Tag Archives: Plano

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.

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.