  By using recent $H(z)$ and SNe Ia data, we reconstruct the evolution of
kinematic parameters $H(z)$, $q(z)$, jerk and snap, using a model-independent,
non-parametric method, namely, the Gaussian Processes. Throughout the present
analysis, we have allowed for a spatial curvature prior, based on Planck 18 [1]
constraints. In the case of SNe Ia, we modify a python package (GaPP) [2] in
order to obtain the reconstruction of the fourth derivative of a function,
thereby allowing us to obtain the snap from comoving distances. Furthermore,
using a method of importance sampling, we combine $H(z)$ and SNe Ia
reconstructions in order to find joint constraints for the kinematic
parameters. We find for the current values of the parameters: $H_0 =67.2 \pm
6.2$ km/s/Mpc, $q_0 = -0.60^{+0.21}_{-0.18}$, $j_0=0.90^{+0.75}_{-0.65}$,
$s_0=-0.57^{+0.52}_{-0.31}$ at 1$\sigma$ c.l. We find that these
reconstructions are compatible with the predictions from flat $\Lambda$CDM
model, at least for 2$\sigma$ confidence intervals.
