is mexico city polycentric - a trip attraction capacity approach (delgado 2009)

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2009 46: 2187Urban StudManuel Suárez and Javier Delgado

Is Mexico City Polycentric? A Trip Attraction Capacity Approach

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Other studies suggest contradictory evi-dence of sub-centre formation. While there is evidence of co-location of jobs and housing in some industrial suburban locations (Cruz Rodriguez and Duhau, 2001), a job accessi-bility study (Suárez and Delgado, 2007) found that there is high urban structure effi ciency in the centre, which rapidly decreases as dis-tance to it increases. This study found that low job accessibility in suburban locations had been accentuated between 1990 and 2000.

Is Mexico City Polycentric? A Trip Attraction Capacity ApproachManuel Suárez and Javier Delgado

[Paper first received, July 2007; in final form, April 2008]

Abstract

The article explores whether Mexico City is a polycentric metropolis. Building upon previous methodologies, an alternative criterion is proposed for identifying employment centres, using a jobs to working residents ratio, while taking into account economic informality. Although a small set of minor sub-centres is traced, it is found that most jobs are concentrated in a large central agglomeration, with a moderate percentage of jobs concentrated in corridor-like shapes. Within this central agglomeration, are found inner nodes and corridor-like structures that had been identifi ed in previous research as sub-centres. Additionally, economic specialisation is identifi ed with the use of location quotients and the results are compared with those of previous methodologies. It is concluded that Mexico City has a hybrid, although still predominantly monocentric, urban form.

1. Introduction

Recent research suggests that Mexico City is in a period of transformation from a mono-centric to a polycentric urban form. With the use of distinct methodologies, Aguilar and Alvarado (2005) found 32 sub-centres using tract-level data, while Graizbord and Acuña (2005) found 14 using municipal-level data. The economic centres found in these two studies do not match.

0042-0980 Print/1360-063X Online © 2009 Urban Studies Journal Limited

DOI: 10.1177/0042098009339429

Manuel Suárez is in the Instituto de Geografía, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, México DF, 04510, México. E-mail: [email protected].

Javier Delgado is in the Instituto de Geografía, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, México DF, 04510, México. E-mail: [email protected].

46(10) 2187–2211, September 2009



2188 MANUEL SUÁREZ AND JAVIER DELGADO

In a historical analysis of population and job suburbanisation, Suárez (2007) found that, between 1950 and 2000, there was an acceler-ated rate of population suburbanisation and a slow decentralisation of jobs.

The purpose of this article is to research whether Mexico City has evolved into a poly-centric metropolis, or, that not being the case, to identify the present-day urban form of the city. In doing so, we present an alternative methodology and criteria for employment centre identifi cation that build upon previ-ous methods.

While most studies in the US have used a combination of employment density and employment volume thresholds to identify sub-centres, we use a trip attraction capacity approach, based on both employment and working population concentration dynamics throughout the city. Additionally, given that more than 40 per cent of economic activity in the city is informal, our method is applied both to formal employment and to the formal and informal sectors combined. Finally, we classify employment areas in the city accord-ing to size, trip attraction capacity and eco-nomic specialisation.

For the moment, the focus of our research is mainly methodological and exploratory. However, determining the urban form of Mexico City may have important implica-tions for public policy. It will allow, through further research, the evaluation of the success of the (admittedly few) metro-wide planning policies, the effects of transport on urban development and growth, and the effi ciency of the urban system and its environmental impacts, among others.

The rest of the article is divided into fi ve sections. In the next section, we give a general description of Mexico City, our study area. Basic demographic and economic profi les are presented, as well as an outline of urban structure and transport. The third section

summarises theoretical issues surrounding our study. We describe previous methods used for employment centre identifi cation for diverse cities and build upon these to present the criteria upon which we base our methodology. That methodology is then described in section 4. In a fi fth section, we present results and include a comparison with an earlier study of Mexico City, along with an alternative methodology used in pre-vious research in the US, although applied to our study area. Finally, we present a set of conclusions, discussion questions and needs for further research.

2. Employment and Population Suburbanisation in Mexico City, 1950–2000

Mexico City reached a population of 18.5 mil-lion in 2000. It comprises the Federal District and 75 municipalities of two contiguous states.

Although there has been a set of legal planning frameworks, regulations, general and partial plans since 1936 to generate em-ployment sub-centres, experts would agree that most urban development plans, for Mexico City, have been designed once devel-opment occurs, or have constantly fallen behind this process (Ward, 2000; Bazant, 2001; Cervero, 1998). Development has been mostly market driven (Cervero, 1998). Most of the plans made for the city have been de-signed for the Federal District and not for the metropolitan area as whole, lacking both physical planning as well as implementation strategies (Ward, 2000).

In the early 2000s, federal, state and muni-cipal authorities developed a master plan for the metropolitan area [Plan de Ordenamiento Territorial de la Zona Metropolitana de la Ciudad de México], which includes the devel-opment of metropolitan sub-centres. As in the



IS MEXICO CITY POLYCENTRIC? 2189

past, with the Conurbation Commission in 1979 and the Programme for the Metropolitan Region in 1984, this recent metro-wide plan, has no implementation strategies. Munici-palities are not legally bound to fulfi l the plan directives and, in many cases, have no institutional capacity to do so. In part, this is the result of the limitations to local author-ities amended in the 115th Constitutional Article, which diminishes the capabilities of metropolitan and regional development and planning (Legorreta, 1994; Iracheta, 1997).

While there is evidence of job suburban-isation having taken place in the past 50 years, population suburbanisation by far exceeds it (Suárez, 2007). Since the 1980s, predominant land uses in the central city and fi rst urban ring have shifted towards a service economy (Delgado, 1988; Cruz Rodriguez, 2001). The service jobs agglomeration has displaced residential uses towards the periphery and the central city has shown a decrease in total population, although with a slight increase

in the number of working residents (Suárez and Delgado, 2007). Population and eco-nomic census data show that population suburbanisation has been accompanied by some local job growth; however, in absolute terms, there has been a continuing concen-tration of economic activity in the centre. This raises doubts as to whether job growth out-side the central city has been suffi cient for the development of employment sub-centres.

Figure 1 shows Delgado’s (1988) urban ring confi guration1 (including main roads and highways), which we use only for the purpose of a general description of the area. Table 1 shows the ratio of percentages of employ-ment to working residents’ concentrations, for each urban ring across a period of 50 years. In the 1950s, the population concen-tration gradient was steeper than the job gradient, because much of the industry, the most important economic sector of the time, was located on the outskirts of the city, in the fi rst and second rings.

Table 1. Mexico City: formal employment to working residents’ percentage ratios by urban ring, 1950–2000

1950a 1960b 1970 1980c 1990 2000

Central city 63:77 60:61 51:40 40:21 39:15 36:11First ring 24:17 24:30 27:38 30:30 29:34 27:31Second ring 10:3 12:5 17:17 23:35 22:31 22:30Third ring 3:2 3:3 3:4 5:11 8:16 11:22Fringe 1:2 1:2 1:2 2:3 2:4 3:6Total employment 611 082 839 774 1 271 814 1 715 065 1 997 467 3 135 074 Working residents 850 353 1 551 610 2 101 471 2 226 783 3 859 133 5 306 073

a Simple linear extrapolation of economic data fi gures with data from 1965 and 1955 due to data constraints for 1950 and 1945.b Simple linear interpolation of economic data fi gures with data from 1965 and 1955 due to data constraints for 1960.c Simple linear interpolation of population fi gures with data from 1970 and 1990 due to data uncertainty for 1980.Note: Secondary and tertiary sectors excluding electricity and transport sub-sectors due to data constraints from 1950 to 1980.Sources: authors’ calculations using 1950, 1960, 1970, 1980, 1990 and 2000 population censuses; 1955, 1965, 1969, 1979, 1989 and 1999 economic censuses.




Over the course of time, the central city’s share of metropolitan employment has been steadily declining. However, overall employ-ment growth has been such that the central city’s percentage share has still consistently equated to the highest absolute growth. In contrast, since the 1970s, the central city has experienced negative population growth in both absolute and proportional terms.

The fi rst ring shows a similar behaviour to that of the central city, albeit lagged in time. Conversely, rings two and three, and the fringe, show a constant increase in the share of both population and employment over the 50-year period. Working population growth has increased and has surpassed employment growth by more than double in the fi rst and second rings, and by more than triple in the

Figure 1. Mexico City: urban ring confi guration, main roads and highways

Sources: Delgado (1988); GDF (2000).




Figure 2. Mexico City: share of formal jobs and working residents growth per urban ring 1990–2000

Sources: authors’ calculations using 1989 and 1999 economic censuses and 1990 and 2000 population censuses.

third ring and the fringe. While, in the 1950s, the proportion of jobs was higher than the proportion of working population in rings one, two and three, this ratio has been defi n-itely reversed since that time.

Figure 2 shows employment and working population growth for each urban ring, as the share of metropolitan growth as a whole, between 1990 and 2000. On the one hand, while working population growth was mini-mal in the central city, it was especially high in the second and fourth rings. On the other hand, close to 40 per cent of metropolitan job growth took place in the city centre, de-clining steadily as distance to it increased. This suggests that the role of the central city, in economic terms, is far from having been reduced, especially since fi gures imply that, relative to working population growth, the number of work trips to the central city has

steadily increased in both absolute and pro-portional terms.

Figure 3 shows a suburbanisation index of working residents and employment from 1950 to 2000, expressed as the normalised dis-tance from the centre at which jobs and resi-dents are, on average, located (see Appendix). Although job suburbanisation is evident, es-pecially in the industrial sector, the working population shows more of a sprawl-type growth. Thus, the question we look at in the fol-lowing sections is whether employment sub-urbanisation has led to sub-centre generation.

3. Literature Review

Research suggests that the monocentric model is insuffi cient to explain today’s cities (Anas and Kim, 1996; Giuliano and Small, 1991; Song, 1992). Evidence of job suburbanisation




and sprawl has led several scholars to devel-op different methods for identifying urban sub-centres. Although the tacit consensus is that sub-centres exist, there is no universally accepted criterion for their detection and classifi cation.

3.1 Theory and Debate

According to Fujita et al. (2001), the mono-centric economy is only sustainable until the population growth reaches a critical level, after which a system of cities would tend to emerge. When, due to population growth, equilibrium is unattainable, there is a shift into a non-monocentric economy. Thus, urban effi ciency holds as the cause for the

transformation to a polycentric urban form. If polycentric urban forms are indeed more effi cient in terms of travel times, allowing co-location (for example, Levinson and Kumar, 1994; Wachs et al., 1993; Gordon et al., 1991), suburban centres should, at the same time, have a direct and moderate impact on a small number of the working population in near-by areas who travel to work, and an indirect and slight impact on overall metropolitan commuting times and congestion. The more centres that exist, the less proportional impact each will have on the overall urban structure.

This effi ciency is, however, subject to debate. From one point of view, the effi ciency of urban polycentrism is in doubt, because multiple

Figure 3. Mexico City: index of employment and population suburbanisation 1950–2000

Notes: secondary sector excludes electricity, tertiary sector excludes transport due to data constraints from 1950 to 1980. Sources: authors’ calculations using 1950, 1960, 1970, 1980, 1990 and 2000 population censuses; 1955, 1965, 1969, 1979, 1989 and 1999 economic censuses.




sub-centres are bound to attract trips from all places in a city, generating wider dis-persion between origins and destinations (Bertraud, 2004). Indeed, for a sample of US and Japanese cities, Hamilton (1982) observed that commuting distances would be eight times shorter if cities were completely mono-centric. Similarly, Cervero and Wu (1998) showed that VMT increased with job subur-banisation. The spatial mismatch hypothesis literature would suggest that it is accessibil-ity that makes the urban structure effi cient (Cervero et al., 1997). Policies such as jobs–housing balance have been recommended in order to develop sustainable urban villages (Cervero 1996), but criticised from the land market angle (Giuliano and Small 1993).

From a regional perspective, the European Spatial Planning Observation Network (ESPON) has intensively encouraged planned polycentrism. ESPON research assures that polycentric structures should

stimulate the functional division of labour, as well as fl ows and level of co-operation between neighbouring cities … promoting the balanced and multiscalar types of urban networks that are the most benefi cial … both for the core and for the peripheries (ESPON, 2005, p. 3).

Finally, from a global view, research sug-gests that terciarisation, IT development and global networking could affect the structural and physical changes of cities and regions due to emerging global economic roles (Scott, 2001; Graham and Marvin, 2001). New forms of social interaction, changes in production and the dependence on commu-nication networks such as the Internet, could infl uence location choices (Saxenian, 1996), thus having an impact on urban form and travel patterns.

In any case, to enter these debates from the perspective of a mega-city in a developing country, we start off by identifying current urban form in Mexico City.

3.2 Previous Methods for Sub-centre Identifi cationBogart and Ferry (1999) defi ne centres as places with both a high density and a high quantity of employment. Likewise, accord-ing to Song (1992), urban centres should in-corporate adjacent high-density zones that, together, are large enough to exert a signifi -cant infl uence on the urban structure. While it is intuitive to state that, as employment volumes and densities increase, the likelihood of exerting an infl uence on urban structure also increases, the threshold from which the infl uence on urban structure becomes sig-nifi cant is unclear. It is also unclear what the extent of this infl uence should be.

There have been different approaches to identifying urban centres. At the regional level, ESPON has used the concept of func-tional urban areas (FUAs) to defi ne nodes within regions, consisting of core and fringe municipalities. At the metropolitan scale, Thurstain-Goodwin and Unwin (2000) have located town centres using kernel density esti-mation and a town centredness index. Batty (2001) has explored the emergence of poly-nucleated cities through agent simulation.

Economic sub-centres have been also iden-tifi ed through the visual inspection of den-sity maps (Gordon et al., 1986) and volume of employment and job specialisation methods (Dunphy, 1982). Most studies, however, have used minimum density thresholds (McDonald, 1987), or minimum density thresholds in addition to a minimum concentration of jobs (Giuliano and Small, 1991; Song 1992; McDonald and Prather, 1994; Cervero and Wu, 1997). In any event, the number of sub-centres that can be found depends on the criteria employed to identify them.

Giuliano and Small (1991) developed what seems to have been the most widely used methodology to identify sub-centres. It consists of two steps. First, a density thresh-old is set and urban tracts that surpass it are




identifi ed as potential sub-centres. The fi nal list of sub-centres is composed of sets of contiguous tracts that individually meet the density criterion and that, together, surpass an employment volume threshold.

With this method, Cervero and Wu (1997) found 22 sub-centres in the San Francisco Bay Area, setting the density threshold above the metropolitan average (17.3 jobs/ha) [7 jobs/acre] and the sum of jobs at 10 000. Giuliano and Small (1991) used a minimum density threshold of 24.7 jobs/ha [10 jobs/acre] and a sum of 10 000 jobs for their study of Los Angeles, while Song (1992) established the thresholds at 37 jobs/ha (15 jobs/acre) and a sum of 35 000 jobs. It is evident that if these three thresholds were applied to the same city, each would fi nd a different number of sub-centres. The issue is one of criterion. Furthermore, as discussed by McMillen (2001), most of these previous methods for sub-centre identifi cation require ample local knowledge in order to produce reasonable results; with the question being: how reason-able is reasonable?

With this in mind, several scholars have developed alternative methods of identify-ing density peaks, taking into account the space dynamics that exist in cities. McMillen and McDonald (1997) argue that the job density criterion is more objective if, instead of selecting an arbitrary threshold, employ-ment density is regressed with distance to the CBD. Taking this approach, places that are near the CBD will require higher densities to meet the density criterion than those that are further away. Thus, the number of sub-centres close to the CBD is less likely to be overestimated, while the number of distant sub-centres will less likely be underestimated. McDonald and Prather (1994) substantiated that regressing the natural log of employ-ment density on distance provides the best functional form of predicting the density gra-dient under the assumption of monocentricity. Still, whereas a sub-centre is an area with an

employment density that is significantly higher than would be expected based on its distance to the CBD, sub-centres should also have a signifi cant infl uence on the local den-sity gradient (McMillen, 2001).

McMillen (2001) and McMillen and McDonald (1997) suggested that density peaks around secondary employment centres in a polycentric city were better modelled using a non-parametric locally weighted regres-sion (LWR) (also known as a geographically weighted regression) than using an OLS re-gression. Nevertheless, this method requires an ad hoc knowledge of polycentrism in a given city, which is modelled in a fi rst stage of an LWR. However, McMillen (2003) suggests that using a hybrid methodology that com-bines the McMillen and McDonald (1997) and McMillen (2001) non-parametric approach with the Giuliano and Small (1991) approach, offers advantages over both methodologies.

This hybrid methodology, in a fi rst stage, uses LWR to select tracts that show peaks in the density gradient. In a second stage, selected tracts are grouped, based on contiguity, into subsets. Each subset enters the fi nal list of sub-centres if the sum of jobs within the set of contiguous tracts meets an employment volume threshold. Nevertheless, the choice of a job-volume threshold as an additional criterion for employment centre identifi ca-tion remains a subjective issue.

3.3 Previous Methods Used For Sub-centre Identifi cation in Mexico City

For Mexico City, Aguilar and Alvarado (2005) determined that tracts that had at least 5500 jobs defi ned sub-centres. Sub-centres would be composed of more than one tract if adja-cent tracts had high employment concentra-tions, although the authors did not explain what this ‘high concentration’ meant. They argued for the use of number of jobs instead of job density, due to the differences in the size of tracts. This is ambiguous, since it is pre-cisely because of the difference in land area




that density is used to determine job concen-tration differences. This study found 35 sub-centres, concentrating 25 per cent of the jobs in the city. Additionally, the authors classi-fi ed centres into fi ve job-size groups, as well as into fi ve economic specialisation groups: industry, commerce, services, commercial-industrial and service-industrial.

In a parallel study, at the municipal level, Graizbord and Acuña (2005) located centres by analysing what they call ‘remarkable fl ows’ (fl ujos sobresalientes). They observed the number of trips between pairs of muni-cipalities that exceeded the expected number of trips estimated with the use of contingency tables. Using this method, the authors found eight primary centres and six secondary centres. Yet, since chi-squared tests, if signifi -cant, suggest that a distribution is not prob-abilistic, this study shows, in any case, that some origins and destinations share certain characteristics that raise the number of observed interdestination commuting fl ows above the expected number of fl ows. How-ever, this does not mean that these destinations are actually centres. This is true, especially since, due to the characteristics of contingency tables, a destination zone j will have less attraction capability from at least one origin i, if there is a higher number of observed trips than of expected trips from another i origin. In fact, in this study, the municipality with the second-highest job–resident ratio and concentration of jobs in the metropolitan area did not show up as either a primary or a secondary centre.

To our knowledge, there have been no at-tempts to identify sub-centres in Mexico City using land use maps. Potentially, land use maps could be used to defi ne sub-centres, by selecting offi ce, commercial and industrial uses with high employment density. At least for Mexico City, this option remains impossible due to differences in land use classifi cations between the Federal District and the State of Mexico, the lack of city-wide

geo-referenced land use maps and even their sole existence in many municipalities!

3.4 Proposed Approach for Sub-centre Identifi cation

Although the method used by Graizbord and Acuña (2005) is statistically defi cient, the idea of trip attraction underpinning it is an outstanding contribution; a concept missing from previous sub-centre iden-tifi cation techniques. While the rest of the aforementioned studies take into account employment data, none of them considers the relationship between employment and residential locations.

According to Perroux (1950), a centre is a place that attracts centripetal forces and from which centrifugal forces emanate. In classic location theory, residential zones comprise residual lands for which no economic uses have been found. Economic activity repre-sents the dominant land use and out-bids residential uses (Alonso 1964). Conforming to these ideas, urban centres and sub-centres should be defi ned not only by the degree of concentration of jobs, but also by their capacity for attracting work trips.

Just as the density of employment centres, and residential density around these, should be expected to vary with distance to the CBD, the volume of employment in each centre cannot be expected to be a fi xed number. Our proposition is, instead of using a fi xed em-ployment volume threshold for a series of contiguous tracts, or one for central locations and a slightly lower one for suburban centres (based on local knowledge), to use a threshold that is dynamic in space, that can be set for each location in relation to its capacity for attracting work trips from other places in the city, once the concentration of working resi-dents in nearby areas has been considered. With this approach, surpassing the density gradient is also a necessary criterion for iden-tifying an employment centre, given that, otherwise, a small shop in the middle of, say,




a regional park, would be considered an employ-ment centre, which obviously cannot be true.

Thus, to identify sub-centres in Mexico City, we use the following defi nition: any point of the city at which there is a peak in the employ-ment density gradient and a concentration of jobs relative to the working population concentration, large enough to attract more work trips than those it is able to generate, within a given distance to it.

4. Methodology

The method employed in this article to iden-tify urban sub-centres is based on two concepts used in previous research: job concentration; and trip attraction capacity. However, we use a different approach. Since there is no con-vincing argument to prove that Mexico City is a polycentric metro area, we initially assume that it is still in a monocentric stage.

The fi rst assumption in identifying employ-ment centres, taken from the work of McMillen (2003, 2001), is that employment centres are places that surpass a smoothed density gra-dient. Based on the work of Graizbord and Acuña (2005), the second assumption of this analysis is that a centre should have the capacity of attracting workers from other places. Although almost any place where there are jobs will show some degree of cross-commuting (Graizbord and Santillan, 2005), a given place may only be said to have a ‘real’ attraction capacity if it has a higher concentration of jobs than resident workers. In effect, an area that can attract more work trips than those it is able to generate. Thus, the area that is identifi ed as the centre is the area contiguous to the CBD (see Figure 1) that meets both criteria. The same is true of sub-centres, however, these will be physically separated from the centre. Additionally, corridors may be identifi ed and classifi ed according to their shape.

The application of these criteria towards identifying employment centres in Mexico City presents two methodological problems.

The fi rst problem is that over 40 per cent of the jobs in the city are informal and do not appear in the economic census data. Add-itionally, there is no recent origin–destination survey and the US census transport package equivalent only shows origins and destin-ations between municipalities, which repre-sent areas that are too large for the purpose of identifying employment centres.

Since our methodology requires calculat-ing jobs to working resident ratios, if we were to use economic census data alone, we would only analyse formal employment and not the overall urban form of the city, a product of all economic activity. Therefore, by way of compensation, we adjust the number of jobs per tract for informal work. However, because of the rough nature of our informality adjustment procedure, due to the aforementioned data constraints, we run the sub-centre identifi cation analysis twice: once for formal jobs alone and a second time having adjusted for the total number of jobs.

The second problem is data aggregation at the tract level. Tracts are artifi cial statistical sampling areas with defi ned limits and have no evident socioeconomic/spatial meaning, in addition to being delimited with popula-tion data and not with economic data. This is especially problematic when applying our criterion of jobs to working residents ratio, since there is no reason to assume that urban tracts can be self-contained areas. Further-more, McMillen (2001) shows that tract size has a direct infl uence on sub-centre production, as small tracts are more likely to show peaks of employment density. For this reason it is important to work with areas of equal size.

4.1 Informality Adjustment

To resolve the informality problem, we com-bine three data sources. The fi rst is the 1999 Economic Census, which shows the number of established jobs per tract (Et) that can be expected to represent formal employment. This database divides jobs into industrial, commercial and service jobs.2 Second is the




2000 Population Census, which contains the number of working residents per tract (Wt). The third is the 10 per cent sample census database (similar to PUMS data in the US). This database presents personal records including: sector of employment, occupation and type of work, as well as place of work at the municipal level (the lowest aggregation level at which personal records are released in Mexico).

We employed an informal worker iden-tifi cation method used previously (Suárez, 2007). Drawing on the 10 per cent sample census database, the proportion of informal and formal workers per municipality was calculated by selecting persons whose type of work was self-employment or day labour, although excluding professionals (such as architects and doctors) and those dedicated to health, education, fi nance, telecommuni-cations, government-owned industry or services, or economic sub-sectors heavily regulated by government.3 Since this data-base contains a place of work variable, we were able to estimate the number of informal (Im) and formal (Fm) jobs per municipality, which we subsequently prorated among tracts.

We cannot expect, however, the number of formal jobs per municipality estimated from the 10 per cent sample to match perfectly the economic census data.4 However, we know that the total number of informal and formal jobs J should equal the total number of work-ing residents W. Thus, our task is to estimate the total number of informal and formal jobs by tract Jt, by prorating the difference of W minus E, considering the proportion of informal jobs per municipality, and assuming that, within tracts of municipalities, informal jobs follow the proportional distribution of the formal sector (equation (1)).

J W EI

I F

E

EEt

m

m m

t

m

t= −( )⋅+

⋅ + (1)

such that J = W, assuming that F ≈ E

where, Jt is the total number of jobs in tract t; W is the total number of working residents (Population Census, tract-level data); E is the total number of formal (established) jobs (Economic Census, tract-level data); Im is the number of informal jobs (estimated) in municipality m (10 per cent sample, Population Census); Fm is the number of formal jobs (estimated) in municipality m (10 per cent sample, Population Census); Et is the number of formal (established) jobs in tract t (Economic Census, tract-level data); and Em is the number of formal (established) jobs in municipality m (Economic Census, tract-level data)

4.2 Generation of GIS Neighbourhoods

While most studies search for adjacent tracts that meet the density and concentration criteria, this often results in maps that show pockets within the identifi ed centres or sub-centres, due to the fact that one or more tracts fail to meet these conditions (for example, because they hold a large park, contain sur-viving residential areas within economic centres, or comprise warehouse areas that serve the local industry). Some studies have resolved this by using tract proximity instead of tract contiguity (McMillen and McDonald, 1998), while others have ignored the issue completely. Furthermore, although tracts represent the preferred level of aggregation due to their small size, they do generate spatial autocorrelation problems.

To approach this problem, we use GIS-generated neighbourhoods of 1.6-km (1 mile) radii in order to provide focal statistics for each urban hectare of the city. This method undergoes two assumptions. First, that tracts are not self-contained entities and, second-ly, that the edge of a given tract is likely to resemble more closely the edge of the adja-cent tract than its own opposite edge. The 1.6-km threshold was selected, as it represents a 20-minute walk. This is usually the maxi-mum distance that a person is willing to walk




without using alternative forms of transport. It is also, in general, the maximum by-law allowable walking distance to schools and the longest walking option between transit points in most Internet trip planners in the US. No information of this sort is available for Mexico, although it is probable that accept-able walking distances are longer in Mexico City than in any US metropolitan area.

To generate neighbourhoods, we calcul-ated job and working resident densities (per hectare) for each tract in our study area. Vector information was then converted into one-hectare grid cells. This resulted in a series of maps that show how many jobs and how many working residents are located within each hectare of the city, assuming a homogeneous distribution within tracts. The next step was to create 1.6-km-radius neighbourhood sums of working population and jobs. This oper-ation produced base maps that indicate the number of jobs and working residents that are within 1.6 km of each hectare of the city. Both a formal employment and total jobs map (formal plus informal) were generated, as well as three other maps indicating the number of industrial, commercial and service jobs, in order to identify economic specialisation.

4.3 Centre and Sub-centre Identifi cation

To identify ‘attraction areas’, we divided the job map by the resident map. Their quotient represents the jobs to working residents ratio within a 1.6-km radius of each urban hectare. Hectares with values greater than one (more jobs than working residents) met the fi rst criterion and thus were considered attrac-tion areas.

The average density gradient (within a 1.6 km radius of each urban hectare) was cal-culated using OLS regression. The regression used the natural log of employment density as a dependent variable which, according to McDonald and Prather (1994), gives the best functional form under the assumption

of monocentricity. Following the analysis by McMillen (2001) to control for urban con-struction limits, due to steep hills and pro-tected natural areas in the south and west of the city, four distance predictors were used, each representing the four cardinal bearings to the CBD.5 In using four distance sets, the distance value to a bearing becomes negative when its opposite bearing is positive. Thus, to avoid colinearity, all negative distance values were coded as zero. For example, all sites that are located to the north-east would have south and west distances coded as zero, while sites located to the south-west would have zero values for north and east distances. Given that all our cases represent areas of the same size, it is not necessary to assign weights to different cases (see McDonald and Prather, 1994; and Frankena, 1978).

Since we performed the analysis for both formal and total jobs, two separate employ-ment density regressions were run. Hectares with formal job densities that exceeded the predicted formal density gradient at the 95 per cent confi dence level were selected in one map as ‘high formal job concentration areas’. Likewise, hectares with total job densities that exceeded the predicted total jobs den-sity gradient were selected in another map as ‘high total job concentration areas’.

Finally, areas that met both trip attraction and density criteria were considered as part of the centre, as sub-centres, or as corridors, depending upon their contiguity to the CBD and their shape, again in two separate maps: one representing formal job agglomerations and another representing total job agglom-erations, which we expected to reveal some-what different urban forms.

4.4 Classifi cation of Sub-centres

Once we identifi ed job agglomeration areas, we proceeded to classify them into three types: either as part of the centre, as sub-centres, or as corridors. The extent of the centre was




identifi ed as the job agglomeration area con-tiguous to the CBD. Sub-centres were iden-tifi ed as those agglomeration areas having a physical separation from the centre of more than 1.6-km. Corridors were identi-fi ed mainly by shape and were classifi ed into three sub-types. Adjacent corridors (ACs) would be those contiguous to the centre where, along a main road or highway, there is an agglomeration through which the centre loses its overall shape. Segmented corridors (SCs) were considered a series of job agglom-eration areas having a distance of no more than 1.6-km between them, aligned along main roads or highways. Finally, adjacent segmented corridors (ASCs) are the result of combining the two previous types.

Additionally, we classifi ed job agglomer-ation areas into industrial, commercial or service, according to the highest location quotient in the area. Finally, we calculated the proportion of jobs in each type of agglom-eration, in order to determine the extent to which each explains the overall urban form.

4.5 Comparison with Previous Methods

Since our methodology differs from previous-ly used methods in the use of GIS-generated neighbourhoods and the trip attraction criterion, we found it necessary to compare our results with those of other methodologies. Thus, we compared our identified centre and sub-centres with those found by Aguilar and Alvarado (2005). Additionally, we ran a separate analysis using the hybrid method proposed by McMillen (2003), which com-bines the McMillen (2001) and Giuliano and Small (1991) methodologies for identifying employment centres.

The hybrid methodology consists in run-ning a locally weighted regression (LWR) on the natural log of employment density, using the X and Y co-ordinates of the tract centroids as predictors. Sites that exceed the predicted density at the 95 per cent confi dence

level are selected as potential sub-centres. In a second step, contiguous tracts that meet the fi rst criterion and that, together, meet an employment volume threshold, are selected to comprise the final list of sub-centres. Interested readers should consult McMillen (2003, 2001) and McMillen and McDonald (1997) for a complete description of this methodology.

In our case, we followed the hybrid meth-odology. However, in the LWR step, we used a bicubic weighting function on a window size of 50 per cent of the nearest cases, instead of the tricubic function used in the original methodology. According to McMillen (2003), any weighting function can be used without signifi cantly altering the results. A second difference is that we used four distance to the CBD predictors (as described previously), because they provide a higher correlation (more than double) between the smoothed densities surface and the observed surface, without inducing additional colinearity, when compared with the model that only uses X and Y distances as predictors. We ran two separate LWRs, one for formal employ-ment and a second one for the combined formal and informal sectors.

Our volume of employment threshold cri-teria involved selecting adjacent tracts that exceeded the predicted density surface at the 95 per cent confi dence level and which, together, summed more than 5500 jobs for formal employment and 10 000 jobs for total employment. These thresholds were chosen since Giuliano and Small (1991) sug-gest that a 10 000 jobs threshold should be used, as it represents the size of a large fac-tory. However, since the number of jobs in the economic census represents only 58 per cent of all jobs, a 5500 jobs threshold seems ade-quate in terms of comparing methodologies when using only formal employment data. It is also the threshold used by Aguilar and Alvarado (2005) for Mexico City.




5. Results

5.1 Formal Centre and Sub-centres

Figure 4 shows the formal employment centre and sub-centres. It additionally shows the average trip attraction capacity from areas out-side the 1.6-km radius of each hectare, rep-resented as a job to working resident ratio. Results reveal a large Central Agglomeration

(CA) that elongates to the north and south of the CBD and which turns into three corridor-like shapes. An AC lies to the east, in the Iztacalco and Iztapalapa area; an ASC to the west, in the Lomas–Santa Fe area; and a second ASC to the north-west, in the Naucalpan–Tlalnepantla area.

Within the CA, there appear to be areas with higher job density that form inner nodes and

Figure 4. Mexico City: formal employment centres and trip attraction capacity in 1.6-km radii

Sources: authors’ methodology using the 1999 Economic Census and the 2000 Population Census.




corridors that are situated along main roads of the city. The CBD elongates from east to west across Reforma Avenue (see main roads in Figure 1) into the Lomas–Santa Fe area and then turns along the highway to Toluca, a city located 40 km west of Mexico City. From north to south, the CA shows a higher job concentration along Insurgentes Avenue, a 50-km road that connects the south of Mexico City with the highway to the city of Pachuca, crossing the centre of the city and the Ecatepec area. Similarly, the Iztacalco–Iztapalapa corridor is situated along Zaragoza Avenue, which then becomes the highway to the city of Puebla.

According to the results, there are eight areas that may qualify as sub-centres: four of them are very close to the CA and four others are at greater distances from it. The largest concentration of jobs in these sub-centres is found to the north, in the Cuautitlán area, an industrial park (see Table 2). The Periférico beltway appears to delimit the overall form of the CA. To the west, the area around the Periférico, which does not show up as an em-ployment centre, lies in the segment of the beltway that was built during the 1990s. The rest of the beltway was built during the 1960s and 1970s.

Considering only formal employment, the areas identifi ed as employment centres and sub-centres account for 13 per cent of the urban area of the city (Table 2). Together, they hold close to 58 per cent of formal jobs, of which only 5 per cent are concentrated in sub-centres that account for 2.6 per cent of the urban area. These same areas, as a whole, account for the location of only 15 per cent of working residents. Within the CA, only 13 per cent of jobs are concentrated in ACs and ASCs, and these corridors show a smaller job to resident worker resident ratio than the rest of the CA (Table 2).

The highest jobs to resident workers ratios appear in the Cuautitlán, Ecatepec and Naucalpan–Tlalnepantla areas. This is probably

due to the fact that industrial areas permit mixed uses to a lesser extent than employment centres with tertiary activity, as well as to data aggregation effects.

5.2 Employment Centres and Sub-centres Adjusting for Informality

Figure 5 shows results adjusting for informality. Using this approach, the Naucalpan–Tlalnepantla corridor becomes the Naucalpan–Cuautitlán ASC, as it practically converges with the Cuautitlán area. The southern edges of the formal CA, depicted in Figure 4, con-nect along the Periférico beltway. The Coapa and Xochimilco sub-centres actually become nodes within the CA. Similarly, the Iztacalco–Iztapalapa formal corridor joins the Periférico corridor and is almost completely integrated into the CA. To the north-east of the CA, the Ecatepec area also appears to have the form of a corridor, not of a sub-centre. Four add-itional sub-centres may be identifi ed when adjusting for informality: Ixtapaluca and La Paz, along the highway to Puebla; Chiconcuac, an artisan textiles town; Coacalco, a small industrial area along the highway that con-nects the Ecatepec and Cuautitlán industrial parks; and one more in the Tepotzotlán area. The latter appears to be consolidating into a corridor. Within the CA, the inner corridor and node structure are maintained.

Adjusting for informality, 70 per cent of all jobs in the metro area are agglomerated in the CA, including ACs and ASCs, while less than 2 per cent are concentrated in sub-centres (Table 3). These areas represent 23 per cent of the urban area of the city and hold close to 26 per cent of resident workers. Job to resident worker ratios are considerably higher when considering informal jobs in all employment centres as a whole.

5.3 Economic Specialisation

Figure 6 shows job specialisation as the high-est location quotient in the area. The CA is predominantly tertiary, with services along




Tabl

e 2.

M

exic

o C

ity: s

elec

ted

char

acte

rist

ics

of fo

rmal

em

ploy

men

t cen

tres

Den

omin

atio

nTy

pea

Wor

king

re

side

nts

Form

al jo

bs

Form

al jo

b de

nsit

y(j

obs/

ha)

Perc

enta

ge

of w

orki

ng

resi

dent

s of

MA

Perc

enta

ge

of fo

rmal

jo

bs o

f MA

Perc

enta

ge o

f ar

ea o

f MA

(u

rban

) Jo

bs:W

R

Cen

tral

agg

lom

erat

ion

(C

A)

Cen

tre

+ A

Cs

and

ASC

s 89

5 68

62

053

970

79.3

13.6

52.9

10.9

2.3

Lom

as–S

anta

Fé

ASC

13 3

7125

868

19.8

0.2

0.7

0.5

1.9

Nau

calp

an–T

laln

epan

tla

ASC

42 3

0310

4 95

352

.40.

62.

70.

82.

5Iz

taca

lco–

Izta

pala

paA

C21

9 71

736

2 37

575

.73.

39.

32.

01.

6C

uau

titl

ánSu

b-ce

ntr

e20

858

58 6

0922

.50.

31.

51.

12.

8Te

pozo

tlán

Sub-

cen

tre

916

2 02

15.

80.

00.

10.

12.

2E

cate

pec

Sub-

cen

tres

(2)

11 9

6329

733

47.8

0.2

0.8

0.3

2.5

Coa

paSu

b-ce

ntr

e29

684

52 9

4444

.70.

41.

40.

51.

8X

och

imilc

oSu

b-ce

ntr

e11

760

12 0

3933

.50.

20.

30.

21.

0Te

xcoc

oSu

b-ce

ntr

e6

219

10 1

3717

.00.

10.

30.

31.

6C

hal

coSu

b-ce

ntr

e8

327

13 5

1532

.30.

10.

30.

21.

6A

tiza

pán

de

ZSu

b-ce

ntr

e41

383

413

.50.

00.

00.

02.

0C

A w

ith

out

corr

idor

s62

0 29

51

560

774

87.7

9.4

40.2

7.5

2.5

All

AC

s an

d A

SCs

275

391

493

196

60.9

4.2

12.7

3.4

1.8

All

sub-

cen

tres

90 1

40.0

179

832.

029

.01.

42.

62.

0A

ll em

ploy

men

t ce

ntr

es98

5 82

62

233

802

69.6

14.9

57.6

13.5

2.3

Met

ro a

rea

tota

l

6 60

3 81

13

880

420

16.3

100

100.

010

0.0

0.6

a AC

: adj

acen

t co

rrid

ors

as d

efi n

ed in

text

; ASC

: adj

acen

t se

gmen

ted

corr

idor

s as

defi

ned

in te

xt.

Sour

ces:

au

thor

s’ c

alcu

lati

ons

usi

ng

data

from

th

e 19

99 E

con

omic

Cen

sus

and

the

2000

Pop

ula

tion

Cen

sus.




Insurgentes Avenue and with commercial activity to the sides of the services areas. Industry is concentrated in the corridors, except in the Lomas-Santa Fe area, which shows tertiary activity. Five of the eight sub-centres are essentially manufacturing towns, while Texcoco, Tlahuac and Chiconcuac show mainly tertiary activity.

5.4 Comparison with Previous MethodsA comparison of our results with those of pre-viously used methods is depicted in Figure 7. The sub-centres identifi ed by Aguilar and Alvarado (2005) are mostly contained with-in our identifi ed formal CA. For the specifi c case of Tlalpan, the areas of this municipal-ity that show up as part of an employment

Figure 5. Mexico City: combined formal and informal employment centres and trip attraction capacity in 1.6-km radii

Sources: authors’ methodology using the 1999 Economic Census and the 2000 Population Census.




Tabl

e 3.

M

exic

o C

ity: s

elec

ted

char

acte

rist

ics

of c

ombi

ned

form

al a

nd in

form

al e

mpl

oym

ent c

entr

es

Denomination

Typea

Working residents

Jobs

Job density (jobs/ha)

Percentage of working residents of MA

Percentage of jobs of MA

Percentage of urban area of MA

Primary/secondary specialisation

Cen

tral

agg

lom

erat

ion

C

ente

r pl

us

AC

s an

d A

SCs

1 64

9 02

24

614

624

90.3

25.0

69.9

21.5

Serv

ices

/In

dust

rial

Lom

as–S

anta

Fé

AC

30

902

71 2

7823

.40.

51.

11.

3Se

rvic

es/C

omm

erci

alN

auca

lpan

–Cu

auti

tlán

A

SC

232

055

622

319

57.1

3.5

9.4

4.6

Indu

stri

alE

cate

pec

ASC

54

634

118

495

47.7

0.8

1.8

1.0

Indu

stri

alPe

rifé

rico

A

C

155

636

291

826

49.4

2.4

4.4

2.5

Serv

ices

Izta

pala

pa

ASC

4

950

5 03

820

.90.

10.

10.

1In

dust

rial

/Com

mer

cial

Tepo

zotl

án

Sub-

cen

tres

(2)

2 26

58

496

9.8

0.0

0.1

0.4

Indu

stri

alC

oaca

lco

Sub-

cen

tre

423

573

23.9

0.0

0.0

0.0

Indu

stri

alC

hic

oncu

ac

Sub-

cen

tre

4 17

65

990

17.9

0.1

0.1

0.1

Com

mer

cial

Texc

oco

Sub-

cen

tre

13 8

0525

196

21.4

0.2

0.4

0.5

Serv

ices

/Com

mer

cial

La P

az

Sub-

cen

tre

7 94

217

094

35.5

0.1

0.3

0.2

Indu

stri

alT

lahu

ac

Sub-

cen

tre

9 79

215

905

37.2

0.1

0.2

0.2

Serv

ices

Ixta

palu

ca

Sub-

cen

tre

849

1 22

420

.70.

00.

00.

0In

dust

rial

Ch

alco

Su

b-ce

ntr

e 10

126

14 1

1727

.40.

20.

20.

2In

dust

rial

/Ser

vice

sA

tiza

pán

de

Z

Sub-

cen

tre

8 73

416

081

36.4

0.1

0.2

0.2

Indu

stri

al

CA

wit

hou

t co

rrid

ors

1 17

0 84

53

505

668

122.

917

.753

.112

.0Se

rvic

es/C

omm

erci

alA

ll A

Cs

and

ASC

s47

8 17

71

108

956

49.1

7.2

16.8

9.5

Indu

stri

al/S

ervi

ces

All

sub-

cen

tres

58 1

1210

4 67

624

.20.

91.

61.

8In

dust

rial

/Ser

vice

sTo

tal E

mpl

oym

ent

cen

tres

1 70

7 13

44

719

300

85.1

25.9

71.5

23.3

Indu

stri

al/S

ervi

ces

Met

ro a

rea

tota

l

6 60

3 81

16

603

811

27.8

100

100

100

a AC

: adj

acen

t co

rrid

ors

as d

efi n

ed in

text

; ASC

: adj

acen

t se

gmen

ted

corr

idor

s as

defi

ned

in te

xt.

Sour

ces:

au

thor

s’ c

alcu

lati

ons

usi

ng

data

from

th

e 19

99 E

con

omic

Cen

sus

and

the

2000

Pop

ula

tion

Cen

sus.




centre are, in our results, aligned to the south of the Periférico Corridor. Aguilar and Alvarado, however, place the centre further south, towards the municipality’s adminis-trative centre.’

The rest of Aguilar and Alvarado’s sub-centres are mostly aligned along the higher jobs to resident workers’ ratio areas, along Reforma and Insurgentes Avenues, depicted in Figure 4. They are also mostly contained within the areas that would be identified if using the hybrid McMillen–Giuliano methodology. However, since between these points the observed densities surpass the density gradient, and since the areas around them show similar characteristics in terms of job concentration, it is likely that Aguilar and Alvarado (2005) found potential nodes

within the bounds of the CA, not sub-centres as such.

In the case of the results of the hybrid McMillen–Giuliano method, we found no signifi cant difference between the fi nal list of centres produced using the formal and the combined formal and informal employ-ment. Only 10 additional tracts showed up as economic centres when controlling for informality at the edges of the identified formal employment centres. Thus, for sim-plicity, we only depict the results of the com-bined formal plus informal jobs analysis in Figure 7. This no-dissimilarity result is explained by the fact that the differences between the chosen employment volume thresholds are basically proportional to the ratios of the formal to combined formal and

Figure 6. Mexico City: economic specialisation in employment centres

Source: authors’ calculations with data from the 1999 Economic Census.




Figu

re 7

. M

exic

o C

ity: c

ompa

riso

n of

em

ploy

men

t cen

tres

bet

wee

n th

ree

met

hodo

logi

es

Sour

ces:

Agu

ilar

and

Alv

arad

o (2

005)

an

d au

thor

s’ c

alcu

lati

ons

usi

ng

data

from

th

e 19

99 E

con

omic

Cen

sus

and

the

2000

Pop

ula

tion

Cen

sus.




informal sectors. In our view, this reveals an additional limitation to the employment volume threshold approach.

Relative to our methodology, the hybrid McMillen–Giuliano method would seem to overestimate the extent of the centre, its corridors and sub-centres. For example, using that methodology, the Nezahualcoyotl, Izatapalapa La Paz and V. Chalco areas, to the east, show up as large employment centres, almost connected to the CA. However, the working residents densities in these areas are so much higher than the job densities that they have higher trip generation rates than attraction rates. Thus, a proportion of resi-dent workers will end up making work trips to the centre, because there are simply not enough jobs nearby to accommodate them all. For this reason, these areas cannot be considered employment centres, even if they do show high job concentrations. Indeed, Iztapalapa and Netzahualcoyotl are among the densest and most populated municipal-ities of the metropolitan area. Together, they hold more than 16 per cent of the city’s popu-lation, at densities that exceed, by more than double, the average gross urban population density of the metro area. It follows that there will be a high concentration of local services and commerce to meet the local demand–not enough, however, to make sub-centres out of them.

6. Discussion

6.1 About the Methodology

We have proposed an alternative criterion for identifying employment centres. It is based on identifying peaks in the density gradient, in combination with determining the areas that have a trip attraction to trip generation ratio greater than one. Due to the nature of this method, we use GIS-generated neighbourhoods of 1.6-km radius. We consider that we have resolved the issue of arbitrarily setting a volume of employment

in contiguous fi xed areas present in previous research, by introducing a more objective criterion that also responds to population location dynamics in the city. Still, a question arises as to how to establish objectively the self-containment radius, which we have set at 1.6-km. For Mexico City, setting the radius at half that distance (800 metres) slightly increases the number of sub-centres (especi-ally at the edges of the CA) and also increases the number and length of ASCs.6 Conversely, using double the distance (3.2 km) slightly reduces the number of sub-centres and expands the area of the CA. At least for our study area, the results produced using the three different radii do not reveal material differences that would cause us to re-evaluate our general conclusions on urban form.

A second question is whether an LWR could be used in predicting the density surface that is already the product of GIS employment neighbourhoods. In our analysis, we used an OLS regression that predicted the log of em-ployment density, since our initial assumption was that the city was still in a monocentric stage. However, it remains inconclusive whether using LWR with this form of aggre-gate data may potentially enhance results.

Finally, we should add that some high-income residential areas might well be em-ployment centres for some types of informal jobs (domestic service, gardeners, chauffeurs), which our methodology is not able to capture.7 This form of disaggregated employment represents very low density and dispersed job centres for a very specifi c sector of the informal working population. Identifi cation of such job centres would require a different methodological approach.

6.2 Is Mexico City Polycentric?

Based on our criteria, it is diffi cult to ascertain whether Mexico City is indeed a polycentric city. If anything, polycentrism would have to be in its initial stages. Although we have identifi ed several potential sub-centres, these




only account for less than 5 per cent of for-mal employment and less than 2 per cent of the total number of jobs in the city when additionally considering informal economic activity.

Instead, nearly 60 per cent of formal jobs and over 70 per cent of total jobs are located within what we have denominated the central agglomeration, which includes a very large area contiguous to the CBD that meets our density and employment concentration cri-teria. This area includes a series of corridors along highway exits that connect Mexico City with four other cities of central Mexico. Corridors account for 13 per cent and 17 per cent of formal and total jobs respectively. The rest of the CA accounts for 40 per cent and 53 per cent of these job type divisions respectively. The internal structure of the CA seems to be composed of a subset of inner corridors and nodes that show higher den-sities and higher trip attraction capacity.

Due to these characteristics, it is our hy-pothesis that the CA evolved from a small centre with a set of corridors that have ex-panded linearly and concentrically through the years to amass the area that we have now identifi ed. This would also explain, at least in part, the elongated nature of its shape. More research is required in this respect.

Results suggest a hybrid urban form. The CA is a dispersed centre, with strip develop-ment characteristics at its edges along main highways, and with very small employment sub-centres, most of which could actually be considered only local centres. Although the central agglomeration is far from being round, the way jobs are agglomerated prompts us to conclude that Mexico City is still in a primarily monocentric stage. Results using the hybrid McMillen–Giuliano methodology would lead to that same conclusion, but with a larger CA, longer corridors and fewer sub-central employment agglomerations. Our interpretation of maps and fi gures is, never-theless, subject to discussion.

As regards informality, we are aware of the rough nature of our adjustment algorithm. It is likely, however, that the effi ciency of its fi t increases with distance to the CBD, be-cause the proportion of urban areas within municipalities also diminishes with distance. In any event, it is still worth noting the differ-ences found between formal employment and total job agglomerations. While, when controlling for informality, the percentage of jobs concentrated in the central agglom-eration is higher, more sub-centres appear. Also, the CA that includes informal jobs is larger than the formal CA. This suggests that job accessibility increases when including informal economic activity and would, in turn, imply that workers in the informal sector have shorter work trips. This is consistent with the fi ndings of Suárez (2007), who found that low-income workers in the informal sector have shorter trips, probably due to lo-cation strategies of informal work activities as a function of residential location.

6.3 Further Research Agenda

Our fi ndings on urban form suggest that any policy intents to generate sub-centres in the Mexico City metro area have not been quite successful, especially if less than 5 per cent of jobs are located within our identifi ed sub-centres. Still, it is worth asking, whether the higher attraction areas within the CA have developed as such because of the effects of planning policies and transport infrastruc-ture, or if their development has been mar-ket driven. That is, what are the historical, economical and political determinants of current urban structure and form, including the location characteristics of the informal sector?

Further research should also look at the effects of urban form on travel time, urban effi ciency, economic development and the en-vironment. Who benefi ts from the current urban form? How effi cient is the spatial ar-rangement of the city in terms of transport?




And, in any case, what policy measures should be taken to improve access to jobs? Do exist-ing employment agglomerations provide the dynamism needed for the city’s economic development potential? Finally, with regard to the environment, what is the effect of the jobs–housing spatial arrangement on emis-sions and exposure?

These are, among others, questions whose answers will determine what some of the future metropolitan-wide planning needs will be.

Notes

1. Delgado’s urban rings are based on historical stages of conurbation. Alternative ring confi g-urations show slight changes of municipalities within rings, but none that would affect sig-nifi cantly the statistics we present.

2. All our calculations exclude the agricultural sector.

3. NAICS codes: 211, 221, 222, 481, 482, 3254, 517, 52, 54, 55, 61 and 62.

4. A correlation between Economic Census (established) jobs per municipality and our selection of formal jobs in the 10 per cent census database revealed a Pearson’s coeffi cient of determination of r2 = 0.83.

5. The cited study originally uses X and Y distances to the CBD as predictors. However, using four distance predictors increases the fi t of the model from an R2 of 0.35 to an R2 of 0.58.

6. We performed a sensitivity analysis using different distance radii. We do not present these results, since they add no signifi cant insights regarding our main research question.

7. We thank an anonymous referee for making this point.

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Appendix

The suburbanisation index measures the nor-malised distance from the centre at which jobs or residents are, on average, located in the metro-politan area, relative to the centre of the city and its farthest edge. It varies from zero (all jobs or residents concentrated at the centre) to one (all jobs or residents concentrated at the farthest edge of the city). Intermediate values may be interpreted as the proportion of the distance between the city’s CBD and its farthest edge where jobs or working population are located, on average.

I P Dki j jj

= ⋅∑

where, Iki is the suburbanisation index of eco-nomic sector i [manufacturing, commerce, services] of category k [jobs or working popula-tion in economic sector i]; Pj is the proportion of jobs or working population in economic sector i in zone j relative to the metropolitan area; Dj is the normalised distance between zone j and the CBD.



is mexico city polycentric - a trip attraction capacity approach (delgado 2009)

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