<p>Resampling approaches to approximate the sampling distribution of the statistic constructed from irregularly spaced data are still far from well-developed. We propose a novel approach, the Wiener-type integral approximation (WIA), as a complement to the existing approaches. It uses a Wiener-type integral (WI) to approximate the sampling distribution of a statistic. Meanwhile, the variance of the WI is consistent to that of the statistic. The construction of the WI involves integrating a localized version of the statistic with respect to standard Gaussian white noise, offering a general class of resampling estimators based on irregularly spaced spatial data. The proposed WIA imitates the second-order dependence of multiple statistics very well, enabling it to approximate the sampling distribution of multivariate statistics that can achieve the joint asymptotic normality. In this paper, we demonstrate the applicability of WIA to various important statistics, including the sample mean, sample variance, auto-covariance estimator, and discrete Fourier transform. Moreover, through simulation studies, the finite sample performance of the WIA is investigated, in comparison with some competitive approaches.</p>