The　wide　application　of　information　computing　technology　has　allowed　for　the　emergence　of　big　data　on　tracing　human　activities.　Therefore,　it　provides　an　opportunity　to　explore　temporal　profile　of　population　changes　in　geographical　area　subdivisions.　In　this　paper,　we　present　a　multi-step　method　to　characterize　and　approximate　temporal　changes　of　population　in　a　geographical　area　subdivision　using　eigen　decomposition.　Datasets　in　weekday　and　weekend　are　decomposed　to　obtain　the　principal　temporal　change　profiles　in　Xiamen,　China.　The　Principal　Components　are　common　patterns　of　temporal　population　changes　shared　by　most　geographical　area　subdivisions.　Its　corresponding　elements　in　eigenvectors　could　be　regard　as　a　coefficient　to　principal　components.　Then,　a　measure,　which　is　the　similarity　of　each　eigenvector　to　a　basis　vector,　that　could　characterize　the　temporal　population　change　is　established.　Based　on　this,　the　coupling　interaction　between　population　changes　and　land　use　characteristics　is　explored　using　this　measure.　It　shows　that　it　is　restricted　by　land　use　characteristics　and　also　is　a　reflection　of　population　changes　over　time.　These　results　provided　an　insight　on　understanding　temporal　population　change　patterns　and　it　would　help　to　improve　urban　planning　and　establish　a　job-housing　balance.　©　The　Authors,　published　by　EDP　Sciences　2020.