Studying the sources of errors in memory recall has proven invaluable for understanding the mechanisms of working memory (WM). While non-spatial memory features (e.g. colour, orientation) can be analysed using mixture models to separate the influence of imprecision, guessing, and misbinding (the tendency to confuse features that belong to different memoranda), such methods are not currently available for spatial WM tasks.Here we present a method to isolate sources of spatial error in tasks where participants have to report the spatial location of an item in memory, using two-dimensional mixture models. The method recovers simulated parameters well, and is robust to the influence of response distributions and biases, and number of distractors and trials. The recovered parameters are better estimates of the true parameters than the previously available behavioural metrics used in these tasks, such as mean target distance or nearest neighbour distance, suggesting our method is more sensitive to underlying error sources. To demonstrate the model, we fit data from a complex spatial WM task, and show the recovered parameters correspond well with previous spatial WM findings, and with recovered parameters on a one-dimensional analogue of this task, suggesting convergent validity for this two-dimensional modelling approach. Because an extra dimension is available for every response, spatial tasks turn out to be much better for separating misbinding from imprecision and guessing than in one-dimensional tasks. Code for these models is freely available in the MemToolbox2D package and is integrated to work with the commonly used Matlab package MemToolbox.