The　People　with　Small　Activity　Space　(PwSAS)　refers　to　the　residents　with　a　small　range　of　daily　activity　locations.　Their　demand　for　urban　public　resources　is　mainly　concentrated　in　the　area　around　their　home.　Analyzing　the　spatial　and　temporal　characteristics　of　their　activities　can　help　to　better　realize　the　equalization　and　precise　allocation　of　urban　public　resources.　However,　little　attention　has　been　paid　to　this　kind　of　people　in　current　researches.　This　study　proposed　a　research　method　to　identify　the　spatial　distribution　of　PwSAS　based　on　mobile　phone　signaling　data.　Firstly,　we　identified　each　user＇s　home　location　and　stay　location.　An　indicator　of　HmaxD,　the　maximum　distance　from　the　home　location,　was　proposed　to　measure　the　activity　space　range　centered　on　the　home　location.　This　indicator　was　also　used　to　filter　the　PwSAS.　Secondly,　we　transformed　the　traditional　trajectory　into　a　new　form　in　a　＇time-distance＇　coordinate　based　on　the　distance　between　the　location　of　each　record　and　the　home　location.　An　area-based　approach　was　constructed　to　measure　the　similarity　between　different　trajectories.　Then　an　optimized　hierarchical　clustering　algorithm　was　applied　to　identify　typical　activity　patterns　of　PwSAS　based　on　the　similarity　approach.　Finally,　the　spatial　distribution　patterns　were　analyzed　based　on　the　home　locations　of　the　users　belonging　to　each　pattern.　A　signaling　dataset,　a　typical　type　of　mobile　phone　location　data　of　Shanghai,　was　used　to　test　the　effectiveness　of　the　method.　We　found　that:　(1)　the　areabased　trajectory　similarity　method　constructed　based　on　＇time-distance＇　framework　can　reflect　the　spatiotemporal　characteristics　of　users＇　activities　based　on　home　location,　and　the　hierarchical　clustering　algorithm　merged　level　by　level　can　significantly　improve　the　efficiency　of　mining　typical　activity　patterns.　This　means　that　the　proposed　method　can　effectively　support　the　mining　of　the　mobility　patterns　of　urban　residents;　and　(2)　in　the　suburbs,　the　commercial　centers　and　places　with　many　factories　or　universities　tended　to　have　more　PwSAS;　While,　the　transition　area　in　the　suburban　had　less　PwSAS.　Therefore,　the　method　proposed　in　this　paper　can　be　used　to　analyze　the　temporal　and　spatial　distribution　characteristics　of　people　in　a　small　activity　area　in　a　city　and　can　provide　support　for　the　current　large　cities＇　decision　to　build　community　life　circles.　©　2021,　Science　Press.　All　right　reserved.