No abstract

No abstract

No abstract

ObjectivesTo identify spatial disparities and demographic characteristics of short stature, we analysed the prevalence of short stature collected in a nationwide health survey.SettingsData were obtained from the 2014 Chinese National Survey on Students Constitution and Health (a cross-sectional study of China). Participants came from 30 provinces, autonomous regions, and municipalities (except Tibet, Hong Kong, Macao, and Taiwan).ParticipantsThere were 213 795 Han school children between 7 and 18 years old enrolled in our study. All participants were sampled by stratified cluster.Primary and secondary outcome measuresShort stature; Chinese and WHO age-specific and gender-specific height growth references were used for short stature assessment.ResultsThe age-standardised and age–gender-standardised prevalence of short stature nationwide was 3.70% and 2.69% according to Chinese and WHO growth references, respectively. The short stature prevalence differed significantly among age groups, urban and rural areas, and regions with different socioeconomic development levels (all p<0.0001). The prevalence was 2.23% in urban versus 5.12% in rural areas (p<0.001). The prevalence was 2.60% in developed, 3.72% in intermediately developed, and 4.69% in underdeveloped regions (p<0.0001). These values were all according to China’s growth reference, but similar patterns were observed on prevalence based on the WHO reference. The spatial distribution of prevalence of short stature presented a clustered pattern. Moran’s I value was 0.474 (p<0.001) and 0.478 (p<0.001) according to the Chinese and WHO growth references, respectively. The southwest part of China showed a higher prevalence of short stature, whereas lower prevalence of short stature was observed mainly in the northeast part of China.ConclusionsThere is an appreciably high prevalence of short stature in rural, underdeveloped areas of China. There are high prevalence spatial clusters of short stature in southwestern China. This provides corroborating evidence for a tailored strategy on short stature prevention and reduction in special areas.

No abstract

The objective, connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper. Big geodata may be categorized into two domains: big earth observation data and big human behavior data. A description of big geodata includes, in addition to the "5Vs" (volume, velocity, value, variety and veracity), a further five features, that is, granularity, scope, density, skewness and precision. Based on this approach, the essence of mining big geodata includes four aspects. First, flow space, where flow replaces points in traditional space, will become the new presentation form for big human behavior data. Second, the objectives for mining big geodata are the spatial patterns and the spatial relationships. Third, the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data, namely heterogeneity and homogeneity, may change with scale. Fourth, data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships. The big geodata mining methods may be categorized into two types in view of the mining objective, i.e., classification mining and relationship mining. Future research will be faced by a number of issues, including the aggregation and connection of big geodata, the effective evaluation of the mining results and the challenge for mining to reveal "non-trivial" knowledge.

No abstract

As a symbol of Chinese culture, Chinese traditional-style architecture defines the unique characteristics of Chinese cities. The visual qualities and spatial distribution of architecture represent the image of a city, which affects the psychological states of the residents and can induce positive or negative social outcomes. Hence, it is important to study the visual perception of Chinese traditional-style buildings in China. Previous works have been restricted by the lack of data sources and techniques, which were not quantitative and comprehensive. In this paper, we proposed a deep learning model for automatically predicting the presence of Chinese traditional-style buildings and developed two view indicators to quantify the pedestrians’ visual perceptions of buildings. Using this model, Chinese traditional-style buildings were automatically segmented in streetscape images within the Fifth Ring Road of Beijing and then the perception of Chinese traditional-style buildings was quantified with two view indictors. This model can also help to automatically predict the perception of Chinese traditional-style buildings for new urban regions in China, and more importantly, the two view indicators provide a new quantitative method for measuring the urban visual perception in street level, which is of great significance for the quantitative research of tourism route and urban planning.