Calculating Intraurban Agglomeration of Economic Units with Planar and Network K-Functions: A Comparative Analysis

Abstract

According to recent research, one of the most promising strategies for intraurban job growth lies promoting localized clusters that produce goods and services which are primarily sold within a single city, metropolitan area, or urban region. However, in order to design urban policies to create or reinforce local clusters, the first challenge is to measure in a reliable way the clustering tendencies of different kinds of economic units in intraurban space. The aim is to compare the similarities and differences in results obtained from two methods designed to measure global clustering tendencies (the planar and network K-functions) in terms of characterization, scale, and intensity of intraurban localization patterns for tertiary economic units in a Latin American metropolis. It is concluded that the network K-function is a more appropriate method for measuring agglomeration patterns, scale, and intensity at the intra-urban level.

Key words:

• location
• pattern
• agglomeration
• retail activities
• network K-function
• Mexico

Acknowledgements

This work was supported by Consejo Nacional de Ciencia y Tecnología (Mexico) grant.

Notes

² The idea is to analyze the global tendency of concentration and dispersion of tertiary firms in the CBD of the Toluca Metropolitan Area using two different methods (K-planar and K-network functions) in order to clarify which method is the most convenient for exploring spatial global tendencies in the study area. In the future this research project will advance toward detecting the exact location of tertiary hotspots and the spatial associations (co-location) between firms of different sectors by using appropriate methods. Although it is critically important for spatial economic analysis to identify the locations of economic units hot spots, “the reliability of pinpointing specific locations is likely to be undermined if not preceded by a global view analysis” (Lu and Chen, 2006, p. 3).

³ Pedestrian flows were measured according to the methodology designed by MTC (2003), with some adaptations to the particular characteristics of the study area. The threshold value used for defining the limits of the CBD was derived from the relationship between the decay rates of the intensity of pedestrian flows as distance increased from the peak location of highest pedestrian traffic in the CBD. This threshold value was 6,700 pedestrians from 9:00 a.m. to 9:00 p.m. Details are covered by Garrocho and Flores (2009).

⁴ Whereas the K-function does not detect specific clusters (only providing global trends), specific knowledge of the study area indicates the presence of well-defined corridors (such as electronics and furniture) at close range, which the network K-function clearly identifies.