Recent　years　have　witnessed　an　increasing　use　of　big　data　in　mobility　research.　Such　efforts　have　led　to　many　insights　on　the　travel　behavior　and　activity　patterns　of　people.　Despite　these　achievements,　the　data　veracity　issue　and　its　impact　on　the　processes　of　knowledge　discovery　have　seldom　been　discussed.　In　this　research,　we　investigate　the　veracity　issue　of　mobile　signaling　data　(MSD)　when　they　are　used　to　characterize　human　mobility　patterns.　We　first　discuss　the　location　uncertainty　issues　in　MSD　that　would　hinder　accurate　estimations　of　human　mobility　patterns,　followed　by　an　examination　of　two　existing　methods　for　addressing　these　issues　(clustering-based　method　and　time　window-based　method).　We　then　propose　a　new　approach　that　can　overcome　some　of　the　limitations　of　these　two　methods.　By　applying　all　three　methods　to　a　large-scale　mobile　signaling　data　set,　we　find　that　the　choice　of　preprocessing　methods　could　lead　to　changes　in　the　data　characteristics.　Such　changes,　which　are　nontrivial,　will　further　affect　the　characterization　and　interpretation　of　human　mobility　patterns.　By　computing　four　mobility　indicators　(number　of　origin-destination　trips,　number　of　activity　locations,　total　stay　time,　and　activity　entropy)　from　the　outputs　of　the　three　methods,　we　illustrate　their　varying　impacts　on　individual　mobility　estimations　relevant　to　location　uncertainty　issues.　Our　analysis　results　call　for　more　attention　to　the　veracity　issue　in　data-driven　mobility　research　and　its　implications　for　replicability　and　reproducibility　of　geospatial　research.