•Population stress areas refers to those with populations growing faster than the available lands.•Population growth is associated with the decrease of land developability.•Low population stress is ...found to be in the Midwest and the traditional “Deep South” counties.•High population stress is found to be in Southeast Coast, Washington, Northern Texas, and the Southwest.•The population stress concept can act as a basis towards developing coherent sustainable land use policies.
The past century has witnessed rapidly increasing population-land conflicts due to exponential population growth and its many consequences. Although the measures of population-land conflicts are many, there lacks a model that appropriately considers both the social and physical contexts of population-land conflicts. In this study we introduce the concept of population stress, which identifies areas with populations growing faster than the lands available for sustainable development. Specifically, population stress areas are identified by comparing population growth and land development as measured by land developability in the contiguous United States from 2001 to 2011. Our approach is based on a combination of spatial multicriteria analysis, zonal statistics, and spatiotemporal modelling. We found that the population growth of a county is associated with the decrease of land developability, along with the spatial influences of surrounding counties. The Midwest and the traditional “Deep South” counties would have less population stress with future land development, whereas the Southeast Coast, Washington State, Northern Texas, and the Southwest would face more stress due to population growth that is faster than the loss of suitable lands for development. The factors contributing to population stress may differ from place to place. Our "population stress" concept is useful and innovative for understanding population stress due to land development and can be applied to other regions as well as global research. It can act as a basis towards developing coherent sustainable land use policies. Coordination among local governments and across different levels of governments in the twenty-first century is a must for effective land use planning.
Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent ...advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.
Air temperature is an essential component in microclimate and environmental health research, but difficult to map in urban environments because of strong temperature gradients. We introduce a spatial ...regression approach to map the peak daytime air temperature relative to a reference station on typical hot summer days using Vancouver, Canada as a case study. Three regression models, ordinary least squares regression, support vector machine, and random forest, were all calibrated using Landsat TM/ETM+ data and field observations from two sources: Environment Canada and the Weather Underground. Results based on cross-validation indicate that the random forest model produced the lowest prediction errors (RMSE=2.31°C). Some weather stations were consistently cooler/hotter than the reference station and were predicted well, while other stations, particularly those close to the ocean, showed greater temperature variability and were predicted with greater errors. A few stations, most of which were from the Weather Underground data set, were very poorly predicted and possibly unrepresentative of air temperature in the area. The random forest model generally produced a sensible map of temperature distribution in the area. The spatial regression approach appears useful for mapping intra-urban air temperature variability and can easily be applied to other cities.
•Maximum daily air temperature is mapped for a complex urban environment.•Random forest outperforms other regression models.•Temperature distribution was mapped satisfactorily from Landsat and elevation data.•Governmental and volunteered data were used for model cal/val.
Temperature is associated with mortality risk across cities. However, there is lack of study investigating the summer effect on mortality associated with mental/behavioral disorders, especially in ...cities with subtropical climate. In addition, summer mortality in subtropical cities is different from tropical cities, and previous studies have not investigated the urban environmental inequality on heat mortality associated with mental/behavioral disorders. A register-based study was developed to estimate the temperature effects on decedents on days with 50
th
percentile of average daily temperature between 2007 and 2014 in Hong Kong (
n
= 133,359). Poisson regression was firstly applied to estimate the incidence rate ratio (IRR) from the summer temperature effects on all-cause mortality, cardiovascular mortality, respiratory mortality, and mortality associated with mental/behavioral disorders. For a 1 °C increase in average temperature on days with temperature ≥ 24.51 °C, IRRs of mortality associated with mental and behavioral disorders on lag 0 and lag 1 days were 1.033 1.004, 1.062 and 1.030 1.002, 1.060, while temperature effects on cardiovascular mortality and respiratory mortality during normal summer days (not extreme heat events) were not significant. A further investigation with linear regression has shown that decedents with mental/behavioral disorders on higher temperature days resided in areas with lower percentage of sky view, lower percentage of vegetation cover, higher level of neighborhood-level PM
2.5
, higher level of neighborhood-level NO, and higher level of neighborhood-level black carbon (BC). In order to develop protocols for community healthcare based on the “Leaving no one behind” scheme documented in the 2016 Sustainable Development Goals report of the United Nations, it is necessary to include heat effects on mental/behavioral disorders, especially people with dementia, for community planning and healthcare development.
Machine-learning is the automated process of uncovering patterns in large datasets using computer-based statistical models, where a fitted model may then be used for prediction purposes on new data. ...Despite the growing number of machine-learning algorithms that have been developed, relatively few studies have provided a comparison of an array of different learners — typically, model comparison studies have been restricted to a comparison of only a few models. This study evaluates and compares a suite of 10 machine-learners as classification algorithms for the prediction of soil taxonomic units in the Lower Fraser Valley, British Columbia, Canada.
A variety of machine-learners (CART, CART with bagging, Random Forest, k-nearest neighbor, nearest shrunken centroid, artificial neural network, multinomial logistic regression, logistic model trees, and support vector machine) were tested in the extraction of the complex relationships between soil taxonomic units (great groups and orders) from a conventional soil survey and a suite of 20 environmental covariates representing the topography, climate, and vegetation of the study area. Methods used to extract training data from a soil survey included by-polygon, equal-class, area-weighted, and area-weighted with random over sampling (ROS) approaches. The fitted models, which consist of the soil-environmental relationships, were then used to predict soil great groups and orders for the entire study area at a 100m spatial resolution. The resulting maps were validated using 262 points from legacy soil data.
On average, the area-weighted sampling approach for developing training data from a soil survey was most effective. Using a validation of R=1 cell, the k-nearest neighbor and support vector machine with radial basis function resulted in the highest accuracy of 72% for great groups using ROS; however, models such as CART with bagging, logistic model trees, and Random Forest were preferred due to the speed of parameterization and the interpretability of the results while resulting in similar accuracies ranging from 65–70% using the area-weighted sampling approach. Model choice and sample design greatly influenced outputs. This study provides a comprehensive comparison of machine-learning techniques for classification purposes in soil science and may assist in model selection for digital soil mapping and geomorphic modeling studies in the future.
•Soil taxonomic units were mapped for the Lower Fraser Valley.•10 machine-learning algorithms were compared.•Four methods of developing training data were compared.•Sampling from soil surveys using an area-weighted approach was most effective.•Choice of model and sampling design greatly influences outputs.
•Inequality in urban greenspace exposure is assessed for 303 cities in China.•Dynamic inequality is characterized using multi-source geospatial data.•Severe inequality in greenspace exposure is ...pervasive in Chinese cities.•Dry cold climate and urban densification contribute to high inequality.
Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities.
Air temperature (T
a
) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based ...observations cannot accurately capture the spatial distribution of T
a
, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximum T
a
in British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimate T
a
. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observed T
a
and spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimating T
a
from MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximum T
a
, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C, R
2
= 0.74) than the linear regression model (MAE = 2.41°C, R
2
= 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximum T
a
at a resolution of 1 km in British Columbia in the summer of 2003-2012 was derived, and the spatial distribution of summer T
a
in this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain.
Being far away from friends and family, and sometimes facing hardships at work and in society, foreign domestic workers in Hong Kong have a strong need for access to social space to gather together ...and to empower each other. At the same time, social space can satisfy their needs for privacy, which has been stripped away from them due to the mandatory live-in rule in the employer's home. In view of this, we devised an explorative case study to probe into the significance and usage patterns of social space by foreign domestic workers and report our findings using their own statements and experiences. We found that the dual-functional social space is an important physical attribute that aided the development of their social identity, and that they have achieved partial success in sharing-or taking over-the social space that was never intended for the sake of their well-being.
Apparent temperature is more closely related to mortality during extreme heat events than other temperature variables, yet spatial epidemiology studies typically use skin temperature (also known as ...land surface temperature) to quantify heat exposure because it is relatively easy to map from satellite data. An empirical approach to map apparent temperature at the neighborhood scale, which relies on publicly available weather station observations and spatial data layers combined in a random forest regression model, was demonstrated for greater Vancouver, Canada. Model errors were acceptable (cross-validated RMSE=2.04°C) and the resulting map of apparent temperature, calibrated for a typical hot summer day, corresponded well with past temperature research in the area. A comparison with field measurements as well as similar maps of skin temperature and air temperature revealed that skin temperature was poorly correlated with both air temperature (R2=0.38) and apparent temperature (R2=0.39). While the latter two were more similar (R2=0.87), apparent temperature was predicted to exceed air temperature by more than 5°C in several urban areas as well as around the confluence of the Pitt and Fraser rivers. We conclude that skin temperature is not a suitable proxy for human heat exposure, and that spatial epidemiology studies could benefit from mapping apparent temperature, using an approach similar to the one reported here, to better quantify differences in heat exposure that exist across an urban landscape.
Display omitted
•Maximum daily apparent temperature (Humidex) is mapped for a complex urban environment•MODIS, Landsat and DEM data combined to develop regression model•Spatial Distribution: apparent temperature is broadly similar to that of air temperature, but different from skin temperature•Some locations are hotspots of apparent temperature, up to 5 degrees above corresponding air temperature
Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative ...contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error.
We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals UIs) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year. Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older.
Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change −0·2% 95% UI −1·5 to 1·0; 2019 prevalence 9·58 cases per 1000 people 95% IU 8·51 to 10·8, 2010 prevalence 96·0 cases per 1000 people 86·0 to 107·0). Age-standardised prevalence of avoidable blindness decreased by −15·4% –16·8 to −14·3, while avoidable MSVI showed no change (0·5% –0·8 to 1·6). However, the number of cases increased for both avoidable blindness (10·8% 8·9 to 12·4) and MSVI (31·5% 30·0 to 33·1). The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15·2 million cases 9% IU 12·7–18·0), followed by glaucoma (3·6 million cases 2·8–4·4), undercorrected refractive error (2·3 million cases 1·8–2·8), age-related macular degeneration (1·8 million cases 1·3–2·4), and diabetic retinopathy (0·86 million cases 0·59–1·23). Leading causes of MSVI were undercorrected refractive error (86·1 million cases 74·2–101·0) and cataract (78·8 million cases 67·2–91·4).
Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached.
Brien Holden Vision Institute, Fondation Théa, The Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg.
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