This is part of a workshop prepared for WorldComp’08 titled “Why and How GeoDa Software Works as a Powerful and Intuitive Method for Geovisualizing Demographic Data” held in Las Vegas, Nevada in July 2008. Funded by fellowship from the Center for Spatially Integrated Social Science, University of California, Santa Barbara. http://www.world-academy-of-science.org/worldcomp08/ws/tutorials/tutorial_byrne J. Kevin Byrne is a Professor in the Science Baccalaureate Program in Visualization + Creative Management and Adjunct Professor, Sustainable Design Program, Minneapolis College of Art and Design, Minneapolis, Minnesota USA. http://kevinbyrne.info

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2. More Guns, Less Crime?
A debate between John Lott, author of More Guns, Less Crime, and Douglas Weil, research director of
Handgun Control, Inc. Transcript from July 1, 1998
Timehost: …John Lott of the University of Chicago, whose book More Guns, Less Crime argues that
allowing citizens to carry concealed weapons actually lowers crime rates. We are also joined by someone
who is in direct opposition to Mr. Lott's theory. He is Douglas Weil, director of research for Handgun
Control, Inc. and the Center To Prevent Handgun Violence. Mr. Lott, perhaps you could first outline what
your book has to say about concealed weapons. We've had a lot of questions about it already.
John Lott: I find that just as criminals can be deterred by higher arrest or conviction rates, they can also be
deterred by the fact that would-be victims might be able to defend themselves with a gun. Criminals are
less likely to commit a crime as the probability that a victim is going to be able to defend themselves
increases.
Douglas Weil First, there's no evidence that we have any significant increase in gun carrying, which means
criminals are not likely to face an increased risk of an armed victim. Most important, when Lott's research
was published, a number of academic researchers looked at this methods and his conclusions and
determined his research was fundamentally flawed. The criticism was so convincing that even Gary Kleck,
a criminologist whose work is often cited by John Lott and the NRA, has dismissed Lott's conclusions.
Kleck wrote in his book, Targeting Guns, that "more likely the declines in crime coinciding with relaxation of
carry laws were largely attributable to other factors not controlled for in the Lott Mustard analysis."

3. Gun Ownership by State
In 2001 the Behavioral Risk Factor Surveillance System (BRFSS) in
North Carolina surveyed 201,881 respondents nationwide, asking
them, "Are any firearms now kept in or around your home? Include
those kept in a garage, outdoor storage area, car, truck, or other
motor vehicle." Here are the results.
Yes No
Total Number Respondents % Respondents %
All Participants 201881 67,786 31.7 134095 68.3
Alabama 2,623 1,294 51.7 1,329 48.3
Alaska 2,716 1,627 57.8 1,089 42.2
Arizona 3,066 989 31.1 2,077 68.9
Arkansas 2,780 1,431 55.3 1,349 44.7
California* 3,897 846 21.3 3,051 78.7

4. BJS Home Page Data for Analysis Data Online Crime Trends State Level
State and national level crime trend estimates
Source: FBI's Uniform Crime Reports Variables: UCR offenses (counts and rates)
(UCR) Violent Crimes: murder and nonnegligent manslaughter,
forcible rape, robbery, aggravated assault
Years available: Since 1960 Property Crimes: burglary, larceny/theft, motor vehicle theft
(See definitions)
Select a table type:
State by State and OR Trends in one variable OR One year of data
national trends
- Multiple jurisdictions - Multiple jurisdictions
> One jurisdiction
> One variable - Multiple variables
- Multiple variables
- Multiple years > One year
- Multiple years
Example: Example:
Example:
Robbery Crime in 2003
Reported crime in Alabama
Year Alabama Alaska Arizona Violent Violent
Violent Violent Population crime crime
crime crime
State coverage total rate
Year Population total rate 2001 5,584 514 8,868
2002 5,962 489 8,000
Alabama 4,500,752 19,331 429.5
2001 4,464,356 19,582 438.6 2003 6,038 444 7,619
Alaska 648,818 3,850 593.4
2002 4,478,896 19,931 445.0
Arizona 5,580,811 28,638 513.2
2003 4,500,752 19,331 429.5

6. Bivariate Scatterplot: Guns - Crime
% gun
?
owner-
ship
violent crimes /
100k pop.

7. violent crime gun ownership
(per 100,000 pop) (in %)
98.2 - 175.7 8.7 - 16.7
More Guns,
175.8 - 297.0 18.0 - 25.5
297.1 - 396.5 30.0 - 35.9
Less Crime?
396.6 - 468.1 38.4 - 42.3
468.2 - 606.8 42.8 - 47.7
606.9 - 761.1
50.7 - 59.7

9. Parallel Coodinates for Guns and Crime Data
% Gun
Vars:
ownership
% H.S.
grad. rate
Violent crimes
/ 100k pop.

17. Atlanta Region Homicides
Homicides and selected socio-economic characteristics for counties surrounding Atlanta, GA
aggregated for three time periods: 1979-84 (steady decline in homicides), 1984-88 (stable p
1988-93 (steady increase in homicides).
S. Messner, L. Anselin, D. Hawkins, G. Deane, S. Tolnay, R. Baller (2000). An Atlas of the S
Patterning of County-Level Homicide, 1960-1990. Pittsburgh, PA, National Consortium on V
Research (NCOVR).
Variables
Variable Description
NAME county name
STATE_NAME state name
STATE_FIPS state fips code (character)
CNTY_FIPS county fips code (character)
FIPS combined state and county fips code (character)
FIPSNO fips code as numeric variable
HR7984 homicide rate per 100,000 (1979-84)
HR8488 homicide rate per 100,000 (1984-88)
HR8893 homicide rate per 100,000 (1988-93)
HC7984 homicide count (1979-84)
HC8488 homicide count (1984-88)
HC8893 homicide count (1988-93)
PO7984 population total (1979-84)
PO8488 population total (1984-88)
PO8893 population total (1988-93)
PE77 police expenditures per capita, 1977
PE82 police expenditures per capita, 1982
PE87 police expenditures per capita, 1987
RDAC80 resource deprivation/affluence composite variable, 1980
RDAC85 resource deprivation/affluence composite variable, 1985
RDAC90 resource deprivation/affluence composite variable, 1990

18. Parallel Coodinates for Georgia Homicide Data
Resource
Vars:
deprivation
Homicides
/ 100k pop.
Police ex-
pend. / cap.

26. Case examples credits (slide #, author/s, affiliation, source)
Slide 2, Yahoo Chat and Time.com, website
Slide 3, Washington Post, website
Slide 4, Bureau of Justice Statistics, U.S. Department of
Justice, web portal
Slide 8, Statemaster.com, web portal
Slide 15, Stephen Haas, Statistical Analysis Center /
WV Division of Criminal Justice, Powerpoint presentation
Slide 16, SAL, University of Illinois, web portal

27. Spatial Interaction of
Two Key Indicators of
Sustainability Among
Mäori of New Zealand
J. Kevin Byrne, July 20, 2007

28. Tables to be used as source matter:
Per capita ecological footprints and land productivity of New Zealand regions,
1997/98
Ecological surplus/deficit and degree of overshoot of New Zealand regions,
1997/98
Source: Ecological Footprints of New Zealand and its Regions
Percentage of Regional Population Belonging to Mäori Ethnic Group, 2001
Source: Statistics New Zealand
NZDE2001 Index of Deprivation, 2001
Source: Department of Health, Wellington School of Medicine and Health
Sciences
• Index purpose: resource allocation, research, advocacy.
• Variables in order of decreasing weight in index: income, employment,
communication, transport, support, qualifications, living space, owned
home.
• The index is an ordinal scale ranging from 1 to 10, where 1 represents the
areas with least deprived scores and 10 with the most.

35. Idealized Process
Question: What is the relationship between eco-consumption,
indigenous ethnicity, and social deprivation in New Zealand?
Thesis: Eco-consumption and indigenous ethnicity are
negatively correlated, i.e. Mäori are low eco-consumers.
One reason may be due to cultural roots of environmental
conservation as inherent within Mäoritanga.
Research + data gathering: three tables required, collect and clean up in
Excel
Analysis: Import into Geoda, and do parallel coordinate plotting,
mapping and trivariate modeling in 3D
Synthesis - diagrams: parallel coordinate plots; maps: choropleth,
cartogram, and possibly trivariate 3D maps
Confirm or disconfirm thesis
New question/s
Feedback
Reflect on learning

36. Idealized Process (concluded)
Findings Summarized:
• Lowest EF = Lowest % Mäori Ethnicity = Lowest
Deprivation
• Moderate EF = Highest % Mäori Ethnicity = Highest
Deprivation
• Regions of Gisborne and Nelson are situated at
opposite ends of the three-variable PCP continuum
Confirm thesis: provisionally yes, but with interesting caveats
New questions: In what specific ways might Kiwi social
deprivation constrain eco-conservation motivations
and/or behavior? Does wine increase Marlborough’s EF? 
Why is the Nelson Region comparatively so small in size?
Feedback: TBD, Reflections on learning: TBD

38. Recycling Patterns of 140 Households in Midland Hills Neighborhood, Roseville, November 24, 2007
Roselawn Avenue
Bins Counted
3.0 - 3.5 Data Source: for parcels, Metropolitan Council;
2.0 - 2.5 for bin counts, Kevin Byrne
1.0 - 1.5
0.0

40. Recycling Participation on 21xx-22xx Draper Avenue, Roseville, 4 sample weeks in 2007
Draper Avenue
Roselawn Avenue
Bins Count
(one month total)
Data Source: for parcels, Metropolitan Council;
7.5 - 10.5 for bin counts, Kevin Byrne
4.5 - 6.5
2.0 - 4.0
0.0 - 1.0