Measuring & Analyzing Households by Social Class by PUMA

.. a social class is a population or household group typically referred to as a lower, middle and upper class. The size of the population or households in a social class is often determined in relationship to an interval related to the median household income of an area — from two-thirds of median household income to twice the median household income (MHI). Subsequent blog posts will address a broader definition for class determination. By better understanding composition and determinants of social class for an area, we might better understand and improve on income inequality and create new opportunities. This is a multi-part blog post on social class analytics. Click Follow at right to receive updates.

Percent Population in Households by Social Class by PUMA
.. Los Angeles area .. click for larger view.

Los Angeles metro area by social class .. PUMAs shown with black boundaries; pointer at Los Angeles-Orange County line

Percent of Households by Social Class by PUMA
.. Los Angeles area .. click for larger view.

Los Angeles metro area by social class .. PUMAs shown with black boundaries; pointer at Los Angeles-Orange County line

Using American Community Survey Microdata
We use of the American Community Survey microdata or “public use microdata samples” (PUMS) http://proximityone.com/pums.htm to develop estimates of population and households by middle class, lower class and upper class by “public use microdata area” (PUMA) http://proximityone.com/puma.htm. Microdata files are comprised of anonymized individual respondent data within PUMAs. The approximate 2,800 PUMAs cover the U.S. wall-to-wall and must have 100,000 population or more. 2010 and 2020 vintages PUMAs may be examined and compared with other geography using the VDA Web GIS http://proximityone.com/vda.htm with the MetroDynamics Project.

Social Class Participation by PUMA
Using custom software, the PUMA (ACS 2021 1 year data in this case), individual housing records are summarized for each PUMA. An estimate is developed for the lower, middle and upper class based on an algorithm.

Examine patterns of social class stratification using VDA Web GIS anywhere in U.S.
The estimates are then integrated into a PUMA shapefile. The PUMA shapefile is added to a Geographic Information System (GIS). Access this shapefile/layers using VDA Web GIS to examine patterns of social class, such as the graphics shown above, or in combination with other geography and subject matter.

About VDA GIS
VDA Web GIS is a decision-making information resource designed to help stakeholders create and apply insight. Use VDA Web GIS with only a Web browser; nothing to install; GIS experience not required. VDA Web GIS has been developed and is maintained by Warren Glimpse, ProximityOne (Alexandria, VA) and Takashi Hamilton, Tsukasa Consulting (Osaka, Japan).

Data Analytics Web Sessions
Join us in the every Tuesday, Thursday Data Analytics Web Sessions. See how you can use VDA Web GIS and access different subject matter for related geography. Get your geographic, demographic, data access & use questions answered. Discuss applications with others.

About the Author
Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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. Join Warren on LinkedIn.

Examine Neighborhood Demographics for any City

.. examine neighborhood demographics for any city (or county, school district ..) using the no fee, no registration new beta version of the Visual Data Analytics (VDA2) Web GIS. Nothing to install, access with any Web browser. Use this unique and powerful resource to make custom maps similar to the one shown below.

Patterns of Economic Prosperity by Neighborhood in Tampa, FL Area
Examine patterns of economic prosperity by neighborhood in context of a selected county, city or other geography. The following view shows patterns of median household income by census tract in Tampa, FL area. The bold black border shows the city boundary for Tampa; the thematic pattern shows colors associated with intervals of median household income ($MHI). The $MHI layer is set with a see through transparency enabling a view of the underlying topology. Create a view similar to this for any of the 19,500 cities in the U.S. See detailed steps to develop this view in the notes below the graphic.

Map Your Own Map View
Follow these stpe to create your own neighborhood by city map view.  Use other features of VDA2 to access demographic-economic data in a tabular or visual form.

More About VDA2 Web GIS
New in VDA2, not available in VDA1, are the table/query operations. View/analyze data for any layer in a spreadsheet/grid form.  Sort and perform queries on subject matter of interest. Click a button in the data grid to zoom to that geographic area in the map window.  The following graphic shows the VDA2 start-up view after clicking the Query/Table On/Off button. This button (shown below map window at right by pointer) toggles the table view on/off.

.. click graphic for larger view.

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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.

Housing Value Appreciation

.. U.S. housing prices rose nationwide in August, up 1.5% from the previous month, based on the FHFA Housing Price Index (HPI). Housing prices rose 8.0% from August 2019 to August 2020.

If you purchased a housing unit in 2019Q2 at $260,200 (the ACS 2019 median housing value), the value of the unit in 2020Q2 would be $271,000, an increase of 4.2%. A good deal in this era of low interest rates.

U.S. housing prices posted a strong increase in August .. the 1.5% increase is the largest one-month price increase observed since the start of the HPI measurement in 1991. This large month-over-month gain contributes to an already strong increase in prices over the summer. These price gains can be attributed to the historically low interest rates, rebounding housing demand and continued supply constraints.

The HPI has various limitations as a measure to assess the housing market. One important limitation is that it a measure in isolation; other related demographic-economic measures are not included. This is unlike the American Community Survey (ACS) estimates of the median housing value ($MHV), used as an annual, year-over-year measure of housing value appreciation.

Median Housing Value
The U.S. ACS 1-year estimate of median housing value ($MHV) increased from $229,700 in 2018 to $240,500 in 2019. The ACS 2020 estimate, which will be impacted by the pandemic, will not be available until September 2021. The ProximityOne 2020 estimate of $MHV is $270,500.

Click this API link to view a CSV-like file showing the 2019 median household income and median housing value by state. Join me in a Data Analytics Web Session (see below) to integrate these data into a map view like shown below. Add other data.

Patterns of Median Housing Value by State

– view developed using ProximityOne CV XE GIS
– click graphic for larger view

An advantage of using the ACS or ACS-like $MHV data is that this measure is synchronized with other related measures, like total population, total housing units, housing units by tenure and age built and so on. Though a popular measure to assess geographically comparable housing values, the $MHV has many limitations. A key limitation is that few survey responders really know the value of their home. Other limitations have to do with the definition itself and how the data are collected/developed. ACS $MHV measures value of only occupied housing units and excludes houses on 10 or more acres and housing units in multi-unit structures. See more. While there are other Federal sources of $MHV, it remains that the usabilty aspects of the ACS or ACS-like measures are second to none.

Learn more — Join me in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for national scope statistical programs and 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.

Business Establishment Characteristics by County

.. what are the number and types of businesses underlying county economies of interest? What is the employment size by type of business establishment? What scope of wages, earnings do they contribute? Learn more here.

The pandemic impact on businesses remains in flux .. this post tools and data that can be used to examine pre-pandemic business establishments and employment pattern characteristics by county. By examining pre-pandemic conditions, we can better assess the impact of how and why business, demographic and economic change and impact as we move forward. The magnitude and duration of the impact on businesses will vary by community/area and become more measurable in the months ahead. The “How & Where of Business Establishment/Employment Change” will be updated later in 2020. See related, more detailed web section. See related section focused business establishments by ZIP code.

Where Things are Made by County
The following graphic shows patterns of the number of manufacturing establishments (NAICS 31) by county for the U.S. 48 contiguous states. Inset legend in map view shows number of establishments by interval/color. View/examine all U.S. states and areas using the related GIS project. Create custom maps similar to this view for your regions of interest depicting establishments, employment or payroll for your type of business selection(s). Click graphic for larger view with more detail; expand browser window for best quality view.

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

The above view shows patterns for only one type of business. Data are tabulated more than 2,000 NAICS/type of business codes. These data may be examined by county using the interactive table. Use the GIS tools and related GIS project to develop variations of the views shown here.

Using the Interactive Table
The 10 largest counties based on the number of manufacturing establishments are shown in the static graphic below. Click for larger view.

Use the interactive table to dynamically create similar rankings on employment size or payroll. Set a query for a county, metro or state of interest.

Updates
These data update in June 2020. Follow the blog (click button at upper right) to receive updates.

Learn more — Join us in the Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

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.

Patterns of Income in America’s Largest Cities

The retreat in personal and household income resulting from the pandemic will be historic and substantial. How long term? Which cities of what size and location will be affected the most? We start to study patterns and trends as new data become available in the next several weeks.

America’s largest 629 cities accounted for a group population of 121,228,560, or 37.1%, of the total U.S. population (327,167,434) in 2018. All of these cities are in Metropolitan Statistical Areas (MSAs). With contiguous cities and places, these urban areas account for more than 80% of the U.S. population. These cities, each with 65,000 population or more, are shown as markers in the thematic pattern view below. See more about cities/places and city/place 2010-2018 demographic trends.

Patterns of Economic Prosperity: America’s Largest Cities
– cities with 2018 population 65,000+ shown as markers
– markers show level of 2018 median household income
– data used to develop this veiw were extracted using GeoFinder.
– click map for larger view; expand browser to full screen for best quality view.

– view developed using ProximityOne CV XE GIS software and related GIS project.

Top 25 Largest Cities based on Median Household Income

About America’s Largest Cities & Economic Characteristics
The set of the 629 America’s largest cities is based on data from the 2018 American Community Survey 1-year estimates (ACS 2018). ACS 2018 1-year estimates, by design, provide data only for areas 65,000 population or more. The ACS 2018 data are the only source of income and related economic data for national scope each/all cities/places (29,853) on an annual and more recent basis. These data will update with 2019 estimates in September 2020. ACS-based data reflecting the impact of the pandemic will not be available until September 2021.

Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

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.

Financing America’s Schools

.. as we look to restarting America’s economy, the nation’s public school systems face many questions and logistical issues.  Among these issues are a wide range of financial challenges.  Financing America’s public K-12 schools is supported by a mix of Federal, state and local funding.  In FY 2017, 13,311 regular public school districts reported total revenues of $679,925 billion derived from $51,212 billion federal (7.5%), $317,434 billion state (46.7%) and $311,278 billion local (45.8%) sources. These data are based on the school district finances (F-33) program data released by the Census Bureau in May 2019. States and individual school districts vary widely on the make-up/distribution of these federal, state and local sources.  See more about this topic and related K-12 schools topics in the ProximityOne K-12 schools main Web section.

Use tools and data reviewed here to examine K-12 school district finances — sources and uses of funds for FY 2017. View, sort, query, compare school district sources and uses of funds using the interactive table below in this section. Create/view profiles for district(s) of interest.

Data and resources reviewed here update in early May 2020 with new FY 2018 sources and uses of funds data and related school/school district financial data. We examine the implications of COVID-19 for school systems as we look to Restarting the Economy. Join the User Group to receive updates.

Percent Federal Revenue by School District, FY 2017
The following graphic shows patterns of percent Federal revenue by school district (unified and secondary), FY 2017. Click graphic for larger view. Expand browser to full window for best quality view.

– view developed using ProximityOne CV XE GIS software and related GIS project.
– use these tools on your computer to examine these data & related data.

Percent State Revenue by School District, FY 2017
The following graphic shows patterns of percent State revenue by school district (unified and secondary), FY 2017. Click graphic for larger view. Expand browser to full window for best quality view.

– view developed using ProximityOne CV XE GIS software and related GIS project.
– use these tools on your computer to examine these data & related data.

Interactive Table
The following static graphic shows the 10 Texas school districts having the largest Federal revenue. Sources of Federal revenue by program are also shown. Create views like this for any of the sources and uses of funds items for your selection of school districts. Use the interactive table (separate page) for dynamic analysis of individual school districts in context of U.S. overall, states or metros. Select by state or metro and rank based on any of several selected revenue by source and per student expenditure by category.

– click graphic for larger view

Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

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.

Analyzing Patterns of COVID-19

.. as COVID-19 impacts our demographics, economy and way of life, we look for answers about where we are and what lies ahead.  Here we review data on COVID-19 incidence and tools to analyze those data. In the coming days, weeks, we plan to augment these tools and data. See more below.  See related Web section for more detail.

Use new resources to examine/analyze patterns of COVID-19 incidence in context of related demographic-economic characteristics. Resources include the narrative/interpretative portion, interactive table and GIS tools and project/files. Data and tools are updated daily. There is no fee to use any of these resources.

COVID-19 Incidence by County in the Atlanta Metro Area
The following graphic shows patterns of COVID-19 incidence (number of cases per 1,000 population) by county. See color patterns in the inset legend. This view shows counties labeled with name and number of confirmed COVID-19 cases. Use the GIS project described below to create variations of this view and develop similar views for any metro.

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

The GIS resources and interactive table below makes use of the COVID-19 confirmed cases data updated daily by the New York Times. See more about the New York Times U.S. tracking page.

COVID-19 Incidence — U.S. by County
Similar to the above view, the following graphic shows patterns of COVID-19 incidence (number of cases per 1,000 population) by county for the U.S. See color patterns in the inset legend. This view shows counties labeled with name and number of confirmed COVID-19 cases. Use the GIS project described below to create variations of this view and develop similar views for any metro. Click graphic for larger, more detailed view. Expand browser window for best quality view.

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

Using GIS Tools & Resources
Use the Geographic Information System (GIS) CV XE GIS software and GIS project to view maps and geospatially analyze patterns COVID-19 cases in context of related demographic-economic data. The GIS project automatically opens with the following view:

.. see details about using the mapping/GIS resources.
.. create map views for your areas of interest.

COVID-19 Confirmed Cases by County Interactive Table
Use the interactive table to view, rank, query compare patterns of COVID-19 cases. See related demographic-economic interactive tables.

The following static graphic illustrates use of the table to view daily patterns of COVID-19 cases in Cobb County, GA.

Use the interactive table to examine counties of interest.

Situation & Outlook Web Sessions
Join me in a Situation & Outlook Web Session where we discuss topics relating to measuring and interpreting the where, what, when, how and how much demographic-economic change is occurring and it’s impact.

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.

Census 2020 Residential Address Counts by Block

.. using Census 2020 residential address count data to examine change since 2010 .. the Census Bureau has released preliminary Census 2020 residential address counts by Census 2010 census block. These data, count of residential addresses and group quarters addresses, reflect updates as of October 2019 and do not represent final Census 2020 counts. The data will continue to be updated to support Census 2020. See related Web section with more detail and updates.

Importance and Use
These data are of immediate value in determining and analyzing how the number of housing units have changed, 2010 to 2019. Since the data are at the census block level, they may be aggregated to any other Census-defined summary level/type of geographic area such as block group, tract, ZIP code, city, county, school district, etc. These data are also important as they give us a “year in advance look” at how small area demographics are changing since 2010. Before this, the most recent census block data were from Census 2010. A lot has happened in many areas. These data provide insights into that change. The Census 2020 block level data will be released in early 2021 for Census 2020 census block geography. So, another important feature of these data is that they are summarized for Census 2010 census block boundaries. Census 2010 and 2020 block boundaries may differ, particularly in areas experiencing larger demographic growth/change. An important limitation is that they are counts, subject to change as the Census data are collected/tabulated.

Comparing Census 2010 Housing Units with Census 2020 Address Counts
The following graphic shows patterns of Census 2010 housing counts with the Census 2020 (late 2019 vintage) residential address counts by census block. This view is focused on census tract 3608100700 (tract 000700) in Queens County, NY (code shown near center of graphic). Individual blocks are labeled with block code (4 digits) with the Census 2010 housing units (yellow label) and Census 2020 residential address count (green label) shown below the block code. As an example, the block located at the pointer has block code 3006 (or full national scope unique block code 36-081-00700-3006) with a Census 2010 count 44 housing units and a Census 2020 (late 2019 count) of 232 residential addresses. Click graphic for larger view. Expand browser window to full screen for best quality view.

.. view created with ProximityOne CV XE GIS software and related GIS project.

More About Using these Data
We have summarized these data at the census tract level and are evaluating their use, in combination with other data, to develop current estimates and projections to 2025.

Data Analytics Web Sessions
Join me in a Demographics 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.

Neighborhood Median Family Income: Measuring Economic Well-Being

.. Median Family Income ($MFI) and Median Household Income ($MHI) are two measures of economic well-being. Based on the 2018 American Community Survey 1-year (ACS) data, the U.S. 2018 $MFI was estimated to be $76,401 while the $MHI was estimated to be $61,937 .. both in 2018/current dollars. Create insights into patterns of well-being by neighborhood using geospatial analysis. $MFI patterns are illustrated by the following thematic pattern map.

Patterns of Economic Prosperity by Neighborhood/Census Tract
The following view shows patterns of $MFI by census tract for the inner beltway area of Houston/Harris County, TX. Income interval color patterns are shown in the inset legend. Tracts are labeled with $MFI. Click graphic for larger view. Expand browser window for best quality view. Larger view shows tracts labeled with tract code. It is easy to see how west Houston and east Houston areas differ.

– view developed with ProximityOne CV XE GIS software and related GIS project.
– these $MFI data are based on the 2018 ACS 5-year estimates.

This section focuses on $MFI but could just as well focus on $MHI and yet other related income measures. $MFI will almost always be greater that $MHI, generally by a large margin. See the U.S. 2018 $MFI and $MHI in context of related demographic-economic measure here. See more about the distinctions/definitions of families and and households below.

The ACS data are a unique source of income and related data at the neighborhood or sub-county level. View more about accessing and using the 2018 ACS 5-year estimates.

Family Definition
A family is a group of two people or more (one of whom is the householder) related by birth, marriage, or adoption and residing together; all such people (including related subfamily members) are considered as members of one family. The number of families is equal to the number of family households. However, the count of family members differs from the count of family household members because family household members include any non-relatives living in the household.

Related … an unmarried partner, also known as a domestic partner, is specifically defined as a person who shares a close personal relationship with the reference person. … Same-sex unmarried-partner families or households – reference person and unmarried partner are both male or female.

Household Definition
A household consists of all the people who occupy a housing unit. A house, an apartment or other group of rooms, or a single room, is regarded as a housing unit when it is occupied or intended for occupancy as separate living quarters; that is, when the occupants do not live with any other persons in the structure and there is direct access from the outside or through a common hall.

A household includes the related family members and all the unrelated people, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. There are two major categories of households, “family” and “nonfamily”.

Situation & Outlook Weekly Web Sessions
Join me in a Situation & Outlook web session to discuss more details about demographic-economic estimates and projections.

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.

Examining How Metro GDP is Changing

.. which metros had the largest 2018 real GDP? How did they change since 2010? How to they compare on a per capita basis? What about metros of interest to you? Read on …

As an investor, business or stakeholder in a metro, it is important to know how and where the economy is changing … and how one or selected metros relate to the U.S. and other metros. Is metro X changing in a different direction than metro Y? By how much, why and is there a pattern? What does the healthcare sector, for example, contribute to a metro’s gross domestic product (GDP)? How does it compare to peer metros? How is the healthcare industry trending? Metro GDP data can provide insights and answers to these important questions.  See related main Web page.

In 2018, per capita real gross domestic product (GDP) in MSAs ranged from $19,299 (The Villages, FL MSA) to $196,277 (Midland, TX MSA). The percent change in per capita real GDP by metro, 2010 to 2018 ranged from -24% (New Orleans-Metairie, LA MSA) to 126.6% (Midland, TX MSA). Use the interactive table to view these and related data.

Change in Per Capita Real GDP by Metro; 2010-2018
The following graphic shows patterns of change in per capita real GDP by metro (MSA) from 2010 to 2018. Label shows 2018 rank of the metro among all 384 MSAs based on 2018 per capita real GDP. Click graphic for larger view. Expand browser to full window for best quality view.

— view created using ProximityOne CV XE GIS and associated GIS project

Top 25 Metros (MSAs) based on 2018 per capita real GDP
The following graphic shows the top 25 metros (MSAs) based on 2018 per capita real GDP labeled with rank. Click graphic for larger view. Expand browser to full window for best quality view.

— view created using ProximityOne CV XE GIS and associated GIS project
 
Using the Interactive Table – 10 largest metros based on 2018 real GDP
— insights into comparative analytics and trends.
— view, rank, compare districts based on your criteria.
— example, which metros have the largest 2018 real GDP?
Use the interactive table to examine GDP characteristics and trends of metros. The following view illustrates use of the table. This view shows use a query to show the ten metros ranked on 2018 real GDP. Click graphic for larger view.

Try using the interactive table to examine metros of interest.

Demographic-Economic Analytics Web Sessions
Join me in a Demographics 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.