Tag Archives: Dallas metro

Developing Geographic Relationship Data

.. tools and methods to build and use geographic relationship files … which census blocks or block groups intersect with one or a set of school attendance zones (SAZ)? How to determine which counties are touched by a metropolitan area? Which are contained within a metropolitan area? Which pipelines having selected attributes pass through water in a designated geographic extent? This section reviews use of the Shp2Shp tool and methods to develop a geographic relationship file by relating any two separate otherwise unrelated shapefiles. See relasted Web page for a more detiled review of using Shp2Shp.

As an example, use Shp2Shp to view/determine block groups intersecting with custom defined study/market/service area(s) … the only practical method of obtaining these codes for demographic-economic analysis.

– the custom defined polygon was created using the CV XE GIS AddShapes tool.

Many geodemographic analyses require knowing how geometries geospatially relate to other geometries. Examples include congressional/legislative redistricting, sales/service territory management and school district attendance zones.

The CV XE GIS Shape-to-Shape (Shp2Shp) relational analysis feature provides many geospatial processing operations useful to meet these needs. Shp2Shp determines geographic/spatial relationships of shapes in two shapefiles and provides information to the user about these relationships. Shp2Shp uses the DE-9IM topological model and provides an extended array of geographic and subject matter for the spatially related geometries. Sh2Shp helps users extend visual analysis of geographically based subject matter. Examples:
• county(s) that touch (are adjacent to) a specified county.
• block groups(s) that touch (are adjacent to) a specified block group.
• census blocks correspond to a specified school attendance zone.
• attributes of block groups crossed by a delivery route.

Block Groups that Touch a Selected Block Group
The following graphic illustrates the results of using the Shp2Shp tool to determine which block groups touch block group 48-85-030530-2 — a block group located within McKinney, TX. Shp2Shp determines which block groups touch this block group, then selects/depicts (crosshatch pattern) these block groups in the corresponding GIS map view.

Geographic Reference File
In the process, Shp2Shp creates a geographic relationship file as illustrated below. There are six block groups touching the specified block group. As shown in the above view, one of these block groups touches only at one point. The table below (derived from the XLS file output by Shp2Shp) shows six rows corresponding to the six touching block groups. The table contains two columns; column one corresponds to the field GEOID from Layer 1 (the output field as specified in edit box 1.2 in above graphic) and column 2 corresponds to the field GEOID from Layer 2 (the output field as specified in edit box 2.2 in above graphic). The Layer 1 column has a constant value because a query was set (geoid=’480850305302′) as shown in edit box 1.3. in the above graphic. Any field in the layer dataset could have been chosen. The GEOID may be used more often for subsequent steps using the GRF and further described below. It is coincidental that both layers/shapefiles have the field named “GEOID”.

Layer 1 Layer 2
480850305302 480850305272
480850305302 480850305281
480850305302 480850305301
480850305302 480850305311
480850305302 480850305271
480850305302 480850305312

Note that in the above example, only the geocodes are output for each geography/shape meeting the type of geospatial relationship. Any filed within either shapefile may be selected for output (e.g., name, demographic-economic field value, etc.)

How it Works — Shp2Shp Operations
The following graphic shows the settings used to develop the map view shown above.

See related section providing details on using the Shp2Shp tool.

Geographic Relationships Supported
The Select Relationships dropdown shown in the above graphic is used to determine what type of spatial relationship is to be used. Options include:
• Equality
• Disjoint
• Intersect
• Touch
• Overlap
• Cross
• Within
• Contains
See more about the DE-9IM topological model used by Shp2Shp.

Try it Yourself

See full details on how you can use any version, including the no fee versin, of CV XE GIS to use the Shp2Shp tools. Here are two examples what you can d. Use any of the geospatial relatoinships. Apply your own queries.

Using Touch Operation
Select the type of geographic operation as Touch. Click Find Matches button. The map view now shows as:

Using Contains Operation
Click RevertAll button. Select the type of geographic operation as Contains. Click Find Matches button. The map view now shows as:

Relating Census Block and School Attendance Zones
The graphic shown below illustrates census blocks intersecting with Joyner Elementary School attendance zone located in Guilford County Schools, NC (see district profile). The attendance zone is shown with bold blue boundary. Joyner ES SAZ intersecting blocks are shown with black boundaries and labeled with Census 2010 total population (item P0010001 as described in table below graphic). Joyner ES is shown with red marker in lower right.


– view developed using CV XE GIS and related GIS project; click graphic for larger view

See more about this application in this related Web section.

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.

New Monthly Residential Construction by Metro Updates

.. tools, data & methods to assess the housing situation, examine housing supply and demand market conditions, and how metros of interest are changing.  New July 2016 building permits (new housing units authorized) and over-the year monthly data are now available for each metropolitan statistical area (MSA).

Use the interactive table to view, query, rank, compare data by metro. Map and geospatially analyze construction patterns with the CV XE GIS software and ready-to-use GIS project/datasets – see details.

Updated Resources to Examine Residential Construction Patterns
Metro Situation & Outlook Reports
.. metro by metro … examples: Houston, Los Angeles, Chicago, Atlanta.
County Annual U.S. by county
County & City/Place Monthly

Patterns of New Authorized Residential Units by Metropolitan Area
The following graphic shows value of single unit structures units authorized  by metro. Larger view shows more details including a mini-profile of housing units authorized detail. Create similar views for preferred time periods and different residential unit attributes using the GIS project.  Zoom-in to areas of interest.  Label the geography as desired.  Add your own data.

View created with CV XE GIS. Click graphic for larger view.

The time lag from reference date to access date of these data is one month, contributing to both the freshness of the data and importance of the data as a leading economic indicator. The importance of these data transcends issues concerning housing market conditions alone.  These data are one part of a mix of demographic-economic factors required to understand housing market conditions and the local/regional economy. These data are a part of the process to develop the ProximityOne county and sub-county demographic-economic estimates and projections.

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.

Characteristics of Largest 50 U.S. Metropolitan Areas

.. the total population of the largest 50 U.S. metropolitan areas as of 2014 (latest official estimates) was 174,886,265. These 50 metros account for 58.3% of the population in all 917 metropolitan areas and 54.8% of the total U.S. population. By either measure, more than half of the U.S. population resides in these 50 metros. Use tools and data resources described in this section to view and analyze these metros.

View the list of these metros by population rank in the scroll section provided below. Click on a metro link to view the Metro Situation & Outlook Report for that metro. The report provides extensive details on the geographic-demographic-economic attributes of the metro.

Use the largest_50_metros GIS project described in this section to map and explore characteristics of these metros. Create zoom-in views of metros/regions of interest. Label geography. Add other geography and data. The largest_50_metros GIS project/datasets includes all U.S. metros and has been used to develop the views in this section:
View 1 .. 50 Largest Metros Ranked on 2014 Total Population
View 2 .. All Metropolitan & Micropolitan Statistical Areas
View 3 .. Patterns of Percent Population Change 2010-2014
See details about using the largest_50_metros GIS project below in this section.

50 Largest Metros Ranked on 2014 Total Population
The following graphic shows the 50 largest metros by 2014 population rank (blue metros are the largest 10). Click graphic for larger view, more detail and legend color/data intervals (expand browser window for best view).

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

All Metropolitan & Micropolitan Statistical Areas
The following graphic shows all metropolitan statistical areas (blue) and micropolitan statistical areas (orange). Click graphic for larger view, more detail and legend color/data intervals (expand browser window for best view).

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

Patterns of Percent Population Change 2010-2014
The following graphic shows the percent change in total population from 2010 to 2014 for all metros. Click graphic for larger view, more detail and legend color/data intervals (expand browser window for best view). This map illustrates the relative ease to gain insights into patterns of population change using geospatial data analytics tools.

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

50 Largest Metros Ranked on 2014 Total Population — scroll section
Click link to view Metro Situation & Outlook Report

Largest 50 Metros GIS Project/Datasets
1. Install the ProximityOne CV XE GIS
… omit this step if CV XE GIS software already installed.
… run the CV XE GIS installer
… take all defaults during installation
2. Download the Largest 50 Metros GIS project fileset
… requires ProximityOne User Group ID (join now)
… unzip Largest 50 Metros GIS project files to local folder c:\largest_metros
3. Open the large_50_metros1.gis project
… after completing the above steps, click File>Open>Dialog
… open the file named c:\largest_metros\largest_50_metros1.gis
4. Done .. the start-up view is similar to the graphic shown at the top of this section.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using metropolitan area 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.

Dallas, TX Metro Situation & Outlook

… examining characteristics, patterns and change for the Dallas-Fort Worth-Arlington, TX metropolitan area … the total population of the metro changed from 6,452,725 in 2010 to 6,954,330 in 2014, a change of 501,605 (7.77%). Among all 917 metros, this metro was ranked number 4 in 2010 and 4 in 2014, based on total population …
• How will the market for single family homes change over the next 5 years?
• How does economic prosperity in this metro compare to others?
• What are the patterns in metro rental income and rental vacancy rates?
• How do patterns vary within the metro by county/neighborhood?
• How are demographic-economic characteristics trending?

We examine these types of topics in this section. Stakeholders can replicate applications reviewed here for this and other metros. Select any metro.

.. this section now continuously updated … see Dallas Metro Situation & Outlook; see related Texas Demographic-Economic Characteristics.

Metropolitan areas include approximately 94 percent of the U.S. population — 85 percent in metropolitan statistical areas (MSAs) and 9 percent in micropolitan statistical areas (MISAs). Of 3,143 counties in the United States, 1,167 are in the 381 MSAs in the U.S. and 641 counties are in the 536 MISAs (1,335 counties are in non-metro areas).

Focus on Dallas-Fort Worth-Arlington, TX MSA
This section is focused on the Dallas-Fort Worth-Arlington, TX MSA; Core-Based Statistical Area (CBSA) 19100. It is not intended to be a study of the metro but rather review recent and trending decision-making data that can be brought together to examine patterns and change and develop insights. The data, tools and methods can be applied to any metro.
– See a more detailed version of this document focused on this metro.

The Dallas-Fort Worth-Arlington, TX MSA is shown in the graphic below. The 7-county metropolitan statistical area is shown with bold boundary; counties appear with black boundaries and county name/geographic code labels.

Click graphic for larger view and details. Map developed using CV XE GIS.

Patterns of Economic Prosperity by Neighborhood
Median household income by census tract

Click graphic for larger view and map legend. Map developed using CV XE GIS.

Develop variations of this map view using the Mapping Texas Neighborhood Patterns GIS resources.

Fortune 1000 Companies
This metro is home to 40 Fortune 1000 companies including AT&T, American Airlines, Comerica, Dean Foods, Exxon Mobil, Fluor Corporation, J.C. Penney, Kimberly-Clark, Lennox International, Michaels Stores, Neiman Marcus, RadioShack, Southwest Airlines, Tenet Healthcare and many others.

Principal Cities
Metro principal cities (about principal cities) … click the link to view city profile   Arlington .. Dallas .. Denton .. Fort Worth .. Irving .. Plano .. Richardson

Overview of Selected Demographic-Economic Characteristics
The total population of the Dallas-Fort Worth-Arlington, TX metro changed from 6,452,725 in 2010 to 6,954,330 in 2014, a change of 501,605 (7.77%). Among all 917 metros, this metro was ranked number 4 in 2010 and 4 in 2014, based on total population. Annual net migration was 62,320 (2011), 77,089 (2012), 57,645 (2013), 74,176 (2014). View annual population estimates and components of change table.

This metro is projected to have a total population in 2020 of 7,418,541. The projected population change from 2010 to 2020 is 965,816 (15.0%). The population ages 65 years and over is projected to change from 592,695 (2010) to 1,031,937 (2020), a change of 439,242 (74.1%). See more about population projections.

Based on per capita personal income (PCPI), this metro was ranked number 61 in 2008 and 76 in 2014. among the 917 metros for which personal income was estimated.The PCPI changed from $44,697 in 2008 to $49,506 in 2014, a change of $4,809 (10.8%). Per capita personal income (PCPI) is a comprehensive measure of individual economic well-being. Use the interactive table to compare PCPI in this metro to other metros.

282 metropolitan statistical areas, of the total 381, experienced an increase in real Gross Domestic Product (GDP) between 2009 and 2014. This metro ranked number 5 among the 381 metros based on 2014 GDP. The GDP (millions of current dollars) changed from $355,756 in 2009 to $504,358 in 2014 a change of $148,602 (41.77%). Real GDP (millions of real, inflation adjusted, dollars) changed from $355,756 in 2009 to $460,154 in 2014, a change of $104,398 (29.35%). GDP is the most comprehensive measure of metro economic activity. GDP is the sum of the GDP originating in all industries in the metro.

View additional selected details about the metro …
Population Characteristics & Trends
–  Component City Characteristics
–  Component County Characteristics
– General Demographic Characteristics
Housing Characteristics & Trends
Total Housing Units
General Housing Characteristics
Residential Construction; Housing Units Authorized & Value
Housing Price Index
Economic Characteristics & Trends
Economic Profile
– Gross Domestic Product
Establishments, Employment & Earnings by Type of Business
Labor Market Characteristics & Trends
Education Infrastructure
Component School District Characteristics
Component Higher Education Institution Characteristics

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about this metro, comparing this metro to peer group metros and use of data analytics to develop further detail related 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.

K-12 Data Analytics: Dallas ISD, Texas

.. using tools and data analytics methods for analysis of K-12 schools located in Dallas ISD school district, Texas .. use the interactive table in related section to examine demographic-economic characteristics of Dallas County by block group. Apply these tools and methods to your schools, school districts and related areas of interest. Contact me for details. Tools and methods described here can help leadership, staff and stakeholders answer key questions and facilitate strategic planning. See the related main Web section for more details and tools access.

Examining the Current Situation
While most schools and school districts know a lot about the students, often less is known about children’s living environment and the broader school district community. See the demographic-economic profile for Dallas ISD total population (compare to Dallas city). These profiles tell a story. It helps stakeholders know “where we are now.”

Use the annually updated School District Special Tabulation that provides similar data but for the grade relevant school age population by type of enrollment universe. See Dallas ISD Children’s Demographic-Economic Profile by Universe of Enrollment.

Dallas ISD School District in Context
Dallas ISD (bold brown boundary) shown in regional context.

  — view created using CV XE GIS and associated GIS Project

School Locations in Context
Zoom-in to Dallas ISD. Pointer at county boundary; Dallas ISD is located in Dallas County. Schools are shown by different types of markers. See markers/style in legend at left. Using the query feature, it is possible to identify charter schools as one type of marker, irrespective of grade range.

Use the national scope interactive tables to examine characteristics of individual public schools and individual private schools. Rank, query, compare and contrast schools within a state or on a national basis.


  — view created using CV XE GIS and associated GIS Project

Zoom-in View of Schools; School Characteristics
Further zoom-in shows schools labeled with name.  The identify tool is used to show a mini-profile of a school by clicking on the Edna Rowe school marker.
The partial view of the profile shows free and reduced lunch participation and enrollment by grade.

  — view created using CV XE GIS and associated GIS Project

School Attendance Zones
Elementary school attendance (black boundaries) are shown in the next graphic. Dallas ISD has three types of attendance zones (elementary, middle, high school). The query feature is used to show only elementary zones. Click graphic for larger view showing more detail.

  — view created using CV XE GIS and associated GIS Project

Cities/Places & School District
Cities/places are shown in the next graphic (cross-hatch pattern) in context with the school district. Places are non-incorporated areas of population concentration. Click graphic for larger view showing more detail; adjacent city/places shown with yellow diagonal cross-hatch pattern.


  — view created using CV XE GIS and associated GIS Project

Road Infrastructure
Roads/streets are added to the view as shown in the next graphic. Vital to student transportation planning and management, the street density view also helps view the scope of build-out. Click graphic for larger view showing more detail; mini-profile shows attributes of street segment by school/pointer.

  — view created using CV XE GIS and associated GIS Project

Schools in Context of Urban/Rural Geography
Census blocks are categorized as urban or rural based on Census 2010. The graphic below shows urban census blocks with an orange fill pattern. It is easy see that most of Dallas ISD is urban; but a large area in the southeast part of the district is rural. See related K-12 Schools by Urban/Rural Status
.

  — view created using CV XE GIS and associated GIS Project

Percent Population in Poverty by Census Tract
Census tracts are statistically defined geographic areas covering the U.S. wall-to-wall (73,057 areas). The view below shows patterns of percent population in poverty by census tract. Click on graphic to view larger view. Choose from hundreds of demographic-economic measures to assess patterns of well-being, age distribution, housing structure and age, educational attainment, housing value, race/origin, employment, language spoken at home among many others.

  — view created using CV XE GIS and associated GIS Project

See zoom-in view of Edna Rowe school vicinity with tract and %poverty labels

Patterns of Economic Prosperity by Block Group
Block groups are statistically defined geographic areas covering the U.S. wall-to-wall (217,740 areas) and nest within census tracts (see above). Block groups (BGs) provide a finer geographic granularity compared to census tracts. The view below shows patterns of economic prosperity as measured by the median household income (MHI). MHI interval/color patterns are shown in the highlighted layer at left of the map. Click on graphic to view larger version that uses the MHI as a label for each BG. Choose from hundreds of demographic-economic measures to assess patterns of well-being. This view illustrates use of transparency setting to “see through” the pattern layer to view the topology/road infrastructure.


  — view created using CV XE GIS and associated GIS Project

Dallas County Block Group Demographic-Economic Characteristics
Use the interactive table to view, rank compare Dallas County block group demographic-economic characteristics. See about Dallas County demographic trends. All block groups for the county are included in the table. Optionally key in an address using the location-based demographics tool to determine the block group code of interest. You can then use the Find button below the table to locate that block group. See about using block group codes — a 12 character code uniquely identifying that area.

Block Groups in Vicinity of School — Interactive Table
The graphic below illustrates use is the Dallas County block group interactive table. Block groups in census tract “48113012206” are shown in the table. This tract was selected as it contains the Edna Rowe school (location used above in maps). The school’s address was used in the location-based lookup tool. Do this for any school or address in Dallas County. It is easy to see that the Gini Index is low indicating high degree of income equality. It is easy to see and compare the number and type of housing units, median income, housing values, and rent. Try this process yourself:
1 – enter an address using this tool to obtain a census tract code see this example.
2 – below the interactive table click ShowAll button, enter your 11 character tract code, click Find.
All block groups in this tract will show as rows in the table.

Data Analytics Lab Session
Join me in a Data Analytics Lab session. There is no fee. Discuss how tools and methods reviewed in this section can be applied 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.

Evolution of Census Tracts: 1970-2010

… examining statistical area geographic change … in the world of small area demographic-economic data analysis, census tracts are often a preferred level of geography. Subdivisions of counties (or county equivalent), census tracts cover the U.S. from wall-to-wall. Each county is comprised of one or more census tracts. Averaging 1,200 population, tract geography often corresponds to neighborhood areas. For Census 2010, there were 73,057 census tracts defined. Their reasonably static geography between each decennial census is an important feature for many applications. See related more detailed Web section

Annual census tract demographic-economic updates from the American Community Survey 5-year estimates (ACS0913), make census tracts even more appealing. But it has not always been that way. And, longitudinal comparison of demographic-economic change at the tract level can be challenging where tract geography and codes change with a new decennial census.

2010 marked the 100th anniversary of the census tract. Extensive use of the census tract started with the 1970 census and has evolved since then. This section illustrates how census tracts evolved between 1970 and 2010 using GIS resources. A GIS project was developed that includes census tract shapefiles for each census 1970 through 2010.

Visualizing Demographic Patterns by Census Tract
The following graphic shows patterns of economic prosperity by Census 2010 census tract in the Dallas, Texas metro area (Dallas metro component counties & demographic change). Census tract geography and demographic patterns are reviewed for part of Collin County. The following view shows median household income (MHI), based on ACS 2013 5-year estimates, by census tract. See MHI intervals/colors in legend at left of map. Boundaries/patterns are shown for Census 2010 tracts “0316.??” (black boundaries) in context of 1970 census tract “031600”. Historical views of this area, illustrating how tract boundaries have changed over time, are shown later in this section.

– Click graphic for larger view showing Census 2010 tract codes.
– View developed with CV XE GIS.
Click to view tract area (red boundaries) in context of broader region

1970 Census Tracts
A very small part of the U.S. was covered by 1970 census tracts.
The following view shows 1970 census tracts with orange fill pattern.

  View developed with CV XE GIS.

1980 Census Tracts
A larger part of the U.S. was covered by 1980 census tracts. The following view shows 1980 census tracts with orange fill pattern.

  View developed with CV XE GIS.

A Brief History
Initial census tract data was with the 1910 census and included a handful of cities. Starting with the 1940 census, census tracts became an official statistical geography tabulation area. Starting with the 1970 census, and the first more extensive data in machine-readable form (magnetic tapes used with mainframe computers), census tracts became a more popular geography for the analysis of small area data. For both the 1970 and 1980 censuses, census tracts did not fully cover the U.S. For the 1990 census, census tracts and the quasi-equivalent “block numbering areas” (BNAs) covered the U.S. wall-to-wall. Starting with Census 2000, BNAs were retired and transformed into census tracts. Use of census tracts for demographic-economic analysis has continued gain in popularity. Now, census tract estimates are available annually from the American Community Survey 5-year estimates (ACS0913). Access ACS 5-year estimates via interactive tables.

1970 Census Tracts: Collin County; Dallas Metro
1970 census tract “031600” shown with bold black boundary.

  View developed with CV XE GIS.

1980 Census Tracts: Collin County; Dallas Metro
1980 census tracts 0316.?? (same general geography as covered by tract “031600” in 1970) shown with bold black boundary. This view shows tracts labeled with the 6-character census tract code, unique within county.

  View developed with CV XE GIS.

1990 Census Tracts: Collin County; Dallas Metro
1990 census tracts 0316.?? (same general geography as covered by tract “031600” in 1970) shown with bold black boundary. This view shows tracts labeled with the census tract “base” plus “suffix” code separated by a decimal point. In 1980, note that there is no 1130.01 or 1130.02 as shown above for the 1970 vintage tracts . The codes 1130.03 and 113003 are equivalent. The 6-character, no decimal version, is preferred in all cases when used as a geocode.


  View developed with CV XE GIS.

2000 Census Tracts: Collin County; Dallas Metro
2000 census tracts 0316.?? (same general geography as covered by tract “031600” in 1970) shown with bold black boundary.

  View developed with CV XE GIS.

2010 Census Tracts: Collin County; Dallas Metro
2010 census tracts 0316.?? (same general geography as covered by tract “031600” in 1970) shown with bold black boundary.

  View developed with CV XE GIS.

Equivalencing Census 2000 and Census 2010 Tract Geography
As shown above, the area covered by Census 2000 tracts 1130.15 and 1130.18 become Census 2010 tract 1146.00. Comparing the above map views for Census 2000 and Census 2010 shows (upper left tracts) shows Census 2000 tract 031644 is split into Census 2010 tracts 031656, 031657 and 031658. To compare demographic change for Census 2000 tract 031644 requires combining data tabulated for Census 2010 tracts 031656, 031657, 031658 and other partial Census 2010 tracts intersecting with Census 2000 tract 031644. See more about these relationships at Census 2010 Demographics for Census 2000 Geography. Use the interactive table in that section to view the relationship among these tracts. The graphic shown below illustrates use of that table. A query has been placed on Census 2000 tract 031644 (see button below table and query value 48085031644). The table nowe shows rows only for Census 2000 031644. See the corresponding Census 2010 tracts in columns to right.

The following graphic shows the relationship between these tracts.

To replicate this view in the interactive table, follow these steps:
• Click ShowAll button below table.
• Key in Census 2000 tract code 48085031644 to right of Find in GeoID00 button.
• Click Find in GeoID00 button.
• The view above appears in the table.

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.