School District Revenue & Expenditure Patterns, FY 2016

.. many school districts are adopting 4-day school weeks.  Part of the reason is shortage of funds.  The amount spent per student for public elementary-secondary education for all 50 states and D.C. increased by 3.2 percent to $11,762 during the 2016 fiscal year, based on new data from the Census Bureau released May 21, 2018. The increase in spending in 2016 was due in part to the increase in revenue across all 50 states and D.C. In 2016, public elementary-secondary education revenue, from all sources, amounted to $670.9 billion, up 4.6 percent from the prior year. This is the largest increase since 2007. Yet for many districts this is not enough.

This section provides access to tools and data to to examine K-12 school district finances — sources and uses of funds for FY 2016. The Census Bureau collects these data annually to meet to needs of the National Center for Education Statistics. ProximityOne restructures and integrates these data with other data for GIS/geospatial analysis using the CV XE GIS tools and School District GeoDemographic Information System (SDGIS).

View annual school district finances Web sections: FY 2014 .. FY 2015 .. FY 2016
• Use interactive table to examine school system finances
• Create/view profile for a district(s) of interest.

Current Spending per Student by School District, FY 2016
The following graphic shows patterns of current spending per student by school district, FY 2016, for Texas and adjacent areas. The four largest Texas metros are shown with the bold brown boundary; counties with gray boundaries. Color/fill patterns and corresponding values are shown in the inset legend. Click graphic for larger view showing a partial mini-profile for Houston ISD (at pointer in map).

– view developed using the CV XE GIS analytical tools.
– use these tools on your computer to examine these data & related geography/subject matter.

Data Analytics Web Sessions
Join me in a Data Analytics Web Session, every Tuesday, where we review access to and use of data, tools and methods relating to GeoStatistical Data Analytics Learning. We review current topical issues and data — and how you can access/use tools/data to meet your needs/interests.

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.
 

School District Demographic Trends: 2010-2016

.. while enrollment in many school districts is growing, for many it is declining — these include some of the largest districts. Declining enrollment in school districts can result in school closings that destabilize neighborhoods, cause layoffs of essential staff and concerns that the students who remain are some of the neediest and most difficult to educate. See related narrative.

Based on total population, the largest 10 school districts in 2016 (see table below), all experienced an increase in population over the period 2010-2016. Five of these districts had a decrease in school age population (ages 5-17 years). Five of these districts had a decrease in the number of related children in poverty in families ages 5-17 years.

See the related Web section that provides tools to analyze annual demographic data for each U.S. school district for the period 2010 through 2016. This post summarizes selected details. These data include Census Bureau official 2016 estimates available for all districts. Developed for use as inputs for the ESEA Title I allocation formula, the data have broader uses of interest to school district demographics stakeholders. The 2016 estimates were released in November 2016; 2017 estimates become available in late 2018. ProximityOne uses these data in combination with other data to develop school district current estimates and annual projections through 2022 with related drill-down demographic-economic subject matter. Use the interactive table in the Web section to view, rank, compare demographic characteristics of districts of interest.

Largest 10 School Districts based on 2016 Population Age 5-17

Patterns of 2016 School Age Population in Poverty by School District
The graphic below shows school districts with total 2016 population of 1,000 or more by poverty incidence. Markers show the population ages 5-17 in families in poverty as a percent of population ages 5-17. Salmon markers: 40-50%. Red markers: 50% or more.

– view developed with CVGIS software and related GIS project.

School District Demographic Trends Interactive Table
Use the interactive table to view, rank, compare demographic characteristics of districts of interest.

More About K-12 Education & Children’s Demographics
See the related section on School District Demographic Trends 2010-2016:
http://proximityone.com/sdtrends.htm.

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 L

Creating Custom School District Maps

…tools & data to map & geospatially analyze school districts. Ready-to-use state-by-state GIS projects may be downloaded enabling you to view and create custom maps almost instantly. Benefit from the power of using GIS software to perform tasks not available on Web-based mapping options. Use the latest school district and related shapefiles. See more information about using these resources in this related Web section.

Federal Revenue per Student by School District
Create views similar to the one shown below. Optionally combine layers as illstrated here by showing four Texas metros.

.. view developed with CV XE GIS and related GIS project.

Extending Reference and Analytical Possibilities

Texas by School District
Examine reference maps at the state, regional or local level. Optionally combine with roads/streets and other layers.

Patterns of Economic Prosperity by School District
Select from many ready-to-use demographic-economic subject matter items to create custom pattern views.

Drill-down — Houston Metro Area by School District
Zoom-in to a school district of interest. Set attributes of district as shown here.
–

County/School District
Visually examine the boundaries or school districts and counties. This view shows Harris County, TX area; select a county of interest.

Drill-down to Street Level
Add road/street and other layers. Drill-down within Fort Bend ISD, Houston metro, showing general earth surface features with streets layers. Mouse used to click on street (see pointer) and display mini-profile of street segment attributes.

Use for Analysis, Reference or in the Classroom
Schools and teachers: consider using these resources for classroom use. Familiarize students about how GIS resources can be used with a minimum of learning time and no cost. Enable students to use their own geography and adapt that learning to more general geography. See related Mapping Statistical Data ready-to-use GIS projects.

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.

National Children & Education Statistics Program Updates

.. NCES Program updates .. tools, data & methodology to examine national scope children & education .. school, school district & extended geographic-statistical data with drill-down to school and intersection level. See more about the NCES Program below.

New this Week
– ACS 2015 school district demographic-economic interactive tables
– view, compare, analyze selected/all U.S. school districts
– more focused blog updates coming soon.

School Districts with Highest Median Household Income
Use the interactive table to examine economic characteristics of school districts. Below is a list of the 10 school districts having the highest median household income developed using the Economic Characteristics interactive table. Develop similar views for metros and states of interest.

– ranked on item E062 — median household income.
– click graphic for larger view.

Use GIS tools to develop thematic pattern maps such as the one shown below with NCES GIS projects. Select from hundreds of statistical measures. Create your own regional;/district views. Integrate other data.

Patterns of Economic Prosperity by School District
– median household income (item E062 in table)

– view developed with CVGIS software & related GIS project and data.
– click graphic for larger view.

See the School Districts Economic Characteristics Interactive Table.

About the National Children & Education Statistics Program
The National Children & Education Statistics (NCES) Program provides access to tools, data & methodology to examine national scope children’s demographics & education-related characteristics. These resources enable stakeholders to view and analyze detailed geographic and statistical data at the school, neighborhood, community, attendance zone, school district and higher level geography. Integrate these data with drill-down demographic-economic data to the census block and intersection levels. Examine characteristics of schools, school districts and education data with related and higher level geography including urban/rural, cities, counties, metros, state and the U.S.

See NCES Main Section.

Contents: Summary of NCES Program Resources
Click a link to view more detail on a selected topic.
• Updates: New Resources, Events & Related Topics
• Analytics, Blogs, Studies
• Using Software Tools & Datasets
• 01 Mapping & Visual Analysis Tools
• 02 School District Annual Demographic-Economic Data Resources
• 03 Children’s Demographics & Living Environment by School District
• 04 School District Enrollment & Operational Characteristics
• 05 School District Finances: Sources & Uses of Funds
• 06 School District Geographic Size & Characteristics
• 07 School District-ZIP Code Area Relationship Table
• 08 K-12 Public Schools
• 09 K-12 Private Schools
• 10 K-12 Public School Attendance Zones
• 11 K-12 Public Schools by Urban/Rural Status
• 12 Census Tract Demographic-Economic Characteristics
• 13 Metropolitan Area Situation & Outlook Reports

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.

School District Demographic Trends: 2010-2015

.. data and tools to examine how school districts of interest are changing … based on total population, the largest 10 school districts in 2015, all experienced an increase in total population over the period 2010-2015. Five of these districts had a decrease in school age population (ages 5-17 years). Four of these districts had a decrease in the number of related children in families ages 5-17 years. See characteristics of districts in this interactive table. See the related Web section for more details.

School Districts with 2015 Population 100,000 or More
More than 600 of the total 13,245 school districts have a total 2015 population of 100,000 or more (red markers).

– view developed with CVGIS software and related GIS project.

Using New 2015 Estimates Released December 2016
– for use in 2017 ESEA Title I Allocations
Analyze annual demographic data for each U.S. school district for the period 2010 through 2015. These data include the Federal official 2015 estimates available for all districts. Developed for use as inputs for the ESEA Title I allocation formula, the data have broader uses of interest to school district demographics stakeholders. Use the interactive table in this section to view, rank, compare, query demographic characteristics of districts of interest.

The annual estimates for each school district include:
• total population
• number of children ages 5 to 17
• number of related children ages 5 to 17 in families in poverty

Using Interactive Data Tools
Use the interactive table to view, rank, compare, query ZIP codes based on a selection of demographic measures. The following graphics illustrate how the table can be used. Click graphic for larger view.

Total Population — 10 districts with largest change 2010-15
– ranked descending on rightmost column

– click graphic for larger view.

School Age Population — 10 districts with largest change 2010-15
– ranked descending on rightmost column

– click graphic for larger view.

Related Children Ages 5-17 in Poverty
– 10 districts with largest change 2010-15
– ranked descending on rightmost column

– click graphic for larger view.

Try it yourself. Use the table to examine a set of districts on your selected criteria in for a state/area of interest.

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.

K-12 School System Revenue & Expenditure Patterns

.. while we might first associate K-12 school systems with children and education, they are also an important part of the economy .. jobs, businesses and more. Total expenditure by public school systems, mostly school districts, was $613.6 billion in fiscal year 2014, up 2.6 percent from 2013. These data are based on the latest survey of public school systems conducted by the Census Bureau and sponsored by the U.S. Department of Education. This section provides an overview of these data and tools to access and view/analyze the data. See related full Web section for data access and more details.

Current Spending per Student by School District, FY 2014
… Houston metro area .. click graphic for larger view.

  … view developed using the CV XE GIS software.

.. click graphic to view all states table

Using the Interactive Table
Use the interactive table in the related Web section to examine sources and uses of funds in school districts of interest. The following graphic illustrates use of the table to view selected characteristics (select from many other items) of districts in the Houston metro. Districts are ranked on percent total revenue received from Federal sources. Click graphics for larger view.

Create Analytical Profiles for Districts of Interest
Follow steps in this related section to create analytical profiles for selected districts.

Illustrative comparative analysis profile for two districts
.. graphic shows partial view .. open full xls profile

Join me in a Data Analytics Lab session to discuss more details about accessing these data 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.

Texas School District Demographic Trends

.. new data, new insights .. in the 2015-16 school year, there were 21 school districts in Texas (of a total 1,025) with enrollment of 50,000 or more students. Among these districts, six districts experienced an enrollment decline between the 2011-12 and 2015-16 school years. Four districts experienced more than 10-percent increase in enrollment (Frisco, Katy, Conroe and Klein). Use the interactive table to view, rank, query and compare Texas school districts by annual enrollment, 2011-12 to 2015-16 , and change over the period. This section provides access to data analytics tools to examine patterns and characteristics of enrollment for Texas local education agencies. Use the GIS project and datasets described here to examine districts and regions of interest. See the full Web page for more comprehensive version of topics reviewed here.

Enrollment Change Patterns: Texas School Districts. 2011-2015
The following graphic shows patterns of the percent enrollment change by Texas school district during the period 2011-12 to 2015-16 school year. The percent change intervals/colors as depicted in legend panel at left of map window. Create custom maps similar to this view for your regions of interest. Examine alternative patterns such as percent change for different time periods, enrollment change or enrollment level. Set queries to include school district by peer group. Click graphic for larger view with more detail; expand browser window for best quality view.

View developed with CV XE GIS software using the Texas school districts GIS project.

The following views (click link) show a zoom-in with districts labeled with name and 2015-16 enrollment .. install the software and GIS project on your Windows computer for alternative and more detailed views.
• Dallas Metro Region
• Houston Metro Region
• San Antonio-Austin Metro Region

School Districts by Locale Code
The following view shows patterns of school districts by locale code. Examine districts based locale code in the interactive table below. See more about locale code below in this section.

View developed with CV XE GIS software using the Texas school districts project.

Additional School District Demographic-Economic Data
Use the following interactive tables to view attributes of individual school districts in context of others. These are national scope tables; select only Texas (or other state) using tools below table on respective pages. Compare Texas (or other state) school districts by national scope peer group size.
• General Demographics
• Social Characteristics
• Economic Characteristics
• Housing Characteristics
More about data analytics and analyzing the school district community.

Enrollment by Texas Local Education Agency: 2011-12 – 2015-16
— Interactive Table
The following graphic illustrates use of the interactive table. This view shows districts having 2015-16 enrollment 50,000 or more ranked in descending order on the enrollment percent change from the 2011-12 school year to the 2015-16 school year. See the full interactive table to perform similar operations. Click graphic for larger view.

See about other demographic-economic interactive tables.

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.

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.

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.