Many current research efforts undertake the solar flare classification task using the Space-weather HMI Active Region Patch (SHARP) parameters as the predictors. The SHARP parameters are scalar quantities based on spatial average or integration of physical quantities derived from the vector magnetic field, which loses information of the two-dimensional spatial distribution of the field and related quantities. In this paper, we construct two new sets of spatial features to expand the feature set used for the flare classification task. The first set uses the idea of topological data analysis to summarize the geometric information of the distributions of various SHARP parameters. The second set utilizes tools coming from spatial statistics to analyze the vertical magnetic field component Br and summarize its spatial variations and clustering patterns. All features are constructed within regions near the polarity inversion lines (PILs) and classification performances using the new features are compared against the SHARP parameters (also along PIL). We found that using the new features can improve the skill of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. This potentially suggests that even using a single channel Br, instead of all SHARP parameters, one can still derive strongly predictive features for flare classification.