Land　use　drivers　that　best　describe　land　use　patterns　quantitatively　are　often　selected　through　regression　analysis.　A　problem　using　conventional　statistical　methods　in　spatial　land　use　analysis　is　that　these　methods　assume　the　data　to　be　statistically　independent　while　spatial　land　use　data　have　the　tendency　to　be　dependent,　known　as　spatial　autocorrelation.　Two　different　scales　of　study　area,　Fujian　Province　and　Longhai　county　are　selected.　In　this　paper,　Moran＇s　I　is　used　to　describe　spatial　autocorrelation　of　dependent　and　independent　variables　and　spatial　autoregressive　models　which　incorporate　both　regression　and　spatial　autocorrelation　are　constructed.　5　main　land　use　types　in　Fujian　Province,　9　main　land　use　types　in　Longhai　county　and　all　candidate　land　use　driving　factors　show　positive　spatial　autocorrelation.　The　occurrence　of　spatial　autocorrelation　is　highly　dependent　on　the　aggregation　level.　Results　also　show　that　spatial　autoregressive　models　yield　residuals　without　spatial　autocorrelation　and　have　a　better　goodness-of-fit.　The　spatial　autoregressive　model　is　statistically　sound　in　the　presence　of　spatially　dependent　data　in　contrast　with　the　standard　linear　model.