UWA’s Online Offender’s database

Structure and Analyses

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

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.smartconservationsoftware.org

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

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

SMART – analysis at Grid level

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

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

Improving patrol effectiveness

• Developing an approach to improve ranger effectiveness using these models

• Predictions for snares is we could double the number found

Tested this approach in QENP

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

Offenders database

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

Fingerprint option recently developed

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

fingerprints

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

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

Arrests over time

Time of day arrests made

Detection of offender

Reasons for arrests

Repeat offender frequency

Verdicts

Percentage of successful prosecutions

Penalties per crime type

Trend in fines

Trends in prison terms

Comparison of Courts

Why an online database?

Security and Law

Enforcement Unit

Parks/ Reserves

Towns Airport

• 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

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

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

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

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

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.