Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)

Population Density Prediction of Wuhan City Based on the Linear Regression Model

Authors
Cheng Wang, Yu Xia
Corresponding Author
Cheng Wang
Available Online December 2017.
DOI
10.2991/mcei-17.2017.82How to use a DOI?
Keywords
Population density; Linear regression model; Main influencing factors
Abstract

In this paper, the problem of population density prediction of Wuhan City is studied, and a prediction model based on linear regression model is presented. Then, considering three main factors, i.e., GDP, employment quantity and completed area of housing construction, which affect the population density of Wuhan City, an empirical analysis is given according to the data information from statistical yearbook of Wuhan City from 2010 to 2014. The result provides valuable decision references for the population management department.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
Series
Advances in Computer Science Research
Publication Date
December 2017
ISBN
978-94-6252-430-9
ISSN
2352-538X
DOI
10.2991/mcei-17.2017.82How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Cheng Wang
AU  - Yu Xia
PY  - 2017/12
DA  - 2017/12
TI  - Population Density Prediction of Wuhan City Based on the Linear Regression Model
BT  - Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SP  - 378
EP  - 381
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.82
DO  - 10.2991/mcei-17.2017.82
ID  - Wang2017/12
ER  -