Understanding　the　relationship　between　the　built　environment　and　the　ride-hailing　ridership　is　crucial　to　the　prediction　of　the　demand　for　ride-hailing　and　the　formulation　of　the　strategy　for　upgrading　the　built　environment.　However,　the　existing　studies　on　ride-hailing　ignore　the　scale　effect　and　zone　effect　of　the　modifiable　area　unit　problem　(MAUP),　and　show　a　lack　of　consideration　for　the　elastic　relationship　with　spatial　heterogeneity　between　built　environment　variables　and　ride-hailing　ridership.　Taking　Chengdu　as　an　example,　this　paper　selects　12　independent　variables　based　on　the　＂5Ds＂　(density,　diversity,　design,　destination　accessibility　and　distance　to　transit)　of　the　built　environment,　the　dependent　variables　are　the　density　of　ride-hailing　pick-ups　in　the　morning　and　evening　peak　hours,　and　11　spatial　units　are　proposed　according　to　different　scales　and　zoning　methods　for　the　aggregation　of　built　environment　variables　and　ride-hailing　pick-ups.　With　the　goal　of　global　optimal　goodness-of-fit,　we　determined　the　optimal　spatial　unit　by　using　the　log-linear　Ordinary　Least-Squares　(OLS)　model.　A　multi-scale　geographically　weighted　elastic　regression　(MGWER)　model　is　formulated　to　explore　the　relative　effect　of　the　built　environment　on　the　ride-hailing　ridership　and　spatial　heterogeneity.　The　average　value　of　positive　elastic　local　regression　coefficient　of　different　variables　is　used　to　measure　the　relative　positive　impact　of　built　environment　factors,　and　the　absolute　value　of　the　average　value　of　negative　elastic　local　regression　coefficient　is　used　to　measure　the　relative　negative　impact　of　built　environment　factors.　The　results　show　that:　(1)　The　MGWER　model　under　the　community　unit　division　has　the　best　global　goodness-of-fit.　(2)　Different　built　environment　variables　have　different　elastic　impacts　on　the　demand　for　ride-hailing.　For　the　morning　peak　hours　and　evening　peak　hours,　the　top　three　built　environment　factors　with　positive　impacts　are　ranked　as　follows:　commercial　POI　density　＞　average　house　price　＞　population　density,　and　distance　to　CBD　has　the　highest　negative　impacts　on　pick-up　ridership.　(3)　The　different　local　elasticity　coefficients　of　the　built　environment　factors　at　different　stations　are　discussed,　which　indicate　the　spatial　heterogeneity　of　the　ride-hailing　ridership.　The　optimal　community　zoning　method　can　provide　a　basis　for　the　zoning　and　scheduling　management　of　ride-hailing.　The　results　of　the　built　environment　variables　with　greater　impact　are　conducive　to　the　formulation　of　targeted　urban　renewal　strategies　in　the　process　of　adjusting　the　ridership　of　ride-hailing.