Boom and Bust of Technology Companies
at the Turn of the 21st Century
Contest webpage: www.public.iastate.edu/~hofmann/infovis/
Authors and Affiliations:
- Heike Hofmann, Iowa State University, email@example.com
- Hadley Wickham, Iowa State University, firstname.lastname@example.org
- Dianne Cook, Iowa State University, email@example.com
- Junjie Sun, Iowa State University, firstname.lastname@example.org
- Christian Röttger, Iowa State University, email@example.com
- R especially packages RMySQL, maps,
maptools, RColorBrewer, ash
This data contains information on 84472 technology companies between 1989-2003.
The companies produced 154912 unique products in this period. This period of
time is notable for technology innovations such as the rise of the internet,
the dot-com bubble and crash, Y2K, the 9/11 tragedy and changes between democratic
and republican control of government.
TASK 1: Trends and multivariate relationships
1.1 Trends in technology companies and products over time
1.2 Trends by industry type
- Process: Each of the variables is tabulated by industry
type and year. Results are plotted as overlaid lines in separate
plots for each variable. The timeline starts at 1992 because the
industry type is not defined until then.
- Companies: There is a dramatic increase in the number of telecommunications
companies in 1999 to a peak of 650 million in 2001. The number of software
companies is high throughout the time period, with a sharper rise 2000-2001
and then a drop. Subcomponent companies were the second highest industry
type until 1999, and the number of these companies levelled out at 200
million from 1998. A curiousity is the industry type NON (primarily non-technology
companies). It has a suspiciously political pattern: the number of NON
companies drops at the beginning of Clinton's years and stay low until
- Products: The number of products by industry type is very
similar to the number of companies.
- Sales: The dominating industry type for sales for the entire
period is NON (primarily non-technology companies). This is a
surprise because we would not have expected this seemingly
miscellaneous industry type to be dominant in sales. Perhaps these
are companies that have a broad variety of products, of which
technology products are a subset, but sales are recorded across
all. There is a dramatic increase in sales for this industry type
after 2000. The next two largest categories in sales are
telecommunications and energy.
- Employees: The patterns for employees follows those for
sales, in primarily non-technology companies and
telecommunications. Subcomponent companies are the second biggest
employers until 2000 when they get bumped by telecommunications.
- Caption for exhibit: Overview of number of companies,
products, volume of sales and number of employees by year and
1.3 Is there anything between the East and West Coast?
- Process: The density of company counts was computed with
respect to geographic location, for each year. The results are
displayed as colored maps and animated over time.
- Results in absolute scale give insights into the geographic areas that
dominate the country - ie the East Coast and California. Apart from that,
Seattle, the Twin Cities, Chicago and Houston are visible.
- Steady growth is visible along the East Coast between Boston
and Washington D.C. as well as on the West Coast in San Francisco
and Los Angeles until 2001. After 2001 this trend inverses and
these areas fall victim to huge decreases in the number of
- Losses in the East are localized with their center in
Manhattan. Manhattan loses 14% of its technology companies in
2001. Close by areas such as Long Island and Upper New Jersey do
not show similarly dramatic losses.
- Caption for exhibit: Growth and decline in number of
companies by geographic location are displayed on a map of the
U.S. Steady growth in the number of companies occurs along the East
Coast and California. After 2001 these areas show huge losses in the
number of companies. The losses along the East Coast have their center
in Manhattan, which loses 14% of all of its technology
companies.Center of the losses on the West Coast is San Francisco -
16% of all companies are gone until 2002. Losses on the East Coast are
different insofar, as dramatic losses are restricted to Manhattan,
whereas both Long Island and Upper New Jersey see only very slight
losses in the range of less than 10%.
TASK 2: Clusters
2.1 Is local growth fueled by natural disasters?
- Process: The density of relative company counts was
computed with respect to geographic location, for each year. The
results are displayed as colored maps as small multiples and
animated over time. Relative growth is measured as the difference
in the number of companies of two consecutive years divided by the
number of companies in the earlier year.
- The relative scale tells about pockets of locally dramatic
increase in technology company activity.
- Some of these pockets seem to coincide with weather related
disasters - between 1992 and 1999 all but one area can be matched
with floods, hurricanes and other mostly weather related
disasters. This points to federal emergency funds as a stimulant for
- Relative growth is high in various areas between 1998 and
1999. This coincides with the boom of internet/web related
- Caption for exhibit:
The density of relative company counts was computed with respect to
geographic location, for each year. Results are displayed as colored
maps and animated over time. The relative scale reveals pockets of
locally dramatic increase in technology company activity. Many of
these appear after natural disasters.
- Supporting information
2.2 Software is out -- services are in
- Process: Top 20 products were picked for each year,
where top products were defined to be those products offered by the
most companies. The development of each of these products is shown
between 1989 and 2003 in absolute number of companies (left) and
their relative market share (right).
- Top products are very stable - between 1989 and 2003 only 45
different products appear among the top 20 products at least
- The number of companies offering one of the top products
increases over time, indicating that market competition is
- Products can be classified mainly as either service or
software. The number of companies offering software products is
very stable over time, the number of companies offering service
products increases dramatically over time. Companies offering
internet/web related services take the market by storm after
1997. After 2001 the number of companies offering services
decreases for all products, following the general trend of the
market. Software companies do not follow this trend but remain
- "Losers" are companies offering custom application
software, "winners" among software products are
- Top 20 services offered from the beginning of the time period are non-computer
related: waste management, soil or water analysis.
- Holding/parent companies are ranked number one throughout the
- 1993 wasn't a good year for high-tech products - the number of
companies takes a dip across all top products. .
- Caption for exhibit: Line plots of top products between
1989 and 2003. Products shown are among the top 20 products
(i.e. products offered by the most companies) at some time between
1989 and 2003. Shades of orange indicate services, shades of blue
indicate software products. The three green lines are software
services. Black lines correspond to holding companies.
Overall, the number of companies offering one of the top 20 products
increases (sign of higher competition?). Early on, most products are
software related products - after 1997 services dominate the
market. Custom programming software products seem to take the worst
dip of all - they seem to drop out of fashion after 1991. While other
software products still increase slightly, they do not experience the
boom of service products. The only software products that do
particularly well are software services (green lines), which seem to
jump on the service bandwagon. On the other hand, software products do
not seem to suffer from the same decrease after 2001 as almost all of
the service products. The dark red lines are internet/web related
products. They exist only after 1997 (some relationship with Windows
97?) and take the market in a storm. Orange colored products
correspond to non-computer related services, such as waste management,
soil analysis and water analysis. These services existed from the
beginning of the time period and remain among the top 20 products
2.3 High market concentration in biochemical companies
TASK 3: Unusual features
3.1 There's something strange about Harris County, Texas!
- Process: The numbers for each county for each year are
aggregated yielding summary statistics for each county: number of
companies, number of employees, volume of sales, number of products,
number of different products. Summary statistics over the time period
for each county are produced to characterize the longitudinal
data. Geographic location, using latitude and longitude, is added to
the county summaries. The data is compiled into an xml metadata set
for ggobi, so that different aspects of the data can be probed
quickly. The strange pattern in Harris County was investigated further
by making detailed calculations in R, subsetting the data into just
Harris County and making further calculations.
- Insights: Most counties follow a pattern of increasing
number of companies over time, and a strong drop after 2000. There
is one noticeable expection to this pattern: Harris County, TX. This
county has a dramatic increase of 110 companies from 2000-2003,
which represents a 14% increase. There is only one other county with
an increase or more than 10 companies during this period. Is there
something unique in Harris County, TX? Harris County, TX, is the
home of the Johnson Space Center. It is also the county where George
Herbert Walker Bush claims a homestead exemption on his
residence. The increase in number of companies is explained mostly
by a 50% increase in energy companies, from 117 to 172, with 26% (62
to 91) explained by primarily non-technology related
companies. (Aerospace companies are included in this industry type.)
Sales and number of employees increase from 2001-2003 but not so
much differently from other counties. The number of different
products jumps, and this is noticeably different from other
- Caption for exhibit: Harris County, TX, has a noticeably
different trend than all other counties after 2001. The number of
companies in the county actually increases, by 110 companies from 800,
by 14%. It is the home of the Johnson Space Center, and also of
G. H. W. Bush.
- Supporting information
3.2 Sales switch up between counties in Detroit, MI
- Process: The same county aggregated data is used,
focusing on sales. The extreme values are sequentially filtered by
hiding the county with the highest sales in the plot. (This includes
New York County, NY, Cook County, IL, Hennepin County, MN.) A strange
pattern was revealed and we investigated this by highlighting the
counties involved and zooming in on a map to explore the geographic
- Insights: One county, Wayne County, MI, has a strange
sales pattern. It has strong but flat sales from 1989 to 1997, and
then drops dramatically. On closer inspection there is another county
with the inverse pattern, which is Oakland County, MI. Both counties
are in Detroit, MI. The switch is observed in the zoomed map view of
the state: from 1997 to 1998 the high sales switch from Wayne to
Oakland County. One reason for this switch might be the activities of
the Mayor, Dennis Archer.
- Caption for exhibit: Wayne County and Oakland County, MI
swap the dominance of sales between 1997 and 1998.
- Supporting information
3.3 Strange Values for Market Concentration
- Process: The HHI (introduced in Task 3) is plotted
against year. Results are displayed as line plots, with and without
the outliers. Further calculations are made to check the data.
Two outlier values (red dots in top picture) excluded in bottom picture
- Insights: Two industry types, MAN and DEF, stood out by exhibiting
sudden, huge jumps in 1992 and 1994, respectively. The results were recalculated
after excluding the two sales values which caused the jumps. In both instances,
one firm has ten-fold increases in sales for one year, followed by a fall
back to original levels the next year. This gives it a near-monopoly HHI score
for that year explaining the HHI spike.
We might be tempted to speculate about the NATO mission in
Bosnia etc. But the high sales represent a tenfold increase,
falling the year afterwards to the former level and staying
there. Therefore we suspect errors in data entry here.
- Caption for exhibit: HHI is plotted against year
measuring the amount of market concentration. There are two extreme
values in industries MAN and DEF. Together with the fact that the high
sales figures are strictly one-off, a tenfold increase followed by a
fall back to original levels, we suspect an error in data entry here.
TASK 4: Other findings
We spent of lot of energy early in the data release finding anomalies in the data
and reporting these. This resulted in numerous revisions of the competition data.
Some of the problems were fixed but there still seem to be numerous problems with
this data. WIth data sets of this size, mainting quality is a very difficult problem.
Here are some of the irregularities we found:
4.1 Can so many companies really be founded in 2000?
- Process: The counts for companies founded are plotted
against years, together and separated by industry type.
- Insights: There is a big spike in number of companies
founded in the year 2000. This doesn't look plausible. It exists
before and after the final data cleaning for the competition. The
spike exists for every industry type. In the original data the number
of companies founded in 1999 is 4081 and it jumps to 13433 in 2000. In
the cleaned data the number of companies founded almost doubles from
4132 in 1999, to 7352 in 2000, and then drops to 804 in 2001. This is
- Caption for exhibit: Counts for companies founded are
plotted against years separated by industry type. The year 2000 has an
implausible spike in number of companies founded, across industry
4.2 Why are there companies in the database before it is
- Process: Founding year is plotted against the first year
in the database. The values are jittered slightly to spread ties
- Insights: Notice the points above the diagonal, in the
upper left half of the plot? There are many companies founded after
they appear in the database. Year 2000 is particularly
problematic. The left plot shows the original posted data, which has
more problems. The right plot shows the final competition data after
cleaning. After the data cleaning, there are still 504 companies that
appear in the database before their founding year.
- Caption for exhibit: (Left) Data before final cleaning. (Right)
Data after final cleaning. Founding year plotted against first year in the
database, with ties jittered slightly. There are many companies who appear
in the database before they are founded. In the revised data set this got
improved but still exists.
We were very surprised by many of our observations on the data. Initial disbelief
was followed by intensive number crunching to check the values and extensive
internet searches to find plausible explanations. Particularly, the
potential relationship of local growth in companies with natural
disasters and the increasing trend in the number of comapnies in Harris County, TX, did come as
We arrived at the association of natural disasters and local hot spots
by an astute observation by one of the team members. The chaotic
popping up of hot spots around the country looked spurious, until one
person asked at the 93-94 hotspot in Iowa: "When were the floods in
Iowa?" This led to extensive searches of geographic locations and
natural disasters, and it cascaded into ways to explain many
hotspots. Mostly, these could be found in the 93-99 period when Clinton
was in government. Only then we started to come across accusations in online news stories about suspect use of FEMA funding during the Clinton administration. Letterman cracked a top 10 joke
related to FEMA. Not all of the hotspots can be explained this way. We would also like to point out that this association between local economic activity
and disasters is purely a proposal, not a conclusive finding.
The results on Harris County, TX, arose immediately from the
longitudinal plots of county counts. The trend stands out in the
graphic, in a manner probably not so detectable numerically. Checking
the numbers and finding no other county in the USA that is even close
to this trend was also a surprise. Identifying it as a county in
Texas, was a tad surprising, and even further surprising to find
accidentally that it is the residence of the current president's
dad. There are many attractions, such as the Johnson Space Center, in
Harris County, but this association raises big questions about
When we started exploring the data, we expected to see the bubble pop in Silicon
Valley, some economic effects in the New York region after September
11, 2001, the effects of Microsoft developing in the Seattle area. And
we saw these. We also had other expectations that did not pan out:
companies that move a lot might be more likely to go bankrupt
(disappear from the database), that there might be movement from away
from the coasts after the bust to the mountain states and the
Midwest. There is some movement of companies but these results were
Thanks to Georges Grinstein, Urska Cvek, Mark Derthick and Marjan
Trutschl for such intriguing data, and the enormous amount of work
that was clearly needed to pull it together.