Are advertisers spending effectively according to media’s ability?

The chart shows how much time individuals spend with each medium (as a percentage of their total media consumption hours) and advertising spent per medium (as a percentage of total advertising spending).

It reveals that ad spending on newspapers and magazines is higher than on TV. We assume that more money is invested in newspaper/magazine advertising, even though individuals spend more hours with TV, because readers are more targeted audiences.

Share of Average Time Spent per Day with Select Media by US Adults vs. US Ad Spending Share, 2009-2012 (% of total)
Average Time Spent per Day with Major Media by US Adults, 2009-2012 (minutes)

 

2009 2010 2011 2012
minutes Time spentshare Ad spendingshare minutes Time spentshare Ad spendingshare minutes Time spentshare Ad spendingshare minutes Time spentshare Ad spendingshare
TV and video 267 42.20% 36.50% 264 40.90% 38.40% 274 40.40% 38.30% 278 39.80% 38.90%
Online 146 23.10% 15.20% 155 24.00% 16.60% 167 24.60% 19.30% 173 24.80% 20.90%
Radio 98 15.50% 9.70% 96 14.90% 9.90% 94 13.90% 9.60% 92 13.20% 9.30%
Mobile (nonvoice) 22 3.50% 0.30% 34 5.30% 0.50% 54 8.00% 0.90% 82 11.70% 1.60%
Print* 55 8.7%** 27.30% 50 7.7%** 24.70% 44 6.5%** 22.60% 38 5.4%** 20.70%
—Newspapers 33 5.20% 16.80% 30 4.60% 14.80% 26 3.80% 13.10% 22 3.10% 11.50%
—Magazines 22 3.50% 10.50% 20 3.10% 9.90% 18 2.70% 9.60% 16 2.30% 9.20%
Other 44 47 45 36
Total 632 646 678 699

Source: eMarketer, Sep & Oct 2012

 

2009 2010 2011 2012 Targeted(least=1, Most=7)
Print $0.52 $0.53 $0.56 $0.62 3
—Magazines $0.50 $0.53 $0.58 $0.65 4
—Newspapers $0.53 $0.53 $0.55 $0.60 5
TV $0.14 $0.16 $0.15 $0.16 1
Online $0.11 $0.12 $0.13 $0.14 6
Radio $0.10 $0.11 $0.11 $0.12 2
Mobile (nonvoice) $0.01 $0.01 $0.01 $0.01 7
Total $0.17 $0.17 $0.16 $0.16

Source: eMarketer, Sep & Oct 2012

If the level of targeting possible is what determines in which medium advertising dollars are invested, Internet and cell phone advertising will likely increase in the near future.

 

Project Snow Job — Jane Sasseen and Kevin R. Convey

Data-Driven Journalism

Pitch for Project Snow Job

Kevin R. Convey and Jane Sasseen

21 Nov. 2012

Pitch

 

 

We propose to comb the city’s 311 complaint records to try to map street snow-clearance complaints either from last winter or from the last several winters to see which neighborhoods of New York City generate the most complaints on the subject, and thus, we infer, get the worst service from the city.

 

Why this story and why now?

 

As winter settles in and the temperature drops, residents of the city’s five boroughs brace themselves for the inevitable: the first big snowstorm of the year and the accompanying blizzard of complaints about snow clearance along the city’s thousands of miles of roads.

 

Some residents see conspiracies: Was the mayor’s upper-east-side townhouse plowed out first? Other see socio-economic favoritism: How come the city’s poorest neighborhoods always seem to be the last to see a snowplow? And others see only incompetence: Can’t this city do anything right? We intend to comb and chart the data to see if any of these ideas hold water, or whether another explanation suggests itself.

 

If we are able to examine several winters in a row, we’ll also explore whether there have been any significant improvements or declines, and why. Do residents who complain a lot one year see improved service the next? Or do the same neighborhoods remain forgotten, year after year?

 

Hypothesis

 

Our guess: Lower-income neighborhoods get less snowplowing attention. Best guess at the official explanation: Most remote neighborhoods with least-traveled thoroughfares and narrowest streets are harder to get to, harder to clear and take longer than, say, Manhattan avenues.

 

The Data

 

Available on NYC Open Data, but needs massive combing and refinement:

https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9

 

Potential Sources

 

Mayor’s Office

Head of NYC public works John J. Doherty or his designate

City Council President Christine Quinn, long-time critic of snowplowing efforts

Bill DiBlasio, NYC public advocate

Presidents of worst-served boroughs

Public policy professor TBA

(All phoners)

 

Presentation

 

Uncertain at this stage. Heat or intensity map embedded in Hype/Tumult format a possibility.

Story Pitch #2: Do deer hunts have any impact on Lyme disease?

For inhabitants of the leafy suburbs and rural areas of the Northeast, Lyme disease is now virtually a year-round problem. Deer ticks, which transmit the bacterial infection, abound in grass and leaf litter and are active in any temperature above freezing.

We tend to blame the prevalence of Lyme disease on resurgent populations of white-tailed deer. However, a study published earlier this year by ecologists at University of California-Santa Cruz suggests that we’ve fingered the wrong mammal. So-called “deer ticks” also thrive on smaller animals like rats, voles and rabbits, whose populations are also booming. A big factor in the success of these small varmints is declining numbers of their traditional predator, the red fox, which is being displaced by coyotes. In fact, researchers looking at New York State have found poor spatial correlation between deer populations and Lyme disease, but close correlation between coyote abundance and fox rarity.

Despite these findings, “deer management”—open hunts on white-tailed deer—continues to be a major strategy for municipalities looking to control Lyme disease. There are other, additional benefits to keeping deer populations down, such as improvements to traffic safety. However, as a disease control measure, deer management may be a dead-end strategy.

It would take some heavy-hitting statistics to provide a definitive answer on this question. However, I’d like to take a stab at it by looking at year-to-year Lyme disease rates in a county with an aggressive deer management program. Connecticut seems particularly gung-ho on deer management, and they have a lot of data through their Department of Environmental Protection.

Data Sources:

Centers for Disease Control county-level Lyme Disease data http://www.cdc.gov/lyme/stats/index.html

[This data is problematic because it only covers the period from 1992-2006; I’m going to try to get something more recent]

2011 Connecticut Deer Program Summary

http://www.ct.gov/dep/lib/dep/wildlife/pdf_files/game/deersum2011.pdf

Mobile Phone Culture Lingers in Finland, Even As Nokia’s Importance Diminishes

  • Finland is unique among is fellow Nordic countries in that its cell phone service per 100 residents is among the highest in the world
  • Many of the counties whose residents have more than one cell phone on average tend to have characteristics very different than Finland’s such as Saudi Arabia, Russia, Oman, Maldives and Libya
  • All Nordic countries had early cell phone adoption and they all increased steadily until a leveling off in 2007 — except Finland, which kept rising
  • Nokia, Finland’s largest company, plays a dominant role in the economy, contributing up to 25% of output, even though the health of the company in the past few years hasn’t been good. It’s very alumnus that a non-commodities company has so much power. Nokia is predicted to have less influence in the future by the Economist.
Cell phones service per 100 inhabitants - Nordic Countires

Cell phones service per 100 inhabitants – Nordic Countires

 

related links:

 

Fema: How some states pay for other’s bad weather.

By Emma Thorne & Ezra Eeman

Gino Vitale owns 16 buildings in Red Hook – a low-lying, high-risk area of Brooklyn that fell under mandatory evacuation order during October’s Hurricane Sandy. Just a few days after the superstorm, Vitale, in an interview with Time.com, estimated the damage to his properties to be about $600,000. You’d think he could count on support from his insurance company and from FEMA – but for many homeowners, it’s just not that simple.

Vitale, for one, received just $4,000 in insurance money to help repair the $80,000 devastation Hurricane Irene inflicted on his homes in 2011. With this project, we’re hoping to show how a situation like Vitale’s can happen: how the flood insurance system, both public and private, works – and more to the point, if it works.

Currently, the estimated damage from Hurricane Sandy stands at $5 to $10 billion. Taxpayers could be left paying for half of that, thanks to shortfalls in insurance coverage. We’ll be using data from FEMA and the National Flood Insurance Program (found here, here, and here) to compare the number of flood insurance claims in each state against the average payouts on those claims, and how they’ve changed over the last 10 years. What we want to explore here is whether or not there’s an inequality in the national flood insurance system that needs to be addressed. Some states, for example, are much more prone to massive weather events and flooding, yet their residents pay the same flat-rate NFIP premiums as do people in drier parts of the country. In this instance, private insurance company would raise premiums for those who continue to live and rebuild in high-flood-risk areas, but the national system does not – essentially leaving some states to pay for others’ bad weather. Is this a fair and efficient way to run the system?

Potential expert sources:
Rita Hollada, flood insurance agent and former representative to the NFIP
http://www.pianet.com/news/press-releases/2012/holladawinsaward051012

Someone from New York State Floodplain and Stormwater Managers Association?
http://ny.floods.org/images/NFIP_Reform_White_Paper_NYSFSMA.pdf

Matthew E. Kahn, Professor at UCLA’s Institute of the Environment, the Department of Economics, and the Department of Public Policy.
http://blogs.hbr.org/cs/2012/11/the_problem_with_fema_no_one_is_talking_about.html