City level
Citation:
Ye,Y., Zhang, Z., Zhang,X.,Zeng,W., (2019). Human-scale Quality on Streets: A Large-scale and Efficient Analytical Approach Based on Street View Images and New Urban Analytical Tools. Urban Planning International, doi:10.22217/upi.2018.490
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Contact:
Yu Ye
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College of Architecture and Planning
Tongji University, Shanghai, China
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Abstract:
This study provides an operational framework about street quality measurement by the means of large-scale data analysis at the humanistic scale and the results can be regarded as the benchmark for the renewal of urban street space. Taking Hongkou District and Yangpu District of Shanghai as an example, based on Street View Images (SVI) data, this paper takes advantage of machine learning to extract spatial feature, then uses neural network (ANN) to measure the quality of street places with wide distribution and fine resolution. Besides that, an evaluation matrix established by overlapping analysis will combine quality evaluation with network accessibility analysis (sDNA). Finally, we find out those “potential streets” and provide fine theoretical foundation for urban micro-renewal.
The analysis was made by Zhaoxi Zhang and published in the report "Understanding Physical Activities in China using the massive Gudong App Records" in collaboration with Being City Lab, WHO China Office and Gudong Company.
Citation:
Chen, L., Zhang, Z., & Long, Y. (2021). Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app. PLoS One, 16(12), e0260570. doi:10.1371/journal.pone.0260570
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Contact:
Ying Long
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School of Architecture,
Tsinghua University, Beijing, China
Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app
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Abstract:
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.
Citation:
Amegbor, P. M., Zhang, Z., Dalgaard, R., & Sabel, C. E. (2020). Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors. Sci Rep, 10(1), 20019. doi:10.1038/s41598-020-76856-y
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Contact:
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NYU School of Global Public Health
708 Broadway New York, NY 10003, USA
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Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors
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Abstract:
In this study, we examine the concepts of spatial dependence and spatial heterogeneity in the effect of macro-level and micro-level factors on stunting among children aged under five in Uganda. We conducted a cross-sectional analysis of 3624 Ugandan children aged under five, using data from the 2016 Ugandan Demographic and Health Survey. Multilevel mixed-effect analysis, spatial regression methods and multi-scale geographically weight regression (MGWR) analysis were employed to examine the association between our predictors and stunting as well as to analyse spatial dependence and variability in the association. Approximately 28% of children were stunted. In the multilevel analysis, the effect of drought, diurnal temperature and livestock per km2 on stunting was modified by child, parent and household factors. Likewise, the contextual factors had a modifiable effect on the association between child’s sex, mother’s education and stunting. The results of the spatial regression models indicate a significant spatial error dependence in the residuals. The MGWR suggests rainfall and diurnal temperature had spatial varying associations with stunting. The spatial heterogeneity of rainfall and diurnal temperature as predictors of stunting suggest some areas in Uganda might be more sensitive to variability in these climatic conditions in relation to stunting than others.
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