A method for urban population density prediction at 30m resolution

ABSTRACT

This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities.

KEYWORDS:

• GIS
• population density
• dasymetric
• Cartosat
• Bangalore
• building height
• street density

Acknowledgments

I thank Carlo de Franchis and Rahul Sami for assistance with S2P software. Officials in the Urban Development Department (Govt. of Karnataka), Bangalore Development Authority and Bangalore Water Supply and Sewerage Board provided access to various datasets. Mohan Rao provided data related to the boundaries of informal settlements in Bangalore. I thank Shriya Anand for detailed discussions and feedback on drafts of the paper. A version of this paper was submitted as a chapter of my doctoral dissertation at UC Berkeley. I thank John Radke for detailed comments on drafts of my dissertation chapter. I also thank two anonymous reviewers who provided valuable feedback which helped improve this manuscript. All errors in the manuscript remain my responsibility.

Disclosure statement

No potential conflict of interest was reported by the author.

Geolocation information

Bangalore (Bengaluru), 12°58ʹ23.81”N 77°35ʹ57.04”E

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

Part of the work described in this paper was enabled by a Junior Research Fellowship awarded by the American Institute of Indian Studies. This manuscript was completed with support from the PEAK Urban program, funded by United Kingdom Research and Innovation – Global Challenge Research Fund, Grant Ref: ES/P011055/1.