Category Archives: Schools

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.

Examining School District Demographic-Economic Patterns

.. the importance of understanding the demographic-economic make-up and trends for school districts can hardly be overstated. Community and educational challenges and opportunities are shaped by demographic-economic dynamics. Only by knowing “where we are” can we develop the most effective plans for improvement.

This section reviews tools, resources and methods that you can use to access, integrate and analyze demographic-economic data. The U.S. national scope ACS 2014 (released December 2015) School District Demographic-Economic Dataset contains approximately 600 subject matter items tabulated for each school district organized into four subject matter groups:
  • General Demographics
  • Social Characteristics
  • Economic Characteristics
  • Housing Characteristics
See similar interactive tables for: Census Tracts | ZIP Codes | State, Metro & County.

These data provide information and insights not available by examining data of students and schools alone — or any other data. See more about the importance of these data. Data are based on the American Community Survey (ACS)2014 5-year estimates for school districts defined as of the 2013-14 school year.

Patterns of Educational Attainment by School District
This view shows percent population 25 years and over with bachelor’s degree by school district; Texas and south central U.S. The thematic pattern shows item S067 shown in the interactive table. Click graphic for larger view, more detail and legend color/data intervals. This map illustrates the relative ease to gain insights into school district patterns using geospatial data analytics tools.

– View developed using CV XE GIS and related GIS project.

Using GIS software and project datasets, you can create zoom-in views, label geographic objects, add your own data, select different subject matter, change intervals/colors and perform a wide range of geospatial analyses.

Get a Custom Map for Your Area of Interest
Use this form to request a no fee map graphic similar to the one shown above for a state of interest. Enter the request with state name in the text section; e.g., “Requesting school district pattern map for Illinois.”

Using the Interactive Table
Use the interactive table in this related section to view, query, rank, compare social characteristics of the population among a set of school district — or view characteristics of a selected district.

The following graphic illustrates use of the interactive table; click graphic for larger view. This view shows school districts in the Dallas, TX metro ranked in descending order on item S067 (Percent bachelor’s degree or higher). Highland Park ISD has the highest value of 83.1%.

Try it for a metro of interest — get metro 5-character code here. Go to the Social Characteristics table, then:
  • Click ShowAll button below table.
  • Click AvgHHSize… button below table.
  • Paste the 5-character metro code in the edit box to right of CBSA> button.
    … overwriting the value 19100.
  • Click the CBSA> button.
  • Click the S067 column header; click again to sort in other direction.
    … Done!

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using school & school district geography and using demographic-economic data.  Learn more about integrating these data with other geography, your data and use of data analytics that apply to your situation.

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.

Mapping Statistical Data

.. GIS tools & data resources that you can use for statistical mapping & visual data analysis … Geographic Information Systems (GIS) provide flexible and powerful capabilities to combine maps with data. In our increasingly data rich environment, we often experience “drowning in data.” GIS tools can help harness disparate and voluminous data and assist with data linkage. This section provides links to other sections that provide information on no cost GIS software and “production” GIS projects and datasets that you can use.

Patterns of Per Capita Personal Income Change 2008-14 by County
— relative to U.S. PCPI 2008-14 change
To illustrate, the following graphic shows patterns of per capita personal income change 2008 to 2014 by county relative to the U.S. See more information. Click graphic for larger view with legend and additional details. Make variations of this map view using resources described in this section. Optionally integrate your own data.

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

GIS provides us with a way to improve collaboration; we can more easily comprehend and understand geographic relationships and patterns among “variables” and statistical data. As we reduce tabular data to visual representations, we are better able to communicate “what the data are telling us” among stakeholders and teams/committees. This second dimension, learning what the data are telling us, provides the power of creating insights for more effective decision-making.

Mapping Statistical Data Topics
Most applications presented in this section involve use of Windows-based desktop GIS software. The software and GIS project files and datasets are installed on your computer. These resources are available for use by members of the User Group at no fee.  Click a link below to view additional details about a topic of interest.  There you find a description of the scope and use of the data/geography, steps to access and use the GIS projects/datasets and getting started tutorials.
World by Country
U.S. by State
U.S. by Congressional District
U.S. by Metropolitan Area
U.S. by County
U.S. by City/Place
U.S. by ZIP Code Area
State by Census Tract (each/all states)
State by Block Group
State by Census Block
K-12 Schools & School District Data Analytics

Applications make use of a range of statistical data from the Federal Statistical System, and other sources, integrated with shapefiles from the Census Bureau TIGER/Line shapefiles, OpenStreetMaps, and other sources.

Join me in a Data Analytics Lab session to discuss accessing, integrating and using these resources … and linking these data/geography with other data that relate to your situation.

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.

Researcher & Story Development Tools & Resources

Innovative and results-oriented story writing often requires geographic, demographic, economic and related data. Research results and stories must increasingly use graphics and visual renderings of data  to be effective, retain reader attention and be responsive to needs. A picture is worth a thousand words. In our expanding everything-integrated world, stories must draw on a range of authoritative data resources that can be knit-together.

Journalists and authors require easy and access to these data in a consumable form. Researchers and students share these needs. Grant writers benefit by including relevant data in proposals and well as unique maps and graphics. Economists and analysts creating interpretative summaries of statistical data releases (“how does this impact us?”) require related data and tools to effectively communicate conclusions and inferences.

This section reviews access to a few of these tools and resources that are available to researchers and story writers — as well as tools to map and otherwise visualize these data. These resources offer the customization or specific detail often not available to meet specific needs. Most of the resources reviewed here are available at no fee. Some resources are available only to ProximityOne User Groupmembers. Join the User Group now. See terms of use. See related Web section.

Navigating the Federal Statistical Resources
… topics below include these and a broader set of resources.
http://proximityone.com/fss.htm

Interactive Geographic-Demographic-Economic Tables
… view, query, rank, compare attributes for many types of geography.
http://proximityone.com/rankingtables.htm

Interactive Location-Based Demographic-Economic Data Tool
… key in address, immediate display of ACS demographic-economic data.
… block group, tract, place, county, state data; latitude-longitude, geocodes.
http://proximityone.com/location_based_demographics.htm

Custom Mapping Tools (Windows software tool)
… create custom maps for proposed service areas.
CV XE GIS

Address Code Your Data (Windows software tool)
… show your address/location data on maps.
http://proximityone.com/apigeocoder.htm

Chart Graphics; Population Pyramids (Windows software tool)
… age-cohort chart graphics for your county or school district.
http://proximityone.com/chartgraphics.htm

Demographic-Economic Data Extraction Tool (Windows software tool)
… use this API-tool to extract your selected subject matter data.
… census block, block group, other geographic levels
… Census 2000, Census 2010, ACS 2010, 2011, 2012, 2013
… create your own files for use with Excel/other software.
http://proximityone.com/dede.htm

Custom maps can enhance your proposal; make custom map graphics:

Make Custom Congressional District Maps
… create custom maps for individual or custom grouped congressional districts.
… CD 113 and CD 114 boundaries are the same, based on maps submitted by states to the Census Bureau.
http://proximityone.com/cd113_maps.htm

Make Custom City Maps
… create custom maps for cities of interest; examine in context of other geography.
http://proximityone.com/citymaps.htm

Make Custom Metro Maps
… create custom maps for metropolitan or micropolitan statistical areas.
http://proximityone.com/metromaps.htm

Make Custom Neighborhood Maps
… create thematic/pattern maps; access related demographic-economic data by neighborhood.
http://proximityone.com/neighborhood_patterns.htm

Make Custom Block Group Maps
… create custom maps for small, sub-neighborhood areas.
http://proximityone.com/mapping_bg.htm

Make Custom Census Block Maps
… create custom maps for areas by census block – smallest geographic area with detailed demographics.
http://proximityone.com/mapping_census_blocks.htm

America’s Communities Program
… demographic-economic profiles for individual cities.
http://proximityone.com/acp.htm

School District Community Profiles
… demographic-economic multi-part profiles for individual school districts.
http://proximityone.com/sddep.htm

School District Characteristics
http://proximityone.com/sddmi.htm

K-12 Public Schools Characteristics – individual and all schools
http://proximityone.com/k12publicschools.htm

K-12 Private Schools Characteristics – individual and all schools
http://proximityone.com/k12privatecchools.htm

Charter Schools Characteristics & Patterns – individual and all schools
http://proximityone.com/sch1314_charter.htm

County Population Trends; Annual Projections to 2020 by Age Group
… population trends profiles for individual counties … how is school age population changing? 65 & over?
http://proximityone.com/outlook2020.htm
most recent county official estimates – click link in table
county trend profile – example for Cook County, IL; all counties available

Metropolitan Area Characteristics
… geographic & demographic composition profiles for individual metros.
Current Vintage Metropolitan Areas
2015 Updates: New and Modified Metros
Metropolitan Area Median Income and Housing Value: 2013-14

State Legislative District Characteristics
… geographic & demographic composition profiles for individual state legislative districts.
http://proximityone.com/sld2013.htm

Congressional District Characteristics
… geographic & demographic composition profiles for individual congressional districts.
http://proximityone.com/cd113.htm
114th Congressional Districts: Median Income and Housing Value: 2013-14

Census Tract Demographic-Economic Patterns
Main Census Tracts section interactive tables includes all tracts:
General Demographics | Social Characteristics | Economic Characteristics | Housing Characteristics

ZIP Code Demographic-Economic Patterns
Main ZIP Code section … interactive tables include all ZIP code areas:
General Demographics | Social Characteristics | Economic Characteristics | Housing Characteristics

More about ProximityOne Demographic-Economic Projections
Outlook 2020 | Outlook 2030 | Outlook 2060 | Quarterly 3 year
• integrated multi-sourced Situation & Outlook demographic-economic data

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.

Charter Schools: Characteristics & Patterns

.. some might say that traditional public schools have a monopoly on K-12 education. Monopolies tend to stifle competition. Without competition, existing K-12 schools and infrastructure might tend to lag, and offer declining or poor quality educational opportunities. Traditional K-12 schools infrastructure might be inhibited due to the impact of unions and perhaps less than optimal HR operations. It might be argued that such situations more typically exist in areas with lower economic prosperity. Can charter schools improve the competitive framework among K-12 schools? Can charter school also improve on the quality of educational opportunities served?

Charter schools are schools that provide free elementary and/or secondary education to eligible students under a specific charter granted by the state legislature or other authority. This section provides information on the approximate 6,400 charter schools identified by the U.S. Department of Education for the 2013-14 school year. See the more detailed related Web section.

Charter Schools, 2013-14
Using GIS tools, it is easy to see the geographic distribution of charter schools and how some counties/sub-state regions have concentrations. America’s 6,934 charter schools (red markers) as of the 2013-14 school year.

— view developed using CV XE GIS; click graphic for larger view showing more detail.

Charter Schools, 2013-14, Houston Region
The following view illustrates a zoom-in view of the Houston, TX region. Charter schools (red markers) are shown in context of school districts (black boundary, yellow label. It is easy to see the relative few charter schools in Spring Branch ISD (see pointer) compared to Alief ISD and Houston ISD

— view developed using CV XE GIS; click graphic for larger view showing more detail.

Additional Selected Views
Chicago, IL
Dallas, TX
Florida
Los Angeles, CA
Washington, DC
New York, NY

Selected Characteristics
All K-12 Schools
• All K-12 schools with enrollment: 96,307
• Enrollment of all K-12 schools: 50,195,195
Charter Schools
• Charter schools with enrollment: 6,375
• Enrollment of all charter schools:
– Total population enrollment: 2,519,245
– Hispanic population (of any race) enrollment: 755,696
– Black, non-Hispanic population enrollment: 682,421
– White, non-Hispanic population enrollment: 878,375
• Enrollment of charter schools with 50+ enrollment (5,986): 2,507,498
• Charter schools having 12th grade enrollment: 2,276
• Charter schools having 20 or more 12th grade enrollment: 1,648
• Charter schools with Free/Reduced Lunch Fee participation: 5,908
• Charter schools Title I Eligible (Public Law 103-382): 4,451

Next Steps
Can charter school improve on the quality of educational opportunities served? Data and tools reviewed here can help answer that and related questions. Contact me about using these resources. There is no broad, blanket conclusion. It is somewhat a case-by-case, district-by-district, situation that needs to be examined. Weak and under-staffed/under-funded charter schools cannot alone lead to improvement.

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.

Voting District Geography & Demographics

.. voting district, or election precinct, demographics are typically hard to acquire, making analysis of voting districts a challenging task. Voting district analysis is of interest for many reasons. Voting districts are the lowest common denominator for most election areas — from city council to the U.S. House of Representatives. This section illustrates how voting district (VTD) geography can be examined in context of other geography and how voting district demographics can be analyzed using GIS resources. These applications make use of Fairfax County, VA,
part of the Washington, DC metro, but can be developed for most counties/areas across the U.S.

Voting Districts & Neighborhood Patterns
The following view uses the Fairfax County GIS project to examine voting districts (black boundaries) in context of neighborhood economic prosperity. The current vintage VTD shapefile (213 VTDs) was obtained from the County. This view shows median household income by census tract. Using the GIS layer editor, different types of demographic-economic subject matter (such as educational attainment, housing value, language spoken at home …) could be used.

… Click graphic for larger view. View developed using CV XE GIS.

Voting District Scope and Vintages
As of Census 2010, there were 177,808 VTDs covering the U.S. While shapefiles for Census 2010 vintage VTDs are available as a part of the TIGER geographic database, most of these areas have now been updated owing to redistricting and the 2012 and 2014 elections. VTD boundaries can change frequently. While Census 2010 demographic data were tabulated for Census 2010 VTDs, these demographics of less interest to analyzing post-redistricting scenarios due to the changing VTD geography.

Voting District Drill-down Demographics
The following view uses the same Fairfax County GIS project to examine census block demographics by voting district. The graphic shows a zoom-in view focused on the Sherwood VTD in southeast Fairfax County. Using the CV XE GIS site analysis tool, all census blocks are selected within the Sherwood VTD (cross-hatched). A subset of blocks could have been selected to examine just part of the VTD. The table to the right of the map shows the aggregated total population and housing units for this VTD. As of Census 2010 there were 1,380 population in this VTD. Other demographic attributes, such as population by age, gender, or race/origin could be integrated into the shapefile using data sourced from Census 2010 Summary File 1.

… Click graphic for larger view. View developed using CV XE GIS.

There are 27 census blocks that comprise the Sherwood VTD. Using the View File button in the above operation, these 27 census block records can be viewed using the CV XE GIS dBrowser tool. A partial view of the records is shown below. This file can be exported for use with other software.

… Click graphic for larger view. View developed using CV XE GIS.

VTDs and Schools & School Attendance Zones
Using the GIS project, the attendance zone and schools layers can be checked/shown. School locations can be examined by VTD; VTDs intersecting attendance zones can be examined. What VTDs are relevant to which schools/attendance zones?  What is the demographic composition of these VTDs?  The following view shows high schools (red markers) and high school attendance zones (red boundaries).  Note in the legend to the left of map view, that different types of schools and attendance zones can be viewed in wide-ranging combinations. Other types of geography can be added to the mix such as voting districts.  The VTD/precincts layer is not shown in the following view so that the view of schools and attendance zones is not obstructed.  It can be added to the view by clicking the checkbox by the Precincts layer in the legend panel to the left of the map view.

… Click graphic for larger view. View developed using CV XE GIS.

VTDs and One Person, One Vote
In May 2015, the Supreme Court agreed to consider redefining ‘one-person, one-vote’ principle. See USATODAY story. How might this ruling impact election precinct geography?

Issues to be examined in upcoming sections include determining the size of the voting age population by VTD and the size of the citizen voting age population. These attributes could be examined at the block group level of geography, not reviewed here. See additional information on the citizen voting age population.

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.

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.