Size Distribution of Building Lots and Density of Buildings and Road Networks: Theoretical Derivation Based on Gibrat’s Law and Empirical Study of Downtown Districts in Tokyo

Hiroyuki Usui and Yasushi Asami

International Regional Science Review, 2020, vol. 43, issue 3, 229-253

Abstract: The concept of density lacks the ability to explain the diversity of physical elements of urban form such as building lot sizes. Thus, urban planners tend to discuss the validity of density values without taking into consideration the variation of building lot sizes due to the limited data available on building lot shapes. Our objective is to discuss the potential of building density and road network density in order to estimate the size distribution of building lots at the district scale in downtown Tokyo. The study finds that (1) building lot sizes approximately follow the lognormal distribution whose parameters, mean, and variance are formulated by gross building density, the coefficient of variation of building lots, road network density, and average road width by removing large building lots and (2) the value of the coefficient of variation is approximately equal to one. As a practical problem, we discuss how to determine the maximum building density by considering the variation of building lot sizes. It was found that the maximum building density can be determined based on a stochastic approach. These findings are expected to provide urban planners with a theoretical basis for discussing the validity of density values.

Keywords: building lot; size; lognormal distribution; road; density (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0160017619826270 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:inrsre:v:43:y:2020:i:3:p:229-253

DOI: 10.1177/0160017619826270

Access Statistics for this article

More articles in International Regional Science Review
Bibliographic data for series maintained by SAGE Publications ().