Urban　traffic　violation　behavior　plays　an　important　role　in　traffic　accidents.　Analyzing　the　spatial　and　temporal　distribution　of　traffic　violation　behavior　can　support　related　decision　makings　for　traffic　management　and　the　optimization　of　the　surroundings　of　the　hotspots.　Due　to　the　limitation　in　data　acquisition,　existing　studies　paid　little　attention　to　the　variation　of　the　spatial　and　temporal　patterns　between　different　violation　behavior　types.　There　is　a　lack　of　analysis　framework　to　support　the　decision　makings　in　traffic　violation　behavior　treatment.　In　this　study,　we　propose　a　traffic-violation-behavior　treatment-oriented　analysis　framework　based　on　the　spatial　and　temporal　hotspot　approach.　Two　analyses　are　designed　and　conducted　to　support　the　traffic　violation　behavior　treatment:　(1)　analyzing　the　temporal　pattern　of　each　spatial　hotspot　to　support　the　reasoning　analysis　and　precise　treatment　policy　makings　at　the　local　scale;　(2)　analyzing　the　spatial　pattern　of　the　hotspots　during　typical　periods　(e.g.,　morning　and　evening　rush　hours)　to　support　the　reasoning　analysis　and　the　optimization　of　the　allocation　of　police　resources　on　a　global　scale.　We　use　a　dataset　of　Fuzhou　city　acquired　in　2017　to　verify　the　proposed　method.　The　spatial　and　temporal　patterns　of　the　motor　traffic　violation　behavior　and　the　non-motor　type　are　analyzed　and　compared.　We　find　that:　(1)　the　traffic　violation　behavior　exhibits　a　double　peak　hourly　pattern　at　9:00　am　and　4:00　pm　during　a　day,　respectively.　The　morning　peak　is　obviously　higher　than　the　evening　peak.　The　traffic　violation　behavior　more　likely　happens　during　weekdays　than　weekends;　(2)　the　traffic　violation　behavior　mainly　concentrates　at　the　core-　built　area　within　the　second　ring　highway　and　several　hotspots　in　the　suburban　area　including　the　shopping　mall　of　Cangshan　Wanda　and　the　exit　of　the　Kuiqi　tunnel　oriented　to　Mawei;　(3)　motor　and　non-　motor　traffic　violation　exhibit　different　temporal　and　spatial　patterns.　Non-motor　traffic　violation　frequencies　exhibit　both　larger　hourly　and　weekday-weekend　differences,　and　mainly　concentrates　at　the　road　crosses　with　big　traffic　volume　of　both　motor　cars　and　e-　bikes/pedestrian.　While　the　motor　traffic　violation　exhibits　more　stable　patterns　across　the　hours　in　a　day　and　the　days　in　a　week,　and　mainly　happens　around　the　critical　places　such　as　large　hospitals,　shopping　malls,　and　complex　overpasses;　(4)　the　spatial　scales　affect　the　patterns　of　the　spatial　hotspots　of　the　traffic　violation　behavior.　The　spatial　autocorrelation　of　the　traffic　violation　increases　with　the　scale　size　rapidly　before　1500　m　and　keeps　around　0.6　afterward.　Motor　traffic　violation　exhibits　lower　spatial　autocorrelation　than　the　non-motor.　The　above　findings　validate　the　effectiveness　of　the　proposed　method.　It　can　help　to　guide　the　construction　of　the　traffic　violation　behavior　treatment　platform　and　further　optimize　the　allocation　of　the　police　resources　and　improve　the　effectiveness　of　the　law　enforcement　for　the　traffic　violation　behavior.　©　2022,　Science　Press.　All　right　reserved.