ObjectivesTo　examine　the　causal　impact　of　small　businesses　on　street　theft　and　the　underlying　mechanisms.MethodsThe　＂Cleanup　Holes　in　the　Wall＂　campaign　in　Beijing,　China,　provides　a　rare　opportunity　for　a　natural　experiment.　Drawing　on　street　view　images　processed　by　deep　learning　algorithms　and　other　big　data　sources　such　as　court　judgments　and　location-based　service　(LBS)　population,　we　use　difference-in-difference　(DID)　models　to　investigate　how　the　disappearance　of　small　businesses　leads　to　changes　in　the　occurrence　of　theft.　We　further　examine　the　mechanisms　by　introducing　mediators,　including　ambient　population　and　social　activity.ResultsThe　treatment　units　that　experienced　a　mass　loss　of　small　businesses　showed　a　significant　reduction　in　street　theft　compared　to　the　control　units　that　were　less　affected　by　the　cleanup　campaign.　Ambient　population　and　social　activity　played　a　mediating　role　in　promoting　and　deterring　crime,　respectively,　with　the　former　dominating.　The　results　remain　robust　after　including　covariates　in　the　models,　balancing　covariates　using　the　propensity　score　matching　method,　and　adopting　alternative　thresholds　to　classify　the　treatment　group.ConclusionsThere　are　two　competing　yet　coexisting　mechanisms　through　which　small　businesses　influence　street　theft.　On　the　one　hand,　commercial　premises　provide　large　numbers　of　criminal　opportunities　for　potential　offenders;　on　the　other　hand,　they　are　central　to　local　social　control　and　order.　While　small　businesses　exercise　a　certain　amount　of　natural　surveillance　power,　as　a　whole,　they　function　primarily　as　crime　generators.　Implications　for　implementing　targeted　policies　tailored　to　the　nature　of　small　businesses　are　discussed.