Over　the　past　decade,　bike-sharing　has　garnered　significant　attention　in　both　research　and　practice.　As　a　manpower-driven　transportation　mode,　the　usage　of　bikes　seems　more　sensitive　to　trip　length,　since　one　could　take　a　shared　bike　to　a　destination　where　is　too　far　to　walk,　or　choose　it　for　simply　replacing　walking　when　going　to　a　nearby　place.　This　paper　identifies　a　threshold　of　bike-sharing　trip　lengths　from　bike-sharing　trace　data,　and　employs　the　Semiparametric　Geographically　Weighted　Poisson　Regression　model　to　investigate　the　relationship　between　built　environment　and　bike-sharing　demand　with　different　lengths　by　considering　the　heterogeneity　in　the　relationship.　Results　show　that　built　environment　has　heterogeneous　effects　on　the　bike-sharing　demand　in　urban　areas,　and　the　effects　differ　across　groups　with　trip　lengths.　The　findings　contribute　to　understanding　the　relationships　between　built　environment　and　bike-sharing　demand,　and　providing　support　for　the　placements　and　dispatching　of　shared　bikes.　©　2024　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.