Next Article in Journal
Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection
Previous Article in Journal
Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics
Previous Article in Special Issue
Recent Surface Water Extent of Lake Chad from Multispectral Sensors and GRACE
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(8), 2558; https://doi.org/10.3390/s18082558

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

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 25 June 2018 / Revised: 25 July 2018 / Accepted: 3 August 2018 / Published: 4 August 2018
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
View Full-Text   |   Download PDF [15243 KB, uploaded 6 August 2018]   |  

Abstract

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. View Full-Text
Keywords: population spatialization; multi-level method; China’s first national geoinformation survey; correlation analysis; overlay analysis population spatialization; multi-level method; China’s first national geoinformation survey; correlation analysis; overlay analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Li, L.; Li, J.; Jiang, Z.; Zhao, L.; Zhao, P. 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. Sensors 2018, 18, 2558.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top