Tag Archives: Urban Rural

Navigating the U.S. Federal Statistical System

.. an overview of the Federal statistical system and how to access data  .. the U.S. Federal Statistical System offers a vast array of diverse data resources that are useful in wide-ranging planning and analytical applications. Many of these data resources, such as census block level demographics from the decennial census, are unique in scope and content; in many cases there are no alternative data resources.

But there are issues/challenges for the data user to navigate the Federal Statistical System. Examples … the Bureau of Labor Statistics releases total employment data by county quarterly and monthly through multiple programs. The data values differ, for methodological reasons, but the net result can be confusion. The Census Bureau releases total employment data from many statistical programs by county both annually and more frequently. Where are these alternative total employment data and how can they be accessed? How do these various measures differ and which data are right for my situation? This section provides basic statistical program information. Subsequent updates will provide more detail.

This section provides an overview of the U.S. Federal Statistical System (FSS) and information that can help stakeholders navigate access to selected types of data produced by the FSS. While the FSS is focused on agencies that collect, develop and make available statistical data, there is a broader set of data and resources that relate to accessing and using these data. As technology and related data analytics resources have evolved, access to and use of these data is closely associated with the development of geographic data by Federal statistical and other agencies and Geographic Information Systems (GIS).

The FSS is a decentralized set of agencies that collect, develop and make available statistical and geographic data. The OMB Office of Statistical Programs and Standards (SPS) provides a FSS coordinative role. The SPS establishes statistical policies and standards, identifies priorities for improving programs, evaluates statistical agency budgets, reviews and approves Federal agency information collections involving statistical methods, and coordinates U.S. participation in international statistical activities.

While the FSS spans more than 100 agencies, the 13 “principal statistical agencies” have statistical work as their principal mission. Excluding funding for the decennial census ($919.3 million requested for the Decennial Census for FY 2016), approximately 38 percent ($2,486.9 million of the $6,486.6 million total proposed for FY 2016 President’s budget request) of overall funding for Federal statistical activities (of the Executive Branch) provides resources for these 13 agencies. The principal statistical agencies include:
Census Bureau (Commerce)
Bureau of Economic Analysis (Commerce)
Bureau of Justice Statistics (Justice)
Bureau of Labor Statistics (Labor)
Bureau of Transportation Statistics (Transportation)
Economic Research Service (Agriculture)
Energy Information Administration (Energy)
National Agricultural Statistics Service (Agriculture)
National Center for Education Statistics (Education)
National Center for Health Statistics (CDC/HHS)
National Center for Science and Engineering Statistics NSF/Independent
Office of Research, Evaluation, and Statistics — SSA/Independent
Statistics of Income (IRS)

The remaining 62 percent of the FY 2016 budget involves more than 100 programs that conduct statistical activities in conjunction with another program mission. These statistical programs are components within a Federal department or other agency. They include a broad set of centers, institutes, and organizations in addition to the 13 principal statistical agencies.

There are also Federal agencies whose statistical activities are not part of the Executive Branch. These agencies include the Congressional Budget Office, which develops and applies projection models for the budgetary impact of current and proposed Federal programs; the Federal Reserve Board, which compiles the widely used Flow of Funds report and other statistical series and periodically conducts the Survey of Consumer Finances; and the U.S. Government Accountability Office, which uses statistical data in evaluations of government programs.

Guide to Navigating the Federal Statistical System
The following graphic is a snapshot of the Guide to Navigating the Federal Statistical System. See http://proximityone.com/fss.htm to access the entire guide.

– click graphic for larger view.

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

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

State and Regional Decision-Making Information

Organized on a state-by-state basis, use tools and geographic, demographic and economic data resources in these sections to facilitate planning and analysis. Updated frequently, these sections provide a unique means to access to multi-sourced data to develop insights into patterns, characteristics and trends on wide-ranging issues. Bookmark the related main Web page; keep up-to-date.

Using these Resources
Knowing “where we are” and “how things have changed” are key factors in knowing about the where, when and how of future change — and how that change might impact you. There are many sources of this knowledge. Often the required data do not knit together in an ideal manner. Key data are available for different types of geography, become available at different points in time and are often not the perfect subject matter. These sections provide access to relevant data and a means to consume the data more effectively than might otherwise be possible. Use these data, tools and resources in combination with other data to perform wide-ranging data analytics. See examples.

Select a State/Area

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C.
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Topics for each State — with drill-down to census block
Visual pattern analysis tools … using GIS resources
Digital Map Database
Situation & Outlook
Metropolitan Areas
Congressional Districts
Counties
Cities/Places
Census Tracts
ZIP Code Areas
K-12 Education, Schools & School Districts
Block Groups
Census Blocks

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

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

Analyzing County Health Patterns

.. as the population ages, migrates and otherwise changes, health status and healthcare needs change by location, type and in other ways. Data on health status, characteristics and trends continue to become more available, particularly at the county geographic level … but these data are often difficult to locate, integrate and use in a combined manner.

VA Hospitals/Facilities in Context of Urban/Rural Areas
The following graphic illustrates how Veterans Administration hospitals and facilities (red markers) can be viewed in context of urban/rural patterns. Urban areas are shown with orange fill pattern. The Appalachia 405 county area is shown with black bold boundary. Use (GIS) resources to examine additional patterns such as the distribution of veterans by census tract based on the American Community Survey (ACS) data.

— view created using CV XE GIS and associated county health GIS Project
— click graphic for larger view showing details.

County Health Analytics
This section provides an overview of accessing, integrating and analyzing demographic, economic and health data with a focus on county and sub-county geography. Geographic information system (GIS) tools are used to visually and geospatially analyze health-related patterns and characteristics. Applications reviewed here are developed using the CV XE GIS software and associated U.S. national scale health GIS Project. See more detail in the related Web section.

The County Health Patterns GIS project includes data from:
• ProximityOne CountyTrends and Situation & Outlook
… view individual county population & components of change trends
… click county link in this interactive table
• Robert Wood Johnson Foundation County Health Rankings
Appalachian Regional Commission economic status
• Other sources. See additional information

Additional ProximityOne ready-to-use shapefiles could be added containing all data from the American Community Survey demographic-economic profiles. The same scope of subject matter, annually updated, is available at the ZIP code, census tract, county and other geography. See related interactive tables (four related web sections) for subject matter details.

The CV XE GIS software is used with the County Health Patterns GIS project to develop views/applications shown below. These views/applications illustrate how the health analytics resources can be used. Select from wide ranging alternative measures.

Patterns of Population Change — %Change 2010-2014 — U.S. by County
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset.

— view created using CV XE GIS and associated county health GIS Project
— click graphic for larger view showing details.

Site Analysis & Patterns of Population Change
— %Change 2010-2014 — Houston Metro Area
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset. This view also illustrates use of the Site Analysis tool to aggregate and display population by year 2010 through 2014.

— click graphic for larger view showing details.

Patterns of Population Change
— %Change 2010-2014 — Missouri Area Counties
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset. This view also illustrates use of the Metros layer to show outlines of Missouri metropolitan statistical areas (bold red/brown boundary). Counties are labeled with the 2014 population estimate.

— click graphic for larger view showing details.

Patterns of Percent Smokers — U.S. by County
The following view shows patterns of percent smokers by county using the County Health Rankings RMD layer/dataset. Choose from a list of wide-ranging health-related subject matter items.

— click graphic for larger view showing details.

Patterns of Food Insecurity — U.S. by County
The following view shows patterns of food insecurity by county using the County Health Rankings AMD layer/dataset. Choose from a list of wide-ranging health-related subject matter items.

— view created using CV XE GIS and associated county health GIS Project
— click graphic for larger view showing details.

Patterns of Food Insecurity — Appalachia Region
The following view shows a zoom-in of the above view.

— click graphic for larger view showing details.

Patterns of Economic Distress — Appalachia Region
The following view shows patterns of economic distress based on an index developed by the Appalachian Regional Commission. Economic characteristics of an area can have a direct impact on health and well-being and access to healthcare resources.

— click graphic for larger view showing details.

An upcoming section will review more detail about analyzing regional health and healthcare issues with drill-down to census tract and other sub-county geography.

Join us in a Data Analytics Web Session where we review and discuss use of tools and resources like those covered in this section.

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

K-12 Schools Data Analytics

.. resources to analyze patterns and trends .. how to improve K-12 education opportunities and outcomes? How might the K-12 improvements impact your community? Higher education? What resources and methods can be used to better understand where we are … and where we are going? How can we use data to better understand patterns, where and how change will occur … and how change will impact us? Have we got the best/right data to answer the right questions? Data Analytics can help K-12 schools, school districts, leadership and stakeholders answer these questions and better achieve visions and goals.

This section provides illustrative data analytics views and application examples using a K-12 school district Geographic Information System (GIS) project and related datasets. The McKinney ISD, TX school district, located in the Dallas metro area, is used. A similar data analytics project/fileset can be developed for any school district. The applications use the ProximityOne CV XE GIS software. Most of the files and layers used in the GIS project are described here. Statewide GIS base K-12/community projects/datasets are available for many states; seeMissouri

The ProximityOne Data Analytics team develops the GIS project and related datasets. The software and data are electronically installed on the school/school district/community computers. Knowledge of how to develop the datasets and GIS project are not required by school/school district staff. Specialized staff are not required to operate the software, use the GIS tools and data or perform data analytics applications. Having staff available with these skill sets can extend productivity and results from use of the resources.

McKinney ISD in Context of Counties/Region
McKinney ISD shown with bold black boundary. McKinney city shown as hatched area. Urban census blocks: orange fill pattern; understanding urban/rural status of geography important for many reasons. See more about K-12 schools and urban/rural geography.

  — view created using CV XE GIS and associated LAES GIS Project
— click graphic for larger showing details.

Zoom-in View of McKinney ISD & K-12 Schools
See school by type/level in legend at left of map. View, compare, query, rank K-12 schools of interest using this interactive table. Extensive school level data are integrated and accessible but not shown in these views.

  — click graphic for larger showing details.

Students Shown by Red Markers
Student markers/locations are added to the GIS project by first geocoding the student address data. A shapefile is created and added to the GIS project. Extensive student level data (demographic, performance, other) are integrated and accessible but not shown in these views. Optionally place a query on the students layer to view/analyze those meeting a certain condition such as attending a specific school, enrolled in a certain grade/grade range, having specified test results, etc.

  — click graphic for larger showing details.

Elementary School Zones
See ES Zone layer (blue boundary) at left in legend panel; choose any/all zones. School zone boundaries can facilitate analysis of students attending associated school. The GIS project can facilitate analysis of alternative redistricting plans and enrollment distributions change.

  — click graphic for larger showing details.

Further Zoom-in View
ES Zones labeled with ES Zone name; census block layer turned off (unchecked). The ability to easily navigate across geography or add detail can create visual pattern analysis not possible using tabular data.

  — click graphic for larger showing details.

Using Site Analysis; Focus on Walker ES Zone
Using the Site Analysis operation, a query placed on student layer so only students attending this school are shown (red markers). The CV XE Site Analysis tool used to select a small group of students (cross-hatched in circle). See summary in grid at right; total students 51; 5 students are Hispanic. In the grid, the total number of students is shown with the name “Weight” — the field name of the database item used to dynamically total/sum/count the number of students. In this example, the value of the weight field is always 1 (thus when summed it shows the total number of students). The field could be set to values varying by individual student for a measure such as performance or participation.

  — click graphic for larger showing details.

Using Site Analysis; Focus on Walker ES Zone
Counting all students enrolled in Walker ES; 532 students; 69 Hispanic.

  — click graphic for larger showing details.

Using Site Analysis; Focus on Walker ES Zone; Further Zoom-in 
This view shows all students; note some are not selected as they are residents of Walker ES zone but do not attend that school. Census block layer has been checked on; census blocks are labeled with Census 2010 population.

  — click graphic for larger showing details.

Thematic Pattern Map; Gini Coefficient by Block Group; McKinney ISD Region
Patterns of income inequality as shown by Gini Coefficient by census block group. Block groups are the smallest geography for which richer demographic-economic data are available (from ACS). See more about Gini Coefficient and Income Inequality.

  — click graphic for larger showing legend details.

Patterns of Economic Prosperity by Census Tract
View shows Median Household Income by census tract. Approximately 74,000 census tracts, averaging 4,000 population, cover the U.S. wall-to-wall. The graphic shown below illustrates integrating census tract/neighborhood level demographic-economic data from American Community Survey with attributes of students, schools and other geography.

  — click graphic for larger showing legend details.

Examining Demographic-Economic Characteristics for a Study Area — Study Area Part 1
The view below shows use of the Site Analysis tool to select a set of 5 census tracts in the vicinity of McKinney Boyd HS (blue triangle marker). Any number of tracts can be selected, contiguous or otherwise. The subject matter items to be summarized are D001_13 (total population), D002_13 (male) and D002_13 (female) — ACS 2013 5-year estimates. More or different items could have been selected. The grid at lower right shows aggregated (across 5 tract) values for these three items.

  — click graphic for larger showing legend details.

Database Operations — Export/View Tract Dataset for Study Area — Study Area Part 2
The view below shows the tracts dataset records in a CV XE grid/spreadsheet based on the above Site Analysis operation. This dataset extract is generated, and grid populated, when the View File button is clicked in the Site Analysis operation (see in Part 1 graphic at right). This grid displays the records selected in the above operation. These selected data records can optionally be exported for use with other software.

  — click graphic for larger showing legend details.

Demographic-Economic Profiles for Study Area — Study Area Part 3
Summary demographic-economic profiles are generated for the above 5 census tract study area by clicking the Report button in the Site Analysis operation (see Part 1 graphic at right).
View the McKinney Boyd HS Area 1 Analysis Reports/HTML profiles generated:
General Demographics (DEP1)more about these data; interactive table
Social Characteristics (DEP2)more about these data; interactive table
Economic Characteristics (DEP3)more about these data; interactive table
Housing Characteristics (DEP4)more about these data; interactive table

Patterns of Percent Children in Poverty by School District
The graphic shown below illustrates using visual analysis tools to compare/contrast school district characteristics in region/state. View, compare, query, rank school districts of interest using this interactive table. Data used in the graphic shown below are derived from the from ACS 2013 5-year estimates.

  — click graphic for larger showing legend details.

Children’s Demographics & Living Environment
Most demographic-economic data are developed for the whole population in an area. Data from the annually updatedACS School District Tabulation can help analysts and leadership better understand demographic-economic characteristics of children, and gain insights into needs, in a school district. See the McKinney ISD Children’s Demographic-Economic Profile by Universe of Enrollment.

Predictive Analytics; Demographic-Economic Projections
ProximityOne develops demographic-economic estimates and projections for individual school districts and component area geography such as census tracts. These data help schools and school districts examine how enrollment and the population in the district might change over the next several years. Projections are developed in several ways: by single year of age by gender by race/origin, by type of enrollment (public school, private school, not enrolled), and by demographic-economic characteristics.

Roads & the Digital Map Database
The street/road network used in the GIS project is from an augmented version of the TIGER digital map database. Each street/road segment runs from intersection to intersection creating opportunities for routing and transportation management. The CV XE GIS identify tool is used to click-on a street segment (see pointer). A mini-profile for this segment is displayed as shown in the graphic. The mini-profile shows that this is the 5500 block of Petunia Dr.

  — click graphic for larger showing legend details.

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

ZIP Code Urban/Rural Demographic Patterns

More than half of U.S. ZIP Code areas are 100% rural. Nine ZIP Code areas have 100,000 or more population. ZIP Code area 10028 (Manhattan), 0.3 square mile, has density of 143,683 population per square mile. ZIP code demographics are widely used in analysis largely due to familiarity of ZIP code locations by code and availability of related data, such as address data. The Census 2010 ZIP Code Tabulation Areas (ZCTAs) are generalized geographic representations of U.S. Postal Service ZIP Code service areas. ZCTAs can provide insights into urban/rural patterns within and among ZIP Code areas. See related Web section for more details and data access.

Of the 33,140 ZCTAs identified in Census 2010 Summary File 1, 5,236 ZCTAs were 100% urban and 17,033 ZCTAs were 100% rural. The remainder have some mix of urban/rural population. Use the interactive table in this section to examine Census 2010 ZIP Code Areas by population and housing units by urban/rural status. Land area, water area and population density are also shown.

ZIP Code Urban/Rural Visual Analysis
The graphic below shows ZIP 75034 (see also in ranking table below) located in Frisco ISD, TX area in Dallas, TX metro. ZIP Code areas red boundaries; school districts black boundaries; urban schools green markers; rural schools orange markers; urban areas orange fill pattern. It is easy to see what parts of ZIP 75034 are urban (orange) versus rural (no fill pattern).
Click graphic for larger view and details. View developed using CV XE GIS and related GIS project. See related section on K-12 schools by urban/rural status.

ZIP Code Area Urban Rural Interactive Table
The following graphic shows the ZIP Code areas with largest population as of Census 2010. See interactive table in the related Web section.

Click graphic for larger view.

Related ZIP Code Demographics Interactive Tables
General Demographics .. Social Characteristics .. Economic Characteristics .. Housing Characteristics

More about ZIP Code Areas

Support Using these Resources
Learn more about demographic economic data and related analytical tools. Join us in a Decision-Making Information Web session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants.

ZIP Code Demographics: Asian/Urban-Rural Patterns

Understanding the size, characteristics and distribution of the Asian population is important to Asian community stakeholders as well as business, government and organizations that cater to Asian population interests.  The Asian population is distributed unevenly throughout the U.S. As of Census 2010, and among Metropolitan Statistical Areas (MSAs), the Asian Indian population ranged from a high of 6.6 percent of the total population in the Yuba City, CA MSA to just 44 persons in Farmington, NM MSA.  The New York MSA has the largest number of Asian Indians (526,133).  Examine the Asian population distribution by type and metro using this interactive table. See more about Asian race groups.

This section reviews use of tools, data and related resources to examine the distribution of the Asian population by ZIP code area and in context of urban/rural geography. Create your own map views similar to those shown in this section. Zoom to locations of interest. Modify Asian percent population settings. Label ZIP codes areas in your preferred manner. Add your own related data. See details below.

Asian Population by ZIP Code & Urban/Rural Status
Patterns of percent Asian population by ZIP Code in Houston, TX area are shown in the graphic presented below. The map shows ZIP code areas that have certain percentages of population who are Asian. The orange fill pattern shows urban areas (defined by census block). Areas with no orange fill pattern are rural blocks. See legend at the left of map for details.

ZIP code areas with highest percent Asian population (see pointer) are shown with red crosshatch pattern.
• Click graphic for larger view, zoom-in & ZIP Codes as labels.
• Click this link to view zoom-in, isolating ZIPs with 30% or more Asian.

Other Selected Areas …

Atlanta Area: Asian Population by ZIP Code & Urban/Rural Status
  Orange fill pattern shows urban areas.
  See ZIP code area legend in map view at top of section.

Washington DC: Asian Population by ZIP Code & Urban/Rural Status
  Orange fill pattern shows urban areas.
  See ZIP code area legend in map view at top of section.

New York City: Asian Population by ZIP Code & Urban/Rural Status
  Orange fill pattern shows urban areas.
  See ZIP code area legend in map view at top of section.

San Francisco: Asian Population by ZIP Code & Urban/Rural Status
  Orange fill pattern shows urban areas.
  See ZIP code area legend in map view at top of section.

Los Angeles: Asian Population by ZIP Code & Urban/Rural Status
  Orange fill pattern shows urban areas.
  See ZIP code area legend in map view at top of section.

Using these Resources on Your Computer
Members of the ProximityOne User Group may use the CV XE GIS software and associated GIS project to develop map views like those shown in this section. Join now. No fees.

Support & DMI Web Sessions
Learn more about using resources described in this section. Join us in a Decision-Making Information Web session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants. We can address specific questions about tools to visually analyze patterns.