Category Archives: Block Groups

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.

Citizen Voting Age Population by Block Group

.. tools and resources to access and analyze citizen voting age population by block group … ideally analysts and stakeholders will examine patterns and characteristics of the citizen voting age population (CVAP) by block group. This is partly because of the extreme variability of CVAP within higher level geography — even at the census tract level. This becomes even more important in more densely populated areas. See about ACS 2014 CVAP block group demographics in this related Web section.

Patterns of ACS 2014 CVAP Population by Block Group
— Houston Area
See legend in lower right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

The size, characteristics and distribution of the citizen voting age population by block group is very important.
• Block groups are the most granular geography at which we can study these demographics.
• The size of the citizen voting age population ultimately determines election outcomes.

This section provides thematic pattern map views and analyses of selected metros. These applications can be replicated for any area. They serve as an “analytical basis” that can be augmented with other methods and data (e.g., voter registration, voter propensity, voter turnout, and other election factors) to gain insights into election outcomes under alternative scenarios. Equally important, this information can be used to better equip voters with the potential impact of improved voting activity for their own neighborhood and larger areas (e.g., even congressional districts).

Using these GIS Resources; Obtaining Custom Maps & Analyses
Contact us (or call 888.364.7656) for maps and analyses for areas of interest or to use the integrated, ready-to-use, national scope GIS software, GIS project and datasets. Add your own data; create custom views.

CVAP Block Group Thematic Pattern Map shown below for Selected Areas
• Atlanta
• Chicago
• Los Angeles
• Kansas City
• Washington, DC

Patterns of ACS 2014 CVAP Population by Block Group
— Atlanta Area
See legend in lower right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

Patterns of ACS 2014 CVAP Population by Block Group
— Chicago Area
See legend in upper right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

Patterns of ACS 2014 CVAP Population by Block Group
— Los Angeles Area
See legend in upper right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

Patterns of ACS 2014 CVAP Population by Block Group
— Kansas City Area
See legend in lower right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

Patterns of ACS 2014 CVAP Population by Block Group
— Washington, DC Area
See legend in lower right of graphic for interval/color correspondence. Click graphic for larger view; expand browser window for best quality view.

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

ACS 2014 CVAP Block Group Demographics
For the 217,479 block groups covering the U.S. wall-to-wall, the median citizen population value is 1,165 (291.8 million population) and the median citizen voting age population is 885 (220 million population). The median total population is 1,252 (314 million population). These data are based on the 2014 American Community Survey ACS 2014 CVAP special tabulation completed in early 2016. WHile the focus here is on the total population, the same scope of data is integrated into the shapefiles used here for 13 race/origin population groups. The 13 race/origin groups include:
  • American Indian or Alaska Native Alone
• Asian Alone
• Black Alone
• Native Hawaiian or Other Pacific Islander Alone
• White Alone
• American Indian or Alaska Native and White
• Asian and White
• Black and White
• American Indian or Alaska Native and Black
• Remainder of Two or More Races
• not Hispanic
• Hispanic (of any race)

Related CVAP Sections
Census Tracts; ACS 2009-13 special tabulation
Census Tracts; ACS 2009-13 special tabulation – Hispanic focus
Tracts & Congressional Districts; ACS 2009-13 special tabulation

See this blog post in this full, more detailed 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.

Low and Moderate Income Demographics by Block Group

.. tools and resources to access and use low and moderate income demographics by block group … the U.S. Department of Housing and Urban Development (HUD) sponsored development of the ACS 2006-10 “low and moderate income population (LMI) by block group” special tabulation released in 2015. This is an important data resource for HUD as well as others who need to examine characteristics of the LMI population by block group and related small area geography. See related Web section for more details on topics covered here data.

This section reviews tools to use these data on a national or local level:
1. Site analysis tools
2. GIS software and national scope LMISD BG GIS project
3. National scope BG-level interactive table
See the related Web section for more detailed information.

Patterns of LMI Population by Block Group
The following graphic shows block groups with 51% or more LMI population with orange fill pattern. See related zoom-in views. Click graphic for broader Chicago area view. Expand browser window for best quality view.

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

Block Groups & American Community Survey (ACS) Data
There are several features of block groups that make this an important geography for data analytics. Block groups average 1,200 population and are the the smallest geographic area for which the ACS data are tabulated. The approximate 220,000 block groups are subdivisions of census tracts and cover the U.S. wall-to-wall. Only ACS provides national scope demographic-economic data at the block group level.

Analytical Tools & the ACS LMI Block Group Data
Tools reviewed below make use of one specific block group (170317115002) in the Chicago area. The tools may be applied to any other block group (BG) or set of BGs. Block groups are uniquely identified by the 12 character BG geocode. The BG code 170317115002 refers to block group 2 in census tract 711500 in county 031 in state 17.

1. Study Area Comparative Analysis Reports
Examine characteristics of circular area reports based on block groups within those areas. The following graphic shows a partial view of a 1- and 3-mile radius report developed for lat-lon location (41.7324422, -87.6410861) BG 170317115002 internal lat-lon. Click graphic to view entire report. This type of report can be prepared for any address/location/lat-lon and area size from less that one mile to many miles. These reports include a much larger set of demographic-economic data than included in the HUD LMISD. The comparative analysis structure makes it easy to compare one site/location with another and their difference.

.. report developed with ProximityOne SiteReport.

2. Using GIS Resources
— LMI 51% or More; Chicago Area Block Groups

Block groups with 51% or more LMI population shown with orange fill pattern. Mini profile displayed for block group at pointer – 170317115002 – find in table below. LMI percent shown as 55.03%. LMI-BG layer shown in context with Public Use Microdata Area 03531. Develop custom estimates of the population for this PUMA using the ACS PUMS data. Integrate other types of geography and data. Click graphic for broader Chicago area view. Expand browser window for best quality view.

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

See more information here about using the GIS project used to develop maps in this section and more generally develop your own maps and perform geospatial analysis.

3. Using the Interactive Table
Use the interactive table to view, query, rank and analyze LMI demographics by block group. The following graphic illustrates one application. The state of Arizona is selected using the state selection tool below the table. The Low Income column header cell is dbl-clicked, sorting the table on the estimate of the Low Income population by block group. It is easy to see that BG 040270116002(blue highlighted) has the largest Low Income population among all BGs in Arizona.

Try using the interactive table for geography of interest. The interactive table is a very useful tool when used in combination with the GIS application.

What’s next is data integration. Upcoming posts will review similar, but different, updated block group demographics and their use with data reviewed here. See the main block group Web page.

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.

Healthcare Data Analytics: Market Analysis

What factors best determine where a clinic, hospital or professional practice is located? For those that exist, how to best determine the scope and needs of the market served? Understanding healthcare market dynamics is one way these entities can improve their business and operation by using Health Data Analytics. Professionals skilled with Health Data Analytics can help their organization, or clients, better achieve their vision and improve performance.

This section is focused on analyzing healthcare markets and infrastructure using Geographic Information System (GIS) tools and related data resources. Participants in the Certificate in Data Analytics may optionally use the tools and resources described here. See overview of steps to install and use the GIS project and datasets illustrated in more detail in the related Web section.

Using GIS to Analyze Healthcare Market Characteristics
Illustrating GIS start-up view discussed in this section.

– View developed using CV XE GIS; click graphic for larger view.

Analyzing healthcare markets involves examining characteristics of healthcare facilities in context of competitive position and market potential. Geographic Information System (GIS) tools can be used to knit together geographic, demographic, economic and business data to perform these analyses. This document makes use of the Atlanta area to illustrate applications. In an actual study, the geographic focus could be a city, county, metro, state or some combination, anywhere in the U.S., or the U.S. overall.

Understanding Needs and Visions
A first step involves an assessment of your situation — your needs, visions and data that you have to work with. The results of this assessment and data that you provide help develop/frame a market study in context of GIS project(s).

Market Infrastructure Analytical Framework
The following graphic shows the start-up view of the Atlanta area Healthcare Data Analytics (HCDA) GIS project. This GIS project involves use of many layers and types of data as shown in the legend at left of map window. Selection of the type of geography, scope of geography and scope of subject matter are key elements in setting up the market infrastructure analytical framework. This is a proxy/example for the GIS project that would be developed to meet your needs/application focus.

The above view shows a thematic pattern of median household income by census tract (averaging 4,000 population). Pattern analysis helps you visualize demographic-economic characteristics by census tract — in this example you can easily see patterns of economic prosperity. This example uses median household income; we can draw upon hundreds of subject matter items and depict other types of patterns.

Examining the Healthcare Infrastructure
The graphic below shows selected types of healthcare facilities.
See legend to the left of map:
• Hospitals – blue triangle markers
• Assisted Living Facilities (ALF) – green circle markers
• Nursing Homes – red square markers

Site Analysis — Examining Characteristics of Healthcare Facilities
The yellow circle marker shows the hypothetical location of a prospective new facility. A 5-mile radius site study area — from the yellow marker — is used to select existing nursing homes; characteristics of the competition. Nursing homes show as cross-hatched; circular area is study zone.

Display of the 9 facilities selected above.

See the related Web section to view further details.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using these data in context of data analytics with other geography and other subject matter.  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

Crime Data Analytics

Goto ProximityOne .. examining crime incidence and socioeconomic patterns and analyzing small-area and location-based data.

.. what are the crime patterns in neighborhoods or areas of interest? It is challenging to get useful answers to this type of question. Crime incidence data by location/address are often difficult or not possible to obtain. Even where the location-based crime data are available, the data must be geocoded, e.g., assigned a census block code to each address. Separately demographic-economic must be organized to examine contextually with the crime data.

Integrating Crimes by Location & Patterns of Economic Prosperity
– View developed using CV XE GIS and related GIS project.

Crime Data Analytics. Use the Crime Incidence and Socioeconomic Patterns GIS project and associated datasets to explore relationships between crime and small area demographic-economic characteristics. Follow the steps described below to study patterns and relationships in Kansas City and/or use this framework to develop similar data analytics for other areas.

Framework for a case study. 409 of Missouri’s 4,506 block groups are within the jurisdiction of the Kansas City police department (KCPD) and had one or more crimes in 2014 (latest fully reported year). There were approximately 10,400 crimes recorded by the KCPD in 2014, in the city area spanning four counties. In this section tools and data are used to examine crime patterns in Kansas City, MO. Crime data are included as markers/locations in a GIS project. Crime data are also aggregated to the census block level and examined as summary data (aggregate crimes by census block). Crime data are related to American Community Survey (ACS) 2014 5-year demographic-economic data at the block group geographic level.

To perform these types of analyses, it is important to start with location-based crime data that have been attributed with type of offense (offense code). Ideally, each crime incidence data record includes minimally the offense code and address of the crime. Such location-based crime incidence data have been acquired from the KCPD. These data are used to develop a shapefile that can be included in a GIS project.

Patterns of Crime Incidence in Kansas City, MO
The following graphic shows patterns of crime incidence by census block for the “Plaza Area” within Kansas city. This view shows all types of crimes aggregated to the census block level. Crimes committed where a handgun was involved are shown as black/red circular markers. Click the graphic for a larger view that shows legend and more detail.
– View developed using CV XE GIS and related GIS project.

Related views (click link to view graphic in new window):
Use the GIS project to develop variations of these views. Optionally add your own data.
Lay of the land: Kansas City city (cross hatched) in context of metro
All crimes as markers in Kansas City in 2014

Patterns of Economic Prosperity & Crime Incidence
The following graphic shows patterns of economic prosperity (median household income $MHI) by block group for the same general area as above. This view illustrates how two types of crimes (burglary blue triangle markers and homicide (red/black square markers) can be examined in context. Click the graphic for a larger view that shows legend and more detail.

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

Related views (click link to view graphic in new window):
Use the GIS project to develop variations of these views.
View similar to above, without $MHI layer

Data used to analyze patterns of economic prosperity/$MHI are based on the American Community Survey (ACS) 2014 5-year estimates at the block group geographic level. The same scope of subject matter is available for higher level geography. The GIS project/datasets includes many types of demographic-economic subject matter that can be used to display/analyze different socioeconomic patterns.

Using Block Group Geography/Data
Census Block Groups sit in a “mid-range” geography between census blocks and census tracts. All cover the U.S. wall-to-wall and nest together, census blocks being the lowest common denominator for each. Block Groups (BGs) are the smallest geographic area for which annually updated ACS 5-year estimates data are tabulated.

Advantages of using BG geodemographics include the maximum degree of geographic drill-down (using ACS data) … enabling the most micro-perspective of demographics for a neighborhood or part of study area. A disadvantages of using BG estimates is that typically the smaller area estimates have a relatively higher error of estimate.

Crime Incidence and Socioeconomic Patterns 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 CISP GIS Project fileset
… requires ProximityOne User Group ID (join now)
… unzip CISP GIS project files to local folder c:\crime
3. Open the kcmo_crimes_2014.gis project
… after completing the above steps, click File>Open>Dialog
… open the file named C:\crime\kcmo_crimes_2014.gis
4. Done .. the start-up view shows the crime patterns.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about accessing location-based data and block group demographics and integrating those data into analytical applications.  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.

Linguistic Isolation Patterns by Block Group

Goto ProximityOne Linguistic isolation inhibits the ability of people and households to integrate into neighborhoods, cities and living areas. Opportunities for advancement and participation in society are improved where linguistic isolation is minimal. This section describes tools and data resources to examine patterns of linguistic isolation for block group level geography.

Size and distribution data on speakers of languages other than English and on their English speaking ability are important for many reasons. These data help us understand where populations with special needs exist and how they are changing. The data are used in a wide-ranging legislative, policy, and research applications. Many legal, financial and marketing decisions involving language-based issues make use of data on language use and English-speaking ability.

Data used to analyze patterns of “household linguistic isolation” are based on the American Community Survey (ACS) 2014 5-year estimates at the block groupgeographic level. The same scope of subject matter is available for higher level geography. The following graphic shows patterns of linguistic isolation in Los Angeles County. Block groups colored in red have more than 40-percent of households where no household member age 14 years and over speaks English “very well”. Click graphic for larger view showing more detail and legend.

Patterns of Linguistic Isolation; Los Angeles County, CA

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

The next view shows a zoom-in to the vicinity of the pointer shown in the above map. This view shows block groups labeled with total population. Click graphic for larger view showing more detail and legend.

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

Language Spoken by Households – Tabular View
The table presented below shows data from ACS Table B16002 Households by Linguistic Isolation for block group 1 in census tract 212304 (also referred to as 2123.04) in Los Angeles County (037) California (06); geoid=060372123041. This block group is shown toward the center of the above view with population 1,894. Data for this block group are shown in the rightmost column of the table below. 47.2 percent of households (803) are linguistically isolated (317+0+62).


— “Language Spoken” categories are based on four major language groups.

More About Linguistic Isolation
One definition of a “linguistically isolated household” is a household in which all adults have substantial limitation in communicating English. In the ACS data, a household is classified as “linguistically isolated” if 1) no household member age 14 years and over spoke only English, and 2) no household member age 14 years and over who spoke another language spoke English “very well”.

Like many demographic measures, linguistic isolation tends to be “masked” when analyzing data for larger geographic areas, even census tracts, are used. Block group geography provides an ability to locate linguistic isolation in sub-neighborhood areas.

Using Block Group Geography/Data
Census Block Groups sit in a “mid-range” geography between census blocks and census tracts. All cover the U.S. wall-to-wall and nest together, census blocks being the lowest common denominator for each. Block Groups (BGs) are the smallest geographic area for which annually updated ACS 5-year estimates data are tabulated.

Advantages of using BG geodemographics include the maximum degree of geographic drill-down (using ACS data) … enabling the most micro-perspective of demographics for a neighborhood or part of study area. A disadvantages of using BG estimates is that typically the smaller area estimates have a relatively higher error of estimate.

Summary of Steps to Access and Use these Data
The ACS 2014 5-year Table B16002 data can be accessed for Los Angeles County using the following API call (paste the following text into a browser and press Enter). See more about using Census API operations.

At the end of this string is the text “state:06+county:037”. Change the state and county to “state:36+county:061” to access the data for New York County, NY (Manhattan); and similarly for any any county.

The results of the API call are shown in this text file. These data are easily imported into an Excel file. The DBF version of the data were integrated into the Los Angeles County 2014 block group shapefile using the CV XE GIS software dBMerge feature. The Layer Editor was then used to develop the map legend/color intervals. Join me in aData Analytics Lab session to learn more about these steps/operations.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about accessing block group demographics using API tools and integrating those data into analytical applications.  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.