赵鹏程
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Personal Information
- Supervisor of Master's Candidates
- Name (Pinyin):Zhao Pengcheng
- Date of Birth:1993-09-05
- E-Mail:
- Date of Employment:2019-12-07
- Administrative Position:高级实验师
- Education Level:With Certificate of Graduation for Doctorate Study
- Business Address:武汉大学信息学部遥感信息工程学院（5号楼）315办公室
- Gender:Male
- Contact Information:+86 15972003670
- Status:Employed
- Alma Mater:武汉大学
- Teacher College:School of Remote Sensing and Information Engineering
Other Contact Information
- PostalAddress:
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Methods of Population Spatialization Based on the Classification Information of Buildings from China's First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China
- Date of Publication:2025-01-07
- Hits:
DOI number:
10.3390/s18082558Affiliation of Author(s):
School of Remote Sensing and Information Engineering, Wuhan University, ChinaJournal:
SENSORSKey Words:
population spatialization,multi-level method,China's first national geoinformation survey,correlation analysis,overlay analysisAbstract:
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China's first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories. In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.Co-author:
Pengcheng,Zhao, Lingli,Jiang, Jiansong,Li, Zilong, LinzeIndexed by:
Journal paperDiscipline:
EngineeringDocument Type:
JVolume:
18Issue:
8Translation or Not:
noCN No.:
WOS:000445712400158Date of Publication:
2018-08-01