Modelling spatial pattern of population distribution in 50 largest cities in the world: A geospatial approach

Abstract

In past decades, gradient pattern analysis has been used effectively to characterize the spatial pattern of population distribution in cities worldwide. Most of these studies have focused only on individual case studies or a limited number of cities. However, measuring, analyzing, and understanding complex spatial patterns of city population distribution requires comparative studies that extend beyond the isolated case of cities. Therefore, the present study analyses the spatial pattern of population distribution along the gradient distance from the city centers of the world’s 50 largest cities using LandScan™ population data from 2013 through a geospatial approach. For each city, the city center was demarcated based on a landmark place, and population density was calculated using concentric buffers from the city center. The study mainly identified five basic spatial patterns of population distribution along the gradient distance to the city center. In addition, the study contrasted the spatial patterns of population distribution between cities in developing and developed countries.

Spatial Agglomeration and Diffusion of Population Based on a Regional Density Function Approach: A Case Study of Shandong Province in China

“…(i) Monocentric regional density function The regional density function is used to describe how population density varies with distance from the central city, and is an effective tool for studying the characteristics of population agglomeration and diffusion [13] . Following Alperovich, the following four forms of monocentric area density functions (Table 1) were validated by some scholars [4] :…”

“…The existence of multiple centers with specialized economic, social, and cultural functions, as well as the interaction between the centers, makes monocentric models no longer suitable for describing contemporary urban structures [12] . Therefore, scholars have questioned the application of monocentric models [13] and have begun to apply polycentric regional density functions to explain the spatial structural characteristics and evolutionary trends of regional population density. Heikkila et al [14] proposed three models of population density distribution in polycentric urban areas, followed by Small and Song [15] , Bontjie [1] , McMillen, McDonald [16] and Joseph [17] who applied polycentric regional density functions to Los Angeles, Western Europe, Chicago, and Port-au-Prince.…”

A Comparative Analysis of Museum Accessibility in High-Density Asian Cities: Case Studies from Seoul and Tokyo

“…South Korea and Japan are developed countries [36], and Seoul and Tokyo are high-density cities playing crucial roles in the economic, cultural, and political development of their respective countries [37,38]. Furthermore, the two cities are similar in area and population density [39]. Seoul and Tokyo deindustrialized and developed into cultural cities earlier than other Asian cities [40,41].…”

A mathematical model for population distribution

“…Recent models proposed various equations for population density as a function of the polar radius: 𝑃 = 𝑃(𝜌), some of which are special cases of relation ( 3). An extensive survey with field data collected from the fifty largest cities of the world (comparable to the work of Bertaud and Malpezzi) made by Subasinghe, Wang and Murayama (2022), proposed the categorization of the above cities into five distinct categories with respect to the following equations:…”