Acta Geographica Sinica ›› 2019, Vol. 74 ›› Issue (4): 664-680.doi: 10.11821/dlxb201904004

• Population and Urbanization Research • Previous Articles     Next Articles

Population distribution pattern and influencing factors in Tibet based on random forest model

Chao WANG1(), Aike KAN2(), Yelong ZENG3, Guoqing LI4, Min WANG1, Ren CI5   

  1. 1. School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
    3. Institute of Remote Sensing and Digital Earth, CAS, Beijing 100101, China
    4. School of Resources and Environmental Engineering, Ludong University, Yantai 264025, Shandong, China
    5. Institute of Science & Technology Information of Tibet Autonomous Region, Lhasa 85000, China;
  • Received:2017-08-31 Revised:2019-03-11 Online:2019-04-25 Published:2019-04-23
  • Supported by:
    Natural Science Foundation of Tibet Autonomous Region, No.XZ2017ZRG-100, No.2015ZR-13-56;The National Key Technology R&D Program of China, No.2014BAL07B02-2;Grants Program of China Clean Development Mechanism Fund, No.2014058


Clarifying the spatial pattern of population distribution, its influencing factors and regional differences at the township level is of great guiding significance for formulating sustainable development policies in ecologically fragile areas. Based on the population census data of Tibet at the township level in 2010, the population density and spatial factors were extracted. The density and clustering characteristics of the population distribution were analyzed by spatial statistical method. The multiple linear regression method and the random forest regression method were used to explore the population influencing factors and their regional differences of population distribution. The results showed that: (1) The population density of Tibet at the township level showed a strong spatial non-equilibrium. The general trend was high in the southeast and low in the northwest, and there was a strong spatial coupling between the main rivers and the main traffic trunks in high density area. (2) The "core-edge" characteristic of population clustering was obvious, and roughly to the wave of Borong (Nyalam County)-Gangni (Anduo County) as the demarcation line. (3) In the multiple linear regression method, the artificial surface index had the greatest influence on the population distribution, followed by the nighttime light index and road network density. (4) Random forest method was more accurate than multiple linear regression method to predict the population density, which can be used to sort the importance of the influencing factors. The influencing factors of the first six factors were the night light index, artificial surface index, road network density, industrial output value, GDP and multi-year average temperature, and these factors were positively correlated with population density. Among topographic factors, the contribution rate of elevation and slope was the largest, which was negatively correlated with population density. (5) The influencing factors and their interactions of population distribution in Tibet showed obvious regional differences. The valley was a gathering area for population in the study region, mainly in Lhasa River Valley, Nianchu River Valley and Sanjiang River Valley. (6) Through the analysis of random forest regression, the conceptual model can be used to express the influencing factors of population distribution, and the dominant factors were summarized as land use structure, road accessibility and urbanization level.

Key words: population distribution, influencing factor, township scale, random forest, conceptual model