基于空间搜索的基层社区督查效果影响因素分析

doi:10.16381/j.cnki.issn1003-207x.2024.1188

Abstract

Abstract:

Community governance is a key component of grassroot administration management system in our country， and also serves as the “last mile” for social governance. It also plays a crucial role in maintaining social stability and promoting the harmonious development of communities. With the rapid development of society and the continuous advancement of urbanization， the complexity and diversity of grass-roots communities are increasingly prominent， putting forward higher requirements for community inspection work. Effective community inspection can timely discover and solve problems in grass-roots governance and lay a solid foundation for the stable development of communities. However， previous research related to community governance is basically limited to case studies or qualitative analytics， and rarely adopts quantitative methodologies to analyze the problem. The inspection team and the inspected community are important components in the process of community inspection. To provide managers with more objective and interpretable decision support how the attributes of inspection teams and communities influence the effectiveness of community inspections is analyzed in this paper. The historical inspection records can be regarded as the points in the multi-dimensional space， and identifying the range of attribute values where inspection issues frequently occur can be abstracted as a frequent subset search problem in a two-dimensional attribute space. Logistic regression on the historical inspection records is employed to identify critical two-dimensional attribute combinations. Based on the results， then a mixed-integer quadratic programming model is proposed to search a space where the inspection issues frequently happen， and is transformed into a mixed-integer quadratic programming model. To enhance computational efficiency and tractability， McCormick inequalities are used to convert the model into a linear programming， and the smallest frequent subset in space prone is identified to inspection issues. Thus， the rectangle regions in the two-dimensional space， which do not overlap and are identified through the search process， represent the frequent subsets of inspection issues. To identify the attribute value ranges corresponding to a certain proportion of inspection issues， the proposed optimization model aims to minimize the sum of the areas of all frequent subsets， thereby ensuring that the distribution of inspection records with inspection issues within these subsets is more dense. Based on the practical data of community inspection records， case studies are conducted on the problems of community personnel management and community assistance， and the results demonstrate the effectiveness of the proposed method. The method can be extended to other risk analysis and influencing factor analysis problems， overcoming difficulties such as insignificant regression analysis， the inability of association rules for continuous values， and the complexity of nonlinear programming. The proposed methodology and models also offers an alternative and effective decision support framework for improving the effectiveness of community inspections. This model identifies the minimal infrequent subsets where inspection issues are likely to occur. This research assists grassroots governance departments in focusing on critical issues and provides a scientific basis for decision-making in personnel allocation and resource management. It contributes to enhancing inspection efficiency and improving overall community governance. Additionally， the study offers significant theoretical support for the improvement and development of future grassroots governance systems. The proposed framework provides valuable insights and methods for exploring more scientific and efficient grassroots governance models and facilitates the construction of a more refined and efficient inspection system.

Key words: community governance, logistic regression, spatial search method, frequent subset, mixed-integer quadratic programming

CLC Number:

C931

Yun Wang,Mengyi Sha,Peng Lian. Analytics of Influencing Factors of Community Governance Based on Spatial Search Method[J]. Chinese Journal of Management Science, 2026, 34(7): 218-228.

Figures/Tables 7

符号	定义	符号	定义
$K$	$K$ 为二维空间频发子集编号的集合； $k$ 代表第 $k$ 个频发子集， $k ∈ K$	$x k 1, x k 2$	第k个频发子集对应的督查对象属性值的最小值和最大值，即频发子集端点横坐标的范围
$I$	$I$ 为历史督查记录编号的集合；i为每条历史督查记录的编号，i $∈ I$ ； $I 1$ 为出现问题的督查记录的编号集合， $I 1 ⊆ I$	$y k 1, y k 2$	第k个频发子集对应的督查组属性值的最小值和最大值，即频发子集端点纵坐标的范围
$p i$	第i条督查记录中督查对象的属性值	$w k i$	0-1变量，判断第i条督查记录中督查对象的属性值是否在第k个频发子集的范围内
$q i$	第i条督查记录中督查组的属性值	$v k i$	0-1变量，判断第i条督查记录中督查组的属性值是否在第k个频发子集的范围内
r₁	针对二维关键属性中督查对象的属性，历史督查记录中属性值最大值与最小值的差	$u k i$	0-1辅助变量， $u k i = w k i v k i$
r₂	针对二维关键属性中督查组的属性，历史督查记录中属性值最大值与最小值的差	$a k k 1, b k k 1$ $c k k 1, d k k 1$	辅助变量，分别代表第k个频发子集和第k₁个频发子集顶点横坐标、纵坐标的最大值和最小值
$α$	频发子集内出现督查问题占总问题数目的最低比例	$z k k 1, h k k 1, g 1 k k 1,$ $g 2 k k 1, l 1 k k 1, l 2 k k 1$	0-1辅助变量，用于分析第k个频发子集和第k₁个频发子集的位置关系
$β$	每个频发子集内督查记录占所有督查记录的最低比例	$s 11 k, s 12 k,$ $s 21 k, s 22 k$	辅助变量
M	取值足够大的常数

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一级指标	二级指标	三级指标
督查对象属性	人口属性	总人数、当地常住人口占比
	经济属性	贫困人口数、人均年收入
	进驻社区工作队属性	工作组人数、党员比例、正处级及以上成员占比、大学及以上学历人员占比、来自经管法相关专业人数占比、35岁及以上成员占比、当地成员占比
	社区干部属性	社区干部人数、党员比例、大学及以上学历人员占比、35岁及以上成员占比、当地干部占比
督查组属性	自然属性	成员数、督查时间、35岁及以上成员占比、当地成员占比
	职级属性	正处级及以上成员占比、来自纪检委相关部门的人员比例、组长为正处级及以上成员占比、联络员为正处级及以上成员占比
	专业属性	从事纪检监察工作不低于四年人员比例、参加督查工作不低于三次的人员比例、来自经管法相关专业人员占比、大学及以上学历人员占比

频发子集包含问题记录的最小比例（%）	频发子集	社区干部中党员占比（%）		督查组中来自经管法相关专业人员占比（%）
频发子集包含问题记录的最小比例（%）	频发子集	最小值	最大值	最小值	最大值
30	k=1	57.00	87.50	83.33	94.44
30	k=2	50.00	50.00	83.33	94.44
40	k=1	50.00	100.00	83.33	94.44
40	k=2	60.00	88.89	80.00	80.00

频发子集包含问题记录的最小比例（%）	频发子集	社区干部大学及以上学历人员占比（%）		督查组中来自纪检委相关部门的成员比例（%）
频发子集包含问题记录的最小比例（%）	频发子集	最小值	最大值	最小值	最大值
15	k=1	0.57	37.50	84.62	95.00
15	k=2	62.50	88.89	84.62	92.31
20	k=1	0.57	42.85	83.33	95.00
20	k=2	60.00	92.30	84.62	95.00

Analytics of Influencing Factors of Community Governance Based on Spatial Search Method

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