1.
UWA’s Online Offender’s
database
Structure and Analyses

2.
UWA’s law enforcement budget
• Between 45-95% of UWA’s
budget is invested in law
enforcement at any site
they manage.
• Need to measure
effectiveness of this
expenditure and look at
ways it can be improved
• Tools developed to capture
data to enable this to
happen

3.
MIST and SMART
• UWA developed MIST in
1998
• Tested and updated in
Murchison Falls National
Park
• WCS helped UWA roll it
out to all PAs from 2001
• Taken around World
• SMART partnership
developed to update
MIST and provide more
analysis capabilities
• www.smartconservations
oftware.org

4.
Changing patrol coverage
Kibale National Park, Uganda
LEM was implemented in Kibale
National Park in 2004 and as patrol
coverage maps were produced
wardens started to orient patrols
to areas that had been rarely
patrolled if at all.
2004 2005 2006 2007
2008 2009 2013

5.
Sightings of illegal activities in Kibale National
Park, Uganda
LEM results of sightings of illegal
activities between 2004 and 2009 in
Kibale National Park. Note how there is
an expansion of locations of illegal
activities over time, mainly because
patrol coverage increased
2024 2005
2006 2007
2008 2009

6.
SMART – analysis at Grid level

7.
SMART data capture
• Data collected on Ssmart
phones
• Downloaded directly to
SMART
• Smart-connect will allow
transfer of data across
cellphone networks
• Aiming for real time data

8.
Improving analyses of LEM
data
• Different spatial
patterns among
illegal activities
• Implications for
management –
different patrol
strategies are
needed for each
type of illegal
activity

9.
Improving patrol
effectiveness
• Developing an approach
to improve ranger
effectiveness using these
models
• Predictions for snares is
we could double the
number found

10.
Tested this approach in QENP

11.
Limits to MIST/SMART data
• Captured data from Patrols
including arrests – can map
arrest locations and
summarise basic data on
arrests
• However, did not track
suspects well nor what
happened to them in the
courts
– Often treated as first time
offenders because no record of
previous arrests

12.
Offenders database

13.
Structure of Offender’s database
• Three main tables:
– Suspects
– Arrests
– Court Cases
• Two analysis options
– Summary queries – automatic and
results produced on screen and as
.csv file
– Export data and analyse
independently

14.
Fingerprint option recently developed

15.
Fingerprint reader scans fingerprints and allows
checking of database with previously scanned
fingerprints

16.
Wild Leo and Offenders Database
• Wild Leo was developed by Andrew Lemieux for
storing similar data.
• Problems
– data stored were limited
– Onsite and data could not be accessed elsewhere
• Online offenders database developed
– Deliberately incorporated all fields of Wild Leo and
agreed to stop using Wild Leo at meeting in UWA HQ in
2014
– But for prosecutions UWA needs maps which show
where suspect was arrested – can be done in SMART
now – needs training

17.
Types of analyses that can be made with
Offenders Database data
• Summary queries by Protected area between
specific dates:
– Numbers of arrests and number of offenders
– Number of first, second, third+ time offenders
– Summed numbers and weight of evidence impounded
– Total fines, prison terms and community service days
– Average fine, prison term or community service days for
first time or repeat offenders
– Number of prosecutions and percentage successful

18.
Arrests over time

19.
Time of day arrests made

20.
Detection of offender

21.
Reasons for arrests

22.
Repeat offender frequency

23.
Verdicts

24.
Percentage of successful prosecutions

25.
Penalties per crime type

26.
Trend in fines

27.
Trends in prison terms

28.
Comparison of Courts

29.
Why an online database?
Security and
Law
Enforcement
Unit
Parks/
Reserves
TownsAirport
• Increasing need for
Intelligence information
to tackle wildlife crime
and trafficking
• SLEU uses I2 and
Sentinel but both are
useless without data
• Need data in real time
and ability to update
regularly

30.
Important intelligence data
• Telephone number of suspect
– Can link to records of calls made by others
– Can check address where registered
• Village location and parish (ideally with GPS location
in case follow up needed)
– Can check hotspots of people involved in wildlife crime
• Associates
– Who works together – partners may provide links to
middlemen

31.
Getting the Online Database to work
• Needs leadership from Protected Area Authority
Headquarters – someone needs to push to make sure
all sites collect and enter data
– Only three of the seven conservation areas UWA manages
enter data relatively regularly
• While fairly simple to use there is a need to train in
its use as staff move on and are replaced
• Need a dedicated computer – part of problem with
slow take up in UWA
• Internet connection can be frustrating in Africa –
dongles have been provided and cost $10/month to
top up with data

32.
Data sheets for offline data storage
• Created data sheets for
Suspects, arrests and
court cases
• Allows data to be
collected if in a rush
and then entered later
• Also ensures paper
record which can be
signed by suspect which
may be useful for future
prosecutions

33.
Conclusion
• The offenders database has great potential in
tracking offenders and also providing data for I2 at
UWA HQ
• For offenders database to be most effective it
needs:
 Leadership in UWA HQ requiring the suspects, arrests
and court data to be entered regularly
 WCS working with OSSCube to allow offline data entry –
but with and average 20 arrests/month in each park the
data should be able to be entered when there is
connectivity
 Prosecutions staff need to understand the data will be
used by others and therefore it is important

34.
The End
Offenders Database has
been funded by Uganda
Wildlife Authority, US Fish
and Wildlife Service;
Darwin Foundation,
Wildlife Conservation
Society and
UK IWT Fund.