High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

Summary: Urban sprawl is thought to be a risk factor for childhood obesity primarily because the physical environment it creates discourages children's physical activity while encouraging their sedentary behavior. However, there has not been any review on the association between urban sprawl and childhood obesity. This study filled this research gap by comprehensively reviewing literature focusing on associations between urban sprawl and weight‐related behaviors and outcomes among children and adolescents. Only two longitudinal and three cross‐sectional studies conducted in two countries were identified. Sample sizes ranged from 2324 to 129 781. Four studies used weight status, and only one study used both behaviors and weight status as outcome variables. All positive, negative, and non‐significant associations were reported. This review could not identify a clear association between urban sprawl and childhood obesity. More longitudinal studies are needed for in‐depth analyses on this important topic in more regions, which would be important not only for public health guidelines but also for research, practice, and policies in urban planning.

Summary: Various measures of the obesogenic environment have been proposed and used in childhood obesity research. The variety of measures poses methodological challenges to designing new research because methodological characteristics integral to developing the measures vary across studies. A systematic review has been conducted to examine the associations between different levels of obesogenic environmental measures (objective or perceived) and childhood obesity. The review includes all articles published in the Cochrane Library, PubMed, Web of Science and Scopus by 31 December 2018. A total of 339 associations in 101 studies have been identified from 18 countries, of which 78 are cross‐sectional. Overall, null associations are predominant. Among studies with non‐null associations, negative relationships between healthy food outlets in residential neighbourhoods and childhood obesity is found in seven studies; positive associations between unhealthy food outlets and childhood obesity are found in eight studies, whereas negative associations are found in three studies. Measures of recreational or physical activity facilities around the participants' home are also negatively correlated to childhood obesity in nine out of 15 studies. Results differ by the types of measurement, environmental indicators and geographic units used to characterize obesogenic environments in residential and school neighbourhoods. To improve the study quality and compare reported findings, a reporting standard for spatial epidemiological research should be adopted.

Summary: Residential density is considered an important attribute of the built environment that may be relevant to childhood obesity. However, findings remain inconclusive, and there are no reviews yet on the association between residential density and childhood obesity. This study aimed to systematically review the associations between residential density and weight‐related behaviours and outcomes. A comprehensive literature search was conducted using the Cochrane Library, PubMed and Web of Science for articles published before 1 January 2019. A total of 35 studies conducted in 14 countries were identified, including 33 cross‐sectional studies, one longitudinal study and one containing both study designs. Residential density was measured by Geographic Information Systems in 28 studies within a varied radius from 0.25 to 2 km around the individual residence. Our study found a general positive association between residential density and physical activity (PA); no significant associations were observed. This study provided evidence for a supportive role of residential density in promoting PA among children. However, it remained difficult to draw a conclusion between residential density and childhood obesity. Future longitudinal studies are warranted to confirm this association.

Summary: As an important factor for neighbourhood walkability, the speed limit in the neighbourhood may influence children's physical activity (PA) outdoors, especially active transport, and further their weight status. This review aimed to systematically evaluate the association between neighbourhood speed limit and obesity‐related behaviours and outcomes among children and adolescents. PubMed, Embase and Web of Science were systematically searched for relevant studies published from the inception of the database to 1 January 2019. Sixteen studies were included, with 13 cross‐sectional studies and three longitudinal studies. Speed limit was measured as the percentage/number of high‐speed roads, perception of safe driving speed, perception of speeding and use of traffic‐calming tools in the neighbourhood. Eleven studies measured the use of active transport as the outcome of interest, and seven studies measured PA directly. Eleven studies revealed an association between a lower speed limit and increased PA, whereas one study showed a negative association, and three studies reported non‐significant associations. Only one study associated speed limit with weight status, which reported a non‐significant association. This review generally supported a negative association between speed limit and PA among children and adolescents. More studies are needed to examine their causality, as well as the association between speed limit and weight status, in order to increase the impact of this research area on public health policy making.

As a leading cause of morbidity and premature mortality, obesity has become a major global public health problem. It is therefore important to investigate the spatial variation of obesity prevalence and its associations with environmental and behavioral factors (e.g., food environment, physical activity), to optimize the targeting of scarce public health resources. In this study, the geographic clustering of obesity in the Netherlands was explored by analyzing the local spatial autocorrelation of municipal-level prevalence rates of adulthood obesity (aged ≥19 years) in 2016. The potential influential factors that may be associated with obesity prevalence were first selected from five categories of healthrelated factors through binary and Least Absolute Shrinkage and Selection Operator (LASSO) regressions. Geographically Weighted Regression (GWR) was then used to investigate the spatial variations of the associations between those selected factors and obesity prevalence. The results revealed marked geographic variations in obesity prevalence, with four clusters of high prevalence in the north, south, east, and west, and three clusters of low prevalence in the north and south of the Netherlands. Lack of sports participation, risk of anxiety, falling short of physical activity guidelines, and the number of restaurants around homes were found to be associated with obesity prevalence across municipalities. Our findings show that effective, region-specific strategies are needed to tackle the increasing obesity in the Netherlands.