As the necessity for utilizing deep neural networks on low-powered devices increases, interest in Spiking Neural Networks (SNN) which requires very small amount of power is growing. However, due to the non-differential nature of SNN, it is difficult to apply the learning method applied to the existing neural networks, and accordingly, the performance tends to be slightly lower than the conventional neural networks. Therefore, this paper proposes a spatial spiking attention mechanism to improve event data recognition performance of SNN. The spatial spiking attention mechanism applies spatially different weights to the input according to the firing rate of each pixel of the event image. For performance evaluation, a neuromorphic dataset, N-MNIST is used. As a result of the experiment, it was confirmed that the spiking attention mechanism improves recognition performance when it is applied to SNN.