Understanding How Green Space Naturalness Impacts Public Well-Being: Prospects for Designing Healthier Cities.

Statement of problem: Urbanization has brought significant advancements in human well-being; however, it poses challenges to urban green spaces (UGSs), affecting environmental quality and public health. Research gap: Previous studies have established the importance of UGSs for urban well-being but have not sufficiently explored how the naturalness of these spaces-ranging from untouched natural areas to human-designed landscapes-affects mental health outcomes in the context of developing countries, particularly Brazil. Purpose: This study aimed to bridge the research gap by investigating the relationship between the degree of naturalness in UGSs and mental health among residents of Brazilian metropolitan areas. Method: Data were collected through an online survey involving 2136 respondents from various Brazilian urban regions. The study used Welch's ANOVA and Games-Howell post hoc tests to analyze the impact of UGS naturalness on mental health, considering depression, anxiety, and stress levels. Results and conclusions: The findings revealed that higher degrees of naturalness in UGSs significantly correlate with lower levels of mental distress. These results underscore the necessity of integrating natural elements into urban planning to enhance public health. Practical implications: Urban planners and policymakers are encouraged to prioritize the preservation and creation of naturalistic UGSs in urban environments to improve mental health outcomes. Future directions: Further research should explore the specific attributes of naturalness that most contribute to well-being and examine the scalability of these findings across different cultural and environmental contexts.

Computer-aided classification of successional stage in subtropical Atlantic Forest: a proposal based on fuzzy artificial intelligence.

STATEMENT OF PROBLEM: Due to the continuous variability of the forest regeneration process, patterns of indicator variables with membership in more than one successional stage may occur, making the classification of such stages a challenging and complex task. PURPOSE: This study aims at presenting a comparative analysis of artificial intelligence methods as an alternative for computer-aided classification of successional stages in subtropical Atlantic Forest. As a research hypothesis, the authors consider that a fuzzy inference system should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. MATERIAL AND METHODS: The analyses were carried out using a database of the forest inventory of Santa Catarina, Southern Brazil. The data are composed of 177 sampling units of subtropical Atlantic Forest (mixed ombrophilous forest), characterized according to eighth indicator variables verified from the field by experts. This database was employed to train several machine learning methods under a tenfold cross-validation process. The overall accuracy (θ) and kappa coefficient were used to compare the performance between FIS and neural networks, classifier committees and support vector machine. Then, to verify if the classification by the FIS differed from the one performed by experts, the Kappa index and a statistical significance analysis by Pearson's [Formula: see text] test were determined. The hypotheses were verified with two-way tests at a significance level (α) 0.05, for a test power (1-ß) 0.8 and minimum expected effect size between medium (ρ = 0.3). RESULTS: Statistical significance tests confirmed the hypothesis that FIS achieved the highest performance, with θ = 98.3% and a kappa value equal to 0.93 (almost perfect agreement) and showed no significant difference ([Formula: see text] = 0.047, p = 0.976) in comparison with the classification by experts. CONCLUSIONS: The use of FIS represents a promising alternative as a tool applicable for computer-aided classification of successional stages in subtropical Atlantic Forest. PRACTICAL IMPLICATIONS: The results and conclusions should substantially impact the guidelines and decision-making process for deforestation authorizations and applicable compensation measures, which are based on the forest succession stage.