PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (9): 1119-1127.doi: 10.18306/dlkxjz.2017.09.008

• Special Issue: Urban Cultural Sensing and Computing • Previous Articles     Next Articles

Satisfaction on urban cultural environment and influencing factors

Li CHEN1(), Yunxiao DANG2,*(), Wenzhong ZHANG3, Renfeng MA4   

  1. 1. College of Applied Arts and Sciences, Beijing Union University, Beijing 100191, China
    2. College of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, China
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    4. Department of Geography & Spatial Information Technology, Ningbo University, Ningbo 315211, Zhejiang, China;
  • Online:2017-09-27 Published:2017-09-27
  • Contact: Yunxiao DANG;
  • Supported by:
    National Natural Science Foundation of China, No. 41230632, No. 41601160;Beijing Natural Science Foundation, No.9164027


Urban cultural environment plays an important role in promoting residents' quality of life and livability of cities. Favorable cultural atmosphere and rich cultural life will improve residents' health and well-being. Some scholars even argue that urban cultural environment is becoming one of the most valued elements for cultivating creativity and producing high-quality human resources. As a result, urban cultural environment catches attention of both urban geographers and policymakers. However, the increasing body of literature is mostly focused on urban culture from the perspective of "others", and few studies have discussed the issue from the perspective of "mine". Local residents' subjective perception is very important as it is an important factor that influences residential location choice and accordingly may affect a city's innovation capability. This research aimed to enrich the literature by studying the influencing factors of residents' satisfaction on urban cultural environment. The primary subjective data came from a large-scale survey conducted in 2015 in 40 typical cities of China, while the objective data came from statistic yearbooks. The study adopted a group of hierarchical multilevel models to examine the different influences from the city level and personal level. The survey results show that residents in Jinan, Shanghai, Tianjin, Shenzhen, and Beijing exhibit the highest satisfaction level among the 40 cities, while those who live in Sanya, Harbin, Nanchang, Lasa, and Zhengzhou are the least satisfied. The model results show that all the observed objective urban characteristics related with cultural environment, including cultural consumption, cultural facilities, as well as historical and cultural accumulation, show significant and positive effects on residents' satisfaction level. Some of the demographic characteristics of urban residents (monthly income, age, and occupation) exhibit significant impacts on their satisfaction. Residents with higher income are more satisfied with urban cultural environment than low-income people; residents at middle age are more satisfied than the younger and older groups; while people worked in agriculture, forestry, fishing, and water conservancy industry are less satisfied compared to those who work in government offices or companies. Analysis of interaction between urban hierarchy and personal hierarchy shows that high income residents are more satisfied with urban cultural environment in cities with higher per capita GDP and more theaters as there is higher cultural consumption diversity, whereas low-income residents are more satisfied when living in less prosperous cities. This finding is consistent with exiting studies, that is, so-called high-quality human resources prefer abundant and diversified cultural and entertainment consumption. This study may contribute to clarifying the relationship between individual subjective perception and urban characteristics. Also, the results may inform government policies guiding the development of highly livable cities

Key words: urban cultural environment, satisfaction, influencing factor, multilevel model