Hierarchical　federated　learning　(HFL)　in　wireless　networks　significantly　saves　communication　resources　due　to　edge　aggregation　conducted　in　edge　mobile　computing　(MEC)　servers.　Taking　into　account　the　spatially　correlated　characteristics　of　data　in　wireless　networks,　in　this　paper,　we　analyze　the　performance　of　HFL　with　hybrid　data　distributions,　i.e.　intra-MEC　independent　and　identically　distributed　(IID)　and　inter-MEC　non-IID　data　samples.　We　derive　the　upper　bound　of　the　difference　between　the　achieved　loss　and　the　minimum　one,　which　reveals　the　impacts　of　data　heterogeneity　and　global　aggregation　frequency　on　the　performance　of　HFL.　On　this　basis,　we　propose　an　algorithm　named　FedHelo　which　optimizes　the　aggregation　weights　and　edge/global　aggregation　frequencies　under　the　constraints　of　training　delay　and　clients＇　energy　consumption.　Our　experiments　i)　verify　the　obtained　theoretical　results;　ii)　demonstrate　the　performance　improvement　achieved　by　FedHelo　with　the　optimal　aggregation　weights　and　training/aggregation　frequencies,　especially　in　the　scenario　with　high　data　heterogeneity;　and　iii)　show　the　preference　for　edge　aggregation　in　the　scenario　with　a　tight　delay　or　client＇s　energy　constraint.