In this paper, we propose feature vectors for lithography hotspot detection considering the widths of wires and the distances between wires. In lithography, which is one of the semiconductor manufacturing processes, there is a pattern that is highly likely to cause an undesired short- or open-circuit, called a hotspot. Since lithography simulation used for hotspot detection requires a very long computation time, a method to more quickly detect hotspot candidates is required. In recent years, methods using machine learning have been attracting attention as those to more quickly detect hotspot candidates. In this study, to improve the accuracy of detection, we focus on the widths of wires and the distances between adjacent wires, which can be correlated with undesired open- and short-circuits, respectively. Experimental results showed that our feature vectors perform well and one of our feature vectors outperforms the others including some existing ones, in terms of F1-score and recall.