Missing　data　is　a　common　problem　in　the　analysis　of　geospatial　information.　Existing　methods　introduce　spatiotemporal　dependencies　to　reduce　imputing　errors　yet　ignore　ease　of　use　in　practice.　Classical　interpolation　models　are　easy　to　build　and　apply;　however,　their　imputation　accuracy　is　limited　due　to　their　inability　to　capture　spatiotemporal　characteristics　of　geospatial　data.　Consequently,　a　lightweight　ensemble　model　was　constructed　by　modelling　the　spatiotemporal　dependencies　in　a　classical　interpolation　model.　Temporally,　the　average　correlation　coefficients　were　introduced　into　a　simple　exponential　smoothing　model　to　automatically　select　the　time　window　which　ensured　that　the　sample　data　had　the　strongest　correlation　to　missing　data.　Spatially,　the　Gaussian　equivalent　and　correlation　distances　were　introduced　in　an　inverse　distance-weighting　model,　to　assign　weights　to　each　spatial　neighbor　and　sufficiently　reflect　changes　in　the　spatiotemporal　pattern.　Finally,　estimations　of　the　missing　values　from　temporal　and　spatial　were　aggregated　into　the　final　results　with　an　extreme　learning　machine.　Compared　to　existing　models,　the　proposed　model　achieves　higher　imputation　accuracy　by　lowering　the　mean　absolute　error　by　10.93　to　52.48%　in　the　road　network　dataset　and　by　23.35　to　72.18%　in　the　air　quality　station　dataset　and　exhibits　robust　performance　in　spatiotemporal　mutations.