Geospatial　big　data　analytics　are　changing　the　way　that　businesses　operate,　and　have　enabled　various　intelligent　services　in　smart　cities.　User　mobility　plays　an　important　role　in　many　context-aware　applications,　such　as　location-based　advertisement,　traffic　planning,　and　urban　resource　management.　This　paper　proposes　CityTracker,　a　hidden　Markov　model　(HMM)-based　framework　to　predict　the　temporal-spatial　individual　trajectory　and　analyze　the　representative　citywide　crowd　mobility.　After　the　locations　are　segmented　into　points　of　interests　and　modeled　as　states,　HMM　can　find　the　most　likely　state　sequence　in　a　maximum-likelihood　sense,　and　thus　perform　trajectory　prediction　with　different　orders.　In　addition,　CityTracker　can　integrate　the　individual　trajectories,　achieving　representative　crowd　mobility　visualization　in　the　target　area.　The　proposed　mechanism　is　evaluated　on　a　database　provided　by　HyXen,　which　contains　temporal-geospatial　records　from　thousands　of　smartphones　in　Taipei.　Experimental　results　confirm　the　effectiveness　of　CityTracker,　which　achieves　prediction　distance　errors　of　approximately　1.28　km　and　outperforms　traditional　probabilistic　and　regression-based　methods.　The　results　also　show　the　most　representative　crowd　mobility　behaviors　in　the　map,　which　is　essential　for　citywide　applications.