To reexamine the relationship between leisure-time physical activity (LTPA) and the built environment (BE), this paper takes advantage of the massive amount of data collected by an accelerometer and GPS-based fitness mobile app. Massive LTPA data from more than 3 million users were recorded by Codoon in 500m by 500m grid cells and aggregated to 742 natural cities in mainland China. Six BE indicators were quantified using GIS at the city scale. Robust regression analysis was used to estimate the correlation between LTPA and BE. Five of six BE indicators—connectivity, road density, land use mix, points of interest density, and density of parks and squares—were significantly, positively, independently, and linearly related to LTPA in the regression analysis. The study obtains findings that are consistent with the previous literature but also provides novel insights into the important role of POI density in encouraging LTPA, as well as how the relationship between LTPA and BE varies by time of day. The study also sheds light on the embrace of new technology and new data in public health and urban studies.
Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network to quantify the usage of small public spaces through recorded videos as a reliable and robust method to bridge the literature gap. To start with, we deployed photographic devices to record videos that cover the minimum enclosing square of a small public space for a certain period of time, then utilized a deep convolutional neural network to detect people in these videos and converted their location from image-based position to real-world projected coordinates. To validate the accuracy and robustness of the method, we experimented our approach in a residential community in Beijing, and our results confirmed that the usage of small public spaces could be measured and quantified effectively and efficiently.
Urban spatial structure, which is primarily defined as the spatial distribution of employment and residences, has been of lasting interest to urban economists, geographers, and planners for good reason. This paper proposes a nonparametric method that combines the Jenks natural break method and the Moran’s I to identify a city’s polycentric structure using point-of-interest density. Specifically, a polycentric city consists of one main center and at least one subcenter. A qualified (sub)center should have a significantly higher density of human activity than its immediate surroundings (locally high) and a relatively higher density than all the other subareas in the city (globally high). Treating Chinese cities as the subject, we ultimately identified 70 cities with polycentric structures from 284 prefecture-level cities in China. In addition, regression analyses were conducted to reveal the predictors of polycentricity among the subjects. The regression results indicate that the total population, GDP, average wage, and urban land area of a city all significantly predict polycentricity. As a whole, this paper provides an alternative and transferrable method for identifying main centers and subcenters across cities and to reveal common predictors of polycentricity. The proposed method avoids some of the potential problems in the conventional approach, such as the arbitrariness of thres hold. setting and sensitivity to spatial scales. It can also be replicated rather conveniently, as its input data, such as point-of-interest data, are widely available to the public and the data’s validity can be efficiently checked by field trips or other traditional data sources, such as land-use maps or censuses.
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