Spatial Analysis of Employment and Population Density: The Case of the Agglomeration of Dijon 1999
Baumont, Catherine, Ertur, Cem, Le Gallo, Julie, Geographical Analysis
The aim of this paper is to analyze the intraurban spatial distributions of population and employment in the agglomeration of Dijon (regional capital of Burgundy, France). We study whether this agglomeration has followed the general tendency of job decentralization observed in most urban areas or whether it is still characterized by a monocentric pattern. To that purpose, we use a sample of 136 observations at the communal and at the IRIS (infraurban statistical area) levels with 1999 census data and the employment database SIRENE (INSEE). First, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. Apart from the CBD, few IRIS are found to be statistically significant, a result contrasting with those found using standard methods of subcenter identification with employment cut-offs. Next, in order to examine the spatial distribution of residential population density, we estimate and compare different specifications: exponential negative, spline-exponential, and multicentric density functions. Moreover, spatial autocorrelation, spatial heterogeneity, and outliers are controlled for by using the appropriate maximum likelihood, generalized method of moments, and Bayesian spatial econometric techniques. Our results highlight again the monocentric character of the agglomeration of Dijon.
Over the last decades, there has been considerable interest in the analysis of urban spatial structures. Indeed, urban growth has exhibited complex spatial patterns including both population spread and employment decentralization from the central city towards the suburbs.
The validity of the monocentric model (Alonso 1964; Muth 1969) to explain urban patterns has therefore been questioned since employment decentralization has recently taken a polycentric form, with a number of employment subcenters influencing the spatial distribution of employment and population. The polycentric urban phenomenon has been extensively documented for many years. Most studies have been carried out on North American urban agglomerations: Chicago (McMillen and McDonald 1998a, b), Dallas-Fort Worth (Wadell and Shukla 1993), Los Angeles (Small and Song 1994; Heikkila et al. 1989; Gordon et al. 1986; San Francisco (Cervero and Wu 1997 1998), and Montreal (Coffey et al. 1996). This trend toward employment decentralization is not limited to North American areas (see, for example, Alperovitch and Deutsch 1996 for Jerusalem; Chen 1997 for Taipei; Wu 1998 for Guangzhou; Gaschet 2000 for Bordeaux; Boiteux-Orain and Guillain 2003 for Ile-de-France).
Few studies have been carried out on middle-sized urban areas. Therefore, it is interesting to investigate whether these particular areas have experienced a similar trend toward employment decentralization or whether the monocentric model is still valid to explain employment and population spatial distributions. From an empirical point of view, studying polycentric rather than monocentric urban configurations raises a set of challenges (Baumont and Le Gallo 1999; Anas, Arnott, and Small 1998) which can be summarized as follows. How many economic subcenters can be identified apart from the traditional Central Business District (CBD)? What are their sizes and their boundaries? How do these multiple economic centers influence land values, population, and employment distributions?
In this paper, we are interested in this empirical challenge applied to the agglomeration of Dijon, which is the capital of Burgundy (France). The study covers the territory of the Communaute de l'Agglomeration Dijonnaise (COMADI) in 1999, which is made up of 16 contiguous towns and has a total population of almost 250,000 inhabitants. We study the intraurban employment and population distributions across the agglomeration using spatial statistic and econometric methods. First, we apply subcenter identification methods combined with spatial statistic techniques to study the characteristics of this agglomeration in 1999. …