Healthy Cities

Revolution in Approaches of Assessing Exposure to Built Environment

From Static Residence Based Approach and GIS Data to Individual Mobility Based Approach and Image Data

The health impact of individuals’ exposure to built environment is a key issue in the field of healthy city research. Individuals’ exposure to built environment means someone’s contact with the built environment, especially with the harmful factors. Accurate assessment of exposure to built environment is the basis of research on how built environment influences human health. As for the indicators and data in studies of assessing individuals’ exposure to built environment, indicators from 5D theory like density, diversity, design, destination accessibility and distance to transition were usually used, and to measure these indicators, GIS data were usually used. However, in these studies, less attention is paid to image data that can reflect the human-scale built environment characteristics such as the indicator of neighborhood physical disorder, which lead to limitation of assessment dimension. As for the assessment areas and spatial averaging methods in studies of assessing individuals’ exposure to built environment,  most of them take the neighborhood of individuals’ residence as the assessment area for the whole day, ignoring individuals’ mobility, which can be called the static residence-based approach. But there comes two problems in this approach, the first is that the region-based attributes could be affected by how the residential units are geographically delineated, which is called Uncertain Geographic Context Problem; and the second is that the assessment can be erroneous when people’s mobility is ignored, because people’s daily mobility may amplify or attenuate the exposures they experienced in their residential neighborhoods, which is called the Neighborhood Effect Averaging Problem. The consideration of individuals’ mobility is the common solution to avoid the above problems. Few studies have used the mobility-based approach to assess individuals’ exposure to built environment, however, these studies are mainly based on 5D indicators and GIS data. Thus, individual mobility has not been considered in assessment of exposure to built environment based on image data, which is a combined limitation in assessment indicator and data, as well as in assessment area and spatial averaging method. With the development of science and technology, the available tools for assessing exposure to built environment are becoming more and more abundant. It is suggested that in the future studies of assessing individuals’ exposure to built environment, for assessment data, image data that can reflect the human-scale quality of the built environment should be considered, and for assessment area, individuals’ mobility should be considered. Referring to the assessment of exposure to natural environment, in this article, two assessment methods of individuals’ exposure to built environment based on image data and individuals’ mobility are proposed. The first one is to assess exposure to built environment by overlaying individuals’ spatio-temporal trajectories with spatial distribution map of street view images. By auditing the street view images, the researchers can get the score of human-scale built environment characteristics, then by overlaying the map of built environment characteristics with the map of  individuals’ spatio-temporal trajectories, the researcher can get the time-weighted averaging built environment characteristics that the individual exposed to. The second one is to invite the individual to wear a wearable camera to record the built environment they exposed to. The wearable camera can take photos at regular intervals, and by auditing these photos and calculating the results, the researcher can get the time-weighted averaging built characteristics the individual exposed to. Compared with the two proposed methods, for assessment accuracy, the first one is less accurate because the update frequency of street view images is not high and the spatial coverage area of them is not complete; while the second one is more accurate because the photos taken by wearable camera can record the complete and real-time built environment information. For labor and capital cost, the first proposed method has less capital cost and more labor cost. It is because that the street view images can be freely downloaded but the wearable camera is costly to buy. And although the two proposed methods both have to audit images, on the basis, the first proposed method has to do more work in overlaying the trajectories. For the above reasons, the two proposed methods are suitable in different scenarios. The new methods proposed in this article fill the gap that the assessment of individuals’ exposure to built environment seldom consider the image based human scale built environment characteristics in existing studies, and with the consideration of mobility, the new methods are more accurate compared with the existing static residence-based assessment approach. The new assessment method of individuals’ exposure to built environment will help the exploration of the new theory in the field of healthy city research.
李文越和龙瀛 2021 西部人居环境科学学刊_建成环境暴露.pdf
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Healthy Neighborhood Evaluation System

Neighborhoods are places where people spend the most time in their lives. Neighborhoods have a decisive impact on the residents' health. With several important tasks, including the transformation of old neighborhoods, the maintenance of existing neighborhoods, and the construction of new neighborhoods in the future, a scientific and reasonable evaluation standard is urgently needed to guide the development of healthy neighborhoods. To build the evaluation system, this paper first clarifies the principles for selecting evaluation indicators, which include: 1) the indicators are selected from a humanistic perspective; 2) the pathways between neighborhoods environment and health outcomes are deeply considered; 3) the indicators are selected from multiple scales. Secondly, based on the combined perspectives of urban planning and public health, it identifies the indicators that affect the residents' health in neighborhoods and searches the literature through the quality assessment to provide evidence to support the accuracy and effectiveness of the indicators. Finally, it proposes prospect to the evaluation, including 1) it is urgent to improve and utilize the healthy neighborhoods based on the Chinese condition; 2) advanced technologies need to be widely applied in neighborhoods in the future; 3) the transitions in cities should be considered in the future development of neighborhoods. It hopes that relevant researchers and government leaders to realize the importance and urgency of healthy neighborhoods to build more healthy neighborhoods in China.

张雨洋等 2020 风景园林_健康居住小区.pdf
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Smart technologies help China combat COVID-19 via promoting city resilience

Details are available HERE

龙瀛 2020 城市规划_泛智慧城市技术.pdf
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李伟健和龙瀛 2020 上海城市规划_泛智慧城市技术.pdf
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Assessment of Tobacco Control Policy Based on Baidu Big Data

As the increased attention to human settlements and healthy environment from all over the world, ‘healthy cities’ has become one of the most indispensable topics for urban development. Under such circumstance and the concept of “Health China” proposed in 2016, there has been an increasingly concern on the policy of urban tobacco and smoking control. In 2018, the World Health Organization started to focus on “smoking in cities” problem. In this study, WHO China cooperated with the Baidu Big Data department and Tsinghua University to conduct the spatial analysis and statistics research on the situation of urban smokers and the effects of tobacco control policy in China.


We evaluated people’s change of attention for tobacco-related information by using the massive and spatiotemporal query data and user profile data related to smoking problem in 2013 and 2017 offered by Baidu Big Data department. The data covered 2869 urban districts in China. Besides, we assessed the effects of tobacco control policies in Chinese cities based on the tobacco control policies of various cities. The results showed that there has been an increase in people’s awareness and discussion on the legislative content of smoke-free and the areas with high overall smoking attention were concentrated in the Yangtze River Basin. Meanwhile, the significant increase of people’s attention to e-cigarettes and tobacco tax policy was also found. As for the smoker groups, the proportion of smokers under 24 years old, female smokers and smokers with lower education level increased. We further compared the difference between cities with different levels of tobacco control policies and the results revealed the increase in overall attention on smoking in cities with strict smoking restrict policies. In addition, the attention of smoke-free and cessation increased in cities with smoking restrict policies and especially in those with strict smoking restrict policies. Furthermore, as the area with increasing smoke-free attention were obviously scattered around cities with strict smoking restrict policies, we found the policy may exert influence to surrounding area.


Tsinghua University:

Ying Long, Zhaoxi Zhang (Presenter), Jue Ma, Yuyang Zhang


Baidu Big Data Department:

Jidong Peng, Peng Liu, Shengwen Yang, Lu Meng, Xin Mao, Huadong Li


We thank Gauden GALEA, Xiaopeng JIANG, Kelvin Khow Chuan HENG, Paige SNIDER, Jiani SUN and Xi YIN (in the alphabetic order) from WHO China for their regular inputs and technical discussions.

Slides for the project
基于百度大数据的烟草与健康研究报告 adjusted logos FULL zx
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Media Press
百度大数据携手清华大学助力世界卫生组织,关注“城市吸烟问题” 推动“健康城市”发
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The Forum on Healthy Cities and Physical Activity

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