Understanding　the　complexity　of　urban　expansion　requires　an　analysis　of　the　factors　influencing　the　spatial　and　temporal　processes　of　rural-urban　land　conversion.　This　study　aims　at　building　a　statistical　land　conversion　model　to　assist　in　understanding　land　use　change　patterns.　Specifically,　GIS　coupled　with　a　logistic　regression　model　and　exponential　smoothing　techniques　is　used　for　exploring　the　effects　of　various　factors　on　land　use　change.　These　factors　include　population　density,　slope,　proximity　to　roads,　and　surrounding　land　use,　and　their　influence　on　land　use　change　is　studied　for　generating　a　predictive　model.　Methods　to　reduce　spatial　autocorrelation　in　a　logistic　regression　framework　are　also　discussed.　Primarily,　an　optimal　sampling　scheme　that　can　eliminate　spatial　autocorrelation　while　maintaining　adequate　samples　to　allow　the　model　to　achieve　the　comparable　accuracy　as　the　spatial　autoregressive　model　is　developed.　Since　many　of　the　previous　studies　on　modeling　the　spatial　complexity　of　urban　growth　ignored　temporal　complexity,　a　modified　exponential　smoothing　technique　is　employed　to　produce　a　smoothed　model　from　a　series　of　bi-temporal　models　obtained　from　different　time　periods.　The　proposed　model　is　validated　using　the　multi-temporal　land　use　data　in　New　Castle　County,　DE,　USA.　It　is　demonstrated　that　our　approach　provides　an　effective　option　for　multi-temporal　land　use　change　modeling　and　the　modeling　results　help　interpret　the　land　use　change　patterns.