Extrapolations

This is a collection of long-term forecasts based on quantitative data from diverse sectors. Long-term means 20 years or more. Diverse means forecasts in a wide range of activities such as transportation, education, food, shelter, entertainment, technology, etc. You can help grow the collection. Please check our list of desired indicators and submit suggestions to extrapolations@kk.org. We're also collecting and crossposting any and all attempts to extrapolate the future on Tumblr and Pinterest. You can follow us on Twitter too.

Media


Summary

This collection of data includes the following indicators, dates, and sources:

GLOBAL
ownership vs. access ($ millions), 2009-2019, McKinsey
total media consumption CAGR, 2010-2015, Zenith
total media consumption min/day, 2010/2015-2018, Zenith
total internet consump min/day, 2015/2018, Zenith
mobile internet consump min/day, 2016, Zenith
all other (besides mobile internet) media consump % change, 2016, Zenith
desktop internet consump min/day, 2010/2014/2016, Zenith
traditional cinema consump % change, 2016, Zenith
traditional outdoor consump % change, 2016, Zenith
traditional television consump min/day, % of consump, % change, 2015/2016/2018, Zenith
traditional radio consump % change, 2016, Zenith
Traditional newspapers consump, % change, 2016, Zenith
traditional magazines consump % change, 2016, Zenith
consumer spending, billions, 2020, Activate
digital video streaming
consumer ebooks
digital music streaming
digital newspapers
esports
digital magazines

UNITED STATES PER ADULT
all digital media time/day, 2012-2018, eMarketer
all mobile (nonvoice) media time/day, 2012-2018, eMarketer
mobile radio time/day, 2012-2018, eMarketer
mobile social networks time/day, 2012-2018, eMarketer
mobile video time/day, 2012-2018, eMarketer
mobile other media time/day, 2012-2018, eMarketer
all desk/lap-top media time/day, 2012-2018, eMarketer
desk/lap-top video time/day, 2012-2018, eMarketer
desk/lap-top social media time/day, 2012-2018, eMarketer
desk/lap-top radio time/day, 2012-2018, eMarketer
desk/lap-top other media time/day, 2012-2018, eMarketer
other connected devices time/day, 2012-2018, eMarketer
non-digital TV time/day, 2012-2018, eMarketer
non-digital radio time/day, 2012-2018, eMarketer
non-digital print media time/day, 2012-2018, eMarketer
non-digital newspapers time/day, 2012-2018, eMarketer
non-digital other media time/day, 2012-2018, eMarketer
all digital and non-digital media total time/day, 2012-2018, eMarketer

UNITED STATES, TOTAL AMONG ALL USERS
gaming hours/day and CAGR, 2015/2020, Activate
messaging hours/day and CAGR, 2015/2020, Activate
social media hours/day and CAGR, 2015/2020, Activate
audio hours/day and CAGR, 2015/2020, Activate
video hours/day and CAGR, 2015/2020, Activate

UNITED STATES
subscription video on demand, subscribers by number of services, 2016-2020, Activate
music revenues by sale type, $ and CAGR, 2006-2016, Activate
music streaming revenue, ads vs. paid subscriptions, 2013-2020, Activate
smart speakers household penetration, millions, 2015-2020, Activate
aggregate media spending CAGR, 2016-2020, Activate
internet advertising
internet access
out-of-home advertising
video games
music
tv advertising
business-to-business
book publishing
radio
cinema
tv & video
magazine publishing
newspaper publishing
internet users % of population, 2000-2014, ITU
broadband subscriptions, 2000-2015, ITU
wired-line vs. wireless users, 2000-2014, Activate

In addition to these data sets, I’ve also noted a large set of VR predictions by Jesse Schell.

Findings

General Media Consumption

McKinsey publishes an annual global media spending/revenues report examining the last five years of historic data and forecasting trends for the next five years. The most recent report includes the following ownership vs. access chart:

mckinsey-global-ownership-vs-access-2009-2019

Note: Ownership consists of home video physical sales, physical recorded music sales and recorded music digital downloads. Access consists of OTT digital
video, recorded music digital subscriptions and recorded music ad-supported digital streaming.

Src:
McKinsey&Company. July 2016.
Global Media Report 2015: Global Industry Overview.” P.21.

Citing:
McKinsey & Company, Wilkofsky Gruen Associates

Older McKinsey reports are available, which offer some older, historic data points. For example, the 2014-2018 Outlook includes some historic data going back to 2013.

*

Zenith (part of Publicis Media) is a marking consulting firm. It’s annual, global survey (two years running), “Media Consumption Forecasts,” estimates recent, current, and near-term media consumption patterns. The current report forecasts general media consumption trends two years out, to 2018. The report estimates time spent reading newspapers and magazines, watching television, listening to the radio, visiting the cinema, using the internet, and viewing outdoor advertising while out of the home. 71 countries are covered, and regional estimates are available (although the freely available excerpts below are global).

Excerpts:

Total media consumption
2010-2015: +7.9% (driven by internet consump), avg +1.5% per year
2010: 403 min/day
2015: 435 min/day
2016: +1.4%
2017: +1.2%
2018: 448 min/day, +0.4% (mobile consump levels off)

Total internet consumption
2015: 110 min/day
2018: 31% global media consumption

Mobile internet consumption
2016: +27.7%, 86 min/day, accounting for 71% of internet consump

Overall media consumption
2016: +1.4%

All other media consumption (besides mobile internet)
2016: -3.4%

Desktop internet consumption
2010: 36 min/day
2014: 52 min/day
2016: 36 min/day

Traditional cinema consumption
2016: -0.5%

Traditional outdoor consumption
2016: -0.8%

Traditional television consumption
[declining, but still the most popular medium]
2015: 177 min/day, 41% of media consumption
2016: -1.5%
2018: 38% of media consumption

Traditional radio consumption
2016: -2.4%

Traditional newspapers consumption
2016: -5.6%

Traditional magazines consumption
2016: -6.7%

Note: [The traditional media] figures only refer to time spent with these media in their traditional forms – with printed publications and broadcast television channels and radio stations. Much of the time that consumers spend on the internet is devoted to consuming content that has been produced by traditional publishers and broadcasters.

Src:
Zenith. June 2016.
Media Consumption Forecasts.
Via:
Contact:
Jonathan Barnard, Head of Forecasting, jbarnard1@publicisgroupe.net
Tim Collison, Global Communications Director, tcolliso@publicisgroupe.net

*

eMarketer has been publishing short-term forecasts of general media consumption longer than Zenith. It’s latest forecasts to 2018 show similar trends, although the total time consumed estimates are quite different.

Excerpts:

emarketer-media-consump-2012-2018

While mobile devices enable people to consume media content anywhere at any time, the numbers suggest a saturation point is near—and that increased time spent with one medium will tend to come at the expense of time spent with another, as explored in a new eMarketer report, “US Time Spent with Media: eMarketer’s Updated Estimates for Spring 2016.”

Src:
eMarketer. June 2016.
Growth in Time Spent with Media Is Slowing.

*

Technology strategist Michael Wolf, of Activate, recently presented a number of media forecasts at WSJDLive, The Wall Street Jounral’s global tech conference.

Here are excerpts:

activate-time-with-media-cagr-2015-2020

activate-svod-subs-per-subscriber-2016-2020

activate-music-streaming-subs-vs-ads-2013-2020

activate-music-streaming-rev-2006-2016

activate-smart-speakers-2015-2020

activate-global-consumer-spend-digi-media-2015-2020

Src:
Activate. October 2016.
Tech and Media Outlook 2017.

*

PwC produces an annual 5-year outlook for the entertainment and media industry which includes forecasts of consumer spending and advertising revenues.

Here are forecasts for US aggregate media spending for 2016-2020:

pwc-us-aggregate-media-spending-cagr-2016-2020

Src:
PRNewswire. June 2016.
PwC’s Entertainment & Media Outlook Forecasts U.S. Industry Spending to Reach $720 Billion by 2020.

citing:
PwC. June 2016.
“PwC Global Media and Entertainment Outlook: 2016-2020.”

*

PwC’s Chris Lederer, Partner, PwC’s Strategy&, Entertainment & Media practice, gave the following generalizations and examples from the latest report:

“At the highest level our annual Global entertainment and Media Outlook shows a mature media industry with slowing growth prospects.”

“The countries with large populations under 35 are faster growers than countries with larger aged populations,” Lederer observes.

More specifically, PwC’s analysis found that “on average, E&M spending in the 10 youngest markets is growing three times as rapidly as in the 10 oldest markets.”

pwc-consumer-magazine-growth-cagr-2015-2020

2016 is the year when global Internet advertising revenue will surpass TV advertising

pwc-global-tv-internet-advertising-2011-2020

src:
Damian Radcliffe. August 2016.
PwC’s global media outlook 2016-2020: six key trends.
The Media Briefing.

***

Internet Users

ITU publishes an annual estimate of the percentage of internet users in each country.

United States, 2000-2014

Src:
ITU. 2016.
Percentage of Individuals using the Internet (excel).
[ICT Statistics]

***

Broadband Subscriptions

itu-bu-global-broadband-subs-2000-2015

Src:
Andrew Meola. Jun 2016.
All media consumption is declining – with one exception.
Business Insider.
Citing: ITU

ITU data is here:

Src:
ITU. 2016.
“Key ICT indicators for developed and developing countries and the world (totals and penetration rates).” [XLS]
[ICT Statistics]

***

Wired-Line Vs. Wireless Users

activate-wireline-wireless-penetration-rates-2000-2014

Src:
Activate. October 2016.
Tech and Media Outlook 2017.

***

VR Predictions

Game Designer Jesse Schell has made a large set of 40 (mostly) falsifiable predictions for VR, looking out as far as 2025.

Src:
Jesse Schell. March 2016.
40 VR/AR Predictions – GDC 2016

Tags: , , ,

Posted by cc on November 18, 2016 at 9:00 am | comment count



Occupations


Summary

This collection of data includes the following indicators, dates, and sources:
unemployment rate, 2010-2060, OECD
white collar jobs growth, 1900-2000, AFL-CIO
occupations with most job growth (by number and percent), 2014/2024, BLS
occupations with the largest job declines (by number and percent), 2014/2024, BLS

In addition to these datasets, I’ve noted a couple jobs projections by futurist Thomas Frey, and a study estimating the impacts of future technology on jobs in the next couple decades.

Findings

Unemployment Rate

oecd-unemployment-2010-2060

Src:
Knoema

Citing:
OECD. May 2014.
OECD Long Term Baseline
[data below]

*

NAIRU unemployment rate with non-accelerating inflation rate, 2010-2060

Src:
OECD. May 2014.
Economic Outlook No 95: Long-term baseline projections
Variable: NAIRU unemployment rate with non-accelerating inflation rate
Country: United States

***

White Collar Jobs Growth

White collar occupations as a percent of total US Workforce

aflcio-white-collar-jobs-1900-2000

Src:
Marc Cenedella. January 2010.
Great News! We’ve Become A White-Collar Nation.
Business Insider.

Citing:
AFL-CIO. 2003.
“Current Statistics on White Collar Employees.”

***

Fastest Growing and Declining Occupations

Table 1.4 Occupations with the most job growth (raw numbers), 2014 and projected 2024
(Numbers in thousands)

Src:
Bureau of Labor Statistics. April 2016.
Table 1.4 Occupations with the most job growth, 2014 and projected 2024
(Numbers in thousands).

Employment Projections.

Table 1.4 Occupations with the most job growth (percent increase), 2014 and projected 2024
(Numbers in thousands)

Src:
Bureau of Labor Statistics. April 2016.
Table 1.4 Occupations with the most job growth, 2014 and projected 2024
(Numbers in thousands).

Employment Projections.

Table 1.6 Occupations with the largest job declines (raw numbers), 2014 and projected 2024
(Numbers in thousands)

Src:
Bureau of Labor Statistics. April 2016.
Table 1.6 Occupations with the largest job declines, 2014 and projected 2024
(Numbers in thousands).
Employment Projections.

Table 1.5 Fastest declining occupations, 2014 and projected 2024
(Numbers in thousands)

Src:
Bureau of Labor Statistics. April 2016.
Table 1.5 Fastest declining occupations (percent), 2014 and projected 2024
(Numbers in thousands).”
Employment Projections.

***

General Predictions

162 Future Jobs: Preparing for Jobs that Don’t Yet Exist.
Thomas Frey. March 2014.

*

101 Endangered Jobs by 2030.
Thomas Frey. November 2014.

*

The Future Of Employment: How Susceptible Are Jobs To Computerisation?
Carl Benedikt Frey and Michael A. Osborne. September 2013.
Oxford University.

Abstract:
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to our estimates, about 47 percent of total US employment is at risk. We further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation.

Tags:

Posted by cc on November 17, 2016 at 9:00 am | comment count



Media


Summary

This collection of data includes the following indicators, dates, and sources:

GLOBAL

ownership vs. access ($ millions), 2009-2019, McKinsey

total media consumption CAGR, 2010-2015, Zenith

total media consumption min/day, 2010/2015-2018, Zenith

total internet consump min/day, 2015/2018, Zenith

mobile internet consump min/day, 2016, Zenith

all other (besides mobile internet) media consump % change, 2016, Zenith

desktop internet consump min/day, 2010/2014/2016, Zenith

traditional cinema consump % change, 2016, Zenith

traditional outdoor consump % change, 2016, Zenith

traditional television consump min/day, % of consump, % change, 2015/2016/2018, Zenith

traditional radio consump % change, 2016, Zenith

Traditional newspapers consump, % change, 2016, Zenith

traditional magazines consump % change, 2016, Zenith

consumer spending, billions, 2020, Activate

digital video streaming

consumer ebooks

digital music streaming

digital newspapers

esports

digital magazines

UNITED STATES PER ADULT

all digital media time/day, 2012-2018, eMarketer

all mobile (nonvoice) media time/day, 2012-2018, eMarketer

mobile radio time/day, 2012-2018, eMarketer

mobile social networks time/day, 2012-2018, eMarketer

mobile video time/day, 2012-2018, eMarketer

mobile other media time/day, 2012-2018, eMarketer

all desk/lap-top media time/day, 2012-2018, eMarketer

desk/lap-top video time/day, 2012-2018, eMarketer

desk/lap-top social media time/day, 2012-2018, eMarketer

desk/lap-top radio time/day, 2012-2018, eMarketer

desk/lap-top other media time/day, 2012-2018, eMarketer

other connected devices time/day, 2012-2018, eMarketer

non-digital TV time/day, 2012-2018, eMarketer

non-digital radio time/day, 2012-2018, eMarketer

non-digital print media time/day, 2012-2018, eMarketer

non-digital newspapers time/day, 2012-2018, eMarketer

non-digital other media time/day, 2012-2018, eMarketer

all digital and non-digital media total time/day, 2012-2018, eMarketer

UNITED STATES, TOTAL AMONG ALL USERS

gaming hours/day and CAGR, 2015/2020, Activate

messaging hours/day and CAGR, 2015/2020, Activate

social media hours/day and CAGR, 2015/2020, Activate

audio hours/day and CAGR, 2015/2020, Activate

video hours/day and CAGR, 2015/2020, Activate

UNITED STATES

subscription video on demand, subscribers by number of services, 2016-2020, Activate

music revenues by sale type, $ and CAGR, 2006-2016, Activate

music streaming revenue, ads vs. paid subscriptions, 2013-2020, Activate

smart speakers household penetration, millions, 2015-2020, Activate

aggregate media spending CAGR, 2016-2020, Activate

internet advertising

internet access

out-of-home advertising

video games

music

tv advertising

business-to-business

book publishing

radio

cinema

tv & video

magazine publishing

newspaper publishing

internet users % of population, 2000-2014, ITU

broadband subscriptions, 2000-2015, ITU

wired-line vs. wireless users, 2000-2014, Activate

In addition to these data sets, I’ve also noted a large set of VR predictions by Jesse Schell.

Findings

General Media Consumption

McKinsey publishes an annual global media spending/revenues report examining the last five years of historic data and forecasting trends for the next five years. The most recent report includes the following ownership vs. access chart:

mckinsey-global-ownership-vs-access-2009-2019

Note: Ownership consists of home video physical sales, physical recorded music sales and recorded music digital downloads. Access consists of OTT digital

video, recorded music digital subscriptions and recorded music ad-supported digital streaming.

Src:

McKinsey&Company. July 2016.

Global Media Report 2015: Global Industry Overview.” P.21.

Citing:

McKinsey & Company, Wilkofsky Gruen Associates

Older McKinsey reports are available, which offer some older, historic data points. For example, the 2014-2018 Outlook includes some historic data going back to 2013.

*

Zenith (part of Publicis Media) is a marking consulting firm. It’s annual, global survey (two years running), “Media Consumption Forecasts,” estimates recent, current, and near-term media consumption patterns. The current report forecasts general media consumption trends two years out, to 2018. The report estimates time spent reading newspapers and magazines, watching television, listening to the radio, visiting the cinema, using the internet, and viewing outdoor advertising while out of the home. 71 countries are covered, and regional estimates are available (although the freely available excerpts below are global).

Excerpts:

Total media consumption

2010-2015: +7.9% (driven by internet consump), avg +1.5% per year

2010: 403 min/day

2015: 435 min/day

2016: +1.4%

2017: +1.2%

2018: 448 min/day, +0.4% (mobile consump levels off)

Total internet consumption

2015: 110 min/day

2018: 31% global media consumption

Mobile internet consumption

2016: +27.7%, 86 min/day, accounting for 71% of internet consump

Overall media consumption

2016: +1.4%

All other media consumption (besides mobile internet)

2016: -3.4%

Desktop internet consumption

2010: 36 min/day

2014: 52 min/day

2016: 36 min/day

Traditional cinema consumption

2016: -0.5%

Traditional outdoor consumption

2016: -0.8%

Traditional television consumption

[declining, but still the most popular medium]

2015: 177 min/day, 41% of media consumption

2016: -1.5%

2018: 38% of media consumption

Traditional radio consumption

2016: -2.4%

Traditional newspapers consumption

2016: -5.6%

Traditional magazines consumption

2016: -6.7%

Note: [The traditional media] figures only refer to time spent with these media in their traditional forms – with printed publications and broadcast television channels and radio stations. Much of the time that consumers spend on the internet is devoted to consuming content that has been produced by traditional publishers and broadcasters.

Src:

Zenith. June 2016.

Media Consumption Forecasts.

Via:

Contact:

Jonathan Barnard, Head of Forecasting, jbarnard1@publicisgroupe.net

Tim Collison, Global Communications Director, tcolliso@publicisgroupe.net

*

eMarketer has been publishing short-term forecasts of general media consumption longer than Zenith. It’s latest forecasts to 2018 show similar trends, although the total time consumed estimates are quite different.

Excerpts:

emarketer-media-consump-2012-2018

While mobile devices enable people to consume media content anywhere at any time, the numbers suggest a saturation point is near—and that increased time spent with one medium will tend to come at the expense of time spent with another, as explored in a new eMarketer report, “US Time Spent with Media: eMarketer’s Updated Estimates for Spring 2016.”

Src:

eMarketer. June 2016.

Growth in Time Spent with Media Is Slowing.

*

Technology strategist Michael Wolf, of Activate, recently presented a number of media forecasts at WSJDLive, The Wall Street Jounral’s global tech conference.

Here are excerpts:

activate-time-with-media-cagr-2015-2020

activate-svod-subs-per-subscriber-2016-2020

activate-music-streaming-subs-vs-ads-2013-2020

activate-music-streaming-rev-2006-2016

activate-smart-speakers-2015-2020

activate-global-consumer-spend-digi-media-2015-2020

Src:

Activate. October 2016.

Tech and Media Outlook 2017.

*

PwC produces an annual 5-year outlook for the entertainment and media industry which includes forecasts of consumer spending and advertising revenues.

Here are forecasts for US aggregate media spending for 2016-2020:

pwc-us-aggregate-media-spending-cagr-2016-2020

Src:

PRNewswire. June 2016.

PwC’s Entertainment & Media Outlook Forecasts U.S. Industry Spending to Reach $720 Billion by 2020.

citing:

PwC. June 2016.

“PwC Global Media and Entertainment Outlook: 2016-2020.”

*

PwC’s Chris Lederer, Partner, PwC’s Strategy&, Entertainment & Media practice, gave the following generalizations and examples from the latest report:

“At the highest level our annual Global entertainment and Media Outlook shows a mature media industry with slowing growth prospects.”

“The countries with large populations under 35 are faster growers than countries with larger aged populations,” Lederer observes.

More specifically, PwC’s analysis found that “on average, E&M spending in the 10 youngest markets is growing three times as rapidly as in the 10 oldest markets.”

pwc-consumer-magazine-growth-cagr-2015-2020

2016 is the year when global Internet advertising revenue will surpass TV advertising

pwc-global-tv-internet-advertising-2011-2020

src:

Damian Radcliffe. August 2016.

PwC’s global media outlook 2016-2020: six key trends.

The Media Briefing.

***

Internet Users

ITU publishes an annual estimate of the percentage of internet users in each country.

United States, 2000-2014

Src:

ITU. 2016.

Percentage of Individuals using the Internet (excel).
[ICT Statistics]

***

Broadband Subscriptions

itu-bu-global-broadband-subs-2000-2015

Src:

Andrew Meola. Jun 2016.

All media consumption is declining – with one exception.

Business Insider.

Citing: ITU

ITU data is here:

Src:

ITU. 2016.

“Key ICT indicators for developed and developing countries and the world (totals and penetration rates).” [XLS]
[ICT Statistics]

***

Wired-Line Vs. Wireless Users

activate-wireline-wireless-penetration-rates-2000-2014

Src:

Activate. October 2016.

Tech and Media Outlook 2017.

***

VR Predictions

Game Designer Jesse Schell has made a large set of 40 (mostly) falsifiable predictions for VR, looking out as far as 2025.

Src:

Jesse Schell. March 2016.

40 VR/AR Predictions – GDC 2016

Media was originally published on Extrapolations

Tags: , , ,

Posted by on November 15, 2016 at 9:02 pm | comment count



Occupations


Summary

This collection of data includes the following indicators, dates, and sources:

unemployment rate, 2010-2060, OECD

white collar jobs growth, 1900-2000, AFL-CIO

occupations with most job growth (by number and percent), 2014/2024, BLS

occupations with the largest job declines (by number and percent), 2014/2024, BLS

In addition to these datasets, I’ve noted a couple jobs projections by futurist Thomas Frey, and a study estimating the impacts of future technology on jobs in the next couple decades.

Findings

Unemployment Rate

oecd-unemployment-2010-2060

Src:
Knoema

Citing:

OECD. May 2014.

OECD Long Term Baseline

[data below]

*

NAIRU unemployment rate with non-accelerating inflation rate, 2010-2060

Src:

OECD. May 2014.

Economic Outlook No 95: Long-term baseline projections

Variable: NAIRU unemployment rate with non-accelerating inflation rate

Country: United States

***

White Collar Jobs Growth

White collar occupations as a percent of total US Workforce

aflcio-white-collar-jobs-1900-2000

Src:

Marc Cenedella. January 2010.

Great News! We’ve Become A White-Collar Nation.

Business Insider.

Citing:

AFL-CIO. 2003.

“Current Statistics on White Collar Employees.”

***

Fastest Growing and Declining Occupations

Table 1.4 Occupations with the most job growth (raw numbers), 2014 and projected 2024

(Numbers in thousands)

Src:

Bureau of Labor Statistics. April 2016.

Table 1.4 Occupations with the most job growth, 2014 and projected 2024

(Numbers in thousands).


Employment Projections.

Table 1.4 Occupations with the most job growth (percent increase), 2014 and projected 2024

(Numbers in thousands)

Src:

Bureau of Labor Statistics. April 2016.

Table 1.4 Occupations with the most job growth, 2014 and projected 2024

(Numbers in thousands).


Employment Projections.

Table 1.6 Occupations with the largest job declines (raw numbers), 2014 and projected 2024

(Numbers in thousands)

Src:

Bureau of Labor Statistics. April 2016.

Table 1.6 Occupations with the largest job declines, 2014 and projected 2024

(Numbers in thousands).

Employment Projections.

Table 1.5 Fastest declining occupations, 2014 and projected 2024

(Numbers in thousands)

Src:

Bureau of Labor Statistics. April 2016.

Table 1.5 Fastest declining occupations (percent), 2014 and projected 2024

(Numbers in thousands).”

Employment Projections.

***

General Predictions

162 Future Jobs: Preparing for Jobs that Don’t Yet Exist.

Thomas Frey. March 2014.

*

101 Endangered Jobs by 2030.

Thomas Frey. November 2014.

*

The Future Of Employment: How Susceptible Are Jobs To Computerisation?

Carl Benedikt Frey and Michael A. Osborne. September 2013.

Oxford University.

Abstract:

We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier. Based on these estimates, we examine expected impacts of future computerisation on US labour market outcomes, with the primary objective of analysing the number of jobs at risk and the relationship between an occupation’s probability of computerisation, wages and educational attainment. According to our estimates, about 47 percent of total US employment is at risk. We further provide evidence that wages and educational attainment exhibit a strong negative relationship with an occupation’s probability of computerisation.

Occupations was originally published on Extrapolations

Tags:

Posted by on at 1:01 pm | comment count



Crime


Summary

This collection of data includes the following indicators, dates, and sources:
violent/property crime rates, 1994-2015, U.S. Dept. of Justice
white collar crime prosecutions, 1995-2015, TRAC
executions, 1976-2016 & 1608-2002, Death Penalty Info Center
adult correctional population, 2014-1980, DoJ
incarceration rate, 1925-2014, DoJ and Sourcebook of Criminal Justice Stats
state and federal confinement and community facilities, 1979-2005 (quinquennial), DoJ

Findings

Crime Rates

Crime in the United States
by Volume and Rate per 100,000 Inhabitants, 1994–2015


Google Spreadsheet

Note: The violent crime figures include the offenses of murder, rape (legacy definition), robbery, and aggravated assault.

Note: The property crime figures include the offenses of burglary, larceny-theft, and motor vehicle thefts.

Src for 1996-2015:
U.S. Department of Justice. Accessed October 24, 2016.
Crime in the United States 2015.
Table 1: Crime in the United States.

Src for 1994, 1995:
U.S. Department of Justice. Accessed October 24, 2016.
Crime in the United States 1995.
Section II: Crime Index Offenses Reported [PDF]

*

White Collar Crime

For every 100,000 people in the United States, there are 5,317 arrests that are directly related to white collar crime.

In August 2014, it was reported that there were 512 total white collar prosecutions that occurred in the United States.

On a year to year comparison with August 2013, the 512 incidents are 6.2% lower.

It is estimated that 1 out of every 4 households will become the victim of a white collar crime at some point.

More than 88% of white collar crime incidents are never reported to law enforcement agencies, although about half of all incidents are reported to someone, such as a supervisor.

White collar crime has been moving away from stealing money from companies to stealing money from people. The most frequently cited charge that leads a prosecution attempt is aggravated identity theft. This charge accounts for 18.6% of the total charges that were filed within the last month. Mail fraud or conspiracy charges to commit offenses that defraud the country are also popular charges that are filed. In total, however, bank fraud and wire fraud are still the most popular white collar offenses that are investigated.

Src:
Brandon Gaille. Nov 2014.
34 Surprising White Collar Crimes Statistics.

*

White Collar Crime Prosecutions, 1995-2015

trac-white-collar-crime-prosecutions-1995-2015

trac-white-collar-crime-prosecutions-change-1995-2015

trac-white-collar-crime-top-charges-1995-2015

Src:
TRAC Reports. July 2015.
Federal White Collar Crime Prosecutions At 20-Year Low.
Transactional Records Access Clearinghouse. Syracuse University.

***

Capital Punishment

The Death Penalty Information Center (DPIC) tracks and aggregates historic data on capital punishment in the United States.

DPIC’s modern data set goes back to 1976

dpic-executions-1976-2016

DPIC has also collected execution data going back to 1608 in its “[Espy File],” based on research compiled by M. Watt Espy and John Ortiz Smykla. The Espy data is available in a few different formats at the DPIC website, but Time has published an interactive info graphic showing the total number of executions per year, integrating the Espy data with DPIC’s modern data.

Srcs:

DPIC. Updated October 19, 2016. Accessed November 3, 2016.
Executions by Year.
Executions in the U.S. 1608-2002: The Espy File.
“Espy File Data.” XLS

Chris Wilson. July 24, 2014.
Every Execution in U.S. History in a Single Chart.
Time.

TO DO: EXTRACT ANNUAL TALLIES OF EXECUTIONS FROM ESPY DATA FILE.

***

Incarceration

The Bureau of Justice Statistics tracks data on the US correctional population going back to at least 1980.

bjs-adult-correctional-population2014-1980


Google Spreadsheet

Src:
Bureau of Justice Statistics. Accessed November 3, 2016.
Key Statistic: Total Correctional Population.
“Estimated number of persons supervised by U.S. adult correctional systems, by correctional status, 1980-2014.” [XLS]
U.S. Department of Justice.

*

A compilation of historic incarceration rates, 1925-2014, is published a WikiCommons, citing data from the “Sourcebook of Criminal Justice Statistics.

ualbany-incarceration-rates-1925-2014


Google Spreadsheet

Src:
Smallman12q. January 2010. Accessed November 3, 2016.
(Subsequently updated by other users)
File:U.S. incarceration rates 1925 onwards.png
Wikimedia Commons.

Citing:
U.S. Department of Justice, Bureau of Justice Statistics.
and
University of Albany.
Sourcebook of Criminal Justice Statistics (2003).
Table 6.28, P.500.

*

State and Fed facilities (confinement and community facilities)

The “Census of State and Federal Adult Correctional Facilities” has been conducted every 5 to 7 years since 1974. The census reports include a count of the number of facilities which were included in the survey. Below are excerpts from 1979-2005.

Confinement AND Community Facilities
1979 – 791
state confinement – 568
1984 – 903
state confinement – 694
1990 – 1,287
state confinement – 957
1995 – 1,500
state confinement – 1,084
2000* – 1,668
state confinement – 1,023
2005 – 1,821 (nearly all growth in private facilities)

Note: 2000 was the first year that specifically called out private facilities.

Src:
Bureau of Justice Statistics. 1979-2005.
Census of State and Federal Correctional Facilities.” [multiple reports]
Data Collection: Census Of State And Federal Adult Correctional Facilities.
U.S. Department of Justice.



Government


Summary

This collection of data includes the following indicators, dates, and sources:
passports circulating, 1989-2015, Dept. of State
passports issued, 1974-2015, Dept. of State
licensed drivers, 1960-2014, Dept. of Transportation
biometric ID usage rates in hospitals, 2008-2012, Raymonde Charles et al.
voting rate, 1978-2014 & 1964-2014, Census Bureau
voter registration, 1966-2014 & 1828-1956, Census Bureau & Congressional Quarterly Press
people working in government, 2014/2024, BLS
federal laws passed, 1973-2015 & 1947-2013, GovTrack & Brookings Inst.

Findings

Personal Identification

Passports

U.S. Passports Circulating (1989-2015) and Issued per Year (1974-2015)
Note: As of 1996, passports issued statistics are tabulated for the fiscal year.


Google Spreadsheet

Src:
US Department of State. Accessed October 17, 2016.
“Passports Statistics.”

Note:
The Department of State has been issuing exclusively biometric/electronic passports since 2007.

Here’s a map showing the state-by-state distribution of Americans with active passports, for the year 2013.

expeditioner-percent-americans-with-passport-2013

The map is based on the State Department data given above for the number of passports issued in the last 10 years, divided by the total US population (per the Census Bureau, minus the ~12 million undocumented residents and ~13 million legal permanent residents who cannot obtain passports).

Src:
Matt Stabile. February 2014.
How Many Americans Have A Passport?
The Expeditioner.

*

Licensed Drivers, Vehicle Registrations, and Resident Population (In Millions)
1960-2014


Google Spreadsheet

Src:
Department of Transportation. January 2016.
Highway Statistics 2014.

*

Gov’t Issued ID

The 2012 American National Election Survey asked survey respondents about what type of government-issued photo identification they possessed. The survey indicates that 6.3% of respondents do not have a valid, government-issued photo ID.

These are the total weighted percentages in responses.

Question: Do you have a non-expired Driver’s License?
Variable name: dem3_driver
Has: 83.9%
Does not have: 15.4%
Don’t know: 0%
Refused: 0.6%
Total number of respondents: 6,112

Question: Do you now have a non-expired U.S. passport, or do you not have one?
Variable name: dem3_passport
Has: 40.8%
Does not have: 58.4%
Don’t know: 0.2%
Refused: 0.6%
Total number of respondents: 5,914

Question: (If no valid driver’s license or passport) Do you have another form of non-expired, government issued photo ID, such as a state-issued ID or military ID?
Variable name: dem3_govtid
Has: 5.9%
Does not have: 6.3%
Inapplicable: 87.3%
Don’t know: 0%
Refused: 0.5%
Total number of respondents: 5,914

Src:
American National Election Studies. May 28, 2015.
User’s Guide and Codebook for the ANES 2012 Time Series Study.
The University of Michigan and Stanford University.
Pp. 732-734.

Other source notes:
Description: Demographics3
Position: Pre-election survey, section-item no. 62.7-12, 62.7-13

NOTE: ANES confirmed by email that 2012 was the first year this data was collected. The same questions will be asked in the 2016 post-election survey, and results will be published at the end of the first quarter of 2017.

Although ANES has not collected any previous data on gov’t-issued ID possession rates, a couple other surveys have been conducted recently, in the context of voter ID requirements. For example:

2006 — the Brennan Center (NYU School of Law) commissioned a survey (of 987 voting-age Americans) finding that 11% of respondents did not have ready access to government-issued photo ID.
[Src]

2001 — The Carter-Ford Commission on Election Reform found that 6-11% of voting-age citizens lacked state-issued photo ID.
[Src]

*

Biometric Data Tracking

Biometric Update is a research and news source with a pretty wide range of news articles, research papers, white papers, and overviews describing current biometric identification technologies. The Government Purchasing news articles may be of particular interest. The statistics presented on the site are largely industry-focused.

search terms:
biometric identification adoption rates
mobile driver’s license (Iowa pilot in 2016)
e-driving licenses
e-passports
border management
national IDs

*

White Paper:
Rawlson O’Neil King. June 2014.
National and Civil ID White Paper.
Biometrics Research Group

Gives an overview of the format and use-cases for ePassports, eIDs, and electronic voting enrollment. Briefly describes (emphasizing shortcomings) the US social security number and driver license with regard to their use as identification.

*

Research report:
Smithers Pira. July 2016.
The Future of Personal ID to 2021.
Contact: Stephen Hill shill@smithers.com; Josh Rabb jrabb@smithers.com
Note: This research firm focuses on packaging, paper and print industry supply chains.

Tables & Figures:
Trends in personal identification
The evolution of the ePassport
Market forecast of for personal ID by technology 2016-2021
Unit forecast of (2016-2021):
traditional MRP
ePassports
traditional national ID
eID
driving licences
traditional visas
eVisas
health cards
electoral systems
vital documents
The evolving landscape of personal identification 2016-2021

I have NOT established contact with this firm to inquire about data sharing, but, as noted above, the Department of State has been issuing exclusively biometric/electronic passports since 2007.

*

Research report:
TechNavio, May 2016
Biometrics Market In North America 2016-2020
Contact: americas@technavio.com

Excerpts:

Market research analysts at Technavio have predicted that the biometrics market in North America will grow steadily at a moderate CAGR of more than 12% by 2020.

Owing to an increase in investments and the early adoption of biometric technology, the biometric market in North America will have a constant demand from the government sector. According to this market research analysis, this segment will account for about 40% of the total market share by 2020 and will dominate the market throughout the forecast period.

*

Research report:
TechNavio, October 2015
Global Law Enforcement Biometrics Market 2015-2019
Contact:

Technavio’s analysts forecast the global law enforcement biometrics market to grow at a CAGR of 13.35% over the period 2014-2019.

*

Research report:
TechSci Research, May 2016.
Global Biometrics Market By Type (Fingerprint Recognition, Facial Recognition, Hand/Palm Recognition, Iris Scanner, Voice Recognition, Vein Scanner & Others), By End Use Sector, By Region, Competition Forecast and Opportunities, 2011 – 2021.

Includes market size and forecast value for North American Government applications (as well as other regions and sectors.

*

Data on the use of biometric information by government agencies is fairly hard to come by. Aside from the few reports mentioned above, I’ve found some data on the adoption rates of biometric data within US hospitals. Not sure if this might have any implications for adoption rates in other sectors, like government.

Excerpts:

Biometric devices and their accompanying software in healthcare institutions permit the automatic authentication of patient and provider identity for different purposes such as secure EHR system access, and patient verification31. The most common hospital implementation of biometrics are the use of fingerprint and iris scanning32, 33. The unique authentication methods of biometrics make it difficult to mismatch and forge identities since no two irises or fingerprints are the same.

The use of fingerprint scanning had an average annual adoption increase of 1.23% per year from 2008, leading to a total of 15.9% (n=871) adoption within all hospital respondents (n=5467) in 2012. Iris scanning technology has an average annual adoption rate of 0.02% from the same time frame, and is only currently being used in 13 hospitals (0.02%) in 2012. Only 2.49% (n=136) hospitals in 2012 plan to expand or adopt the fingerprint technology in the following years, in contrast to only 12 hospitals (0.22%) for iris scanning.

Low adoption may be due to the costs in implementing biometrics within existing EHR systems and workflows.

biometrics-use-in-hospitals-2008-2012

Src:
Raymonde Charles Y. Uy et al. November 2015.
The State and Trends of Barcode, RFID, Biometric and Pharmacy Automation Technologies in US Hospitals.
AMIA Annual Symposium Proceedings Archive.

*

Examples of companies/technologies currently being deployed in the US:

Unisys integrates NEC’s facial recognition software in CBP project at JFK airport.
Biometric Update. May 2016

Excerpt:
NEC‘s top NIST rated facial matching technology will be used to compare the image taken during the normal inspection process to the image stored on the traveler’s e-passport. The initial deployment will apply to first time Visa Waiver Program (VWP) travelers and returning U.S. citizens with e-Passports. … Unisys worked together with NEC to develop this entry/exit screening solution, which allows CBP officers to capture a live facial image. The facial image from electronic passports is then compared to the live captured image. If the images do not match, travelers may be subject to additional screening by CBP officers.

***

Voting Registration and Rates

Voting Rates in Congressional and Presidential Elections: 1978 to 2014

census-voting-rates-1978-2014

Src:
U.S. Census Bureau.
Who Votes? Congressional Elections and the American Electorate: 1978–2014.” July 2015.
Figure 2, P. 4.

*

The Current Population Survey collects data on voting and voter registration in November of even-numbered years, and has done so since 1964. It provides information about voting and registration by many characteristics, including age, sex, race, and education. Because the data are from a survey, they are subject to sampling error.


Google Spreadsheet

Src:
U.S. Census Bureau. February 2012.
Table A-1. Reported Voting and Registration by Race, Hispanic Origin, Sex, and Age Groups: November 1964 to 2014 (NOTE: Voting rates corrected February 2012).
Historical Reported Voting Rates.

[XLS file]

*

Voter Turnout in Presidential Elections: 1828 – 2012

Year % Turnout of Voting Age Population
1828 57.6%
1832 55.4%
1836 57.8%
1840 80.2%
1844 78.9%
1848 72.7%
1852 69.6%
1856 78.9%
1860 81.2%
1864 73.8%
1868 78.1%
1872 71.3%
1876 81.8%
1880 79.4%
1884 77.5%
1888 79.3%
1892 74.7%
1896 79.3%
1900 73.2%
1904 65.2%
1908 65.4%
1912 58.8%
1916 61.6%
1920 49.2%
1924 48.9%
1928 56.9%
1932 56.9%
1936 61.0%
1940 62.5%
1944 55.9%
1948 53.0%
1952 63.3%
1956 60.6%

Note: Voting Age Population includes those ineligible to vote such as felons. Because of this, V.A.P. figures are naturally lower than if the Voting Eligible Population (V.E.P.) is used as the denominator.

Note: Figures for 1960-2012 are also given on the source page, which cites data compiled by Gerhard Peters from data obtained from the Federal Election Commission.

Src:
Gerhard Peters and John T. Woolley. Accessed October 24, 2016.
The American Presidency Project: Voter Turnout in Presidential Elections 1828-2012.

Citing:
Sources:
(1824-1956) – Lyn Ragsdale, Vital Statistics on the Presidency (Washington, D.C.: Congressional Quarterly Press, 1998), 132-38.

***

Number of People Working in Gov’t 2014/2024


Google Spreadsheet

Src:
Bureau of Labor Statistics. Accessed October 26, 2016.
Employment Projections.
Industry-occupation matrix data, by occupation — Total, all occupations
[XLS]

***

Laws Passed

GovTrack aggregates publicly available data on the number of federal laws passed during each two-year Congressional session. Data are available going back to 1973.


Google Spreadsheet

Enacted Laws: Enacted bills and joint resolutions (both bills and joint resolutions can be enacted as law)
Passed Resolutions: Passed resolutions (for joint and concurrent resolutions, this means passed both chambers)
Got A Vote: Bills and joint/concurrent resolutions that had a significant vote in one chamber
Failed Legislation: Bills and resolutions that failed a vote on passage or failed a significant vote such as cloture, passage under suspension, or resolving differences
Vetoed Bills (w/o Override): Bills that were vetoed and the veto was not overridden by Congress
Other Legislation: Bills and resolutions that were introduced, referred to committee, or reported by committee but had no further action

Src:
GovTrack. Accessed November 3, 2016.
Congress > Bills > Statistics and Historical Comparison > Bills by Final Status.

*

Brookings Institution also aggregates figures on Congressional activity. This data set covers 1947-2013.


Google Spreadsheet

Src:
Raffaela Wakeman et al. July 9, 2013 (updated August 2014).
“Vital Statistics on Congress: Data on the U.S. Congress – A Joint Effort from Brookings and the American Enterprise Institute.”
[PDF]

Brookings Institution
contact: vitalstatistics@brookings.edu

Tags: ,

Posted by cc on November 14, 2016 at 11:44 pm | comment count



Environment


Summary

This collection of data includes the following indicators, dates, and sources:
Land Use
urban/developed km2, 1992-2100, USGS
forest km2, 1992-2100 & 1982-2012, USGS & USDA
forest (mil hectares), 2010-2030(2100), Pardee
deforestation (mil hectares), 2010-2100, Pardee
cropland km2, 1992-2100 & 1982-2012, USGS & USDA
grassland/shrubland km2, 1992-2100 & 1982-2012, USGS & USDA
hay/pasture km2, 1992-2100 & 1982-2012, USGS & USDA
wetland km2, 1992-2100, USGS
Conservation Reserve Program, 1985-2012, USDA
agricultural demand (mil met tons), 2010-2030(2100), Pardee
agricultural productions (mil met tons), 2010-2030(2100), Pardee
yield in agriculture (tons/hectar), 2010-2030(2100), Pardee
Air Quality
carbon emissions from fossil fuels (bil tons), 2010-2100, Pardee
carbon emissions cumulative change (bil tons), 2010-2100, Pardee
Water Use
water usage (cubic Km), 2010-2030(2100), Pardee
water use cumulative change (cubic Km), 2010-2100, Pardee
Mining
active mines, 2004-2013, CDC
Climate Change
max temperatures, 1950-2099, USGS
precipitation, 1950-2099, USGS
snowpack, 1950-2099, USGS
soil water storage, 1950-2099, USGS
sea-level rise, 1800-2100, NCA citing Kemp, Church, Narem, Parris, Etheridge
sea-level warming, 1900-2010, NCA citing Chavez, Etheridge
extreme precipitation events, 1900-2000, NCA citing Kunkel
water demand change, 2005/2060, NCA citing Brown
water supply risk, 2050, NCA citing Roy et al.
atmospheric carbon dioxide, 1900-2000, NCA citing Etheridge
sea surface pH, 1900-2000, NCA citing Etheridge
Northeaster fisheries shifting north, 1970-2010, NCA citing Pinsky and Fogarty

Findings

Land Use

The U.S. Geological Survey tracks and forecasts several categories of land use, with charts and maps showing change under four different scenarios from 1992 through 2100. The land-use categories include acreage that is 1) urban/developed, 2) forest, 3) cropland, 4) grassland/shrubland, 5) hay/pasture, and 6) wetland.

USGS-urban-dev-and-forest-land-area-1992-2100

USGS-crop-and-grass-land-area-1992-2100

USGS-pasture-and-wetland-land-area-1992-2100

img src:
Terry Sohl, et al. December 2012.
The Completion of Four Spatially Explicit Land-use and Land-cover (LULC) Scenarios for the Conterminous United States.” P.28
U.S. Geological Survey.

*

The USDA’s National Resources Conservation Service (NRCS) has published a periodic National Resources Inventory (NRI) since 1977, with data being collected annually since 2000. The contemporary reports give acreage counts for non-Federal lands, including cropland, Conservation Reserve Program lands, pastureland, rangeland, forest land, and other rural land.

Excerpts:

Table 2 – Land Cover/use of non-Federal rural land, 1982-2012 (quinquennial)
In thousands of acres, with margins of error

USDA-land-use-1982-2012

Note: Acreages for Conservation Reserve Program (CRP) land are established through geospatial processes and administrative records; therefore, statistical margins of error are not applicable and shown as a dashed line (–). CRP was not implemented until 1985.

Table 9 – Changes in land cover/use between 1982 and 2012
In thousands of acres, with margins of error

USDA-land-use-change-1982-2012

Similar tables for five-year intervals within the 1982-2012 are also presented in the report (pp.41-46).

Cropland
A land cover/use category that includes areas used for the production of adapted crops for harvest. Two subcategories of cropland are recognized: cultivated and noncultivated. Cultivated land comprises land in row crops or close-grown crops and also other cultivated cropland; for example, hayland or pastureland that is in a rotation with row or close-grown crops. Noncultivated cropland includes permanent hayland and horticultural cropland.

Pastureland
A land cover/use category of land managed primarily for the production of introduced forage plants for livestock grazing. Pastureland cover may consist of a single species in a pure stand, a grass mixture, or a grass-legume mixture. Management usually consists of cultural treatments: fertilization, weed control, reseeding, renovation, and control of grazing. For the NRI, includes land that has a vegetative cover of grasses, legumes, and/or forbs, regardless of whether or not it is being grazed by livestock.

Rangeland
A broad land cover/use category on which the climax or potential plant cover is composed
principally of native grasses, grasslike plants, forbs or shrubs suitable for grazing and browsing, and introduced forage species that are managed like rangeland. This would include areas where introduced hardy and persistent grasses, such as crested wheatgrass, are planted and such practices as deferred grazing, burning, chaining, and rotational grazing are used, with little or no chemicals or fertilizer being applied. Grasslands, savannas, many wetlands, some deserts, and tundra are considered to be rangeland. Certain communities of low forbs and shrubs, such as mesquite, chaparral, mountain shrub, and pinyon-juniper, are also included as rangeland.

src:
USDA, August 2015.
2012 National Resources Inventory: Summary Report.”
pp. 3-2, 3-39, 3-47, 4-1

*

For projected data on farmlands, 2014-2025, see the “Crop Acreage” section of the Food research round-up, citing “USDA Agricultural Projections to 2025.”

*

International Futures is a forecasting platform developed by Barry Hughes, based at the Pardee Center at the University of Denver. Among its many forecasts are a small set of agriculture and environment indicators.

Excerpt:

Quinquennial agriculture and environment indicators, United States, Working File
Pardee-crops-forest-carbon-emit-water-use-2010-2030

Forecast data through 2100 is available at the site for each indicator, if you click through. For example, here’s the chart for millions of hectares of forest land through 2100:
Pardee-forest-land-2010-2100

The charting tools offer several options, including specialized displays for particular issues. For example, the Advanced Sustainability Analysis, which calculates fossil fuel use, carbon emissions, deforestation, and water use in terms of intensity per million GDP, thousand population, and thousand labor. These calculations are also forecast through 2100, accessible by changing the “select year” parameter of the display.

Raw annual deforestation (million hectares), 2010-2100 (via the Advanced Sustainability Analysis table):
Pardee-raw-annual-deforestation-2010-2100

TO DO: EXTRACT ANNUAL DATA POINTS BY CLICKING THROUGH ON THE INDIVIDUAL INDICATORS FROM THE REPORT. THIS ALSO GIVES ACCESS TO THE FORECAST FIGURES THROUGH 2100.

Src:
International Futures. Accessed August 22, 2016.
Basic Report – USA, Working File.”
Advanced Sustainability Analysis — USA, Working File
The Pardee Center. University of Denver.

*

This 2010 report includes a round-up of recent projections of forest land conversion in the United States. The report discusses socioeconomic drivers of land-use change affecting forest area, and defines five categories of change: afforestation, deforestation, forest fragmentation, forest parcelization, and increased numbers of structures on forest land.

Here’s the summary of projected land-base changes affecting US forests:

Alig-land-change-projections-affecting-forests

Src:
Ralph Alig et al. 2010.
Conversions of Forest Land: Trends, Determinants, Projections, and Policy Considerations.” In Advances in Threat Assessment and Their Application to Forest and Rangeland Management.
U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations.
p.14

Air Quality

Raw annual carbon emissions (billion tons), 2010-2100
Pardee-carbon-emit-raw-annual-2010-2100

Cummulative change in carbon emissions, percent (billion tons), 2010-2100
Pardee-carbon-emit-cum-change-2010-2100

TO DO: EXTRACT ANNUAL DATA POINTS

Src:
International Futures. Accessed August 22, 2016.
Advanced Sustainability Analysis — USA, Working File
The Pardee Center. University of Denver.

*

More data on carbon emissions can be found under the Energy Mix blog post.

Water Use

Water use (cubic kilometers), cumulative change in raw values, percent, 2010-2100 (via the Advanced Sustainability table):
Pardee-water-use-cum-change-2010-2100

TO DO: EXTRACT ANNUAL DATA POINTS

Src:
International Futures. Accessed August 22, 2016.
Advanced Sustainability Analysis — USA, Working File
The Pardee Center. University of Denver.

Mining

MSHA-active-mines-2004-2013

src:
CDC. Accessed August 24, 2016.
Number of Active Mines by Sector and Year, 2004-2013.”
Statistics: All Mining
citing:
Mine Safety and Health Administration (MSHA)

TO DO: CONTACT MSHA TO CONFIRM SOURCE, AND INQUIRE ABOUT OLDER DATA.

Climate Change

In 2013, NASA released climate projection data through 2099 for the continental United States that is being used to quantify climate risks to the nation’s agriculture, forests, rivers and cities.

The excerpts below shows the change for the 1950-2005 historic period versus the 2050-2074 forecast period. Max temperatures are trending up, precipitation is staying about the same, snow pack is trending down (dramatically in the Western mountains), and water stored in the soil column is trending down.

Excerpts:

Max Temperatures – High Emissions Scenario (RCP8.5)
NASA-NEX-DCP30-max-temps-high-emit-1950-2074

Max Temperatures – Low Emissions Scenario (RCP4.5)
NASA-NEX-DCP30-max-temps-low-emit-1950-2074

Precipitation — Low Emissions
NASA-NEX-DCP30-precip-low-emit-1950-2074

Snow Water Equivalent — Low Emissions
NASA-NEX-DCP30-snow-low-emit-1950-2074
(Snow Water Equivalent — the liquid water stored in the snowpack)

Soil Water Storage – Low Emissions
NASA-NEX-DCP30-soil-stor-low-emit-1950-2074
(Soil water storage: the water stored in the soil column)

src:
USGS. Accessed August 24, 2016.
National Climate Change Viewer.”

NASA NEX DCP30 National Climate Change Viewer

The full NEX-DCP30 dataset includes 33 climate models for historical and 21st century simulations for four Representative Concentration Pathways (RCP) greenhouse gas (GHG) emission scenarios developed for AR5. … We include 30 of the 33 models in the viewer that have both RCP4.5 and RCP8.5 data; the remaining two scenarios, RCP2.6 and RCP6, are available in the NEX-DCP30 data set.

The NCCV allows the user to visualize projected changes in climate (maximum and minimum air temperature and precipitation) and the water balance (snow water equivalent, runoff, soil water storage and evaporative deficit) for any state, county and USGS Hydrologic Unit (HUC).

To create a manageable number of permutations for the viewer, we averaged the climate and water balance data into four climatology periods: 1950–2005, 2025–2049, 2050–2074, and 2075–2099.

src:
USGS. May 2014.
U.S. Geological Survey – National Climate Change Viewer: Tutorial and Documentation.”

*

The National Climate Assessment (NCA), conducted by the U.S. Global Change Research Program, aggregates a mix of historic quantitative data, data projections, and qualitative descriptions of the impacts of the climate change trends that are being seen and are most likely to continue. Impacts on seven sectors are described: human health, water, energy, transportation, agriculture, forests, and ecosystems.

Citations are provided in-context below. The source information for the NCA report is at the very bottom.

Excerpts:

NCA-extreme-precip-events-1900-2000

One measure of heavy precipitation events is a two-day precipitation total that is exceeded on average only once in a 5-year period, also known as the once-in-five year
event. As this extreme precipitation index for 1901-2012 shows, the occurrence of such events has become much more common in recent decades. Changes are compared to the period 1901-1960, and do not include Alaska or Hawai‘i. (Figure source: adapted from Kunkel et al. 2013 (7)).
p.25

7. K. E. Kunkel, et al. 2013.
Monitoring and understanding trends in extreme storms: State of knowledge.”
Bulletin of the American Meteorological Society, 94.

Past and Projected Changes in Global Sea Level:
NCA-sea-level-1800-2100

Figure shows estimated, observed, and possible amounts of global sea level rise from 1800 to 2100, relative to the year 2000. Estimates from proxy data (4) (for example, based on sediment records) are shown in red (1800-1890, pink band shows uncertainty), tide gauge data in blue for 1880-2009 (5), and satellite observations are shown in green from 1993 to 2012 (6). The future scenarios range from 0.66 feet to 6.6 feet in 2100 (7). These scenarios are not based on climate model simulations, but rather reflect the range of possible scenarios based on other kinds of scientific studies. The orange line at right shows the currently projected range of sea level rise of 1 to 4 feet by 2100, which falls within the larger risk-based scenario range. The large projected range reflects uncertainty about how glaciers and ice sheets will react to the warming ocean, the warming atmosphere, and changing winds and currents. As seen in the observations, there are year-to-year variations in the trend. (Figure source: Adapted from Parris et al. 2012 (7), with contributions from NASA Jet Propulsion Laboratory).
p.30

4. A. C. Kemp, et al. 2011.
Climate related sea-level variations over the past two millennia.”
Proceedings of the National Academy of Sciences, 108, 11017-11022.

5. J. A. Church, et al. 2011.
Sea-level rise from the late 19th to the early 21st century.”
Surveys in Geophysics, 32, 585-602.

6. R. S. Nerem, et al. 2010.
Estimating mean sea level change from the TOPEX and Jason altimeter missions.”
Marine Geodesy, 33, 435-446.
Article is behind a paywall. Might be able to get even more current data from Nerem’s website.

7. A. Parris, et al. 2012
Global Sea Level Rise Scenarios for the United States National Climate Assessment.”
NOAA Tech Memo OAR CPO-1, 37 pp. National Oceanic and Atmospheric Administration.

NCA-water-withdrawals-2005-2060

The effects of climate change, primarily associated with increasing temperatures and potential evapotranspiration, are projected to significantly increase water demand across most of the United States. Maps show percent change from 2005 to 2060 in projected demand for water assuming (a) change in population and socioeconomic conditions consistent with the A1B emissions scenario (increasing emissions through the middle of this century, with gradual reductions thereafter), but with no change in climate, and (b) combined changes in population, socioeconomic conditions, and climate according to the A1B emissions scenario. (Figure source: Brown et al. 2013 (4).)
p.43

4. T. C. Brown, et al. 2013.
Projecting fresh water withdrawals in the United States under a changing climate.”
Water Resources Research, 49, 1259-1276/

NCA-water-supplies-2050

Climate change is projected to reduce water supplies in some parts of the country. This is true in areas where precipitation is projected to decline, and even in some areas where precipitation is expected to increase. Compared to 10% of counties today, by 2050, 32% of counties will be at high or extreme risk of water shortages. Numbers of counties are in parentheses in key. Projections assume continued increases in greenhouse gas emissions through 2050 and a slow decline thereafter (A1B scenario). (Figure source: Reprinted with permission from Roy et al. 2012 (7). Copyright American Chemical Society).
p.44

7. S. B. Roy, et al. 2012.
Projecting water withdrawal and supply for future decades in the U.S. under climate change scenarios.
Environmental Science & Technology, 46, 2545−2556.

Key Climate Variables Affecting Agricultural Productivity

Frost-free season is projected to lengthen across much of the nation. Taking advantage of the increasing length of the growing season and changing planting dates could allow planting of more diverse crop rotations, which can be an effective adaptation strategy.

The annual maximum number of consecutive dry days (less than 0.01 inches of rain) is projected to increase, especially in the western and southern parts of the nation, negatively affecting crop and animal production. The trend toward more consecutive dry days and higher temperatures will increase evaporation and add stress to limited water resources, affecting irrigation and other water uses.

Hot nights are defined as nights with a minimum temperature higher than 98% of the minimum temperatures between 1971 and 2000. Such nights are projected to increase throughout the nation. High nighttime temperatures can reduce grain yields and increase stress on animals, resulting in reduced rates of meat, milk, and egg production.

p.47

NCA-ocean-warming-1900-2010

Sea surface temperatures for the ocean surrounding the U.S. and its territories have risen by more than 0.9°F over the past century. (Figure source: adapted from Chavez et al. 2011 (3)).
p.58

3. F. P. Chavez, et al. 2011.
Marine primary production in relation to climate variability and change.”
Annual Review of Marine Science, 3, 227-260.

NCA-CO2-ocean-impacts-1900-2000

As heat-trapping gases, primarily carbon dioxide (CO2) (panel A), have increased over the past decades, not only has air temperature increased worldwide, but so has the ocean surface temperature (panel B). The increased ocean temperature, combined with melting of glaciers and ice sheets on land, is leading to higher sea levels (panel C). Increased air and ocean temperatures are also causing the continued, dramatic decline in Arctic sea ice during the summer (panel D). Additionally, the ocean is becoming more acidic as increased atmospheric CO2 dissolves into it (panel E). (CO2 data from Etheridge 2010, Tans and Keeling 2012, and NOAA NCDC 2012; SST data from NOAA NCDC 2012 and Smith et al. 2008; Sea level data from CSIRO 2012 and Church and White 2011; Sea ice data from University of Illinois 2012; pH data from Doney et al. 2012 (4,5)).
p.59

4. D. M. Etheridge, et al. 2010.
Law Dome Ice Core 2000-Year CO2, CH4, and N2O Data, IGBP PAGES/World Data Center for Paleoclimatology.”
Data Contribution Series #2010-070. NOAA/NCDC Paleoclimatology Program, Boulder, CO.

CSIRO. 2012.
The Commonwealth Scientific and Industrial Research Organisation.”

J. A. Church and N. J. White. 2011.
Sea-level rise from the late 19th to the early 21st century.”
Surveys in Geophysics, 32, 585-602.

University of Illinois. 2012.
Sea Ice Dataset
University of Illinois, Department of Atmospheric Sciences.

S. C. Doney, et al. 2012.
Climate change impacts on marine ecosystems.”
Annual Review of Marine Science, 4, 11-37.

5. P. Tans and R. Keeling. 2012.
Trends in Atmospheric Carbon Dioxide, Full Mauna Loa CO2 Record.”
NOAA’s Earth System Research Laboratory.

NCA-fisheries-shifting-north-1970-2010

Ocean species are shifting northward along U.S. coastlines as ocean temperatures rise. As a result, over the past 40 years, more northern ports have gradually increased their landings of four marine species compared to earlier landings. While some species move northward out of an area, other species move in from the south. This kind of information can inform decisions about how to adapt to climate change. Such adaptations take time and have costs, as local knowledge and equipment are geared to the species that have long been present in an area. (Figure source: adapted from Pinsky and Fogerty 2012 (19)).
p.61

19. M. L. Pinsky and M. Fogarty. 2012.
Lagged social-ecological responses to climate and range shifts in fisheries.”
Climatic Change, 115, 883-891.

src:
Jerry M. Melillo, et al. May 2014.
Highlights of Climate Change Impacts in the United States: The Third National Climate Assessment.”
U.S. Global Change Research Program.

TO DO: EXTRACT DATA FROM SOURCES

Tags: , , , ,

Posted by cc on September 8, 2016 at 4:51 pm | comment count



Downtowns


Summary

This collection of data includes the following indicators, dates, and sources:

Acreage
urban acreage, 1992-2100 & 2000-2050 & 1950-2010, USGS & Lincoln Inst & US Census
Population
urban population, 1950-2050, United Nations
young adults by proximity to center, 1990/2012, UVA
elderly by proximity to center, 1990/2012, UVA
white residents by proximity to center, 1990/2012, UVA
black residents by proximity to center, 1990/2012, UVA
population by proximity to center, 1990/2012 & 2000/2010, UVA & US Census
population distribution by proximity to center, 2000/2010, US Census
Jobs
metro area jobs, 2002/2011 5-yr period, US Census
skilled jobs by proximity to center, 1980-2010 decadal, US Census
Education
full-time workers with BA+ by proximity to center, 1980-2010 decadal, US Census
educational attainment by proximity to center, 1990/2012, UVA
Desirability/Walkability
competitiveness of city centers, 2002/2011 5-yr period, City Observatory/US Census
cities most likely to be WalkUPs in the future, GWU
Personal Income
per capita income by proximity to center, 1990/2012, UVA
proportion residents living below poverty line by proximity to center, 1990/2012, UVA
home prices by proximity to center, 1980-2010 decadal, US Census
Housing and Density
occupied housing units by proximity to center, 1990/2012, UVA
percent of rented housing by proximity to center, 1990/2012, UVA
population density by proximity to center, 1990/2012, UVA

NOTE: Might be worth talking to someone from MIT’s Center for Advanced Urbanism, or an editor from City Lab or Next City for suggestions of additional “downtown” indicators and possible data sources, as well as more forecast data, which is quite lacking in this round-up.

keywords:
urban development
central business districts
city centers

data desired but lacking:
traffic congestion
construction trends
walkability (longitudinal data)
energy consumption per capita (vs. rural)

Findings

Urban Acreage

The U.S. Geological Survey tracks and forecasts several categories of land use, with charts and maps showing change under four different scenarios from 1992 through 2100. The land-use categories include acreage that is 1) urban/developed, 2) forest, 3) cropland, 4) grassland/shrubland, 5) hay/pasture, and 6) wetland.

Urban/Developed Land Area in Four Scenarios, 1992-2100
USGS-urban-developed-land-area-1992-2100

Excerpt:

Researchers at the USGS Earth Resources Observation and Science Center have developed the FOREcasting SCEnarios of land-cover change (FORE-SCE) model that is based on consistent and systematic historical data derived from Landsat satellite imagery of rates and spatial patterns of land cover change over many years. As part of USGS research to assess potential greenhouse gas fluxes and carbon storage in vegetated landscapes, FORE-SCE has been used to produce projected, annual land-cover maps from 1992 through 2100 for four future scenarios for the conterminous United States. The land cover maps provide 250-meter resolution information for 17 different land-use and land-cover classes, offering an unprecedented combination of thematic detail and spatial resolution for land-cover projections.

The four land cover scenarios that were modeled are based, in turn, on carefully-defined environmental scenarios from the Intergovernmental Panel on Climate Change (IPCC), ensuring that the projected land cover maps may be used in assessment frameworks consistent with IPCC scenario characteristics. The scenarios vary in terms of both climate and socioeconomic conditions in the future.

The A1B scenario is characterized by very high economic growth, strong technological innovation, and open, global economies and societies. Projected land use changes include sprawling urban growth, agricultural expansion, and heavy use of forested lands for wood products.

The A2 scenario is characterized by moderate economic growth, very high population growth, and less open economies and societies. Land use changes include very high urban growth, massive expansion of agricultural lands to feed a growing population, and loss of natural landscapes.

The B1 scenario has high economic growth, moderate population growth, and a focus on environmental sustainability. Land-use change is modest, with compact urban growth and modest increases in agricultural land.

The B2 scenario has moderate economic growth, low population growth, and a focus on environmental sustainability. Natural landscapes are preserved and even expanded in this scenario, with a reduction in lands devoted to agriculture.

Image Src:
Terry Sohl, et al. December 2012.
The Completion of Four Spatially Explicit Land-use and Land-cover (LULC) Scenarios for the Conterminous United States.” P.28
U.S. Geological Survey.

NOTE: This PDF includes the same charts for Forest, Cropland, Grassland/Shrubland, Hay/Pasture, and Wetland

Excerpt Src:
Jon Campbell. Sep 2014.
Modeling a Changing American Landscape
USGS Science Features.
contact: Terry Sohl, sohl@usgs.gov

TO DO: CONTACT TERRY SOHL, sohl@usgs.gov, FOR DATA POINTS. MIGHT BE AVAILABLE VIA ONE OF THE (HUGE) EXCEL FILES ON THIS PAGE.

*

The Lincoln Institute has also projected urban land coverage based on a 1990-2000 sample of 120 global cities.

Three projections are made based on three different assumptions about density change over time: (a) no density change; (b) a 1 percent annual density decline; and (c) a 2 percent annual density decline.

US Urban Land Cover Projections in Three Scenarios, 2000-2050
Lincoln-Inst-urban-land-cover-2000-2050

Src:
XLS: “Urban Land Cover Projections for Countries and Regions, 2000-2050
Via:
Shlomo Angel, et al. 2010.
Section 3 – Urban, National, and Regional Data.”
Atlas of Urban Expansion. Lincoln Institute of Land Policy.

*

Demographia.com, maintained by Wendell Cox (a pro-automobile urban planner who favors low-density planning), has aggregated a few urbanizations statistics from the US Census Bureau. Indicators include urban land area (square miles), urban population, urban households, population density, and household density, as well as annual percent change for all of the above. Decadal data points are available from 1950 through 2010.

src:
Demographia. 2012.
Urbanization in the United States from 1945.”
Demographia.com, Wendell Cox Consultancy.
citing:
US Census Bureau

TO DO: EXTRACT/CONFIRM DATA.

Urban Population

Proportions of urban and rural population in the current country or area in per cent of the total population, 1950 to 2050.

UN-proportion-urban-rural-population-1950-2050

Total urban and rural population

UN-total-urban-rural-population-1950-2050

Urban population of the current country by size class of its urban agglomerations in 2014.

UN-US-city-size-class-1950-2030

The light blue area is a residual category, which includes all cities and urban agglomerations with a population of less than 300,000 inhabitants. The size classes correspond to the following legend:

UN-US-city-size-class-legend-1950-2030

src:
United Nations. 2014
Country Profile: United States of America,” in World Urbanization Prospects: The 2014 Revision.
Department of Economic and Social Affairs, Population Division.

TO DO: EXTRACT DATA. ANNUAL DATA AVAILABLE HERE.

*

dark red – 2012
orange – 1990

Juday-population-50-metro-1990-2012

Juday-young-adults-50-metro-1990-2012

Juday-elderly-50-metro-1990-2012

Juday-white-residents-50-metro-1990-2012

Juday-black-residents-50-metro-1990-2012

src:
Luke Juday. Accessed August 19, 2016.
The Changing Shape of America’s Metro Areas.”
University of Virginia. Demographics Research Group.
citing: 1990 Census data and the 2008-2012 American Community Survey

*

Based on the Census Bureau’s data for the 51 major metropolitan areas (those with more than 1 million population).

Cox-metro-pop-growth-2000-2010

Cox-metro-pop-growth-and-distro-2000-2010

src:
Wendell Cox. October 2012.
Flocking Elsewhere: The Downtown Growth Story.”
New Geography.

citing:
US Census Bureau. September 2012.
Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010.”

Metro Area Jobs

City Observatory analyzed Census Bureau data from the 2002-2007 period to the 2007-2011 period and finds that after a long period of decentralization, economic and demographic indicators are now favoring city centers.

Excerpts:

When we compared the aggregate economic performance of urban cores to the surrounding metro periphery over the four years from 2007 to 2011, we found that city centers—which we define as the area within 3 miles of the center of each region’s central business district—grew jobs at a 0.5 percent annual rate. Over the same period, employment in the surrounding peripheral portion of metropolitan areas declined 0.1 percent per year. When it comes to job growth, city centers are out-performing the surrounding areas in 21 of the 41 metropolitan areas we examined. This “center-led” growth represents the reversal of a historic trend of job de-centralization that has persisted for the past half century.

CityObs-jobs-change-city-centers-peripheries-2002-2011

src:
Joe Cortright. February 2015.
Surging City Center Job Growth.”
City Observatory.

*

Economists at Columbia University and the Getulio Vargas Foundation have also analyzed Census Bureau data from 1980 to 2010, finding that center cities and downtown living are making a come back. Their analysis is based on data from 27 heavily populated U.S. cities, from the CBD to 35 miles away from the center of the cities. Their report includes an interesting graphic on skilled jobs, although these are not the exclusive focus of the report. Elsewhere in this roundup, I’ve also excerpted a graphic for home prices and and education which follow the same trend.

Excerpts:

Skilled Jobs:
Edlund-et-al-skilled-jobs-by-centrality-1980-2010

src:
Lena Edlund, et al. 2015.
Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”
National Bureau of Economic Research. NBER Working Paper No. 21729.

via:
Eric Jaffe. November 2015.
Why the Wealthy Have Been Returning to City Centers.”
Atlantic City Lab.

Education

Full-time workers with at least a bachelor’s degree working 40 or 50 hours per week:
Edlund-et-al-women-with-degrees-by-centrality-1980-2010
The data for men show similar trends, with shares rising a bit more toward the periphery.

src:
Lena Edlund, et al. 2015.
Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”
National Bureau of Economic Research. NBER Working Paper No. 21729.

via:
Eric Jaffe. November 2015.
Why the Wealthy Have Been Returning to City Centers.”
Atlantic City Lab.

*

dark red – 2012
orange – 1990

Juday-ed-attain-50-metro-1990-2012

red – 1990-2012 change
black – baseline change for whole metro area

Juday-ed-change-50-metro-1990-2012

src:
Luke Juday. Accessed August 19, 2016.
The Changing Shape of America’s Metro Areas.”
University of Virginia. Demographics Research Group.
citing: 1990 Census data and the 2008-2012 American Community Survey

Desirability/Walkability

In City Observatory’s analysis of 2002-2007 and 2007-2011, referred to above, they also came up with a competitiveness calculation which favors city centers.

Excerpt:

We undertook a shift-share analysis that allowed is to separate out the effects of changing industry mix from relative competitiveness. The data make it clear that city centers are more competitive in 2011 than they were in 2007. While city centers had a negative competitive effect in the 2002-07 period, their relative competitiveness for industry has been equal to peripheral locations from 2007-11.

CityObs-competition-city-centers-peripheries-2002-2011

src:
Joe Cortright. February 2015.
Surging City Center Job Growth.”
City Observatory.

*

Researchers at The George Washington University School of Business identified and analyzed 558 walkable urban places (WalkUPs) in the 30 largest metropolitan areas in the United Station, and then ranked the 30 metros according to their current walkable urbanism. This research, published in 2014, is an update to the Brookings Institution’s initial scoring in 2007.

The researchers find that the most walkable urban areas are correlated with substantially higher GDPs per capita and percentages of college graduates over 25 years old. They also find that walkable urban office spaces command a 74% rent-per-sq-foot premium over rents in drivable suburban areas.

They assert that walkable urban development is not limited to center cities, but includes the urbanization of suburbs.

They predict future demand for tens of millions square feet of walkable urban development and hundreds of new WalkUPs.

Walkable urban places are defined as having 1.4 million square feet or more of office space and/or 340,000 square feet or more retail space, as well as a Walk Score value greater than or equal to 70 at the 100 percent location of the WalkUP.

Current Ranking

GWU-walkable-urbanism-rankings-2014

Future Ranking
Note: This ranking refers to the level of expected future walkability, but does not define a particular point in time in the future. Although there are only 6 metro areas currently ranked as highly walkable, 9 are expected to be highly walkable in the future.

GWU-walkable-urbanism-rankings-future

src:
Christopher B. Leinberger & Patrick Lynch. 2014.
Foot Traffic Ahead: Ranking Walkable Urbanism in America’s Largest Metros.”
The George Washington University School of Business.

citing:
CoStar for office and retail data
Walk Score index
Local transit agency web sites
The American Community Survey for educational attainment & population data
U.S. Bureau of Economic Analysis for per capita GDP

Personal Income

dark red – 2012
orange – 1990

Juday-per-cap-income-50-metro-1990-2012

Juday-below-pov-line-50-metro-1990-2012

red – 1990-2012 change
black – baseline change for whole metro area

Juday-income—change-50-metro-1990-2012

src:
Luke Juday. Accessed August 19, 2016.
The Changing Shape of America’s Metro Areas.”
University of Virginia. Demographics Research Group.
citing: 1990 Census data and the 2008-2012 American Community Survey

*

Home Prices:
Edlund-et-al-home-prices-by-centrality-1980-2010

src:
Lena Edlund, et al. 2015.
Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”
National Bureau of Economic Research. NBER Working Paper No. 21729.

via:
Eric Jaffe. November 2015.
Why the Wealthy Have Been Returning to City Centers.”
Atlantic City Lab.

Housing & Density

dark red – 2012
orange – 1990

Juday-occupied-housing-50-metro-1990-2012

Juday-percent-rented-50-metro-1990-2012

Juday-pop-density-50-metro-1990-2012

src:
Luke Juday. Accessed August 19, 2016.
The Changing Shape of America’s Metro Areas.”
University of Virginia. Demographics Research Group.
citing: 1990 Census data and the 2008-2012 American Community Survey

Models of Change

The University of Virginia’s Demographics Research Group has analyzed the demographic trends of 66 major US metropolitan areas with regard to their distance from the city center. City Lab reports that, in many cases, the findings help to support Aaron’s Renn’s “new donut” model of cities, although some cities continue to follow other growth models, referred to as “magnetic” cities and “old donut” cities.

Juday-new-donut-1990-2012

In the “old donut” model, growth and wealth are concentrated in the suburbs.

In the “magnetic” model, growth and income are highest in the center and gradually decline all the way to the periphery.

In the “new donut” model, city centers and outer-ring suburbs have the highest performing indicators, but the inner ring suburbs are characterized by decline.

src:
Tanvi Misra. March 2015.
This Chart Tool Shows How City Centers Are Doing Better Than Inner Suburbs.”
Atlantic City Lab.
citing:
Aaron Renn. September 2014.
The New Donut.”

and also citing:
Luke Juday. March 2015.
The Changing Shape of American Cities.”
University of Virginia. Weldon Cooper Center for Public Service.
which cites: 1990 Census data and the 2008-2012 American Community Survey

The University of Virginia also released a charting tool associated with their report. In addition to showing charts for individual cities, it also includes charts for a composite of the 50 largest metropolitan areas.

The charts below (also excerpted elsewhere in this roundup) show the “new donut” model.

dark red – 2012
orange – 1990

Juday-per-cap-income-50-metro-1990-2012

Juday-ed-attain-50-metro-1990-2012

red – 1990-2012 change
black – baseline change for whole metro area

Juday-income—change-50-metro-1990-2012

Juday-ed-change-50-metro-1990-2012

src:
Luke Juday. Accessed August 19, 2016.
The Changing Shape of America’s Metro Areas.”
University of Virginia. Demographics Research Group.

*

A US Census Bureau report from 2012 also shows a “new donut” model of population change for the period 2000-2010, although Wendell Cox argues that the growth in city centers is over-exaggerated. Cox created the following graphics (also excerpted above) based on the Census Bureau’s data for the 51 major metropolitan areas (those with more than 1 million population).

Cox-metro-pop-growth-2000-2010

Cox-metro-pop-growth-and-distro-2000-2010

Cox puts it this way, and it’s true, based on the data: “The one percent flocked to downtown and the 99 percent flocked to outside downtown.” The literal figures are 1.3% and 98.7%.

src:
Wendell Cox. October 2012.
Flocking Elsewhere: The Downtown Growth Story.”
New Geography.

citing:
US Census Bureau. September 2012.
Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010.”

Other Resources

MIT Center for Advanced Urbanism
CAU’s Twitter feed
“Future of Suburbia” conference program, conference videos

increased mobility for everyone
greater need for proximity
more of the same
more exurbia
more growth at the core
more gentrified neighborhoods with trees and shopping
more dev in the suburbs with skyscrapers and higher density
for a multi-nucleated urban system

Amanda Kolson Hurley. June 6, 2016.
The “Future of Suburbia,” according to MIT.”
Architect.
Somewhat critical review of the MIT Center for Advanced Urbanism conference on suburbs, spring 2016. Lists a few other centers and researchers examining suburbs. Not a lot of data sources though.

Here’s a news article from MIT promoting the conference.

Joel Budd. June 2016.
Healthy, Happy And Hands Free.”
1843 Magazine.

Vision of the future of cities as impacted by driverless cars. Very short.
The elderly, young, and disabled will be more mobile.
Parking lots could be turned into green spaces, or offices or housing.
People might spend less time commuting, or they might commute farther.
Downtowns AND distant villages could both become more appealing.
Suburbs may dwindle.

Reporting:
Atlantic CityLab
Next City



Downtowns


Summary

This collection of data includes the following indicators, dates, and sources:

Acreage

urban acreage, 1992-2100 & 2000-2050 & 1950-2010, USGS & Lincoln Inst & US Census
Population

urban population, 1950-2050, United Nations

young adults by proximity to center, 1990/2012, UVA

elderly by proximity to center, 1990/2012, UVA

white residents by proximity to center, 1990/2012, UVA

black residents by proximity to center, 1990/2012, UVA

population by proximity to center, 1990/2012 & 2000/2010, UVA & US Census

population distribution by proximity to center, 2000/2010, US Census
Jobs

metro area jobs, 2002/2011 5-yr period, US Census

skilled jobs by proximity to center, 1980-2010 decadal, US Census
Education

full-time workers with BA+ by proximity to center, 1980-2010 decadal, US Census

educational attainment by proximity to center, 1990/2012, UVA
Desirability/Walkability

competitiveness of city centers, 2002/2011 5-yr period, City Observatory/US Census

cities most likely to be WalkUPs in the future, GWU
Personal Income

per capita income by proximity to center, 1990/2012, UVA

proportion residents living below poverty line by proximity to center, 1990/2012, UVA

home prices by proximity to center, 1980-2010 decadal, US Census
Housing and Density

occupied housing units by proximity to center, 1990/2012, UVA

percent of rented housing by proximity to center, 1990/2012, UVA

population density by proximity to center, 1990/2012, UVA

NOTE: Might be worth talking to someone from MIT’s Center for Advanced Urbanism, or an editor from City Lab or Next City for suggestions of additional “downtown” indicators and possible data sources, as well as more forecast data, which is quite lacking in this round-up.

keywords:

urban development

central business districts

city centers

data desired but lacking:

traffic congestion

construction trends

walkability (longitudinal data)

energy consumption per capita (vs. rural)

Findings

Urban Acreage

The U.S. Geological Survey tracks and forecasts several categories of land use, with charts and maps showing change under four different scenarios from 1992 through 2100. The land-use categories include acreage that is 1) urban/developed, 2) forest, 3) cropland, 4) grassland/shrubland, 5) hay/pasture, and 6) wetland.

Urban/Developed Land Area in Four Scenarios, 1992-2100
USGS-urban-developed-land-area-1992-2100

Excerpt:

Researchers at the USGS Earth Resources Observation and Science Center have developed the FOREcasting SCEnarios of land-cover change (FORE-SCE) model that is based on consistent and systematic historical data derived from Landsat satellite imagery of rates and spatial patterns of land cover change over many years. As part of USGS research to assess potential greenhouse gas fluxes and carbon storage in vegetated landscapes, FORE-SCE has been used to produce projected, annual land-cover maps from 1992 through 2100 for four future scenarios for the conterminous United States. The land cover maps provide 250-meter resolution information for 17 different land-use and land-cover classes, offering an unprecedented combination of thematic detail and spatial resolution for land-cover projections.

The four land cover scenarios that were modeled are based, in turn, on carefully-defined environmental scenarios from the Intergovernmental Panel on Climate Change (IPCC), ensuring that the projected land cover maps may be used in assessment frameworks consistent with IPCC scenario characteristics. The scenarios vary in terms of both climate and socioeconomic conditions in the future.

The A1B scenario is characterized by very high economic growth, strong technological innovation, and open, global economies and societies. Projected land use changes include sprawling urban growth, agricultural expansion, and heavy use of forested lands for wood products.

The A2 scenario is characterized by moderate economic growth, very high population growth, and less open economies and societies. Land use changes include very high urban growth, massive expansion of agricultural lands to feed a growing population, and loss of natural landscapes.

The B1 scenario has high economic growth, moderate population growth, and a focus on environmental sustainability. Land-use change is modest, with compact urban growth and modest increases in agricultural land.

The B2 scenario has moderate economic growth, low population growth, and a focus on environmental sustainability. Natural landscapes are preserved and even expanded in this scenario, with a reduction in lands devoted to agriculture.

Image Src:

Terry Sohl, et al. December 2012.

The Completion of Four Spatially Explicit Land-use and Land-cover (LULC) Scenarios for the Conterminous United States.” P.28

U.S. Geological Survey.

NOTE: This PDF includes the same charts for Forest, Cropland, Grassland/Shrubland, Hay/Pasture, and Wetland

Excerpt Src:

Jon Campbell. Sep 2014.

Modeling a Changing American Landscape

USGS Science Features.

contact: Terry Sohl, sohl@usgs.gov

TO DO: CONTACT TERRY SOHL, sohl@usgs.gov, FOR DATA POINTS. MIGHT BE AVAILABLE VIA ONE OF THE (HUGE) EXCEL FILES ON THIS PAGE.

*

The Lincoln Institute has also projected urban land coverage based on a 1990-2000 sample of 120 global cities.

Three projections are made based on three different assumptions about density change over time: (a) no density change; (b) a 1 percent annual density decline; and © a 2 percent annual density decline.

US Urban Land Cover Projections in Three Scenarios, 2000-2050
Lincoln-Inst-urban-land-cover-2000-2050

Src:

XLS: “Urban Land Cover Projections for Countries and Regions, 2000-2050

Via:

Shlomo Angel, et al. 2010.

Section 3 – Urban, National, and Regional Data.”

Atlas of Urban Expansion. Lincoln Institute of Land Policy.

*

Demographia.com, maintained by Wendell Cox (a pro-automobile urban planner who favors low-density planning), has aggregated a few urbanizations statistics from the US Census Bureau. Indicators include urban land area (square miles), urban population, urban households, population density, and household density, as well as annual percent change for all of the above. Decadal data points are available from 1950 through 2010.

src:

Demographia. 2012.

Urbanization in the United States from 1945.”

Demographia.com, Wendell Cox Consultancy.

citing:

US Census Bureau

TO DO: EXTRACT/CONFIRM DATA.

Urban Population

Proportions of urban and rural population in the current country or area in per cent of the total population, 1950 to 2050.

UN-proportion-urban-rural-population-1950-2050

Total urban and rural population

UN-total-urban-rural-population-1950-2050

Urban population of the current country by size class of its urban agglomerations in 2014.

UN-US-city-size-class-1950-2030

The light blue area is a residual category, which includes all cities and urban agglomerations with a population of less than 300,000 inhabitants. The size classes correspond to the following legend:

UN-US-city-size-class-legend-1950-2030

src:

United Nations. 2014

Country Profile: United States of America,” in World Urbanization Prospects: The 2014 Revision.

Department of Economic and Social Affairs, Population Division.

TO DO: EXTRACT DATA. ANNUAL DATA AVAILABLE HERE.

*

dark red – 2012

orange – 1990

Juday-population-50-metro-1990-2012

Juday-young-adults-50-metro-1990-2012

Juday-elderly-50-metro-1990-2012

Juday-white-residents-50-metro-1990-2012

Juday-black-residents-50-metro-1990-2012

src:

Luke Juday. Accessed August 19, 2016.

The Changing Shape of America’s Metro Areas.”

University of Virginia. Demographics Research Group.

citing: 1990 Census data and the 2008-2012 American Community Survey

*

Based on the Census Bureau’s data for the 51 major metropolitan areas (those with more than 1 million population).

Cox-metro-pop-growth-2000-2010

Cox-metro-pop-growth-and-distro-2000-2010

src:

Wendell Cox. October 2012.

Flocking Elsewhere: The Downtown Growth Story.”

New Geography.

citing:

US Census Bureau. September 2012.

Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010.”

Metro Area Jobs

City Observatory analyzed Census Bureau data from the 2002-2007 period to the 2007-2011 period and finds that after a long period of decentralization, economic and demographic indicators are now favoring city centers.

Excerpts:

When we compared the aggregate economic performance of urban cores to the surrounding metro periphery over the four years from 2007 to 2011, we found that city centers—which we define as the area within 3 miles of the center of each region’s central business district—grew jobs at a 0.5 percent annual rate. Over the same period, employment in the surrounding peripheral portion of metropolitan areas declined 0.1 percent per year. When it comes to job growth, city centers are out-performing the surrounding areas in 21 of the 41 metropolitan areas we examined. This “center-led” growth represents the reversal of a historic trend of job de-centralization that has persisted for the past half century.

CityObs-jobs-change-city-centers-peripheries-2002-2011

src:

Joe Cortright. February 2015.

Surging City Center Job Growth.”

City Observatory.

*

Economists at Columbia University and the Getulio Vargas Foundation have also analyzed Census Bureau data from 1980 to 2010, finding that center cities and downtown living are making a come back. Their analysis is based on data from 27 heavily populated U.S. cities, from the CBD to 35 miles away from the center of the cities. Their report includes an interesting graphic on skilled jobs, although these are not the exclusive focus of the report. Elsewhere in this roundup, I’ve also excerpted a graphic for home prices and and education which follow the same trend.

Excerpts:

Skilled Jobs:
Edlund-et-al-skilled-jobs-by-centrality-1980-2010

src:

Lena Edlund, et al. 2015.

Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”

National Bureau of Economic Research. NBER Working Paper No. 21729.

via:

Eric Jaffe. November 2015.

Why the Wealthy Have Been Returning to City Centers.”

Atlantic City Lab.

Education

Full-time workers with at least a bachelor’s degree working 40 or 50 hours per week:
Edlund-et-al-women-with-degrees-by-centrality-1980-2010

The data for men show similar trends, with shares rising a bit more toward the periphery.

src:

Lena Edlund, et al. 2015.

Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”

National Bureau of Economic Research. NBER Working Paper No. 21729.

via:

Eric Jaffe. November 2015.

Why the Wealthy Have Been Returning to City Centers.”

Atlantic City Lab.

*

dark red – 2012

orange – 1990

Juday-ed-attain-50-metro-1990-2012

red – 1990-2012 change

black – baseline change for whole metro area

Juday-ed-change-50-metro-1990-2012

src:

Luke Juday. Accessed August 19, 2016.

The Changing Shape of America’s Metro Areas.”

University of Virginia. Demographics Research Group.

citing: 1990 Census data and the 2008-2012 American Community Survey

Desirability/Walkability

In City Observatory’s analysis of 2002-2007 and 2007-2011, referred to above, they also came up with a competitiveness calculation which favors city centers.

Excerpt:

We undertook a shift-share analysis that allowed is to separate out the effects of changing industry mix from relative competitiveness. The data make it clear that city centers are more competitive in 2011 than they were in 2007. While city centers had a negative competitive effect in the 2002-07 period, their relative competitiveness for industry has been equal to peripheral locations from 2007-11.

CityObs-competition-city-centers-peripheries-2002-2011

src:

Joe Cortright. February 2015.

Surging City Center Job Growth.”

City Observatory.

*

Researchers at The George Washington University School of Business identified and analyzed 558 walkable urban places (WalkUPs) in the 30 largest metropolitan areas in the United Station, and then ranked the 30 metros according to their current walkable urbanism. This research, published in 2014, is an update to the Brookings Institution’s initial scoring in 2007.

The researchers find that the most walkable urban areas are correlated with substantially higher GDPs per capita and percentages of college graduates over 25 years old. They also find that walkable urban office spaces command a 74% rent-per-sq-foot premium over rents in drivable suburban areas.

They assert that walkable urban development is not limited to center cities, but includes the urbanization of suburbs.

They predict future demand for tens of millions square feet of walkable urban development and hundreds of new WalkUPs.

Walkable urban places are defined as having 1.4 million square feet or more of office space and/or 340,000 square feet or more retail space, as well as a Walk Score value greater than or equal to 70 at the 100 percent location of the WalkUP.

Current Ranking

GWU-walkable-urbanism-rankings-2014

Future Ranking

Note: This ranking refers to the level of expected future walkability, but does not define a particular point in time in the future. Although there are only 6 metro areas currently ranked as highly walkable, 9 are expected to be highly walkable in the future.

GWU-walkable-urbanism-rankings-future

src:

Christopher B. Leinberger & Patrick Lynch. 2014.

Foot Traffic Ahead: Ranking Walkable Urbanism in America’s Largest Metros.”

The George Washington University School of Business.

citing:

CoStar for office and retail data

Walk Score index

Local transit agency web sites

The American Community Survey for educational attainment & population data

U.S. Bureau of Economic Analysis for per capita GDP

Personal Income

dark red – 2012

orange – 1990

Juday-per-cap-income-50-metro-1990-2012

Juday-below-pov-line-50-metro-1990-2012

red – 1990-2012 change

black – baseline change for whole metro area

Juday-income—change-50-metro-1990-2012

src:

Luke Juday. Accessed August 19, 2016.

The Changing Shape of America’s Metro Areas.”

University of Virginia. Demographics Research Group.

citing: 1990 Census data and the 2008-2012 American Community Survey

*

Home Prices:
Edlund-et-al-home-prices-by-centrality-1980-2010

src:

Lena Edlund, et al. 2015.

Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill.”

National Bureau of Economic Research. NBER Working Paper No. 21729.

via:

Eric Jaffe. November 2015.

Why the Wealthy Have Been Returning to City Centers.”

Atlantic City Lab.

Housing & Density

dark red – 2012

orange – 1990

Juday-occupied-housing-50-metro-1990-2012

Juday-percent-rented-50-metro-1990-2012

Juday-pop-density-50-metro-1990-2012

src:

Luke Juday. Accessed August 19, 2016.

The Changing Shape of America’s Metro Areas.”

University of Virginia. Demographics Research Group.

citing: 1990 Census data and the 2008-2012 American Community Survey

Models of Change

The University of Virginia’s Demographics Research Group has analyzed the demographic trends of 66 major US metropolitan areas with regard to their distance from the city center. City Lab reports that, in many cases, the findings help to support Aaron’s Renn’s “new donut” model of cities, although some cities continue to follow other growth models, referred to as “magnetic” cities and “old donut” cities.

Juday-new-donut-1990-2012

In the “old donut” model, growth and wealth are concentrated in the suburbs.

In the “magnetic” model, growth and income are highest in the center and gradually decline all the way to the periphery.

In the “new donut” model, city centers and outer-ring suburbs have the highest performing indicators, but the inner ring suburbs are characterized by decline.

src:

Tanvi Misra. March 2015.

This Chart Tool Shows How City Centers Are Doing Better Than Inner Suburbs.”

Atlantic City Lab.

citing:

Aaron Renn. September 2014.

The New Donut.”

and also citing:

Luke Juday. March 2015.

The Changing Shape of American Cities.”

University of Virginia. Weldon Cooper Center for Public Service.

which cites: 1990 Census data and the 2008-2012 American Community Survey

The University of Virginia also released a charting tool associated with their report. In addition to showing charts for individual cities, it also includes charts for a composite of the 50 largest metropolitan areas.

The charts below (also excerpted elsewhere in this roundup) show the “new donut” model.

dark red – 2012

orange – 1990

Juday-per-cap-income-50-metro-1990-2012

Juday-ed-attain-50-metro-1990-2012

red – 1990-2012 change

black – baseline change for whole metro area

Juday-income—change-50-metro-1990-2012

Juday-ed-change-50-metro-1990-2012

src:

Luke Juday. Accessed August 19, 2016.

The Changing Shape of America’s Metro Areas.”

University of Virginia. Demographics Research Group.

*

A US Census Bureau report from 2012 also shows a “new donut” model of population change for the period 2000-2010, although Wendell Cox argues that the growth in city centers is over-exaggerated. Cox created the following graphics (also excerpted above) based on the Census Bureau’s data for the 51 major metropolitan areas (those with more than 1 million population).

Cox-metro-pop-growth-2000-2010

Cox-metro-pop-growth-and-distro-2000-2010

Cox puts it this way, and it’s true, based on the data: “The one percent flocked to downtown and the 99 percent flocked to outside downtown.” The literal figures are 1.3% and 98.7%.

src:

Wendell Cox. October 2012.

Flocking Elsewhere: The Downtown Growth Story.”

New Geography.

citing:

US Census Bureau. September 2012.

Patterns of Metropolitan and Micropolitan Population Change: 2000 to 2010.”

Other Resources

MIT Center for Advanced Urbanism
CAU’s Twitter feed

“Future of Suburbia” conference program, conference videos

increased mobility for everyone

greater need for proximity

more of the same

more exurbia

more growth at the core

more gentrified neighborhoods with trees and shopping

more dev in the suburbs with skyscrapers and higher density

for a multi-nucleated urban system

Amanda Kolson Hurley. June 6, 2016.

The “Future of Suburbia,” according to MIT.”

Architect.

Somewhat critical review of the MIT Center for Advanced Urbanism conference on suburbs, spring 2016. Lists a few other centers and researchers examining suburbs. Not a lot of data sources though.

Here’s a news article from MIT promoting the conference.

Joel Budd. June 2016.

Healthy, Happy And Hands Free.”

1843 Magazine.

Vision of the future of cities as impacted by driverless cars. Very short.

The elderly, young, and disabled will be more mobile.

Parking lots could be turned into green spaces, or offices or housing.

People might spend less time commuting, or they might commute farther.

Downtowns AND distant villages could both become more appealing.

Suburbs may dwindle.

Reporting:
Atlantic CityLab
Next City

Downtowns was originally published on Extrapolations



Shopping


Summary

This collection of data includes the following indicators, dates, and sources:

Retail Space
shopping center growth, 1975-2014, Int’l Council of Shopping Centers
number of private retail establishments, 2001-2015, Bureau of Labor Statistics (BLS)
store count by category, 2010/2015/2020, Kantar Retail
store square footage by category, 2010/2015/2020, Kantar Retail
Consumer Spending
indexed personal spending, 1950-2016, Trading Economics
percent personal spending, 2016/2017/2020, Trading Economics
personal expenditures deflated, 1901-2003, BLS
food, clothing, and housing expenditures, 1901-2003, BLS
food expenditures, 1901-2003, BLS
non-necessities expenditures, 1901-2003, BLS
Time Spent Shopping
avg hours per day purchasing consumer goods, 2003-2015, BLS
avg % of people purchasing consumer goods per day, 2003-2015, BLS
avg hours per day for persons so-engaged purchasing consumer goods, 2003-2015, BLS
Online Shopping
retail ecommerce sales as a percent of total US retail sales, 2014-2019, eMarketer
worldwide ecommerce sales growth, 2013-2019, eMarketer
US retail sales as a % of worldwide retail, 2014-2019, eMarketer
US ecommerce sales as a % of total worldwide retail sales, 2014-2019, eMarketer
retail formats % distribution, 2010/2015/2020, Kantar Retail
online share of sales by category, 2009/2014/2020, Kantar Retail
Mall Habits
avg mall visits per month, ~2012, ICSC
avg length of mall visits, ~2012, ICSC
avg monthly spending at malls, 2010/2012, ICSC

Findings

Retail Space
With the U.S. having an estimated 48 square feet of retail space per citizen, the footprint is poised to decline “pretty fast,” Jan Kniffen said.

“On an apples-to-apples basis, we have twice as much per-capita retail space as any other place in the world. The U.K. is second. They’re half of what we are. So, yes, we are the most over-stored place in the world,” he told CNBC’s “Squawk Box.”

In his view, about 400 of America’s 1,100 enclosed malls will fail in the coming years. Of the survivors, about 250 will thrive and the rest will struggle. Likewise, Macy’s probably needs 500 of its roughly 800 existing stores, he said.

src:
Tom DiChristopher. May 12, 2016.
1 in 3 American malls are doomed: Retail consultant Jan Kniffen.
CNBC.

Note: This article, though published a year earlier, takes a completely opposite tone of the one above.
Amanda Kolson Hurley. March 2015.
Shopping Malls Aren’t Actually Dying.
The Atlantic: CityLab.

*

ICSC-shopping-center-growth-1975-2014

Between 2000 and 2008, U.S. shopping center space – gross leasable area – grew by an annual average rate of 2.6% or a net addition of 169 million square feet (sq. ft.) of retail space per year. However, beginning in 2009, the supply increment slowed dramatically to about one-tenth of that pace. In 2013, the addition of new retail supply grew at its slowest pace in more than 40 years. According to Cushman & Wakefield, over the next three years (2014-2016), the addition of new supply will pick up somewhat as 120.5 million sq. ft. are added to U.S. shopping center inventory. This moderated supply growth is a positive sign going forward (see Figure 2).

According to the U.S. Bureau of Labor Statistics, the total number of U.S. private retail establishments grew from 1,023,696 in 2011 to 1,028,242 in 2012 – an increase of 4,546 establishments. Preliminary 2013 figures show a further increase of 7,887 retail establishments over 2012 figures to 1,036,129.

src:
International Council of Shopping Centers. 2014.
Shopping Centers: America’s First and Foremost Marketplace.

citing:
[CoStar]
and BLS

*

The Bureau of Labor Statistics tracks the number of establishments as part of its Workplace Trends tracking. The following chart and data reflect the number of private retail establishments from 2001 through 2015.

BLS-private-retail-establishments-2001-2015-chart

BLS-private-retail-establishments-2001-2015-data

src:
Bureau of Labor Statistics. Accessed August 8, 2016.
Quarterly Census of Employment and Wages
Series Id: ENUUS00020544-45
Area: US Total
Industry: NAICS 44-45 Retail trade
Owner: Private
Type: Number of Establishments
linked via “IAG: Retail Trade – NAICS 44-45

*

Over the next five years, Kantar Retail forecasts that U.S. retail square footage will grow more slowly than the number of stores; in other words, the average store built will be smaller. Conversely, in the previous five years, square footage grew more quickly than stores, resulting in the addition of relatively larger stores (Figure 7).

Kantar-stores-size-2010-2020

As Figure 7 indicates, nearly half of new stores over the next five years will be smaller footprint drug, discounter, and convenience stores. As a result, retailers will need to deliver even greater differentiation and specialization from this growth in available points of commerce. Shrunken versions of larger boxes will not suffice. Shoppers will demand that small formats earn their reason for being. Going forward, we expect greater efforts toward differentiation in the small-box space, with in-store marketing (versus simply merchandising) gaining traction.

src:
Kantar Retail. 2015.
REinvent Format: Adapting to 21st-Century Retailing
Pp.9, 15
Press contact: Victoria Bradshaw, victoria.bradshaw@kantarretail.com

EMAILED KANTAR RETAIL 8/9/16 TO INQUIRE ABOUT ADDITIONAL DATA POINTS (FOR THIS AND ALL OTHER KANTAR CHARTS MENTIONED IN THIS RESEARCH ROUNDUP).

Consumer Spending

Trading Economics make a short, five-year forecast (currently, to 2020) of various US consumption indicators, based on US Bureau of economic Analysis (BEA) data going back to 1969.

Each indicator has its own page summarizing current data and the forecast to 2020, along with a chart which can be expanded to show data back to ~1950. Each indicator’s page also includes a summary of the forecasts for related indicators. For example, the data on the “United States Personal Spending Forecast 2016-2020” page.

TradingEcon-personal-spending-1950-2016

TradingEcon-personal-spending-2016-2020

TradingEcon-consump-indicators-2016-2020
(Note: No units are given in this summary. Generally, the much larger numbers are billions of USD, and the smaller numbers are percentages or index values.)

Trading Economics forecasts are frequently updated (several times per week).

src:
Trading Economics. Accessed August 6, 2016.
United States Personal Spending: Forecast 2016-2020.

*

The Bureau of Labor Statistics published a 100-year compilation of national consumer spending data from 1901 through 2003.

Excerpts:

While no two families spend money in exactly the same manner, indicators suggest that families allocate their expenditures with some regularity and predictability. Consumption patterns indicate the priorities that families place on the satisfaction of the following needs: Food, clothing, housing, heating and energy, health, transportation, furniture and appliances, communication, culture and education, and entertainment.

In this report, economic and demographic profiles of U.S. households in the aggregate, as well as profiles of households in New York City and Boston, are presented. New York City and Boston are included because they are two of the country’s oldest urban areas. The report examines how, over a 100-year period, standards of living have changed as the U.S. economy has progressed from one based on domestic agriculture to one geared toward providing global services.

The report provides an in-depth assessment of U.S. households at nine points in time, beginning with 1901 and ending with 2002–03. The text highlights changes in family structure and economic conditions and examines factors that have altered and influenced both society and households. (P.1)

During the 100-year period, household expenditure patterns also demonstrated great variability. … In real dollars, calculated with 1901 as the base, expenditures also demonstrated a notable increase. In 1901, as noted, the average U.S. family had $769 in expenditures. By 2002–03, that family’s expenditures would have risen to $1,848, a 2.4-fold increase. (Chart 40, pp.66-67)

BLS-expenditures-deflated-1901-2003

The material well-being of families in the United States improved dramatically, as demonstrated by the change over time in the percentage of expenditures allocated for food, clothing, and housing. In 1901, the average U.S. family devoted 79.8 percent of its spending to these necessities. By 2002–03, allocations on necessities had been reduced substantially, for U.S. families to 50.1 percent of spending. (Chart 41, pp.66, 68)

BLS-spending-food-clothing-housing-1901-2003

The continued and significant decline over the century in the share of expenditures allocated for food also reflected improved living standards. In 1901, U.S. households allotted 42.5 percent of their expenditures for food; by 2002–03, food’s share of spending had dropped to just 13.2 percent. (Chart 41, p.68-69)

BLS-spending-food-1901-2003

In 2002–03, the average U.S. family could allocate 49.9 percent ($20,333) of total expenditures for a variety of discretionary consumer goods and services, while the average family in 1901 could allocate only 20.2 percent, or $155, for discretionary spending [eg: family car, reading and education, personal care products, recreation and entertainment, healthcare, alcohol, charitable giving, etc]. (Chart 43, p.70)

BLS-spending-non-necessities-1901-2003

src:
US Bureau of Labor Statistics. May 2006.
100 Years of U.S. Consumer Spending: Data for the Nation, New York City, and Boston.

Time Spent Shopping

The American Time Use Survey has been conducted annually since 2003. Among many other indicators, it tracks the amount of time Americans age 15 and older spend purchasing goods and services.


Google Spreadsheet

src:
Bureau of Labor Statistics. 2003-2015.
American Time Use Survey. ATUS Tables.
“Table A-1. Time spent in detailed primary activities and percent of the civilian population engaging in each activity, averages per day by sex, annual averages.” All years, 2003-2015

NOTE: The “Purchasing goods and services” section of the source table is further itemized. The full itemization is as follows:

Purchasing goods and services
>Consumer goods purchases
>>Grocery shopping
>Professional and personal care services
>>Financial services and banking
>>Medical and care services
>>Personal care services
>Household services
>>Home maintenance, repair, decoration, and construction (not done by self)
>>Vehicle maintenance and repair services (not done by self)
>Government services
>Travel related to purchasing goods and services

The itemization follows from greatest to smallest use of time, EXCEPT for travel, which is the second largest use of time. Grocery shopping generally accounts for about a quarter of the broader consumer goods category, and this is true over the full time period with a slight increase over time (groceries generally hold steady, but the broader category total slowly decreases over time).

Online Shopping

Retail Ecommerce Sales as a Percent of Total Retail Sales in the US, 2014-2019
2014 — 6.4%
2015 — 7.1%
2016 — 7.8%
2017 — 8.4%
2018 — 9.1%
2019 — 9.8%

Retail Ecommerce Sales Worldwide, 2014-2019
Showing total ecommerce retail sales, percent change, and percent of total retail sales.

Emarketer-online-retail-2014-2019

Retail ecommerce sales in North America will rise 14.4% this year to reach $367.44 billion, largely due to increased spending from existing digital shoppers. The region will see consistent double-digit growth through 2019, fueled by expanding online categories, increased average order values and growing mcommerce sales.

Total Retail Sales Share Worldwide, by Region, 2014-2019
Emarketer-total-retail-share-worldwide-2014-2019

Total Ecommerce Sales as a Percent of Total Retail Worldwide, by Region, 2014-2019
Emarketer-ecommerce-share-worldwide-2014-2019

src:
eMarketer. 2015
Worldwide Retail Ecommerce Sales: Emarketer’s Updated Estimates And Forecast Through 2019.”

*

eMarketer’s previous forecasts

US online retail spending
2015 — 7.1%
2016 — 7.7%
2017 — 8.3%
2018 — 8.9%

Emarketer-online-sales-2013-2018

src:
eMarketer. December 2014.
Retail Sales Worldwide Will Top $22 Trillion This Year.”

*

Figure 3: Percent Sales Importance of Retail Formats – 2010 to 2020 [US]

PWC-retail-formats-2010-2020

Non-store retail, driven by online today, and likely mobile and tablet commerce in 2020, is projected to be the fastest growing retail channel in the future. Conversely, Supermarket, Drug and especially Mass channel retailers are likely to face a tough growth environment in the coming 2015-2020 period—even if job and income growth surmount global pressures. Likewise, Supercenters, as a percent of total US sales, will decrease to 12.7% from 14.4% today.

src:
PwC/Kantar Retail. 2012.
Retailing 2020: Winning In A Polarized World.
P.9

*

Kantar-online-sales-shares-by-category-2009-2020

src:
Kantar Retail. 2015.
REinvent Format: Adapting to 21st-Century Retailing
Figure 22: U.S. Online Shares of Sales by Category
P. 21.

Mall Habits

CC’s Note: I’m trying to flesh these single-year data points out. Hoping to get more data from ICSC on the items they’re responsible for below.

*

JCDecaux is an international, outdoor advertising company. They have collected and published the following statistics on mall shopping (most stats seem to be from 2012).

75% of all Americans visit a mall at least once a month. [2]

On average, shoppers visit 3.4 times per month and stay
1 hour and 24 minutes. [1]

Average Time Spent Shopping by Age [1]
Ages 14-17: . . . . . . . . 95 minutes
Ages 18-24: . . . . . . . . 81 minutes
Ages 25-34: . . . . . . . . 79 minutes
Ages 35-44: . . . . . . . . 86 minutes
Ages 45-54: . . . . . . . . 88 minutes
Ages 55-64: . . . . . . . . 87 minutes
Ages 65+: . . . . . . . . . 89 minutes

Average Money Spent Per Visit [4]
Specialty Stores — $109.91
Anchor Stores — $92.80

Monthly Shopping Frequency by Age [4]
Ages 12-17: . . . . . . . . 3.7 times
Ages 18-24: . . . . . . . . 3.2 times
Ages 25-34: . . . . . . . . 3.0 times
Ages 35-44: . . . . . . . . 2.9 times
Ages 45-54: . . . . . . . . 2.9 times
Ages 55-64: . . . . . . . . 3.0 times
Ages 65+: . . . . . . . . . 3.6 times

Shopper spending at malls per month increased from $316.80
in 2010 to $330.82 in 2012. [1]

src:
JCDecaux. Accessed August 8, 2016.
The Mall Phenomenon.”

citing
1 – ICSC (International Council of Shopping Centers)
PR: Noelle Malone, nmalone@icsc.org
EMAILED 8/8/16 – GOT A REPLY – EXPECTING UPDATED DATA SOON.
2 – Journal of Shopping Center Research
1994-2007 full text archives freely available, but no search

http://jrdelisle.com/JSCR/

Couldn’t track the source
3 – Glimcher Reality Trust
4 – Alexander Babbage, Inc.

Predictions/Scenarios

2030: Half of America’s shopping malls have closed

“For much of the 20th century, shopping malls were an intrinsic part of American culture. At their peak in the mid-1990s, the country was building 140 new shopping malls every year. But from the early 2000s onward, underperforming and vacant malls – known as “greyfield” and “dead mall” estates – became an emerging problem. In 2007, a year before the Great Recession, no new malls were built in America, for the first time in half a century. Only a single new mall, City Creek Center Mall in Salt Lake City, was built between 2007 and 2012. The economic health of surviving malls continued to decline, with high vacancy rates creating an oversupply glut.*

A number of changes had occurred in shopping and driving habits. More and more people were living in cities, with fewer interested in driving and people in general spending less than before. Tech-savvy Millennials (also known as Generation Y), in particular, had embraced new ways of living. The Internet had made it far easier to identify the cheapest products and to order items without having to be physically there in person. In earlier decades, this had mostly affected digital goods such as music, books and videos, which could be obtained in a matter of seconds – but even clothing was eventually possible to download, thanks to the widespread proliferation of 3D printing in the home.* Many of these abandoned malls are now being converted to other uses, such as housing.”

src:
FutureTimeline.net. Accessed August 1, 2016.
Half of America’s shopping malls have closed.”

*

The Future Real Estate Shopping Experience [undated scenario]

Highlights:

No real estate agents.

VR-based anytime tours that let you see what your stuff would look like in the new place, try different landscaping and remodels.

A machine-learning algorithm monitors your experience to learn your likes and dislikes, recommending other homes to visit.

An AI helps you to write your bid and contact your bank to make an offer on the spot.

src:
Peter Diamandis. March 2015.
Disrupting Real Estate.”

*

Global forces affecting the consumer sector in 2030

Exhibit 1
Five dominant forces — and an underlying set of trends — will drive change in the consumer landscape over the next 15 years.

McKinsey-five-forces-consumption-2030

Exhibit 2
Trends that could most affect consumer companies.

McKinsey-five-forces-impact-consumption-2030

src:
Richard Benson-Armer, Steve Noble, and Alexander Thiel. December 2015.
The Consumer Sector In 2030: Trends And Questions To Consider.”
McKinsey & Company.

*

PwC conducts an annual consumer survey, “Total Retail.” The results of the survey published in 2015

PwC. February 2015.
Physical Store Beats Online as Preferred Purchase Destination for U.S. Shoppers, According to PwC.”

*

Harvard Business Review presented a near-future shopping scenario in the opening of it’s 2011 article on the future of shopping. The article predicted that the scenario would arrive within a handful of years (not quite).

The article closes with some suggestions for how retail stores can use digital technologies innovatively to meet emerging consumer desires.

src:
Darrell K. Rigby. December 2011.
The Future of Shopping.”
Harvard Business Review.

Other Possible Sources/Contacts

Maureen McAvey
Maureen McAvey is the Bucksbaum Family Chair for Retail at the Urban Land Institute (ULI) in Washington, D.C. She concentrates on urban retail and has led several special projects around demographics and future urban development. She is also a senior staff adviser to ULI’s Building Healthy Places Initiative. [Src]
Email: Maureen.McAvey@uli.org

AT Kearney
Conducts surveys of consumer shopping preferences and behaviors.
EG: “The Omnichancel Consumer Preferences Study

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Posted by Claudia Lamar on September 7, 2016 at 9:55 pm | comment count