Client: Estonian Police and Border Guard Board, with the Estonian Academy of Arts
Location: Tallinn (and originally, Tartu), Estonia
Year: 2013–2014
Sector: Origin Story · CPTED · Space Syntax · Public Safety
Building on the team’s own DIY street-lighting sensor, this project tested whether Tallinn’s most accessible streets were also its safest — bringing space syntax into direct dialogue with police crime data.
Does how well-connected a street is — how easy it is to reach and move through — predict how safe it feels or actually is? SPIN Unit, working with the Estonian Academy of Arts, pitched this question directly to the Estonian Police and Border Guard Board.
Grew out of a December 2012 proposal to the Police (“Analyzing the links between urban space and crime — piloting a multi-criteria approach in Tallinn and Tartu”), formalised into a four-phase research plan in February 2014 and backed by a signed EKA–SPIN Unit collaboration agreement (May 2014), with funding support credited to DoRa/Archimedes Foundation and the Estonian Cultural Endowment. Results were presented at an international CPTED conference in August 2014.
Reused the team’s own custom lighting-sensor rig (built for the 2013 EAEA11 night-map project) to survey roughly 400km and 33,000+ GPS-tagged light readings across central Tallinn. These were compared against a space-syntax segment analysis of the street network and police-reported crime by category (theft, robbery, burglary, violence, drugs, graffiti), replicating methodology from Bill Hillier’s UCL crime/space-syntax research in London.
Only Phase 1 (accessibility and lighting, Feb–Aug 2014) is documented as completed; the planned Phases 2–4 (land use and census data, urban typologies, and a final empirical crime-hazard map with planning guidelines, running through Dec 2015) don’t appear in the project files.
- Illumination correlates clearly with street accessibility — better-connected streets are better lit — but the sample was too small to draw firm conclusions about light and crime directly.
- A known data limitation was flagged upfront: crimes are often geocoded to the offender’s home address rather than the crime scene, complicating any true spatial crime map — a caveat borrowed from Rome’s benchmark policing data system.
- Crime types were mapped directly onto specific Estonian Penal Code sections, grouped by expected spatial signature — public-space property crime, public-to-private entry crime, and public-order offences each analysed separately.
- The custom sensor rig was built specifically because commercial light-metering tools don’t scale to city level — a recurring theme in SPIN Unit’s early, hands-on approach to urban data.
An ambitious research roadmap that appears to have delivered its first phase and stopped — still useful as an origin-story example of SPIN Unit’s early, self-built approach to urban sensing, and its willingness to pitch unconventional data-driven ideas directly to institutional clients like the police.