Household　size　and　its　spatial　distributions　reflect　not　only　the　socioeconomic　development　in　a　city　but　also　the　rationality　of　urban　resource　allocation.　Most　existing　studies　rely　heavily　on　census　data　to　explore　the　potentially　influential　factors　using　methods　such　as　macro-statistical　analysis　and　socioeconomic　analysis,　of　which　the　spatial　resolutions　and　geographic　scales　are　constrained.　More　importantly,　the　association　between　the　household　size　distribution　and　the　built　environment　is　oversimplified　or　even　neglected　to　some　extent.　In　this　work,　we　use　massive　mobile　phone　data　combined　with　travel　surveys　of　Beijing　inhabitants＇　data　(TSBI)　to　infer　the　household　size　and　analyze　the　effect　of　spatial　heterogeneity　in　a　finer　spatial　resolution　in　Beijing,　China.　First,　the　machine　learning　method　(i.e.,　support　vector　machine　(SVM))　is　applied　to　identify　the　household　relationships　of　mobile　users,　and　there　are　around　3.44　million　households　(families)　with　different　sizes　are　obtained.　Second,　we　analyze　the　spatial　distribution　patterns　of　household　size　and　its　association　with　built　environmental　indicators　(e.g.,　public　service　density,　public　transportation　density,　etc.).　The　results　exhibit　a　heterogeneous　effect　of　the　regional　built　environment　on　average　household　size　(AHS).　For　instance,　＂commercial　density＂　and　＂administrative　density＂　show　a　negative　impact　on　household　size,　while　＂public　service　density＂　and　＂public　transportation　density＂　show　positive　correlations　with　household　size.　As　a　com-plement　to　census　data,　mobile　phone　data　can　be　used　to　obtain　the　household　size　in　real-time.　This　paper　provides　quantified　evidence　for　government　departments　to　allocate　facilities　in　a　more　targeted,　balanced,　and　reasonable　way　according　to　the　regional　differences　in　household　size,　which　would　potentially　support　the　sustainable　urban　development.