Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.

中文翻译：

---

利用数据中的时空模式进行名义属性推算

自愿地理信息（VGI）中的数据丢失是非专家在模糊的和非正式的制图准则的指导下不可避免地收集数据的结果。尽管已经提出了各种缺失值插补（MVI）技术作为数据清除策略，但它们主要针对非空间数据库中的数值数据属性。估算名义属性值的方法（例如，街道名称）仍然存在很大差距）在地图数据库中。在这里，我们介绍一种称为成员资格插补算法（MIA）的插补算法，该算法以空间数据库为目标，并可以在空间参考记录中插补名义值。通过针对空间对象的隶属度类别，MIA利用数据的时空特征并提出有效的启发式方法来插补类别名称（即隶属度）。实验结果表明，该算法能够在分配街道名称时以较高的准确度（超过94％）估算成员资格。，涵盖了高度多样化的区域环境。MIA在挑战空间环境（例如街道交叉路口）时非常有效。我们的研究是强调时空度量作为名义插补技术的主要驱动力的有效性的第一步。