Does space complexity analysis usually include output space?
Typically, we consider space complexity in terms of Turing machines with:


one read-only input tape
one write-only output tape
however many read-write working tapes you want.


The space usage is the number of cells used on the working tapes, so input and output space typically aren't counted.  (See, e.g., Section 2.5 of Papadimitriou.)

Obviously, we don't want to count input space since, then, every algorithm would have to use linear space.  Likewise, we don't want to count output space as that stops us from distinguishing between problems that are hard simply because the output is big (e.g., given an integer&nbsp;$n$, write the list $1, 2, \dots, n$) and problems that are hard because even computing a single output is hard (e.g., output a list of all $3$-colourings of the input graph).

On the other hand, not all authors do this. Sipser defines space usage as just the number of tape cells read by an ordinary Turing machine (Section&nbsp;8) and the says, more or less, "Oops but that doesn't let us consider any kind of sublinear space, so we'd better redefine it not to include the input" (Section&nbsp;8.4; he's only considering decision problems, so there's no output to worry about).

I'm not a fan of the redefinition approach but, either way, we need to exclude the input and output if we want to talk about any kind of sublinear space bound.

References
C.H.&nbsp;Papadimitriou, Computational Complexity. Addison&ndash;Wesley, 1992.
M.&nbsp; Sipser, Introduction to the Theory of Computation (3rd edition). Cengage Learning, 2013.