Tag Archives: block group demographics

State and Regional Decision-Making Information

Organized on a state-by-state basis, use tools and geographic, demographic and economic data resources in these sections to facilitate planning and analysis. Updated frequently, these sections provide a unique means to access to multi-sourced data to develop insights into patterns, characteristics and trends on wide-ranging issues. Bookmark the related main Web page; keep up-to-date.

Using these Resources
Knowing “where we are” and “how things have changed” are key factors in knowing about the where, when and how of future change — and how that change might impact you. There are many sources of this knowledge. Often the required data do not knit together in an ideal manner. Key data are available for different types of geography, become available at different points in time and are often not the perfect subject matter. These sections provide access to relevant data and a means to consume the data more effectively than might otherwise be possible. Use these data, tools and resources in combination with other data to perform wide-ranging data analytics. See examples.

Select a State/Area

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C.
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Topics for each State — with drill-down to census block
Visual pattern analysis tools … using GIS resources
Digital Map Database
Situation & Outlook
Metropolitan Areas
Congressional Districts
Counties
Cities/Places
Census Tracts
ZIP Code Areas
K-12 Education, Schools & School Districts
Block Groups
Census Blocks

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

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.

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.

Neighborhood Analysis: Block & Block Group Demographics

.. steps to analyze NYC Chelsea area demographics that can be applied to any neighborhood … demographic characteristics of the Chelsea area in New York City, an area west of Avenue of the Americas between 14th and 34th Streets, is radically different from adjacent areas. This topic was covered in a “great wealth divide” New York Times story. This section reviews how census block and block group demographic-economic data can be used to examine these patterns. A GIS project is used that associates census block and block group data for visual analysis Methods summarized here can be applied to any area. Use the tools described in this section to obtain demographic-economic profiles for any neighborhood based on an address. See related Web page for more detail.

See related post on Most Populated New York City Census Blocks.

Study Area in Context of Broader Area
The study area, a group of selected census tracts, is shown as the red cross-hatched area in context of lower Manhattan in the view below.

  — view created using CV XE GIS and associated GIS Project

Zoom-in View of Study Area
The next view shows a zoom-in to the study area. Block groups are shown with a red boundary. Chelsea Park is visible as the green area above the pointer south of 29th street.

  — view created using CV XE GIS and associated GIS Project

Census Block Demographics in Context of Block Groups
The next view shows a further zoom-in showing census blocks with black boundary and block groups with red boundary. Census blocka are shown with a semi-transparent yellow fill pattern (population greater than 4) and gray fill pattern (blocks with population less than 5). The block group containing Chelsea Park (green area above pointer) contains three census blocks, 2 with no population and one with 1,010 population. Block data are from Census 2010; there are no post-Census 2010 block level demographics available. The analysis could be extended to shown wide-ranging demographics at the block level.

  — view created using CV XE GIS and associated GIS Project

Examining Socioeconomic Attributes
In this further zoom-in, Chelsea Park (green area) is shown near the pointer. Census block population labels are turned off for blocks with 5 or more population to help show a less cluttered view. Block groups are labeled with two values. The yellow upper label shows the median housing value (MHV). The green lower label shows median household income (MHI). Both data items are based on the American Community Survey 5-year estimates (ACS 2013) are centric to 2011. The ACS data are updated annually; as of October 2015, the latest data are from ACS 2013; the ACS 2014 data become available December 2015. The ACS 2013 5 year estimates are top-coded at $1,000,001 for MHV and $250,001 for $MHI.


  — view created using CV XE GIS and associated GIS Project

The block group containing Chelsea Park has a median household income of $26,440; the median housing value estimate is not available (too few owner-occupied units to develop MHV estimate). The Chelsea Park block group code is “360610097002” — this code uniquely identifies this block group among all other block groups in the U.S.

The block group immediately to the south of the Chelsea Park block group median household income of $21,750; the median housing value estimate is $1,000,001 (top-coded). The code for this block group code is “360610093006”.

While the MHI for BG 360610093006 might seem like it should be higher, a look at the number of households by income interval explains this number. Almost half of the households in the BG have a household income below $20,000. Analytical options that might be considered include using mean household income or mean family income instead of median.

Compare number of households by household income intervals for these two block groups.

Compare Your Block Group of Interest to Chelsea Park BG
Compare the above BG attributes to any BG of interest:
1. Copy and paste this string into text editor (eg, Notepad) window (do not press enter after paste):
http://factfinder.census.gov/bkmk/table/1.0/en/ACS/13_5YR/B19001/1500000USXXXXXXXXXXX|1500000US360610097002

2. Click here, key in an address then click Find to locate the 11 character BG code.
— scroll down to “2010 Census Blocks” and then further to “GEOID”
— copy the first 11 digits of the GEOID value to clipboard see illustrative graphic.

3. Paste those 11 characters into the URL, replacing the “XXXXXXXXXXXX”; this modification must be exact.

4. Press Enter. A profile appears comparing your BG to the Chelsea Park BG 360610097002.

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.