Tag Archives: pattern analysis

Tools to Analyze County Demographic-Economic Characteristics

.. demographic-economic characteristics of counties are essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine these data for all counties in the U.S. This annual update includes geographic area characteristics based on ACS 2015 data.  The tools/data are organized into four related sections summarized below.

1. General Demographics
View interactive table at http://proximityone.com/us155dp1.htm
Patterns of School Age Population by County
Use GIS tools to visually examine county general demographics as illustrated below. The following view shows patterns of percent population ages 5 to 17 years of age by county — item D001-D004-D018 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

2. Social Characteristics
View interactive table at http://proximityone.com/us155dp2.htm 
Patterns of Educational Attainment by County
– percent college graduate
Use GIS tools to visually examine county social characteristics as illustrated below. The following view shows patterns of percent college graduate by county — item S067 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

3. Economic Characteristics
View interactive table at http://proximityone.com/us155dp3.htm 
Patterns of Median Household Income by County
Use GIS tools to visually examine county economic characteristics as illustrated below. The following view shows patterns median household income by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

4. Housing Characteristics
View interactive table at http://proximityone.com/us155dp4.htm 
Patterns of Median Housing Value by County
Use GIS tools to visually examine county housing characteristics as illustrated below. The following view shows patterns median housing value by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

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.

Personal Consumption Expenditures by State: Updates & Pattern Analysis

.. data and tools to develop insights into personal consumption patterns by state .. growth in state personal consumption expenditures (PCE) – the measure of goods and services purchased by or on behalf of households – decelerated to 3.6 percent on average in 2015 from 4.4 percent in 2014. In 2015, PCE growth ranged from 1.5 percent in Wyoming to 5.0 percent in Florida. PCE by state data for 16 expenditure categories are shown for the U.S. and by state in the interactive table. See related Web section for more detail.

Per Capita Personal Consumption Expenditures
  — Patterns & Characteristics by State

The following graphic shows patterns of percent change in total PCE 2010-2015 by state labeled with 2015 per capita total PCE. Use CVGIS project to examine PCE by types and different years. Integrate additional subject matter and types of geography. Click graphic for larger view with details.

– views developed with CVGIS and related GIS project & datasets.

In 2015, the fastest growing categories of expenditures across all states were food services and accommodations, health care and other nondurable goods. These categories along with housing and utilities were also the largest contributors to growth in total PCE by state.

Per capita PCE by state measures average PCE spending per person in a state. Across all states, per capita total PCE was $38,196. Per capita PCE by state ranged from a high of $49,717 in Massachusetts to a low of $29,330 in Mississippi.

Personal Consumption Expenditure by Category
PCE by state is the state counterpart of the Nation’s personal consumption expenditures (PCE). PCE by state measures the goods and services purchased by or on behalf of households and the net expenditures of nonprofit institutions serving households (NPISHs) by state of residence for all states and DC. PCE by state reflects spending on activities that are attributable to the residents of a state, even when those activities take place outside of the state. Per capita PCE by state measures average PCE spending per person in a state.

Interactive Analysis
The following two graphics illustrate use of the interactive PCE table. View 1 shows Texas by PCE type ranked in ascending order on percent change from 2010 to 2015 (ranked on far right column). View 2 shows Texas by PCE type ranked in descending order on percent change from 2010 to 2015 (ranked on far right column). Use the table to examine characteristics of states of interest. Click graphic for larger view.

Texas by PCE Type; Ranked Ascending on PCPCE Change 2010-15

Texas by PCE Type; Ranked Descending on PCPCE Change 2010-15

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.

Mapping Statistical Data with R

The world of visual and geospatial analysis continues to morph and evolve. So it is with R’s geospatial analysis evolvement. This section is focused on mapping statistical data with R and provides steps you can use to develop Web-based interactive maps in complete HTML structure ready to publish. No coding.

R (more about R) is an open source language and environment for statistical computing and graphics. R has many similarities with the Statistical Analysis System (SAS), but is free … and widely used by an ever increasing user base. R is used throughout the ProximityOne Certificate in Data Analytics course.

For now, in the areas of mapping and geospatial analysis, R is best used in a companion role with Geographic Information System (GIS) software like CV XE GIS. Maybe it will always be that way. R’s features to 1) perform wide-ranging statistical analysis operations and 2) to process and manage shapefiles and relate those and other data to many, many types of data structures are among R’s key strengths.

Mapping with R
The graphic shown below illustrates a Web-based interactive map that has been developed totally using R. The map shows patterns of Census 2010 population for Texas by county. Aside from satellite imagery, which can be added, this application provides the look and feel of a Google maps application. Yet the steps to develop the application are far different and much closer to more traditional GIS software and data structures .. and there are no proprietary constraints. Join us in weekly Data Analytics Lab sessions to learn about developing this type of mapping application and geospatial analysis. See more about this application in narrative below the map.

Create & Publish this Interactive Map or Variation
  … no coding .. see details in Web version of this post.

Click graphic for larger view and details — opens new window with interactive map.. View developed using R.

R generates the final product HTML as shown above. This application illustrates use of a Census countyTIGER/Line shapefile integrated with Census 2010 demographics. Participants in the ProximityOne Data Analytics course learn how to develop the types of maps using a range of TIGER/Line shapefiles from census block to metro to congressional district to state and integrating subject matter from the American Community Survey and many other Federal statistical programs. R and the ProximityOne CV XE GIS tools work together to expand the range of analytics to an unlimited set of possibilities.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using R for mapping, data management and statistical analysis in context of data analytics.  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.

Importance of Census Tracts in Data Analytics

census tracts are important for many reasons. It is easy to misidentify or misunderstand patterns and characteristics within cities, counties and metros which become obfuscated using these higher level, more aggregate, geographies. Many cities and counties that might be experiencing demographic-economic decline will often have bright spots that are groups of a few or many census tracts.

Patterns of Percent Population with Bachelor’s Degree
— by Census Tract; Los Angeles Metro
The following graphic shows percent population age 25 years and over with bachelor’s degree by census tract based on ACS 2014 5 year estimates for a portion of the Los Angeles metro. Accommodating different demographic-economic thresholds/patterns, different legend color/data intervals are used. The pattern layer is set to 80% transparency enabling a view of earth features. Click graphic for larger view, more detail and legend color/data intervals. This map illustrates the geographic level of detail available using census tract demographics and the relative ease to gain insights using geospatial data analytics tools.

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

Get a Custom Map for Your Area of Interest
Use this form to request a no fee map graphic similar to the one shown above for a county of interest. Enter the request with county name and state in the text section; e.g., “Requesting social characteristics tract map for Cook County, IL.”

This section reviews reasons for the importance of census tracts in data analyyics. See related Web sections on tools, resources and methods that you can use to access, integrate and analyze U.S. by census tract general demographics data. The U.S. national scope Census Tracts Demographic-Economic Dataset contains approximately 600 subject matter items tabulated for each census tract organized into four subject matter groups:
General Demographics
Social Characteristics
Economic Characteristics
Housing Characteristics

Importance of Census Tracts for Data Analytics
Census tracts are important for many reasons.  A partial list of reasons is provided below.
• Covering the U.S. wall-to-wall, census tracts are the preferred “small area” geography for superior data analytics.
• The Census Bureau now produces annual tract demographic-economic data from the American Community Survey;  there is an evolving time-series at the tract level creating new analytical opportunities.
• Originally developed to equivalence neighborhoods, many still do.
• Defined by the Census Bureau in collaboration with local groups, tracts typically reflect boundaries meaningful for local area analysis.
• Defined generally for use with each new decennial census, most tract boundaries are stable and non-changing for ten years and many much longer.
• Designed to average 4,000 population, there are more than twice as many census tracts (73,056) than ZIP code areas (33,129).
• Tract boundaries are well-defined; unlike ZIP code areas which are subject to multi-sourced geographic definitions.
• Many data developers (e.g., epidemiologists) use census tract geography to tabulate their own small area data enabling more effective use of those data with Census Bureau census tract data.
• As a statistical geographic area (in contrast to politically defined areas, census tracts are coterminous with counties; data at the census tract level can be aggregated to the county level.
• Small area estimates for tracts are typically more reliable than for block groups.
.. census tracts are comprised on one or more coterminous block groups.
.. on average, a census tract is comprised of three block groups.
• Census tracts are used by many Federal, state and local governments for compliance and program management.

The annually updated American Community Survey provides “richer” demographic-economic characteristics for national scope census tracts. While Census 2010 provides data similar to those items in the General Demographics section, only ACS sourced data provide details on topics such as income and poverty, labor force and employment, housing value and costs, educational participation and attainment, language spoken at home, among many related items. The approximate 600 items accessible via the tract dataset are supplemented by a wide range of additional subject matter. ACS census tract data are updated annually in December of each year.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using census tract geography and 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.

Comparing Census Tract Demographics Over Time

.. it’s about more than census tracts .. this section is about comparing American Community Survey ACS 5-year estimates: 2005-2009 with 2010-2014 … something new and powerful happening this week.

To make good business decisions we need hard data, recent data, trend data … to assess patterns and change and develop reliable, superior plans. Read about the past and then how things have changed for the better.

Imagine that it is 2005. Data from Census 2000 are now 5 years old. There will not be another update for richer demographics for all counties and cities in the foreseeable further. There will not be any update for small area geography such as census tracts or block groups until Census 2010. Businesses are forced to use out-of-date data to assess markets … where and how are opportunities changing? City and neighborhood planners can only make educated guesses to respond to growing needs of various population groups. Federal and state government programs that base funding allocations on demographics are challenged. Changes in the rental vacancy rates for most cities, counties and metros will remain unknown for the foreseeable future.

Fast forward to 2015 and present day reality. The situation is now radically different. First, we can now compare 5-year estimates from the 2009 American Community Survey ACS to those from the 2014 ACS 5 year estimates. Second, we will be able to do that again in 2016 — compare 5-year estimates from ACS 2010 to those from ACS 2015. Health planners can now assess the size and change in special needs population and how that matches up to resources that respond to those needs — rather than guessing. Schools and school districts can better understand how school age population trending and plan for enrollment change. Education agencies are better able to assess how changing demographics among school systems compare to one-another. Businesses can now determine the size of potential markets and how they are trending based on hard data. It is possible to compare changing patterns in rental vacancy rates and rental housing market conditions for all levels of geography down to block group.

The American Community Survey ACS provides a wide range of important statistics about people and housing for every community in the nation. These data are the only source of local estimates for most of the approximately 40 topics it covers for even the smallest communities. It produces statistics for ancestry, language, education, commuting, employment, mortgage status and rent, as well as income, poverty and health insurance. The ACS estimates are tabulated annually as 1-year estimates (e.g., the ACS 2014 1-year estimates) and 5-year estimates (e.g., the ACS 2014 5-year estimates. See a comparison below in this section about scope, advantages/disadvantages, and other usage attributes for the 1-year versus 5-year estimates.

See ACS 2014 5-year main page for additional data access & use details.

Data from the 5-year estimates are available for all geographies down to the block group level regardless of population size. Starting with the ACS 2014 5-year estimates, for the first time, users will be able to compare two non-overlapping five-year periods 2005-09 and 2010-14. Looking ahead, data from the 2006-10 and 2011-15 (available December 2016) will be comparable … and so on. Over several years, a time-series of 5-year estimates, non-overlapping five-year periods, will evolve.

Comparing Geography Between 2005-09 & 2010-14 ACS 5-Year Data
The following graphic summarizes geographic tabulation areas for 2005-09 and 2010-14 ACS 5-year data. Use the corresponding Web table as a reference guide for comparing data over time. Links provided in the table enable you to navigate to selected data access tables. This Web-page table updates with new links; bookmark the page for re-visits.

Updates
Posts later this month will provide updates on this topics; new data and new data analytics tools. Join me in a Data Analytics Lab session to discuss use of these data using analytical tools and methods 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. Contact Warren. Join Warren on LinkedIn.