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Web page: www.uiuc.edu/unit/real SPATIAL ANALYSIS OF EMPLOYMENT AND POPULATION DENSITY: THE CASE OF THE AGGLOMERATION OF DIJON, 1999 by Catherine Baumont, Cem Ertur, and Julie Le Gallo REAL 03-T-6 March, 2003 SPATIAL ANALYSIS OF EMPLOYMENT AND POPULATION DENSITY: * THE CASE OF THE AGGLOMERATION OF DIJON, 1999 Catherine Baumont, Cem Ertur LATEC UMR n°5118 and University of Burgundy, Pôle d'Economie et de Gestion, B.P. 26611, 21066 Dijon Cedex, France. (e-mail: [email protected]; [email protected]) Julie Le Gallo REAL, University of Illinois at Urbana Champaign, 220 Davenport Hall, 607 S. Mathews Av., Urbana, IL 61801-3671, USA. (e-mail: [email protected]) Abstract The aim of this paper is to analyze the intra-urban 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. In that purpose, we use a sample of 136 observations at the communal and at the IRIS (infra-urban 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. Keywords: Bayesian spatial econometrics, exploratory spatial data analysis, outliers, population density, spatial autocorrelation, spatial heterogeneity, subcenters JEL Classification: C11, C21, C52, R12, R14 * An earlier version of this paper was presented at the 49th North American Meeting of the RSAI, Puerto Rico. The authors would like to thank Françoise Bourdon for the help provided on the data and Don Lacombe, James LeSage, Daniel MacMillen and Antonio Paez for their helpful comments. -1- 1. INTRODUCTION 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 MacDonald, 1998a, b), Dallas-Fort Worth (Wadell and Shukla, 1993), Los Angeles (Gordon et al., 1986; Heikkila et al., 1989; Guiliano and Small, 1991; Small and Song, 1994; Sivitanidou, 1996), San Francisco (Cervero and Wu, 1997, 1998), Montréal (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, 2002 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 (Anas et al., 1998; Baumont and Le Gallo, 1999) 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 Communauté de l’Agglomération 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 intra-urban 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. Let us note that the identification of subcenters in this area using the standard methods suggested -2- by Guiliano and Small (1991) has already been carried out in Baumont and Bourdon (2002) with the conclusion in favor of a multicentric urban configuration. In this paper, we underline the relevance of an alternative identification methodology, namely exploratory spatial data analysis (Anselin, 1995, 1996) in the spirit of Scott and Lloyd (1997). Indeed, these methods allow detecting both spatial autocorrelation, in the form of spatial clusters of population or employment, and spatial heterogeneity in the form of differentiated cluster patterns across space. Furthermore, they constitute an improvement over existing methodologies that don't assess the significance of their results and necessitate the definition of arbitrary cut-offs. Our results highlight a monocentric pattern based on the employment density distribution whereas some potential employment subcenters are detected using the total employment distribution. Second, we analyze whether the employment clusters detected in the previous step have a significant influence on the distribution of population using both monocentric and multicentric population density functions. It is well known that the presence of spatial autocorrelation yields inconsistent and inefficient OLS estimators but only a few studies have used spatial econometric techniques in the case of population density functions (Griffith, 1981; Anselin and Can, 1986; Stern, 1993; Griffith and Can, 1996; McMillen, 2002). Given the presence of spatial autocorrelation and spatial heterogeneity in the population density distribution of the COMADI, the appropriate spatial econometric methods are used (Anselin, 2001; LeSage, 1999, 2002). Our results show that the monocentric model explains well the population density provided that spatial autocorrelation, spatial heterogeneity and outliers are taken into account using maximum likelihood, generalized method of moments and Bayesian estimation techniques. The paper is organized as follows. In the following section, we describe the data and the spatial weight matrices used in this study. In the third section, we study the spatial pattern of total employment and employment density using exploratory spatial data analysis. In the fourth section, we provide a spatial econometric analysis of monocentric and multicentric population density functions. Different specifications are estimated and compared: exponential negative, spline-exponential and multicentric density functions. The paper concludes with a summary of key findings. -3- 2. DATA AND SPATIAL WEIGHT MATRIX Our study focuses on a middle-sized urban area named COMADI (Communauté de l'Agglomération Dijonnaise), which is located in the French region of Burgundy. A community of agglomerations is a kind of large town council composed of several towns adjacent to a major city. More precisely, the COMADI is composed of 16 adjacent towns: the central city is Dijon, which is the capital of Burgundy, and 15 suburban towns around Dijon: Ahuy, Chenôve, Chevigny-Saint-Sauveur, Daix, Fontaine-lès-Dijon, Longvic, Marsannay-la- Côte, Neuilly-lès-Dijon, Ouges, Perrigny-lès-Dijon, Plombières-lès-Dijon, Quétigny, Saint- Apollinaire, Sennecey-lès-Dijon and Talant. In order to reduce edge effects present in spatial data analysis, we consider 22 additional suburban towns immediately surrounding the COMADI area. We will label these towns “urban