Hierarchical　federated　learning　(HFL)　in　wireless　networks　significantly　saves　communication　resources　thanks　to　edge　aggregation　in　edge　mobile　computing　(MEC)　servers.　Considering　the　spatially　correlated　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　also　derive　the　performance　impacts　of　data　heterogeneity　and　global　aggregation　interval.　Based　on　our　theoretical　results,　we　further　propose　a　novel　aggregation　weights　design　with　loss-based　heterogeneity　to　accelerate　the　training　of　HFL　and　improve　learning　accuracy.　Our　simulations　verify　the　theoretical　results　and　demonstrate　the　performance　gain　achieved　by　the　proposed　aggregation　weights　design.　Moreover,　we　find　that　the　performance　gain　of　the　proposed　aggregation　weights　design　is　higher　in　a　high-heterogeneity　scenario　than　in　a　low-heterogeneity　one.