Terminology can differ between fields even apparently sharing applications. Based on statistical theory and practice in several fields (time series, spatial series, any application where a response may be smoothed as a function of predictors) I propose simply that a moving average is still a moving average outside a time series context.
There is no good reason to include as part of a definition of moving average that the application must be to time series. In practice, that is likely to be the most common application and also the example that people meet first, but neither fact is decisive in principle.
It's not even crucial that you have
on the dimension or dimensions you are averaging over. (On dimensions, note that averaging over values at neighbouring points in space is often helpful.)
You can always define sets of weights (kernels) that are general enough to cope with such complications. I assert that weights that decline with time or distance from the point being averaged for are often more useful than equal weights. Whether an average should be asymmetric (e.g. only considering "earlier" points) is up for discussion too.
So, to make a key point now explicit, I see no reason to define moving averages as being based on equal weights. In time series analysis, equal weights are often used, but that is a matter of convention or simplicity at most. Basic theory and practice combine to show that equal weights have unfortunate properties in the frequency domain and are especially sensitive to outliers as averages can jump when an outlier leaves or enters the window, often although not always regarded as undesirable.
Note that we can be flexible about what is an average in this context as well as any other. Prefer medians? trimmed means? Being clear about what you're doing is the main imperative about use of terminology.
The term scatter plot smoother fits some applications that are not time series, but clearly not all.