Nighttime　light　(NTL)　intensity　is　highly　associated　with　the　unique　footprint　of　human　activities,　reflecting　the　development　of　socioeconomic　and　urbanization.　Therefore,　better　understanding　of　the　relationship　between　NTL　intensity　and　human　activities　can　help　extend　the　applications　of　NTL　remote　sensing　data.　Different　from　the　global　effect　of　human　activities　on　NTL　intensity　discussed　in　previous　studies,　we　focused　more　attention　to　the　local　effect　caused　by　the　spatial　heterogeneity　of　human　activities　with　the　support　of　the　multiscale　geographically　weighted　regression　(MGWR)　model　in　this　study.　In　particular,　the　Suomi　National　Polar　Orbiting　Partnership/Visible　Infrared　Imaging　Radiometer　Suite　(NPP/VIIRS)　NTL　data　within　Chongqing,　China　were　taken　as　example,　and　the　point　of　interest　(POI)　data　and　road　network　data　were　adopted　to　characterize　the　intensity　of　human　activity　type.　Our　results　show　that　there　is　significant　spatial　variation　in　the　effect　of　human　activities　to　the　NTL　intensity,　since　the　accuracy　of　fitted　MGWR　(adj.R-2:　0.86　and　0.87　in　2018　and　2020,　respectively;　AICc:　4844.63　and　4623.27　in　2018　and　2020,　respectively)　is　better　than　that　of　both　the　traditional　ordinary　least　squares　(OLS)　model　and　the　geographically　weighted　regression　(GWR)　model.　Moreover,　we　found　that　almost　all　human　activity　features　show　strong　spatial　heterogeneity　and　their　contribution　to　NTL　intensity　varies　widely　across　different　regions.　For　instance,　the　contribution　of　road　network　density　is　more　homogeneous,　while　residential　areas　have　an　obviously　heterogeneous　distribution　which　is　associated　with　house　vacancy.　In　addition,　the　contributions　of　the　commercial　event　and　business　also　have　a　significant　spatial　heterogeneity　distribution,　but　show　a　distinct　decrement　when　facing　the　COVID-19　pandemic.　Our　study　successfully　explores　the　relationship　between　NTL　intensity　and　human　activity　features　considering　the　spatial　heterogeneity,　which　aims　to　provide　further　insights　into　the　future　applications　of　NTL　data.