on Population Change
The ranking table described above presents socioeconomic characteristics based on ACS 2006-08 estimates. What all data for all cities?
What about how cities of interest are changing over time? For these views/analyses,
see the ranking table at http://proximityone.com/places.htm.
This table contains the most current official estimates available (7/1/08) for all U.S. incorporated places (19,496).
Operations are similar to the table described above.
To illustrate use of this table, and compare Santa Fe, NM to other cities in New Mexico, first click on the State dropdown below
the table and select NM. As you can see in the State column, now only New Mexico cities are shown.
Dbl-click the column header Pop 7/1/08; you can see the Santa Fe population as of 7/1/2008 was estimated to be 71,831 and that
Santa Fe was the 4th largest city in the state. You can also see that it was the 3rd largest city in the state as of 7/1/2000,
that position now overtaken by Rio Rancho. Of these larger cities, Rio Rancho grew by 51-percent between 7/1/00 and 7/1/08.
Extended Analysis -- Orienteering & Thematic Map Patterns
Using the public domain TIGER/Line shapefiles, the January 2009 New Mexico city/place boundary shapefile
is added to a CommunityViewer GIS project (subject of another Patterns topic).
The next view shows a zoom-in view to Albuquerque (green), Rio Rancho (blue), Santa Fe (orange), and other cities
with yellow fill pattern.
Other geography/layers are added to this project with thematic map views and presented in the Applications Gallery.
Extended Analysis -- More Detailed Demographics
Approximately 500 demographic subject matter items are contained in the ACS 2006-08 demographic profiles.
View the subject matter in the section below the ranking table at http://proximityone.com/places2.htm.
Click the View link for each of DP1, DP2, DP3 and DP4 to see the more detailed subject matter.
These datasets are available in CSV and other formats for further analysis.
What is the True Unemployment Rate? (020310a)
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What is the True Unemployment Rate?
Six alternative measures of labor underutilization have long been available on a monthly basis from the Bureau of Labor Statistics
Current Population Survey (CPS).
We have recently updated the interactive ranking table
(http://proximityone.com/state_employment.htm) showing these alternative unemployment rates
on a state-by-state basis for the 2008 annual averages and the new 2009 annual averages. Using the "U-6" definition for those
unemployed, the Michigan unemployment rate could be as high as 21.6% for 2009 on an annual average basis. See the ranking table
to examine the alternative measures for states of interest.
The official measure of unemployment is referred to as "U-3" in the U-1 to U-6 range of alternative measures
and includes all jobless persons who are available to take a job and have actively sought work in the past four weeks.
Other measures are provide more narrowly (U-1 and U-2) and broadly (U-4 through U-6) defined measures.
The State Employment Webpage
includes state-by-state monthly updates of the latest employment/unemployment situation and
links to a related Google tool to view time-series unemployment charts for one state or set of states.
U.S. by County Employment Situation Update (020210a)
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U.S. by County Employment Situation Update
As of December 2009, more than one-third of all U.S. counties (1,238)
had an unemployment rate of 10-percent or more (1,048 in October).
30 counties had a December 2009 unemployment rate above 20-percent (16 in November).
The two highest county December 2009 unemployment rates were in Imperial County, CA (27.74) and Baraga County, MI (27.62).
19 counties, of the 285 counties with December 2009 employment over 100,000,
had a December 2009 unemployment rate below 6-percent (compared to 28 counties in November 2009).
The two lowest county October 2009 unemployment rates, for counties with 100,000 or more employment,
were in Arlington, VA (3.11) and Lancaster County, NE (3.37).
Of counties with 100,000 or more employment, six of the top thirteen with lowest December 2009 unemployment rates are in the Washington, DC metro.
See more information on sources and uses of these time series data updated
monthly and integrated with broader demographic-economic data for mapping, GIS and extended analysis applications.
December 2009 Unemployment Rate by County (preliminary estimates released 2/2/09)
Updated monthly. See Applications Gallery for additional views.
December 2008 Unemployment Rate by County (revised estimates released 2/2/09)
Graying of America by Metro
The "graying of America" matters to all of us for many reasons.
Some are members of this age group and almost everyone is connected to many people in this age group.
The growth of this age group is about to accelerate as baby-boomers start to turn 65.
We have developed a set of estimates of the population 65 years and over as of 2008 by metropolitan area.
See the interactive ranking table at http://proximityone.com/metro_08_65up.htm.
The population 65 years and over varies widely by metropolitan area.
Based on 2008 population estimates, the percent population ages 65 years and over ranged from 4.77% in the Edwards, CO MISA to
32.24% in the Sebring, FL MISA.
For MSAs over 500,000 population, the percent population ages 65 years and over ranged from the low of 6.4% in Provo-Orem, UT MSA to
the high of 27% in Bradenton-Sarasota-Venice, FL MSA.
Percent Population 65 years and over by CBSA [see larger view]
Percent Population 65 years and over by MSA [see larger view]
Knitting together multi-sourced Federal statistics (020110a)
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Knitting together multi-sourced Federal statistics
Last year we developed the no fee DMIGateway Web tool (http://proximityone.com/dmigateway/guide).
DMIGateway enables access to multi-sourced Federal statistical data using a common user-interface to access data
directly from each agency server. In an upcoming Patterns topic, DMIGateway enhancements will be reviewed.
DMIGateway does not cover all of the important data resources that are available and a focus has been on data for U.S. regional and sub-state geography.
To address the need for more of a roadmap approach to "when, what and where of Federal statistics," the format and content
of the Proximity Calendar (http://proximityone.com/calendar.htm) have been revised and extended.
The Calendar covers
all key multi-sourced Federal statistics and as well as links to Proximity resources where these data have been augmented by form of access, content, geography
and/or time frame. Selected non-Federal statistical data resources are included. HTML links are included to provide easier access overall.
There are many calendars that chronicle release dates for economic indicators. Most of these calendars are organized by agency, and often program, and use different formats.
This makes it difficult to learn more about specific data in a broader context. In the case of the Census Bureau,
the nicely done economic indicators calendar is separate from any parallel calendar for demographic data products.
What do you think?
Insights from the Housing Price Index (012810a)
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Insights from the Housing Price Index
The FHFA
Housing Price Index (HPI) is one of many measures that offers insights into housing prices and markets.
The HPI is a broad measure of the movement of single-family house prices and estimated quarterly by metro.
We have restructured the quarterly FHFA HPI by metro into a ranking table.
Rank your metros of interest at http://proximityone.com/hpi.htm on any HPI estimate for the most recent
five quarters, quarter-to-quarter change, or change from the most recent quarter to the same quarter one year earlier.
The current ranking table (most recent quarter report 2009q3) shows that the El Centro, CA MSA has the highest decline in HPI over the past year
whereas the Wichita Falls, TX MSA has the highest increase in the past year. The ranking table will be updated in late February.
OnTheMap -- All About Jobs
Where are the jobs? The Census Bureau OnTheMap Version 4
(http://lehdmap4.did.census.gov/themap4/)
can help with jobs analysis for large or very small areas ... for metros or more rural cities.
OTM4 is a Web-based GIS resource that is at once easy to use, has relevant ready to use geographic and subject matter data,
and is oriented toward jobs analysis. In just a few minutes, I used OTM4 to create a map of Lenexa, KS with 2008 jobs coded to census block.
One of the classic limitations to Web-based GIS tools is the inability to add your own data.
OTM4 has the excellent feature of being able to download shapefiles for use with desktop GIS applications.
I added the downloaded jobs points shapefile to my existing project file, having my own data layers, and generated the following
view. The project/view includes K-12 schools (blue triangle markers).
The identify tool is used to click on one point (see pointer) and display a miniprofile. The profile shows the census block geocode
and number of 2008 jobs. See larger view of the above map with more detail.
Join me in a one-hour webinar on February 4 where we will review the steps involved to perform these tasks and link in other
TIGER/Line shapefiles and non-Census-sourced shapefiles. Register here.
Foreign-Born Population by Region of Birth (012210a)
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Foreign-Born Population by Region of Birth
How will the changing patterns of foreign-born population by region of birth change our society?
Will the diminishing percentage of European foreign-born population result in a loss of that cultural impact?
Over the next 40-50 years, what will be the impact on English language proficiency and use in America?
In 2007, there were 38.1 million foreign born in the United States, representing about 1 in 8 residents.
Most foreign born were White (46%), Asian (23%), or Some Other Race (21%). Approximately 80 percent of the
foreign-born population were born in Latin America or Asia. These data are based on a
Census Bureau report
(January 2010)
describing the race and Hispanic-origin composition of the foreign-born population in the U.S in 2007.
The report shows that the foreign born have a pattern of race and Hispanic origin reporting that is markedly different from the native
population. Author Elizabeth Grieco notes, "Over the last 40 years, immigration from Latin American and Asia has been the major force changing the
racial and ethnic composition of the American population. The influx of newcomers from these regions has resulted in rapid growth in the
number of Hispanics and Asians in recent decades. For the three
groups with the oldest historical roots in the United States -- non-Hispanic Whites, non-Hispanic Blacks, and American Indian and
Alaska Natives —- natural increase, not immigration, has been the main source of growth since 1970."
The graphic shown below illustrates change in the foreign-born population & region of birth during the past 5 decades.
Percent Foreign-Born Population by Region of Birth: 1960 to 2007
We have developed a ranking table and GIS/mapping application that extend on the national level Census Bureau report.
State level data are provided in a ranking table that enables viewing/analyzing sub-national state-level data
for 2000 and the 2006-08 period.
See the ranking table and additional narrative at State Foreign-Born by Region of Birth Patterns & Trends.
Cities with Median Housing Value $1 Million+ (011910a)
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Cities with Median Housing Value $1 Million+
Before the housing market meltdown, the McMansion era gave forth some eye-popping numbers.
17 U.S. cities/places with population of 20,000 or more have a median housing value (MHV) of $1 million or more.
All but two are in California with the other two in Connecticut.
The number of U.S. cities with MHV over $1 million is higher than seventeen when cities with under 20,000 population are included.
The all U.S. city/place list will be available from the American Community Survey 2005-09 5-year data to be released in late 2010.
The following view shows locations of all places 20,000 population or more as blue markers.
Places with median housing value of $1 million or more are shown as red markers.
Click map for larger views.
Use the interactive ranking table to rank the cities on
median housing value (2nd from furthest right column) or other demographic-economic subject matter items.
Census 2010 and Trend/Pattern Analysis (011410a)
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Census 2010 and Trend/Pattern Analysis< (011410a)
The scope of subject matter data from Census 2010 will be markedly different from that tabulated from Census 2000
... and actually the 1960 Census through Census 2000. For example, there will be no data on income or poverty from Census 2010, as there has been from
the decennial for the past seven censuses. What does this mean to you as a user of these data?
Those who have been users of decennial census data over the past several decades typically examine how their markets, service areas,
neighborhoods, metros, cities, counties, school districts/school cachement areas, etc. have changed from decade to decade.
The most insightful data have been the richer demographics that include educational attainment, employment by occupation/industry, housing value, various income
measures, poverty incidence measures, language spoken at home, among a longer list.
A fundamental change has taken place. The items listed above are no longer tabulated from the decennial census.
The reason for this is that the questions previously asked on the decennial census long form, will not be asked in Census 2010.
There is no Census 2010 long form questionnaire. The result is that we are no longer able to pose the questions like those listed above and get answers
from Census 2010. We will not be able to compare how the median household income or poverty incidence for a county, for example, changed between Census 2000
and Census 2010.
The Census Bureau states, "Accurate data reflecting changes in your community are crucial in apportioning seats in the U.S. House of Representatives and deciding how more than $400 billion per year is allocated for projects like new hospitals and schools."
The implication of this statement is that this allocation of resources will be determined by Census 2010.
It is true that such allocations were determined by Census 2000 for the past decade, but such allocations cannot be determined from Census 2010 alone.
More than half of those allocations are based on items only on the long form questionnaire in previous censuses.
The Census Bureau Director correctly states,
"Just like we can’t survive without roads and bridges, the country doesn’t function well without an updated Census to distribute funds to areas that most need them and to support community decisions about their own future."
The hitch is, and broadly speaking, most community decisions cannot be supported by Census 2010, alone, as with previous decennial censuses.
Still, however, the data that will flow from Census 2010 do play a unique, key and essential role in these uses.
American Community Survey. The richer demographics (first paragraph) required for Community Decisions will be available from the
Census Bureau American Community Survey (ACS).
The first ACS data for all U.S. counties, school districts, census tracts, etc. are scheduled to become available in December 2010.
But, these data are from the ACS 5-year 2005-09 data tabulation period. One might say these estimates are centric to 2007; data from ACS 5-year 2008-12,
centric to 2010, will not be available until December 2013.
Haiti Earthquake 1-12-2010 (011310a)
The U.S. Geological Survey reported the magnitude 7.0 Haiti region earthquake at 21:53:09 UTC on January 12, 2010.
A relief map of the region shows the epicenter near the pointer in the following graphic.
We developed a GIS project
to show this same region with different attributes and more flexibility. The start-up view of the GIS project appears as shown below.
See larger view.
This view shows the epicenter, fault lines, cities and countries.
The next view shows a zoom-in with fault lines turned off.
Download this GIS project for use on your computer. The installer includes ready-to-use
CommunityViewer GIS software and project.
Google Public Data Access (011210a)
In April 2009, Google started an initiative referred to as
"Adding search power to public data."
See also this page.
An interesting aspect to this development is that Google showcased development using two U.S. by county datasets
that are also tracked/used by Proximity. We have taken a step to illustrate how these resources can be linked together
without having any formal or computer-based linkages. Follow the steps in the two examples described below to see how this works.
Annual County Population Patterns.
Use the Google population by county charting tool for each state in the Proximity
CountyTrends-Population Web page (annual data).
Items in the county population ranking table differ from items in the state population ranking table.
State population estimates are available for 2009 whereas the latest county population estimates are for 2008.
To view the Google county chart, click the county name link in the ranking table.
Monthly County Employment Patterns.
Use the Google employment by county charting tool for each state in the Proximity
CountyTrends-Employment Web page (monthly data).
Items in the state employment ranking table differ from items in the county employment ranking table.
The state employment ranking table has a focus on alternative unemployment rates;
some items are updated on a quarterly basis due to data availability.
To view the Google county chart, click the county name link in the ranking table.
Annual State Population Patterns. Use the Google population by state charting tool for each state in the Proximity
StateTrends-Population Web page (annual data).
Items in the state population ranking table differ from items in the county population ranking table.
State population estimates are available for 2009 whereas the latest county population estimates are for 2008.
To view the Google state chart, click the state name link in the ranking table.
Monthly/Quarterly State Employment Patterns. Use the Google employment by state charting tool for each state in the Proximity
StateTrends-Employment Web page (monthly data).
Items in the state employment ranking table differ from items in the county employment ranking table.
The state employment ranking table has a focus on alternative unemployment rates;
some items are updated on a quarterly basis due to data availability.
To view the Google state chart, click the state name link in the ranking table.
Likelihood Models: 2010 & 2012 Election Outcomes (011110a)
As the 2010 and 2012 November elections approach, we seek to assess factors that might make the outcomes different from those of 2008.
That is, what will the vote or percent vote by county be for each major party and candidate?
Clearly, many 'what-ifs' come into play.
The following view shows the 2008 Presidential election vote by county.
See larger view.
To learn more about what might be ahead and examine this at the county level, factors that would shape the 2012 presidential outcome
would include and 'employment situation' factor and an 'incumbent popularity' factor.
The specific candidate and many other factors go into the mix in determining the outcome.
The above map view is based upon a dataset that includes the 2008 presidential vote by candidate and other items not shown in this view.
'Likelihood models' that predict possible 2010 and 2012 outcomes will be reviewed forthcoming Patterns.
The models make use of a mix of variably weighted factors.
Indexes are computed at the county level based on a quantified value for each factor and added to (or updated in) the
dataset. Using the models, data and related tools, other GIS layers can be viewed/examined that depict alternative outcomes (votes, percent vote, etc.) by county.
What is your interest in being a part of this?
Red or blue? Before 2000 in the U.S., and possibly more generally throughout the world, blue has been associated with the more conservative party
and red with the more liberal party. More recently, news organizations have tended to reverse this pattern.
The above map shows blue associated with Republican/McCain and red associated with Democrat/Obama.
What do you think?
U.S. Population Change by County: 1900-2000 (010910a)
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U.S. Population Change by County: 1900-2000 (010910a)
View the graphics below to see how the population density has changed in the U.S. by county between 1900 and 2000.
These views make use of decennial census population data divided by the county land area in square miles.
Land area is based on the 2008 vintage county boundaries. See notes below map views regarding changing county boundaries.
In each graphic the legend at the left shows the color pattern associated with the population density.
19001910192019301940195019601970198019902000
In map views between 1900 and 1940, most areas that show as gray (data NA) are because data are not available for the counties as currently defined.
Several states were not yet formed in 1900 ... AZ(1912) AK(1989) HI(1959) NM(1912) OK(1907).
Most counties formed by 1940 in the 48 contiguous states remain the same as today. However, there are some notable county boundary changes since 1940 such as
Yuma County, AZ being split into La Paz County and Yuma County.
High Unemployment Persists in Many Regions (010810a)
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High Unemployment Persists in Many Regions (010810a)
Based on the latest November data,
our monthly analysis of the national scope county-level employment situation shows that economic distress continues or worsens in largely the same troubled areas.
Meanwhile, a few counties experience very low unemployment rates.
Improvement in the diverse pattern of county-level economic distress can better be served by geographically targeted Federal actions instead of the perceived remedies such as
business tax credits that can apply to any area.
The national unemployment remained near 10.0 percent in December; nonfarm payroll employment edged down in December (-85,000). These national data mask the fact that the employment situation is very uneven by county/region and that most counties in distress continue to experience challenging conditions.
November 2009 Unemployment Rate by County
(preliminary estimates released 1/5/10, updated monthly)
See Applications Gallery
for additional views.
As of November 2009, approximately one-third of all U.S. counties (1,048) had an unemployment rate of 10-percent or more (1,024 in October).
16 counties had a November 2009 unemployment rate above 20-percent (13 in October). The two highest county November 2009 unemployment rates were in Imperial County, CA (29.19) and Baraga County, MI (25.60). 28 counties, of the 285 counties with November 2009 employment over 100,000, had a November 2009 unemployment rate below 6-percent (compared to 23 counties in October 2009). The two lowest county October 2009 unemployment rates, for counties with 100,000 or more employment, were in Arlington, VA (3.80) and Lancaster County, NE (3.88). Of counties with 100,000 or more employment, six of the top fourteen with lowest November 2009 unemployment rates are in the Washington, DC metro.
Examining Voter Participation Rates (010710a)
New estimates of the voting age population estimates as of July 1, 2009 were released by the Census Bureau in January 2010.
The following view shows the percent population 18 years and over based on these estimates (using the downloadable State Trends GIS project).
Using 2008 data, the next view shows the population that voted as a percent of the total population 18 years and over.
The new 2009 population 18 years and over estimates and several measures relating to 2008 voter participation have been added to the State Trends shapefile
dataset and GIS project (see State Trends).
Specific data fields available are listed in the GIS project section.
Install and use the no-fee, no registration State Trends GIS Project on your computer.
See installation details. Create different thematic map views. Zoom, label, capture for your presentations.
GPS & Large Scale Data Collection (010610a)
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GPS & Large Scale Data Collection (010610a)
The role of Global Positioning System (GPS) in Census 2010 may have had some useful impact,
but it was short lived.
Handheld computers (HHCs) using GPS were used to support address canvassing in the fall of 2009.
The HHCs will not be used for any future Census 2010 activities.
GPS technology was an attractive means for the Census Bureau to more accurately geographically code the
location of housing units for Census 2010 -- and related statistical programs.
The Census Bureau has undertaken significant efforts to comprehensively and accurately determine the locations of all housing units
in the U.S. for Census 2010.
Accurate location of housing units is essential for purposes of tabulating respondent data to
census blocks and higher level geography.
If a housing unit is even shown to be on the wrong side of the street, those data may become tabulated as a part of an incorrect
census block and thus potentially incorrect congressional district summary data, as one example.
Even hundreds of such inaccuracies could lead to significantly population counts.
GPS seemed to offer a better and even less expensive way to improve housing unit location data by providing
more exact latitude-longitude coordinates for each unit.
The 140,000 HHCs were part of the Census Field Data Collection Automation (FDCA) program involving a broader computer and
communications network, devices that may have no further role/use. Starting with the dress rehearsal in 2007, field staff experienced wide ranging problems ...
issues with transmission, the device freezing, mapspotting (collecting mapping coordinates)
and difficulties working with large blocks. U.S. Government Accountability Office (GAO) reports detail the nature of issues
and usage of the HHCs and GPS into the fall 2009. See
d10140t and
d10132t.
Knowing Your School District Community (010510a)
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Knowing Your School District Community (010510a)
School districts, defined by individual states, cover the U.S. pretty much wall-to-wall.
I had the opportunity to develop the first set of national scope school district boundary files in connection with
the 1990 Census School District Special Tabulation.
Since 1990, school district boundaries have changed radically in a few states.
While most school districts have not had a boundary change since 1990, most have changed significantly in terms of demographic make-up.
Most of us think about school districts in terms of the schools and students.
We might be less familiar with the "school district community."
While some school districts are coterminous with county or city boundaries, most are not.
A very large number of school districts overlap in various ways with sometimes several counties and cities.
In such cases, the boundary of your school district might be less familiar than say your county or city boundary.
The graphic at right shows McKinney ISD, TX (blue boundary) in context of multi-colored places.
This example shows that McKinney ISD overlaps McKinney place (orange) but does not serve parts of McKinney place/city.
The school district community is the total set of residents within the school district boundary.
This population includes the grade relevant children attending schools in the district, their families, and the
broader population not directly engaged with the district or schools.
See recent estimates for your school districts of interest
(these data are used for U.S. Department of Education Title I allocations).
Some might say ... "I have no kids in these schools, why should I have to pay property taxes for this school district"?
One important answer is that all residents of the school district benefit from a strong and vibrant K-12 school system.
The K-12 school system/district can be the entity that knits-together otherwise diverse neighborhoods.
The more that is invested, and not just in a financial way, the more the benefits that are derived.
Businesses looking to locate in your area always look at the K-12 schools.
A strong school district community is good for business development and aides in maintaining or growing economic prosperity.
Learn about the demographics of your school district community. View an interactive Google map showing your school district
boundary ... example for McKinney ISD.
See this additional information about school district communities;
view characteristics of your school district community.
Hmong: Murky Area of Race versus Ethnicity (010410a)
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Hmong: Murky Area of Race versus Ethnicity (010410a)
Clint Eastwood's movie Gran Torino gave new visibility to Hmong Americans.
Is Hmong a race or an ethnicity? It seems that almost universally, except for the U.S. Federal statistical system, that
Hmong is an ethnicity.
The Census Bureau treats the Hmong population as a race. Following guidelines set by the U.S. Office of Federal Statistical Policy,
now located in OMB, Federal statistical data only identify one ethnicity, Hispanic.
China considers Hmong to be a part of the Miao ethnicity group, one of 56 ethnicities.
Most consider Hmong to be a population culture now centered in Laos and other adjacent countries.
Even for the greatly simplified Census 2010
questionnaire (relative to Census 2000 and prior decennial censuses),
the matter of checking the right boxes and writing in the correct information -- to self-identify yourself as Hmong -- is not simple.
The Laotian American National Alliance provides extensive instruction on how to complete the questionnaire.
See details.
The Hmong population is one example among many others. As major race and ethnicity permutations are examined,
the number of distinctive groups easily gets into the thousands. Some argue that race and ethnicity do not matter, why even collect such data.
It is important - for many reasons. But one key reason is the self identification with the relevant culture/ethnicity.
"Where (geographically) are we, what are our characteristics, how are we changing ..." a few, typical prevailing questions that need answers.
Bono and Intellectual Property (010310a)
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Bono and Intellectual Property (010310a)
Bono offers a Top 10 List for the 2010's in the January 3, 2010 New York Times... 10 ideas that might make the next 10 years more interesting, healthy or civil.
An awesome, creative performer and more, Bono provides views on important patterns for the 2010 decade.
At the top of the list is "Intellectual Property Developers" (IP).
Not surprisingly, the IP focus is pretty much on the audio and video type IP in the high tech era .. and what's happening.
What about data as an IP? The very patterns and trends that Bono addresses are only known (beyond media anecdotals) because someone (many someones) developed
(created) data identfying what is occurring.
English Language Proficiency (010210a)
One time advisor and economics professor Walter Johnson (University of Missouri-Columbia) would evangelize the importance of English.
If there was one course to excel in, make it English.
English language proficiency is a cornerstone of communication,
business, and the economy. Data on English language proficiency equip us to better understand
'where we are' and help enable us to develop plans to identify
problems areas and improve on English language proficiency.
Much of the challenge, and opportunity, to make improvements lies within the realm of K-12 schools and school systems.
How to assess the state of English language proficiency (ELP)
in the context of small area geography and on a national scale ... and how it is trending? This information can
benefit individual communities, schools and neighborhood stakeholders as well as developing and managing national scope
business and education operations. To assess ELP for an area and how it is changing requires data.
It turns out that we are pretty limited in what we know about English language proficiency.
Decennial census
(Census 2000)
data do not tell us about ELP for those who
speak only English. In addition, census data are respondent-based.
Data about language proficiency is in the view of the respondent.
More ...
What Makes an Outstanding K-12 School? (010110a)
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What Makes an Outstanding K-12 School? (010110a)
What set of factors create a lively learning environment and experience in a high school ... making it an outstanding school?
Knowing what these factors are helps us locate schools that are outstanding,
and differentiate them from those that have fewer opportunities.
It can also help parents and other stakeholders compare their school to schools that are considered outstanding.
You can help create change by offering examples from what is being done elsewhere.
Looking at the metrics of your school, and comparing those metrics to schools that are outstanding enables parents and stakeholders to set goals and objectives to move toward
better performance.
Not all factors that contribute to a school’s excellence are within reach of parents to improve the school.
Here are some examples:
• ability to impact availability of funds for teaching and instructional resources.
• change in classrooms, buildings, grounds and physical infrastructure.
• economic distress in an area can limit employment opportunities and impact families.
In the "The Washington region's schools are many splendored things" (
12-31-2009, Washington Post) by Jay Mathews, he observes
it is the "critical mass of parents and educators of varied backgrounds
who are determined to create the lively learning environment" that creates outstanding schools.
While this is a "facilitating and enabling criteria" that can foster a school being outstanding,
there are many other factors that are part of the mix.
Here are some examples:
• the specific set of students, their abilities and their individual motivations.
• the specific set of teachers and their instructional resources.
• impact of grade relevant children not enrolled.
• socioeconomic attributes of the "school district community."
In a sequel to this topic, we will examine creating school-by-school "outstanding index measure," and develop
a national scope ranking table (see other currently available ranking tables).
One of the classic limitations to Web-based GIS tools is the inability to add your own data.
OTM4 has the excellent feature of being able to download shapefiles for use with desktop GIS applications.
I added the downloaded jobs points shapefile to my existing project file, having my own data layers, and generated the following
view. The project/view includes K-12 schools (blue triangle markers).
The identify tool is used to click on one point (see pointer) and display a miniprofile. The profile shows the census block geocode
and number of 2008 jobs. 
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
In map views between 1900 and 1940, most areas that show as gray (data NA) are because data are not available for the counties as currently defined.
Several states were not yet formed in 1900 ... AZ(1912) AK(1989) HI(1959) NM(1912) OK(1907).
Most counties formed by 1940 in the 48 contiguous states remain the same as today. However, there are some notable county boundary changes since 1940 such as
Yuma County, AZ being split into La Paz County and Yuma County.
