Requia Weeberb J, Silva Luciano Moura
School of Public Policy and Government, Fundação Getúlio Vargas, Brasília, Brazil.
Environ Technol. 2025 Jan;46(1):59-71. doi: 10.1080/09593330.2024.2339190. Epub 2024 Apr 15.
In this study, we propose a novel approach for estimating the relationship between neighborhood characteristics and students' academic performance. We propose the concept of urban morphology by Urban Structure Types (USTs). USTs are spatial indicators that describe the urban system through its physical, environmental, and functional characteristics. Our academic performance data includes 344,175 students from 256 public schools in the Federal District (FD), Brazil. This is student-level academic achievement data from 2017 to 2020. We performed the UST mapping in the FD by using visual interpretation. We classified 21 different types of UST. We fit mixed-effects regression models with a student-specific random intercept and slope. The model was adjusted for temporal factors, SES factors, and variables representing the characteristics and the location of each school. Our findings suggest associations between several types of USTs surrounding schools and academic performance. Overall, areas characterized as low population density, with high green index, and high standard residences were associated with an increase in student performance. In contrast, areas that include old buildings near streets, with significant traffic density, and areas with significant exposed soil (areas devasted) were associated with a decrease in student performance. The results of our study support the creation of effective educational and urban planning policies for local interventions. These interventions are likely to translate into healthier schools and improvements in children's behavioral development and learning performance.
在本研究中,我们提出了一种新颖的方法来估算邻里特征与学生学业成绩之间的关系。我们通过城市结构类型(USTs)提出了城市形态的概念。USTs是通过城市的物理、环境和功能特征来描述城市系统的空间指标。我们的学业成绩数据包括来自巴西联邦区(FD)256所公立学校的344,175名学生。这是2017年至2020年的学生层面学业成就数据。我们通过视觉解读在FD进行了UST映射。我们将UST分为21种不同类型。我们拟合了具有学生特定随机截距和斜率的混合效应回归模型。该模型针对时间因素、社会经济地位因素以及代表每所学校特征和位置的变量进行了调整。我们的研究结果表明,学校周边的几种UST类型与学业成绩之间存在关联。总体而言,人口密度低、绿色指数高且高标准住宅多的地区与学生成绩的提高相关。相比之下,街道附近有旧建筑、交通密度大的地区以及有大量裸露土壤(受灾地区)的地区与学生成绩的下降相关。我们的研究结果支持制定有效的教育和城市规划政策以进行地方干预。这些干预措施可能会带来更健康的学校环境,并改善儿童的行为发展和学习成绩。