It　is　of　great　reference　significance　to　exploring　spatial　dependence　of　urban　traffic　activities　and　researching　internal　causes　of　regional　traffic　state　changes　for　road　network　optimization　and　residents＇　travel　behavior　analysis.　Based　on　trajectory　data　of　taxis　in　Ningbo　city　of　China,　this　study　calculates　average　driving　speed　of　taxis　in　different　blocks　during　characteristic　period　and　generates　the　global　Moran＇s　I　and　the　LISA　clustering　diagram.　On　this　basis,　the　spatial　clustering　characteristics　of　congestion　on　working　days　and　non-working　days　are　analyzed.　Furthermore,　in　order　to　further　characterize　the　changes　of　congestion　from　the　perspective　of　spatial　migration,　a　method　of　measuring　geometric　displacement　is　adopted　to　describe　spatio-temporal　migration　trend　of　traffic　states,　four　indicators　designed　to　identify　urban　frequently　congested　areas,　including　migration　direction,　angle,　distance,　and　low-value　area.　The　results　show　that　the　high-clustering　area　are　located　urban　fringe　and　the　low-clustering　area　are　located　at　geometric　center　of　major　urban　areas.　Spatial-temporal　migration　law　of　low-value　areas　in　city-center　is　obvious.　Difference　between　trend　is　compared　with　non-working　days,　the　offset　and　azimuth　of　low-value　area　in　downtown　on　working　days　are　even　bigger.　The　accurate　capture　of　the　characteristics　of　congestion　space　migration　at　the　urban　scale　will　help　to　formulate　more　targeted　congestion　management　strategies.　(C)　2021　Published　by　Elsevier　B.V.