Pattern Identification of Community Engagement Behaviors in a Big Data Environment and Its Impact on Community Health Development

Abstract

Improving residents’ community participation capacity in the big data environment helps to realize common action and resource sharing among subjects and promotes the healthy development of the community. This paper takes the theory of planned behavior and the ladder theory of civic participation as the guide, designs and distributes questionnaires related to residents’ community participation behavior patterns to obtain research data, identifies the community participation behavior patterns using the K-Means clustering algorithm and introduces the PROMETHEE Ⅱ method to measure the community participation behavior patterns. The impact model of community health development based on structural equations was constructed to analyze the degree of impact of community participation behavior patterns on community health development. There are six categories of community participation behavioral patterns, and the overall net flow of complete community participation behavioral patterns is the highest at 0.047. The coefficient of impact of community participation behavioral patterns on community health development in the structural equation model is 0.149, and the test result has a significant impact at the 1% level. Different behaviors of community participation can promote healthy community development and help improve community governance.