Association between Physical Activity and Neighborhood Environment among Middle-Aged Adults in Shanghai

Objective: To determine the perceived neighborhood environment (NE) variables that are associated with physical activity (PA) in urban areas in China. Methods: Parents of students at two junior high schools in Shanghai, one downtown and the other in the suburbs, were recruited to participate in the study. They completed an International Physical Activity Questionnaire (IPAQ) and Neighborhood Environment Walkability Scale-Abbreviated (NEWS-A) survey. Participant physical activity was also objectively measured using accelerometers. Results: Participants from downtown areas were more positively associated with transportation PA and leisure-time PA than respondents living in the suburbs. Residential density was found to be a significant positive predictor of recreational or leisure-based PA. Street connectivity was negatively associated with leisure time PA for respondents. Moderate-vigorous PA was found to be negatively associated with traffic safety. There were no significant associations between environmental factors and transportation PA. Women had higher levels of moderate-vigorous PA than men. Conclusions: The results of this study demonstrate that residential density, street connectivity, and traffic safety have a significant impact on Chinese middle-aged adults’ PA, suggesting urban planning strategies for promoting positive public health outcomes

Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts

There is currently a lack of in-situ environmental data for the calibration and validation of remotely sensed products and for the development and verification of models. Crowdsourcing is increasingly being seen as one potentially powerful way of increasing the supply of in-situ data but there are a number of concerns over the subsequent use of the data, in particular over data quality. This paper examined crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation to determine whether there were significant differences in quality between the answers provided by experts and nonexperts in the domain of remote sensing and therefore the extent to which crowdsourced data describing human impact and land cover can be used in further scientific research. The results showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than nonexperts in identifying the land cover type. This suggests the need to create training materials with more examples in those areas where difficulties in identification were encountered, and to offer some method for contributors to reflect on the information they contribute, perhaps by feeding back the evaluations of their contributed data or by making additional training materials available. Accuracies were also found to be higher when the volunteers were more consistent in their responses at a given location and when they indicated higher confidence, which suggests that these additional pieces of information could be used in the development of robust measures of quality in the future