This　study　presents　a　spatial　approach　for　the　macrolevel　traffic　crashes　analysis　based　on　point-of-interest　(POI)　data　and　other　related　data　from　an　open　source.　The　spatial　autoregression　is　explored　by　Moran＇s　I　Index　with　three　spatial　weight　features　(i.e.,　(a)　Rook,　(b)　Queen,　and　(c)　Euclidean　distance).　The　traditional　Ordinary　Least　Square　(OLS)　model,　the　Spatial　Lag　Model　(SLM),　the　Spatial　Error　Model　(SEM),　and　the　Spatial　Durbin　Model　(SDM)　were　developed　to　describe　the　spatial　correlations　among　2,114　Traffic　Analysis　Zones　(TAZs)　of　Tianjin,　one　of　the　four　municipalities　in　China.　Results　of　the　models　indicated　that　the　SDM　with　the　Rook　spatial　weight　feature　is　found　to　be　the　optimal　spatial　model　to　characterize　the　relationship　of　various　variables　and　crashes.　The　results　show　that　population　density,　consumption　density,　intersection　density,　and　road　density　have　significantly　positive　influence　on　traffic　crashes,　whereas　company　density,　hotel　density,　and　residential　density　have　significant　but　negative　effects　in　the　local　TAZ.　The　spillover　effects　coefficient　of　population　density　and　road　density　are　positive,　indicating　that　the　increase　of　these　variables　in　the　surrounding　TAZs　will　lead　to　the　increase　of　crashes　in　the　target　zone.　The　impacts　of　company　density　and　hotel　density　are　just　the　opposite.　In　general,　the　research　findings　can　help　transportation　planners　and　managers　better　understand　the　general　characteristics　of　traffic　crashes　and　improve　the　situation　of　traffic　security.