Monthly Archives: December 2013

Census Tract Demographic-Economic Interactive Tables

Goto ProximityOne   New U.S. national scope census tract demographic-economic interactive tables are now available.  These tables include approximately 600 subject matter items derived from the American Community Survey 2012 5-year estimates released in December 2013.  Census tracts are subdivisions of counties covering the U.S. wall-to-wall and average approximately 4,000 population.

The tables are organized into four related sections:
• General Demographics
• Social Characteristics
• Economic Characteristics
• Housing Characteristics

Use the interactive ranking tables to view, query, rank, compare demographic-economic characteristics of the population and housing.  A scroll box is provided for each section that lists each of the subject matter items available for each area in the table.

Importance of these Data
These data provide “richer” demographic-economic characteristics for national scope census tracts. While Census 2010 provides data similar to those items in the General Demographics section, only ACS 2010, 2011, 2012 sourced data provide details on topics such as income and poverty, labor force and employment, housing value and costs, educational participation and attainment, language spoken at home, among many related items. The approximate 600 items accessible via the tract dataset are supplemented by a wide range of additional subject matter. ACS 2013 census tract data become available in December 2014.

Occupation-Industry Employment Projections

Goto ProximityOne   For the United States overall, electrical engineers are projected to grow from 306.1 thousand in 2012 to 318.7 thousand in 2022 — an increase of 12.6 thousand or 4.1%. These “demand-driven” projections, new in December 2013, will different from actual employment experienced in 2022 in any given metro or area. Will metros of interest experience a workforce shortage in key highly skilled occupations? How might this impact the regional economy? Cost of labor?

Electrical engineer projections are shown in the following graphic relative to other engineering occupations ranked on projected employment in 2022.

This section is focused on accessing and using these new, very detailed, occupational and industry employment projections for the United States to 2022.  Gain insights into future employment conditions using these resources. Which occupations will growth fastest?  How will change in employment for an occupation be distributed by industry?

Employment Projections Interactive Table
Use this interactive table (link to separate page) to query, view, rank, compare employment projections.  The table shows employment projections by occupation. Click a link in the table to view the projected employment by industry for the selected occupation.

Fastest Growth Occupations
The following graphic illustrates use of the interactive table.  All detailed occupations are ranked in descending order based on change between 2012 and 2022.  Personal care aides increase from 1,190.6 thousand in 2012 to 1,771.4 thousand in 2022 — an increase of 580.5 thousand or 48.8%
Use tools below the table to perform queries on occupations of interest.  Rank on a column of interest by clicking header cell.  Click a link in the Code column to view the distributions of a selected occupation by detailed industry.

Using these Projections & Alternatives
The employment projections in the table are based on December 2013 projections developed by the Bureau of Labor Statistics (BLS).  The projections are provided for 479 detailed occupations and summarized into occupational groups.  These demand-driven projections differ from the ProximityOne Situation & Outlook (S&O) employment projections in several ways. The BLS projections are national level only and provide data for only 2012 and 2022; they are updated every two years.  The S&O projections are available to the county level (less detail), provide annual data to 2030, and are updated quarterly and annually.

The S&O projections are developed using holistic cause and effect simultaneous equation models and reflect expected, projected employment levels.  The BLS projections are based on assumptions about the overall economy growth and do not include extensive cause and effect determinants.  While the BLS projections may provide good demand-based employment conditions, the employment levels may not materialize due to shortages and other factors affecting the supply of labor.

Next Steps
Join us in the next Situation & Outlook quarterly briefing session on January 16, 2014.  We will cover 2030 state and regional demographic-economic patterns and trends based on the latest developments.  See additional details at

Small Area Disability Demographics

Goto ProximityOne   People with disabilities bring unique sets of skills to the workplace, enhancing the strength and diversity of the U.S. labor market. They make up a significant market of consumers, representing more than $200 billion in discretionary spending and creating technological innovation and entrepreneurship. People with disabilities also often rely on various government interventions to maintain their participation in the community. Demographic data about people with disabilities help stakeholders better understand needs and make more informed decisions relating to a wide range of topics concerning people with disabilities.

New Disability Demographics
Disability demographics from the American Community Survey (ACS) have been available for cities, counties and larger areas with population over 65,000 for a few years. New as of December 2013, are ACS 2012 5-year (ACS0812) disability demographics available for all cities, school districts, counties and geographies down to the census tract level — and ZIP code area. Disability demographics are often “masked” when analyzed for larger population areas. Masked, not in the sense of suppressed data, but that concentrations that might be identified at the census tract level may become less prominent when viewed in the aggregate of county or higher level geographies. This section reviews disability-related ACS0812 data available for these small area geographies and how they can be used to facilitate decision-making.

Disability Concepts
Subject matter categories about people with disabilities from ACS generally involve limitations with vision, hearing, cognitive, ambulatory, self-care or independent living difficulty.

• Vision — blindness or serious difficulty seeing even when wearing glasses
• Hearing — deafness or serious difficulty hearing
• Cognitive — difficulty concentrating, remembering or making decisions
• Ambulatory — serious difficulty walking or climbing stairs
• Self-care — difficulty bathing or dressing
• Independent living — difficulty going outside to shop or visit a doctor’s office

Visual Analysis of Disability Patterns by Census Tract
The following view shows the population ages 5-17 years with disabilities (ACS0812 estimates) by census tract for Harris County, Texas (Houston area). The inset legend shows color patterns associated with data from Table B18101 (see below).
The above view was developed using the CV XE GIS software with a GIS project. The GIS project includes a county by census tract layer with Table B18101 (see below) integrated subject matter. This particular view shows patterns of the sum of items B18101007 (males age 5-17 years with disability) and B18101026 (females age 5-17 years with disability). View all items in this table by opening excel file B18101 in the section below. Members of the ProximityOne User Group may download and use this project to develop similar views on their computer. Add other geographies to the view such as school districts or cities. Add your own data from any source. Join the User Group now, no fee.

Disability Subject Matter Data/Tables
View the scope of ACS 2012 5-year estimates subject matter using the interactive table at Sort on the rightmost column and scroll to Disabilities. These same tables are listed below. Click a link on the table number to view a sample of the data (an excel file will open). All data tables are provided for Houston ISD (HISD), Texas school district. The same scope of data are available for any school district.

Table B18101 shows that there are more than 9,000 K-12 school age children with disabilities in Houston ISD (3,493 females, 6,133 males). Using the additional Table B18101 iterations, the distribution of this population can be examined by race and origin. Of course, school age children without disabilities can be impacted by other household members that do have disabilities. Demographics provided in these tables show characteristics for the total population and many age groups. Tables B18102 through B18107 provide insights into the number of persons by age and gender by type of disability. Table B18135 provides data on health insurance coverage. Employment status and workforce data are provided by Tables C18120 and C18121. Earnings and poverty characteristics data are provided by Tables B18140, C18130 and C18131.

ACS 2012 Disability Tables
B18101 — Sex by Age by Disability Status
B18101A — Age by Disability Status:  White alone
B18101B — Age by Disability Status:  Black or African American alone
B18101C — Age by Disability Status: American Indian and Alaska Native alone
B18101D — Age by Disability Status: Asian alone
B18101E — Age by Disability Status: Native Hawaiian and other Pacific Islander
B18101F — Age by Disability Status: Some other race alone
B18101G — Age by Disability Status: Two or more races
B18101H — Age by Disability Status: White alone, not Hispanic or Latino
B18101I — Age by Disability Status: Hispanic or Latino
B18102 — Sex by Age by  Hearing Difficulty
B18103 — Sex by Age by Vision Difficulty
B18104 — Sex by Age by Cognitive Difficulty
B18105 — Sex by Age by Ambulatory Difficulty
B18106 — Sex by Age by Self-Care Difficulty
B18107 — Sex by Age by Independent Living Difficulty
B18135 — Age by Disability Status by Health Insurance Coverage
B18140 — Median Earnings in the Past 12 Months
C18108 — Age by Number of Disabilities
C18120 — Employment by Disability Status
C18121 — Work Experience by Disability Status
C18130 — Age by Disability Status by Poverty Status
C18131 — Ratio of Income to Poverty Level Past 12 Months by Disability

Accessing & Using the Data
Access the above tables using the CV APIGateway. Integrate the disability-related data with other demographic-economic data from ACS 2012 and other data sources. Save data for multi-geography such as all census tracts for a county. Merge these data into a county by tract shapefile and use the CV XE GIS software to visually examine small area patterns of the population with disabilities.

State Population Projections 2030

Goto ProximityOne   Knowing more about what will change when, where and by how much … the U.S. total population increased from Census 2000 281.4 million to Census 2010 308.7 million (9.7 percent). ProximityOne projections show how the U.S. population changes from 2010 to 2060:
• 363.7 million in 2030, a 55.2 million increase from 2010 to 2030 (17.9%)
• 417.7 million in 2060, a 109.2 million increase from 2010 to 2060 (35.4%)

How will population change manifest itself by state, metro, region, county and city? By age and household composition? Use the demographic estimates and projections interactive table to view/rank/compare population change from 2010 to 2060 for the U.S. overall, states, metros and counties. Analyze area patterns and trends and assess how areas of interest relate to each another.

Estimates and projections shown in the table below are a part of a broader set of annually updated current demographic-economic estimates and projections developed by ProximityOne. The estimates and projections are developed using models that knit together a mix of historical birth, death, migration, economic and other data and assumptions.

Population Projections to 2030 and Change by State (scroll section)
Area 2010 Population 2030 Population Change
United States 308,498,560 363,686,916 55,188,356 17.9
Alabama 4,775,157 5,499,905 724,748 15.2
Alaska 708,006 919,640 211,634 29.9
Arizona 6,391,655 7,855,522 1,463,867 22.9
Arkansas 2,908,610 3,462,622 554,012 19.0
California 37,252,610 44,533,261 7,280,651 19.5
Colorado 5,024,675 6,163,650 1,138,975 22.7
Connecticut 3,573,914 3,976,633 402,719 11.3
Delaware 897,843 1,078,635 180,792 20.1
District of Columbia 601,723 764,371 162,648 27.0
Florida 18,798,709 22,392,839 3,594,130 19.1
Georgia 9,674,458 11,910,320 2,235,862 23.1
Hawaii 1,360,211 1,650,029 289,818 21.3
Idaho 1,563,079 1,898,594 335,515 21.5
Illinois 12,820,706 14,549,119 1,728,413 13.5
Indiana 6,474,071 7,440,376 966,305 14.9
Iowa 3,033,554 3,478,730 445,176 14.7
Kansas 2,841,928 3,398,309 556,381 19.6
Kentucky 4,327,238 4,998,884 671,646 15.5
Louisiana 4,528,569 5,445,280 916,711 20.2
Maine 1,327,355 1,350,773 23,418 1.8
Maryland 5,772,717 6,893,977 1,121,260 19.4
Massachusetts 6,547,431 7,470,365 922,934 14.1
Michigan 9,877,689 10,833,205 955,516 9.7
Minnesota 5,295,168 6,136,486 841,318 15.9
Mississippi 2,959,442 3,484,847 525,405 17.8
Missouri 5,977,429 6,794,888 817,459 13.7
Montana 984,485 1,133,910 149,425 15.2
Nebraska 1,817,904 2,154,780 336,876 18.5
Nevada 2,699,291 3,208,465 509,174 18.9
New Hampshire 1,316,156 1,380,056 63,900 4.9
New Jersey 8,791,405 10,252,175 1,460,770 16.6
New Mexico 2,057,118 2,438,390 381,272 18.5
New York 19,376,232 22,414,984 3,038,752 15.7
North Carolina 9,530,122 11,589,035 2,058,913 21.6
North Dakota 668,272 894,071 225,799 33.8
Ohio 11,530,391 12,677,688 1,147,297 10.0
Oklahoma 3,747,249 4,573,147 825,898 22.0
Oregon 3,829,080 4,401,617 572,537 15.0
Pennsylvania 12,698,190 13,732,718 1,034,528 8.1
Rhode Island 1,052,353 1,139,601 87,248 8.3
South Carolina 4,622,889 5,593,128 970,239 21.0
South Dakota 808,660 1,007,980 199,320 24.6
Tennessee 6,338,970 7,433,347 1,094,377 17.3
Texas 25,126,449 31,972,276 6,845,827 27.2
Utah 2,761,145 3,741,353 980,208 35.5
Vermont 624,483 636,650 12,167 1.9
Virginia 7,991,736 9,775,166 1,783,430 22.3
Washington 6,722,621 8,210,522 1,487,901 22.1
West Virginia 1,846,843 1,976,190 129,347 7.0
Wisconsin 5,681,229 6,273,275 592,046 10.4
Wyoming 561,340 695,132 133,792 23.8

The following graphic shows the largest states by projected 2030 population and population change.  Use the separate interactive table to develop this view and examine patterns among other states.  This view was developed by clicking the “View 2010-2020-2030 Change” button below the table and then clicking the Population 7/1/30 column header cell to rank the table on this column in descending order.  Try this operation yourself using the interactive table.

U.S. by State Population: 2010-2020-2030; Ranked on 2030 Projection

Differences between ProximityOne and Census Bureau Projections
The Census Bureau only develops population projections for the U.S. as a whole; there are no post Census 2010 Census-sourced state level projections.  For both current estimates and projections, the Census Bureau uses a “top-down” approach.  For example, Census will release the 2013 U.S. national estimates in late December 2013.  The corresponding 2013 state and county estimates will be released in early to mid-2014, the state estimates controlled to the U.S. estimates and the county estimates controlled to the state estimates.  The most forward looking Census-sourced state and county population data will be for 2013.  Development of Census-sourced population estimates, and the national level population projections use a demographic-only model; there is no direct integration of the economy and business/economic factors.

In contrast,  the ProximityOne projections are developed with a “bottom-up” methodology using cause and effect simultaneous equation models. Projections are first developed using county-level demographic-economic models. County level projections are developed by single year of age by gender by race/origin and then aggregated to state and national levels. The entire projection period extends annually to 2060. View Broward County, FL projections showing more detail. See complete dataset example for Sedgwick County (Wichita), Kansas (XLS).   The holistic modeling does not separate the process of developing current estimates versus projections; the cause and effect relationship is specified over time.

Examining Impact of Demographic Change
An important role for population projections in context of decision-making information is to help us better anticipate where, when and how change will take place — and how it might impact market or service areas and business operations.   To best examine impact analyses, the geographic focus needs to be on the relevant market or service area (e.g., city, group of counties, etc.) geography.  Projections change over time; use of alternative scenario projections is essential.  The ideal approach to impact analysis — how will my markets be impacted in 2030? — is best achieved through through fully integrated cause and effect models and not using only separately developed demographic-economic estimates and projections.  Users of the Situation & Outlook modeling tools can perform fully integrated impact analyses under alternative scenarios.

Next Steps
Join us in the next Situation & Outlook quarterly briefing session on January 16, 2014.  We will cover 2030 state and regional demographic-economic patterns and trends based on the latest developments.  See additional details at

Linguistic Isolation Patterns

Goto ProximityOne  Size and distribution data on speakers of languages other than English and on their English speaking ability are important for many reasons. These data help us understand where populations with special needs exist and how they are changing. The data are used in a wide-ranging legislative, policy, and research applications. Many legal, financial and marketing decisions involving language-based issues make use of data on language use and English-speaking ability.

This post reviews data useful to analyze “household linguistic isolation” based on American Community Survey (ACS) 5-year estimates at the block group geographic level. The same scope of subject matter is available for higher level geography.  The following graphic shows patterns of linguistic isolation in Queens County, NY.  Block groups colored in red have more than 50-percent of households where no household member age 14 years and over speaks English “very well”.

Patterns of Linguistic Isolation; Queens County, NYli_queens

One definition of a “linguistically isolated household” is a household in which all adults have substantial limitation in communicating English. In the ACS data, a household is classified as “linguistically isolated” if 1) no household member age 14 years and over spoke only English, and 2) no household member age 14 years and over who spoke another language spoke English “very well”.

Like many demographic measures, linguistic isolation tends to be “masked” when analyzing data for larger geographic areas, even census tracts, are used. Block group geography provides an ability to locate linguistic isolation in sub-neighborhood areas.

Census Block Groups sit in a “mid-range” geography between census blocks and census tracts. All cover the U.S. wall-to-wall and nest together, census blocks being the lowest common denominator for each. Block Groups (BGs) are the smallest geographic area for which annually updated American Community Survey (BG) data are tabulated.

Advantages of using BG geodemographics include the maximum degree of geographic drill-down (using ACS data) … enabling the most micro-perspective of demographics for a neighborhood or part of study area. A disadvantages of using BG estimates is that typically the smaller area estimates have a relatively higher error of estimate.

Language Spoken by Households
The table presented below shows data from ACS Table B16002 Households by Linguistic Isolation for block group 1 in census tract 046300 in Queens County (081) New York (36); geoid=360810463001. This block group is shown in the above map at the pointer. Data for this block group are shown in the rightmost column of the table below. 62.8 percent of households (610) are linguistically isolated (232+60+91).

Table B16002. Household Language by Households
Item Code Item Description Households
B16002001 Total 610
B16002002   English only 12
B16002003   Spanish language 321
B16002004     No one 14 and over speaks English only or speaks English “very well” 232
B16002005     At least one person 14 and over speaks English only or speaks English “very well” 89
B16002006   Other Indo-European languages: 60
B16002007     No one 14 and over speaks English only or speaks English “very well” 60
B16002008     At least one person 14 and over speaks English only or speaks English “very well” 0
B16002009   Asian & Pacific Island languages: 217
B16002010     No one 14 and over speaks English only or speaks English “very well” 91
B16002011     At least one person 14 and over speaks English only or speaks English “very well” 126
B16002012   Other languages: 0
B16002013     No one 14 and over speaks English only or speaks English “very well” 0
B16002014     At least one person 14 and over speaks English only or speaks English “very well” 0

“Language Spoken” categories are based on four major language groups.

Next Steps
Use the CV APIGateway to access Table B16002 and related data for block groups in cities or counties of interest.  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.

ACS 2012 BlockGroup Data

Goto ProximityOne  There are several things to like about the new American Community Survey 2012 (ACS2012) 5-year demographic-economic data (available 12/17/13). These data are one year more recent data than released in December 2012 (the ACS 2011 5-year data).  As a result, they provide an updated and more current picture. Two, this is a third year sequel to having Census 2010 vintage census tract and block group data available.  This de facto three year mini-time series enables a start to examine trends. Three, these estimates are centric to mid-2010 and thus roughly comparable to what would have been “richer demographics” from Census 2010 (had the long-form not been eliminated).  This enables a rough comparison between 2000 and 2010 (it will be the best opportunity ever).  These data provide unique and powerful measures that facilitate development of decision-making information.  The ACS 5-year block group estimates are the smallest geographic areas for which the Census Bureau develops richer demographic-economic data such as income, educational attainment, employment, housing value among a wide range of related items.

The focus of this post is on block group level data … demographic-economic data tabulated for approximately 220,000 areas averaging 1,200 population covering the U.S. wall-to-wall.  Block groups are one of many geographic levels/areas for which ACS 5-year estimates are tabulated.  While it might seem easy to determine what subject matter data are available at the block group level, it is not easy.  Block group data cannot be accessed via the Census Bureau FactFinder online data access tool, so that presents the first challenge.  Fortunately, the ACS block group data can be accessed using the ProximityOne CV APIGateway tool.

Washington DC Area; Median Household Income by Block Group

Is unemployment data tabulated by block group?  Language spoken at home?  If so, what are the corresponding table numbers? Only selected tables are available at the block group level — a situation unique to the block group level geography.  Presently, the only way to determine availability of a subject matter item or data table is to view Appendix E of the technical documentation.  In that section, listing all tables, there is a column with an indicator showing whether or not the table is available at the block group level.

Struggling with this situation in years past, we have developed this Web page that contains an interactive table to facilitate determining subject matter availability by block group.  The interactive table contains a row for each data table for which ACS 2012 5-year data are tabulated.  In the interactive table, there is a column with an indicator showing whether or not that data table is available at the block group level.  This way all of the data tables can be browsed as well as those only available at the block group level.

Below the interactive table a few tool buttons are available.  Now you can do a keyword search on all tables for specific subject matter words, like “Language Spoken.”  Or, show the whole table and click the block group button to view only those tables available at the block group level.

ACS 2012 Public Use Microdata Sample (PUMS) Data
Soon there be related post focused on the ACS 2012 Public Use Microdata Sample (PUMS) data.  That post will also provide improved data navigation and locator tools.

County Monthly Workforce Trends

Goto ProximityOne  Keep up-to-date with current county monthly workforce patterns and trends using the CV APIGateway. Automatically updated with multi-sourced demographic-economic data, the APIGateway Integrated Profile (IP) provides monthly workforce updates from the Bureau of Labor Statistics (BLS) Census of Employment and Wages (place of work data) and Local Area Unemployment Statistics (LAU – place of residence data). This section is focused on data and analytical tools to examine county workforce trends and patterns using the Local Area Unemployment Statistics data.

A unique and important property of the LAU data is that they are the only source of semi-official data available on a national scale for each/all counties and larger places providing current and monthly data on unemployment and the unemployment rate. The next runner-up to these estimates are the ACS 5 year estimates that are a) for a five year period and b) at least 3 years old (to the mid-point of the estimate period) when they become available. These highly perishable and time-sensitive economic measures are important to understanding the relative and absolute strength or weakness of workforce conditions and the employment sector on a local and regional basis.

The table presented below is a section taken from the APIGateway IP for Santa Clara County, CA (San Jose-Cupertino metro). The monthly/annual LAU data shown in the table are available for each/all counties. The APIGateway IP shows these four workforce series from 2011 forward. It auto-updates monthly with only a two-month lag to the reference time period. View a series as a chart: click graphic; on new page click a link shown in the table. Optionally set a wider time interval.
Santa Clara County, CA Monthly Labor Force Characteristics

Using GIS Resources for Visual Pattern Analysis
Separate from the APIGateway IP, the 2008-2012 annual time series data have been integrated into a U.S. by county shapefile. The graphic shown below illustrates how these tools can be used to examine the change in employment conditions by county since the recession started. This view shows the change in the unemployment rate from 2008 to 2012. Counties with red coloring experienced/are experiencing/ a 3-percent or more increase in the unemployment rate in 2012 compared to 2008. Using this measure alone, it is easy to see the extent to which the economy in many areas is still struggling to compensate for the recession impact and exactly which county/regional areas are impacted and how much. While one county might be prospering, many adjacent or nearby counties are not. View zoom-in to Santa Clara county area.

Interactive Table
Click the graphic shown below to use this interactive table to examine this same set of annual workforce data 2008-2012. Viewing the interactive table, click ShowAll button below table and then FindInName with default value “Santa Clara” to view the 5-year series for the same scope of subject matter as shown above in monthly form. The interactive table provides a convenient structure to view, rank, compare counties using these measures.


Next Steps
The U.S. by county shapefile with workforce data shown in the table and used to develop maps shown in this section are available to ProximityOne User Group members. (join now, no cost). Use these resources to develop wide-ranging variations of this view and analyze tabular data on your own computer; integrate your own data; add other types of geography; zoom-in to specific metros/regions. Visit the CountyTrends Web section for more information on county demographic-economic data and analytics.

School Attendance Zone Demographics

Goto ProximityOne  Elementary-secondary education … wide-ranging decision-making is made possible by visually examining K-12 school catchment areas with related geography and demographic-economic measures. Needs of the school age population can be better analyzed resulting in possible improved educational outcomes. Characteristics of the population served by a school, and its special needs, can be better understood. Awareness of the number of students in areas hit hard by natural disasters could aid relief efforts; the Department of Homeland Security can use this information for planning disaster relief programs in case of a national disaster. The data can aid school districts in distributing funds to schools within its system.

National scope School Attendance Zones (SAZ) boundary files and related demographics are now being developed.  This post reviews use of SAZ boundary files and related subject matter data for areas served by individual schools. SAZ resources are used by schools, school districts, researchers, policy makers and other stakeholders to examine relationships between areas served by schools and related geographic areas (e.g., city/county). Based partly on data developed by the U.S. Department of Education, these resources are now available and will be expanded during 2014. See related Web page with more detail.

Using SAZ Boundary Files & Related Geodemographics
The following views illustrate use of the SAZ boundary files with related geography and associated demographics. The CV XE GIS software is used to develop these views and corresponding GIS project fileset are available to ProximityOne User Group members (join, no fee). These applications illustrate how point location and geographic area attributes, multi-sourced data, can be integrated and geospatially analyzed.

Montgomery County Schools MD Area School Attendance Zones
Part of the Washington, DC metro, the school district boundary is shown by  pointer.  A query has been placed on the SAZ layer so that only elementary SAZ boundaries are shown (red boundaries).

By turning on the school marker layer, schools are shown by blue markers.
A query has been placed on the schools layer so that only elementary schools are shown.

The next view is a zoom-in to the area shown by the pointer in the above graphic.  One SAZ label (yellow) shows the SAZ name. A second SAZ label (white) shows the total population.  The demographic label could be any of hundreds of subject matter items available. The Kemp Mill school marker is clicked showing a profile for the school (partial view).

Characteristics of SAZ Demographics in Adjacent Districts
The next graphic shows SAZ boundaries for Alief ISD, TX and adjacent Houston, TX area school districts. This view illustrates that the same types of school/SAZ analyses as in Maryland can be applied elsewhere in the nation due to the use of standardized approaches to the geography and demographics. A peer group of SAZs might be comprised of SAZs with common attributes but in different school districts — nearby or far away. These resources enable school district leadership, analysts and stakeholders to better understand SAZ demographic attributes.

SAZ Demographics
The scope of SAZ subject matter is similar to that provided now in school district ranking tables and profiles:
• General Demographics —
• Social Characteristics —
• Economic Characteristics —
• Housing Characteristics —

School District Decision-Making Information
See more about data, analytical tools and resources to examine school district characteristics and patterns at

Geocodes & Data Linkage

Goto ProximityOne  Data linkage is a dominant challenge to developers and users of decision-making information — how to get data from one source linked to data from another source for integrated data viewing or analysis.  A simple example is to link census tract data from Census 2010 with census tract data from American Community Survey 2012 5 year estimates.  These data are differently sourced, so the starting place is having a dataset from each source program.  To link the datasets for analysis involves using the common key field, or geocode, between the datasets and creating one merged dataset.

Geocodes are structured handles that uniquely identify a geographic area.  While geocodes might be sometimes be viewed as an alternative name for a geographic area, without them data linkage between differently sourced data can quickly become impossible.  This section reviews the structure and use of some commonly used geocodes developing and using decision-making data.

A geocode is defined as a structured character string containing no spaces that uniquely associates with a geographic area.  In the case of the two census tract datasets, the geocode would be the 11 character string comprised of the FIPS state code (2), FIPS county code (3) and Census 2010 census tract code (6).

The graphic below shows an area in Honolulu County (county FIPS code 003), Hawaii (state FIPS code 15).  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.  The tracts are labeled with only the 6-character tract code; but the 6-character tract codes are only unique within a county.

The full Census 2000 census tract geocode for the area labeled “001902” is 15003001902.  The full Census 2010 census tract geocode for the area labeled “001904” is 15003001904.  Note the geocode has no identification or attribute identifying the vintage of the area (e.g., 2000 versus 2010).  The following graphic illustrates the relationship between these geocodes.

FIPS Codes and Census Geocodes
Federal Information Processing Standard (FIPS) codes have long been a standard for associating/defining geocodes for geographic areas widely in use in Federal programs.  The FIPS codes have been widely adopted outside of government applications, becoming somewhat universal.  FIPS codes do not cover key Census geography including census tracts, block groups and census blocks.

ANSI Geocodes
American National Standards Institute codes (ANSI codes) are standardized numeric or alphabetic codes issued by the American National Standards Institute (ANSI) to ensure uniform identification of geographic entities through all federal government agencies.  ANSI has taken over the management of geographic codes from the National Institute of Standards and Technology (NIST). Under NIST, the codes adhered to the Federal Information Processing Standards (FIPS). ANSI now issues two types of codes. They continue to issue the commonly used FIPS codes, although the acronym has now changed to Federal Information Processing Series, because it is no longer considered the standard. They also issue the Geographic Names Information System (GNIS) Identifiers, which were established by the United States Geological Survey (USGS). Links to FIPS codes, which are the codes most commonly used by the Census Bureau, most other Federal statistical agencies, and thus most non-government data developers/users.

Relating Geography: SF1 header
Census geocodes for the decennial census can be determined in a geographic-relationship manner using the Summary File 1 (SF1) geographic header record/file.   This datasest contains a record for each census tabulation block.  In each record, the corresponding array of associated geocodes, pertaining to where that block is located, are provided (e.g., for that block the census tract, city/place, school district, county, ZIP code area, congressional district, metro/CBSA, etc.).  As an example, it is possible to determine the geocodes for the city/places intersecting with a county or school district using this file.

The SF1 has the limitation that the codes are as of the decennial SF1 tabulation vintage.  As a result, the initial Census 2010 SF1 contains codes for the 112th Congressional Districts, 2009 vintage metros/CBSAs, and cities/places as of 2010.

Merging Subject Matter Data into Shapefiles
Shapefiles are often/typically developed in a manner that includes no subject matter.  For example, most Census Bureau shapefiles do not include any statistical data beyond geographic area (size) and latitude-longitude.  To merge demographic-economic data into a shapefile, thus enabling use of the shapefile to develop thematic pattern maps, requires linking the shapefile dbf (e.g., Census 2010 census tract boundary file) with a subject matter dbf (e.g., Census 2010 demographics or ACS 2011 demographics).  The subject matter dbf is linked to the shapefile dbf using a common key which is the geocode.   The geocodes must be identically defined and of the same vintage.

Next Steps
Join in one of the upcoming Web sessions on Using Shapefiles where applications that illustrate use of geocodes are reviewed.

DMI Mentoring Program

Goto ProximityOne  The Decision-Making Information (DMI) Mentoring Program (DMIMP) is an educational program entered into between Warren Glimpse of ProximityOne and DMIMP participants. The objective of the DMIMP is for participants to develop a certain skill/ability level using using DMI resources and methods. There is no fee for program participants.

Participants in the DMIMP may be of any academic level generally starting at high school junior. Most DMIMP participants are pursuing a graduate degree or engaged in post-doctorate research. Often the DMIMP focus in on a dissertation or research topic.  DMIMP participants must have an approved project(s) or application topic(s).

There is no specific time period for which DMIMP participants are “enrolled.” Typically the time period will be approximately one year. Participants receive DMIMP Certificate of Completion. Presently there are no transferable academic credit hours awarded. Following the award of the Certificate, a forum for continuing involvement, and engagement with peers, is provided.

The DMIMP is comprised of Web-based instructional components. DMIMP participants are enrolled in these instructional segments in a manner similar to online courses.

The DMIMP is focused on the intersection of these foundation elements:

Situation & Outlook database and modeling
Digital Map Database
Geographic Information System software

A customized program is developed for each DMIMP participant.  The program follows a general structure oriented to the participant’s application area(s).

For more information on the DMIMP, send a message and mention DMIMP in the text section.