We present an automatic spatial-segmentation method for a novel magnetic-field hybrid indoor positioning system. Unlike conventional approaches that implement magnetic field alone for the whole experimental space, our approach employs an efficient automatic segmentation method to make up the constraints of magnetic-field indoor positioning after fully evaluating local magnetic-field characteristics. By generating new partitioned databases, this method takes full advantage of characteristic changes in the geomagnetic field according to the distribution of disturbance objects inside buildings. Moreover, it reduces the processing time, as new databases contribute only to the region partition, and no further location calculation is involved. Experimental evaluations carried out with two different sets of hybrid techniques confirm satisfactory performance of the indoor localisation based on automatic spatial segmentation achieving more than a 1 meters' improvement in the average error distance compared to conventional magnetic-field localisation systems.