Spatial prediction of population based on random forest

Ding, Jianjun; Song, Chunqiao; Liu, Ruifan; Guohua, Zhu

doi:10.1109/itaic54216.2022.9836834

Cited by 1 publication

(1 citation statement)

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“…Wang [36], based on building models, utilized the random forest algorithm to spatially simulate the population in Lin'an District, Hangzhou City, Zhejiang Province. Ding [37] trained the random forest model using building outlines and POI data to simulate the spatial distribution of the population in Hubei Province in 2010. These studies demonstrate the feasibility of the random forest method in spatializing population research.…”

Section: Introductionmentioning

confidence: 99%

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

Zhang,

Wang,

Luo

et al. 2024

Sustainability

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The urban agglomeration on the north slope of the Tianshan Mountains is a pivotal place in Western China; it is essential for the economic growth of Xinjiang and acts as a critical bridge between China’s interior and the Asia–Europe continent. Due to unique natural conditions, the local population distribution exhibits distinct regional characteristics. This study employs the spatial lag model (SLM) from conventional spatial analysis and the random forest model (RFM) from contemporary machine learning techniques. It integrates traditional geographic data, including land cover data and nighttime light data, with geographical big data, such as POI (points of interest) and OSM (OpenStreetMap), to build a comprehensive indicator database. Subsequently, it simulates the spatial population distribution within the urban agglomeration on the northern slopes of the Tianshan Mountains in 2020. The accuracy of the results is then compared and assessed against the accuracy of other available population raster datasets, and the spatial distribution pattern in 2020 is analyzed. The findings reveal the following: (1) The result of SLM, combined with multi-source data, predicts the population distribution as a relatively uniform and nearly circular structure, with minimal spatial differentiation. (2) The result of RFM, employing multi-source data, better captures the spatial population distribution, resulting in irregular boundaries that are indicative of strong spatial heterogeneity. (3) Both models demonstrate superior accuracy in simulating population distribution. The spatial lag model’s accuracy surpasses that of the GHS and GPW datasets, albeit still trailing behind WorldPop and LandScan. Meanwhile, the random forest model significantly outperforms the four aforementioned population raster datasets. (4) The population spatial pattern in the urban agglomeration on the north slope of the Tianshan Mountains predominantly consists of four distinct circles, illustrating a “one axis, one center, and multiple focal points” distribution characteristic. Combining the random forest model with geographic big data for spatialized population simulation offers robust scientific validity and practicality. It holds potential for broader application within the urban agglomeration on the Tianshan Mountains and across Xinjiang. This study can offer insights for studies on regional population spatial distributions and inform sustainable development strategies for cities and their populations.

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Section: Introductionmentioning

confidence: 99%