Earth observation data uncover green spaces’ role in mental health

The prevalence of mental health disorders, a key disability cause, is linked to demographic and socioeconomic factors. However, limited data exists on mental health and the urban environment. Urbanization exposes populations to environmental stressors, particularly affecting low-middle-income countries with complex urban arrangements. We used remote sensing and census data to investigate potential connections between environmental factors and mental health disorders. Land cover variables were assessed using the European Space Agency (ESA) global WorldCover product at 10 m resolution together with the database of mental health diagnosed cases (n = 5769) from the Brazilian Unified Health System’s Department of Informatics (DATASUS) from every health facility of the city of Porto Alegre. The association of mental health data with land cover was established with machine learning algorithms and polynomial regression models. The results suggest that higher trees cover at neighborhood level was associated with better mental health index. A lower mental health index was also found to be associated with an higher Human Development Index. Our results highlight the potential of greenness in the city environment to achieve substantially better mental health outcomes.

life satisfaction 21 .Besides, moving to greener areas improves mental health 22 and these areas have lower rates of prescriptions for psychotropic medications for anxiety, depression, and psychosis than urban areas 23 .
Greenness levels are intrinsically linked to various critical ecosystem services, including medicinal resources, food supply, and potable water.Additionally, these green areas serve as natural buffers, mitigating the detrimental impacts of environmental stressors and can act as regulation services such as control of air and noise pollution, as well as moderation of extreme heat 24 .
Around the world, mechanisms for studying the relationship between green areas and mental health are diverse, as are the mechanisms by which green areas promote good mental health.One early theory presented by Kaplan and Kaplan suggests that having access to green areas in urban environments facilitates "psychological restoration" countering the mental fatigue caused by modern living 25 .It has been shown that being close to green spaces can improve psychological health in various ways, such as reducing cortisol levels 26 , buffering the adverse effects of stressful life events 27 , fostering social cohesion 28 and enhancing overall psychological well-being 29,30 .These findings underscore the significance of urban green spaces in promoting mental health and well-being, contributing positively to the quality of life in cities. Urban green spaces foster improved mental health by people simply by being present, nearby, or in view 28 , facilitating the restoration of depleted cognitive capacities 31 , aiding in the recovery from periods of psychosocial stress 32 , and promoting increased optimism 33 .The enhancement of these mental health advantages might be due in part to natural, biodiverse sound environments that calmly reduce chronic noise, and potentially mitigate the impact of socioeconomic disadvantage on mental well-being 34 , but also to social and physical activities enabled by nearby green spaces 35 .
For mental health, assessment techniques can include self-reported measures 36 , as well as subjective well-being indexes 37 .Studies evaluating of green spaces in relation to mental health have used satellite imagery to derive Normalized Difference Vegetation Index (NDVI) 38 , the use of geo-tagged street view images 39 , and other types of Earth observation data 40,41 .Furthermore, diverse indices have been used in prior research to evaluate exposure to urban green spaces, including Land Use/Land Cover (LULC) classifications derived from satellite imagery 41,42 , which has been shown to play a significant role in mental health outcomes.However, there are few studies addressing the relationship between different classes of LULC and mental health in LMICs, as the presence of green spaces has a very irregular distribution, availability, quality and use in these regions 43 .Recently, a potential upward trend in the benefits of urban green spaces was found among people from lower socioeconomic classes in LMICs 38,44 .For example, in the Sao Paulo Metropolitan Area, the 6th most populated city in the world with around 21 million inhabitants, the prevalence of anxiety was negatively correlated with the presence of green spaces, predominantly grassy areas, and positively correlated with roofs and asphalt 41 .
Currently, there exists a lack of comprehensive information regarding the influence of different land use categories on the mental well-being of individuals in the southern region.The authorities have focused on emergency initiatives and symptomatic solutions that are temporary, costly, and often not effective.Given these prevailing circumstances, it is imperative to better comprehend the relationship between various land use patterns, specifically focusing on the presence of green spaces within urban settings and their potential impact on the mental health of the local population.These recommendations should delineate areas where mental health requires heightened attention, as well as pinpoint potential locations for strategic LULC interventions.Such insights are intended to provide valuable guidance to policymakers in shaping future urban management strategies for the city, as well as recommendations to health authorities.In this context, the aim of this study is to examine the connections between Land Use/Land Cover (LULC) and mental health outcomes within the population of Porto Alegre, a city located in southern Brazil with a predominantly low to middle-income demography.

Spatial autocorrelation of mental health cases
A total of 5,769 diagnosed cases of mental health issues were included in the study with initial diagnostic from January 2018 to July 2023.The Global Moran's I spatial statistical analysis (Moran's Index = 0.072, p-value = 0,000 and Z-score = 3706) indicates there is less than 1% likelihood that this clustered pattern results from random chance.The positive value of the Moran's Index indicates clustering effects in the mental health case distribution in the city of Porto Alegre, i.e., the presence of similar clusters close to each other in the data suggests the presence of spatial distribution patterns.

Hot spot analysis
Figure 1 presents the map of the hotspot analysis for the study area that highlights the neighborhoods with high (hot spot) or low (cold spot) proportion of mental health cases.Risk areas are represented by red colours (high rate of mental health cases).The hot spot areas are in Mario Quintana (northeast) and Agronomia, Lomba do Pinheiro, Cascata and Vila São José, mainly in the east zone.Five hotspots are identified and represent 19.93% of the total diagnostic cases.The total area of hotspots is 54,600 km 2 , which equals 11.02% of the city area.The five hotspot neighborhoods are Mario Quintana (Z-score = 2655; p-value = 0.007), Agronomia (Z-score = 3,089; p-value = 0,002), Lomba do Pinheiro (Z-score = 4,190; p-value = 0,000), Cascata (Z-score = 2508; p-value = 0.012) and Vila Sao Jose (Z-score = 2,036; p-value = 0,041).The larger the Z-score and the lower the p-value, the more intense the clustering at that location.

Discussion
Our study investigated the spatial distribution of diagnosed mental health cases in Porto Alegre and their associations with various remotely sensed Land Use/Land Cover (LULC) variables and the Human Development Index (HDI) using a machine learning and polynomial regression model approach.The spatial distribution of diagnosed mental health cases exhibited significant variations across neighborhoods, as well as significant clustering.The latter may reflect socio-economic conditions, other shared characteristics of the communities living in these areas, or similar environmental conditions and stressors.Our machine Learning analysis of the different LULC classes and HDI revealed statistically significant associations with mental health cases.In this study, the LULC class Tree cover and the HDI variable emerged as the most relevant predictors of our Mental Health Index.The Polynomial Regression model demonstrated a negative correlation between the most significant predictors and the Mental Health Index.Specifically, an increase in neighborhood Tree cover above 50% was associated with a decrease in the Mental Health Index.A similar trend was observed for the HDI, where higher neighborhood HDI values (from 0.7 upward) corresponded to lower values of the Mental Health Index.
To the best of our knowledge, our study is the first one to test directly, through machine learning and a modelling technique, the influence of different LULC classes and the HDI on the number of diagnosed on mental health cases in a low-to middle-income city.In Fig. 3 the beginning of the tree cover percentage plot presents a momentary decrease.The areas with low tree cover are typically correspond neighborhoods that are very densely populated and tend to present squares or small groves that were planned to be used mainly for relaxation and rest, indicating that these structures are correlated with lower MHI.The middle of the curve (from about 30% to 55% tree cover) is more related to neighborhoods featuring wastelands and abandoned places, which are areas where the level of greenness is not conducive to generating mental health benefits.At last, the curve presents a high decrease, which is correlated with large parks and even urban forests, areas know to provide mental health benefits.The Tree cover class could be used as a good proxy for green spaces.This class refers to the clustering of tall (15 m or higher) dense vegetation, typically with a closed or dense canopy and has high statistical accuracy 45 .Recent studies have reported that the presence of green spaces is linked to various advantageous health outcomes, including negative associations with the presence of anxiety 41 .Changes to ecosystems resulting from alterations in land use can profoundly impact mental well-being, as they are intricately linked to both individual and community identity www.nature.com/scientificreports/and often form an essential component of culture and overall sense of well-being 46,47 .Our results are in line with these earlier works.Previous research has explored the effects of ethnicity and socioeconomic factors influencing mental health outcomes 48 .However, quality and accessibility of those types of data are usually low, especially in lowerincome communities 49 .The Brazilian Public Unified Health System (SUS) runs the Department of Information Technology (DATASUS) that is responsible for developing, researching, and integrating information technologies to support healthcare systems 50 .DATASUS maintains the systems and applications necessary to record, process, and make available health information 51 , but despite the national consensus about its importance as an instrument of health surveillance, there is around 17.7% of unreported information 52 , making it a challenge for studies such ours.The use of HDI in our model mitigated the lack of reliable national social-economic data in Brazil, and HDI was shown to be one of the most important predictors together with the Tree cover.Our finding that as the HDI increases, the MHI tends to decrease, is in line with previous studies, notably a multi-country study that found that countries with a low HDI had the highest prevalence of depression 53 .A similar study done in Asia 54 showed that depressive disorder had a significant economic burden on individuals and society, which could be a consequence of deprivation [54][55][56][57] .
Our findings also contribute to the growing literature on green space and the Human Development Index on mental health disorders.Our results are consistent with the generic research findings of the health benefits of living in areas with high levels of green space, but several limitations of our study should be acknowledged.Firstly, our research was limited by the lack of information on the severity levels of depression and anxiety.As a result, we were unable to determine how these conditions, in relation to Land Use/Land Cover, are impacted at specific severity levels.Secondly, we determined vegetation density utilizing aerial measures rather than eye-level or other ground-level assessments.This may offer different perceptions of green spaces compared to eye-level evaluations 58 .Thirdly, our study did not consider the physical accessibility or usability of green spaces, such as parks and squares, factors that could significantly contribute to improving mental and overall health.Recent investigations have crafted methodologies to model physical access to urban green spaces, using strategies that account for movement barriers and walking speeds 59,60 .In future studies, these approaches could be used with ours to explore if distance to green spaces, as well as frequency of use and type of interaction with these spaces, is associated with certain mental health conditions.Lastly, our assessment of mental health cases was based on clinician-rating scales through self-reported instruments.While certain symptoms might be better suited for self-reporting, this approach can be influenced by potential biases when patients describe their symptomatology, personality traits, and demographic characteristics such as education, ethnicity, and socioeconomic factors.Despite these potential biases, self-reporting has, in some instances, proven to be an effective mechanism for assessing mental health disorders, such as depression 61 .
Moreover, when assessing the impact of green spaces on human well-being, it is crucial to recognized the disproportionate burden faced by marginalized communities, particularly those with lower socioeconomic status (SES), including low-income individuals and racial or ethnic minorities.These vulnerable populations are often subjected to living in polluted and deteriorated environments, characterized by limited access to quality green spaces, higher exposure to environmental hazards, and inadequate infrastructure 62 .These population may also be more prone to the impacts of future climatic extremes 63 , and the relationship between increased or decreased local urban temperature and mental health should be explored in futures studies.
Finally, our data suggest that the presence of mental health disorders in the city of Porto Alegre was associated with decreased density of tree cover.Residing in areas with a high level of greenness and a high Development Index is associated with lower instances of mental health issues in the urban environment of Porto Alegre.For Porto Alegre, it is advised to carry out and effective tree monitoring maintenance plan based on the Plan Director of Arborization of the municipality, and to create effective guidelines regarding the city's tree.Priority should be given to the hotspot neighborhoods identified in our study.Despite the challenges of significantly increasing green spaces in densely populated city areas, these results remain relevant for policymaking, advocating the potential health benefits that could be achieved through improved urban planning and development initiatives.

Methods
This study was conducted with the approval of the Ethics Research Committee of Unisinos University, under number 61224822.6.0000.5344.

Study area
The study was conducted in the low-middle-income city of Porto Alegre, southern Brazil, situated at 30°1′40″ south and 51°13′43″ west (Fig. 4) covering an area of approximately 495,390 km 2 , divided into 94 neighbourhoods, with a population of approximately 1,332,570 inhabitants in the 2023 demographic census 64 .Porto Alegre has a humid subtropical climate, with four well-defined seasons and significant climate variability 64 .The city has an annual average temperature of 19.5 °C and an average annual rainfall of 112 mm.According to the Brazilian Institute of Geography and Statistics, Porto Alegre has a Human Development Index (HDI) of around 0.805 65 , classified as very high according to the criteria of the United Nations Development Program 66 .According to the World Health Organization, for a city to be considered well-forested, the recommended minimum green area per inhabitant is approximately 12 m2.With about 49 m2 of green areas per inhabitant, Porto Alegre is considered one of the most forested capitals in Brazil, ranking fourth in urban afforestation, with 82.9% of the area covered 65 .On the other hand, Porto Alegre leads the diagnoses of depression and suicide cases among the capitals of Brazil, the Brazilian average is about 11.3% cases per 100,000 inhabitants, while Porto Alegre has a rate of about 17.5% 67 68 was used, specifically the ICD codes F31 and F42.The data was collected precisely for the resident population of Porto Alegre and the address of each mental health diagnosticated case was georeferenced in the ArcGIS software 69 .The data spanned from January of 2018 to July of 2023 and due to the lack of georeferencing limitations (missing address on the database) and duplicate records, our analysis evaluated a sample of 5,769 cases.Subsequently, we create a Mental Health Index (MHI) adapted from the descriptive measures of incidence and prevalence which have been largely used in epidemiology 70 .Here, the Mental Health Index was based on the number of cases divided by the population of each one of the 94 neighbourhoods.To calculate the MHI for each of the neighborhoods, we divided the number of cases at neighborhood level by the number of populations of each neighbourhood and then multiplied by 100.This ensured our continuous dependent variable to be test against the Land Use/Land Cover variables and the Human Development Index.

Human development index
In this study, we considered the Human Development Index (HDI) 66 as a proxy for demographic and socioeconomic conditions, due to known bias in Brazilian studies when collecting income and social data.The HDI is calculated based on four metrics as the geometric mean (equally weighted) of life expectancy, education, and gross income per capita, as follow 71 : where

Figure 2
Figure 2 summarizes the importance of each LULC variable for predicting the Mental Health Index.The goodness-of-fit and accuracy metrics of the Random Forest algorithm are MSE: 0.03, RMSE: 0.19 and MAE: 0.14.The Tree cover percentage and the Human Development Index are the most important predictors

Fig. 1 .Fig. 2 . 8 (
Fig. 1.Hot spot analysis of mental health cases in Porto Alegre, where red colours represent high rate of mental health cases and blue colors low rates (cold spot area), generated in ArcMap® 10.6.1 69 .

Fig. 3 .
Fig. 3. Fitted curves for predicting the Mental Health Index (MHI) in response to significant predictors, which is: Tree cover and Human Development Index.The curve (continuous black line) and the 95 confidence bands (shaded areas) were obtained by fitting the Mental Health Index as function of LULC variables using Polynomial Regression.
the Health component-a long and healthy life-is based on estimates of life expectancy, the Education component-access to education-is calculated with both the expected years of schooling (for children at school entering age) and mean years of schooling (for adults aged 25 an older), and the Income component-a decent HDI = (I Health * I Education * I Income ) 1/3

Fig. 4 .
Fig. 4. The study area map shows Porto Alegre in the left corner in relation to Brazil and in the right, the Land Use/Land Cover (LULC) of the city borders, generated in ArcMap® 10.6.1 69 .