Incoherent　optical　spatial　differentiation　may　allow　high　speed,　low　computational　power　and　low　latency　image　processing.　However,　it　is　a　great　challenge　to　directly　design　an　optical　convolutional　kernel　containing　negative　values　for　image　differentiation.　Therefore,　polarization　or　wavelength-multiplexed　bipolar　point　spread　functions　(PSFs)　followed　by　digital　subtraction　is　proposed　to　achieve　incoherent　optical　edge　detection.　Unfortunately,　these　methods　will　increase　the　device　design　complexity　and　only　support　single　isotropic　edge　detection.　Here　we　proposed　and　experimentally　demonstrated　a　single-shot　incoherent　multifunctional　image　differentiation　with　spatial　tiling　optical　convolution.　As　a　proof　of　concept,　we　specially　designed　four　spatial　tiling　non-negative　optical　convolutional　kernels　based　on　4f　system　through　inverse　design　method,　and　simultaneously　realized　three　types　of　spatial　differential　operators.　The　x　and　y　directions　spatial　differentiation　and　isotropic　edge　detection　can　be　experimentally　demonstrated　simultaneously　after　digital　subtraction.　Our　solution　is　capable　of　relaxing　device　complexity,　increasing　scalability　and　facilitating　intelligent　design,　which　is　of　great　significance　for　accelerating　machine　vision　tasks　under　incoherent　illumination.