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DANA STOVALL – LOCATING THE MARGINALIZED IN MEXICAN CITIES – CRP 386, FALL 2008

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EXECUTIVE SUMMARY

Latin America is characterized by high rates of marginalization and inequality, and the

rapid urbanization throughout the region in the last half-century has exacerbated issues of

substandard living conditions and security for the poor. In a rapidly changing urban

environment, governments and development agencies working in the region need

consistent methodologies for locating the most marginalized populations, as well as

determining area’s most in need of structural interventions. Poverty alleviation policy

can be much more effective when geographically targeted at those in most need.

Mexico has an elaborate government branch working with demographic and geo-spatial

data that has developed various systems and variable-indicators for measuring

marginalization. This report borrows from such studies and uses Geographic Information

Systems (GIS) to create a model for ranking marginalization using a very small unit of

analysis for the Metropolitan Area of Monterrey, located in Nuevo León, Mexico. The

intention was to provide a scaled-down look at the presence of poverty in a Latin

American city that is most often praised for its wealth and advancement in international

business. Research was conducted principally by consulting studies from Mexican

sources such as Instituto Nacional de Estadística y Geografía (INEGI), Consejo Nacional de la Población (CONAPO), and Instituto Tecnológico de Estudios Superiores de Monterrey (ITESM).

The ultimate motivation for this study is to provide a relatively simple, accessible GIS

model for mapping marginalization in Mexican cities. It allows the researcher to locate

the most marginalized areas of the population from an analysis of combined variable-

indicators and then focus on one policy-related variable (i.e. access to running water) for

a specific highly marginalized neighborhood or block. The model is not to be applied

without an understanding of the context and the situation of the various marginalization-

indicators for the specific region being studied. It can easily be modified and allows for

the researcher to select different variables, if appropriate.


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INTRODUCTION

Mapping Marginalization in Mexico

Latin America has been recognized as one of the world’s regions with the highest levels

of inequality in relation to the distribution of income, and although the region’s poverty

levels may not be as extreme as some others, they remain consistently high. The

Economic Commission for Latin America and the Caribbean (ECLAC) has measured that

the amount of households in the region living in poverty has hovered between 35 and

40% since the early 1970s (Montes Avilés 2003). The urban areas of Mexico maintain

average poverty indices very similar to the overall region; in the year 2000, an average of

10% of Mexican households were measured as indigent while another 37.5% were

considered to be poor (Montes Avilés 2003).

As Latin America and the rest of the developing world continue to rapidly urbanize, the

dynamics of poverty and security for the marginalized are changing, and governments as

well as non-profit agencies are looking for effective programming to combat poverty and

ensure safe and sanitary living conditions. In order to maximize the effectiveness of

poverty reduction policy, agencies must ensure aid-resources are directed solely at the

poor. One way of doing so is to target resources geographically, which requires detailed

information on the location of the poor (Fujii 2008). In addition to mitigating aspects of

poverty such as basic nutrition and education, comprehensive policy must specifically

address the structural rehabilitation of settlements that are the result of rapid and

unplanned generation of informal housing.

There is no one way to measure poverty or marginalization; while some methodologies

take into account purely economic factors, others give more weight to social indicators.

The Consejo Nacional de la Población (CONAPO), Mexican federal agency for

demographic and population studies, has developed various methodologies for measuring

marginalization with inclusive variables that take into account access to education, living

conditions, and income.

The Monterrey Metropolitan Area

The Monterrey Metropolitan Area (MMA) is Mexico’s third largest metropolitan area by

population (after Mexico City and Guadalajara), and is located in the northeastern state of

Nuevo Leon. The MMA is officially composed of nine municipalities: Apodaca,

Escobedo, Garcia, Guadalupe, Juarez, Monterrey, San Nicolas de los Garza, San Pedro

Garcia Garza, and Santa Catarina. The MMA was officially created in the year 1984, and

initially included seven municipalities (Garcia and Juarez, located on the outskirts, are the

two municipalities that were more recently added). Two additional adjacent

municipalities, Santiago and Salinas Victoria, are beginning to be considered as part of

the MMA as well. The core of the urban conurbation is the municipality of Monterrey,

which also serves as the capital of state of Nuevo Leon.


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The MMA, like other Mexican cities, has experienced an explosion in population since

the mid-1900s. In the year 1950, the nine municipalities that today form the MMA had a

total population of 339,282, and 87% of these inhabitants were concentrated in the

municipality of Monterrey. In the year 2000, the total population of the MMA reached

3,243,466, composing 84.6% of the total state’s population.

Source: INEGI

As the urban footprint has expanded, the municipality of Monterrey’s share of the

population has decreased. The municipalities that have most recently increased in

population are the outer areas of Apodaca, Escobedo, Juarez, and Garcia, all of which

approximately doubled their population in the 1990s alone. This indicates a rapid

urbanization that transforms municipalities from largely rural areas into the enveloping

network of the MMA.

Monterrey has traditionally been a stronghold for manufacturing and industry, likely a

contributing factor to its rapid expansion from in-migration. In the year 1950, 46% of the

MMA’s economically active population was employed in the manufacturing sector; in

the year 2000 that number had decreased to 26.9%. This indicates a rise in other formal

sectors, such as finance and business, but also a rise in the informal economic sector

brought on by rapid urbanization. While Monterrey is generally considered the best place

in all of Latin America to do business, and is home to the wealthiest municipality in the

country (San Pedro Garcia Garza), this does not guarantee that the economic benefits are

distributed uniformly across the MMA. The Encuesta de Ingresos y Gastos de los Hogares Área Metropolitana de Monterrey (Income and Spending Survey of MMA

Households), conducted in 1995, indicated that the total combined income of the lowest-

earning 80% of the population of the MMA was less than the combined earnings of the

top 10%. When divided into deciles, the top decile earned 27 times the income of the

bottom decile. However, Monterrey’s Gini Coefficient (an internationally-applied

Population Growth MMA 1950-2000


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measurement of inequality in which a measurement of 0 indicates total equality while 1

indicates total inequality) is 0.4938, indicating slightly more equality than the country

overall, with a Gini Coefficient of 0.5187 (Montes Avilés 2003).

The nine municipalities of the MMA all maintain distinct administration of urban

management issues such as transportation, public security, waste management and other

services. Article 115 of the Mexican Federal Constitution states that each municipality is

to be governed by a directly elected body, without any intermediary governing between

the municipal level and the state level. There is even an explicit prohibition of a

governing body composed of various municipalities (such as the County level of

governance in the United States). Such administrative disjunction leads to inefficiency in

all matters of urban management in the MMA. This includes policy approaches to

alleviate poverty, from nutritional programs to housing and infrastructure access.


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PROBLEM STATEMENT & RESEARCH QUESTIONS

The government of Mexico and other similarly structured Latin American countries must

use census and survey data to determine where the most marginalized members of its

population are located, in order to best allocate funds and programming targeted at

poverty alleviation. International development agencies and non-profits would also

greatly benefit from a methodology of systematically locating the most marginalized

areas within the ‘mega-city’ urban conurbations such as the MMA.

The official Mexican agency for geographic and demographic data, the Instituto Nacional de Estadística y Geografía (INEGI), along with the CONAPO, has various systems for

ranking the levels of well being and marginalization for states and municipalities. The

state of Nuevo Leon has consistently ranked high in well-being indices in comparison to

the remainder of the country. One such index of marginalization study carried out in

1995 took into account four dimensions of marginalization: education, housing,

population dispersion, and income. Nuevo Leon was placed in the lowest bracket of

marginalization, along with 3 other states and the Federal District (Mexico is composed

of 31 states and 1 Federal District). Rankings for marginalization have also been

conducted at the municipal level, as indicated by the maps below.

MAP 1.3 Marginalization Ratings for Nuevo León for the year 1995

Monterrey Metropolitan Area

Grade of Marginalization Very low Low Middle High Very High

Source: Consejo Nacional de Población (CONAPO) y Programa de Educación, Salud y Alimentación (PROGRESA), 1998. COMPILED BY CEDEM. Accessed from Montes Avilés, 2003.


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Such studies can be important first steps for allocating government funds and

programming. However, they can also discount the diversity within large geographic

areas as well as the sharp contrast of the very poor living ‘next door’ to the very rich, a

rampant trend throughout Latin America. For this reason, a consistent methodology must

be applied at a low scale of study areas in order to properly identify the most

marginalized zones in need of housing and infrastructure interventions.

The most recent indexes calculated represent the average situation for the inhabitants of the

municipality. However, just like in all big cities, even if the average of some municipalities does

not indicate a high level of marginalization, they can still have towns, neighborhoods, or other

areas that are very marginalized. More disaggregated information is required to locate such areas.

This occurs in the MMA, where analyzing the municipalities comparatively at the national level

doesn’t show high indices of marginalization, but analyzing them individually at a smaller unit,

such as AGEBS,1 proves (in addition to the obvious and undeniable presence of an indigent

population) that marginalization does exist in the MMA (Montes Aviles 2003, p. 294—translated

by author).

This report uses Geographic Information Systems (GIS) to create and test a methodology

for identifying and ranking the most marginalized areas within a metropolitan area. The

case used here is the MMA, but the hope is that upon refinement the model could be

transferred to other municipalities and metropolitan areas within Mexico, and even other

Latin American countries. It could provide a useful tool for international groups seeking

to rank marginalization at a small scale across larger regions.

In addition to identifying the most marginalized areas within the MMA, the report takes a

step further in its analysis by focusing on a specific highly marginalized area to

demonstrate how GIS can be used at the block level to identify which residential clusters

are in need of specific housing or infrastructural improvements.

This report is a ‘trial study’ in nature, so specific attention will be given to caveats

encountered with the data or the analysis/methodology itself. In summary, the main

research questions are:

- How can GIS be used to identify and rank the most marginalized areas within a

Mexican (or other Latin American) metropolitan area?

- How can GIS be used to pinpoint specific neighborhoods and blocks in need of

housing/infrastructure interventions?

- What is the effectiveness of the methodology model and how can it be applied in

future studies?


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Since the trial study at hand focuses on the MMA, more specific research questions are

also proposed:

- Where are the most marginalized areas located within the urbanized area of the

MMA?

- Is there a concentration of highly marginalized areas within any one municipality?

- Within areas determined as highly marginalized, where are the neighborhoods and

blocks in most need of specific types of structural (housing or infrastructure)

intervention?


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METHODOLOGY

The methodology summary is divided into four sub-sections:

1. Acquire Data

2. Map Marginalization Variables at the AGEB level

3. Generate Ranking of AGEBS based on Marginalization Variables

4. Map Example of ‘Block Level Analysis’ Using Infrastructure Variables

1. Acquire Data

The study required two main types of data: spatial data in the form of GIS

shapefiles, and demographic/census data in the form of excel files. Since the

project involves mapping Mexican data, and I am based in the US, I was severely

limited as to the spatial data I could obtain. While INEGI does provide limited

downloadable shapefiles over the Internet, they are not at the level of analysis I

needed for the study. Therefore the methodology was in large part determined by

the data I could access.

Spatial data were obtained from a disc released to me personally by Dr. Peter

Ward, professor at University of Texas-Austin. The disc included shapefiles for

the state of Nuevo Leon, specifically a shapefile of all the AGEBS for the state

and another for all of the blocks. The AGEBS shapefile did not contain more

specific demographic data within its attribute table, while the blocks shapefile did.

The original source for this spatial data is INEGI, and the projection & datum are

Lambert Conformal Conic, GCS North American 1927.

The demographic data that I used in conjunction with the AGEB shapefiles is

from the Censo General de Poblacion y Vivienda 2000 (General Population and

Housing Census 2000), conducted by INEGI. The level of detail I required was

also not available online, but I was able to obtain it through an INEGI generated

software provided to me personally in disc form by a colleague, Cristina Saborio.

The software program (SINCE 2000, Sistema de Informacion Censal 2000. Instituto de Informacion Geografica e Informatica INEGI. Aguascalientes, Mexico 2005) is published by INEGI and available for sale in Mexico. I was able

to use the software to export .dbf files of demographic data at the AGEB level,

which I then opened in excel.

In summary, the data (all from INEGI) used in the analysis were as follows:

o AGEBS shapefiles for Nuevo Leon

o Block shapefiles for Nuevo Leon

o 2000 Census Data for the AGEBS located in the AMM (the specific tables

used were: Employment, Education, Indigenous Language, Migration,

Population, and Housing)


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2. Map Marginalization Variables at the AGEB level

- Determine the 10 variables for the marginalization analysis. A similar

marginalization study by CONAPO with data from 1990 Census was used as a

model to determine the variable indicators for marginalization.

- Generate main ‘Variables’ data table. I used Excel to generate a table with the

percentages for all 10 variables for each AGEB.

- Create ‘AMM_AGEBS’ shapefile. The file I had was for all AGEBs in the state

of Nuevo Leon, so I wanted to reduce it to just the AGEBs in the AMM. In

ArcMap, I selected all the AGEBs that were in the 9 municipalities of the AMM,

and created a layer. I then exported that layer and saved it as ‘AMM_AGEBS.’

- Further Prepare ‘AMM_AGEBS’ shapefile. I joined the ‘Variables’ table to

the new ‘AMM_AGEBS’ shapefile, using the code for AGEB as the common

field. I then noted that I did not have corresponding Census data for some of the

AGEBS in my shapefile. I removed these AGEBS so as to not include them in

the analysis, ending with an ‘AMM_AGEBS’ shapefile with a total of 1070

AGEBS.

- Choose thresholds for variables. I chose to set a unique threshold for each

variable so as to include the top 10% most marginalized AGEBs for that given

variable. I calculated these thresholds in Excel.

- Run query and create shapefile for each variable. In ArcMap, I added the

AMM_AGEBS shapefile. Beginning with the first variable, I ran a query to

select all AGEBs greater than or equal to the threshold value. I created a layer of

those selected AGEBs and exported it as a shapefile. I repeated for each variable,

finishing with ten new shapefiles.

- Generate a map for each variable. In ArcMap, I added the AMM_AGEBS

shapefile and the first variable shapefile to the same data frame on top of it, and

symbolized them appropriately. I repeated for each variable.

3. Generate Ranking of AGEBS based on Marginalization Variables

Combine variables to generate ‘marginalization ranking.’ From ArcMap, I

exported the attribute table for AMM_AGEBS, and then opened it in Excel.

Starting with the first variable column, I replaced all values equal to and over the

threshold with a 1, and all values under the threshold with a 0. After doing this

with all variable columns, I created a new column, ‘GM’ (Grade of

Marginalization), the sum of all the variables columns (now made up of 1’s and

0’s) for each AGEB row. The GM column contained values for the ranking

ranging from 0 to 9. I saved this excel table as “AMM_AGEBS_grados.”

Generate map to represent ranking of “Marginalized Areas in the AMM.”

In ArcMap, I added the AMM_AGEBS shapefile, and joined the


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AMM_AGEBS_grados to it. I symbolized the marginalization grade in three

classes, ‘high,’ ‘medium,’ and ‘low.’ The AGEBs with a value of ‘0’ GM I

labeled as ‘not included in study,’ since they did not pass the threshold for any of

the variables.

Generate Map of Highly Marginalized Areas by Municipality. For this map, I

modified the AMM_AGEBS layer by symbolizing the AGEBs based on their

Municipality Name field; I made each municipality a faded neutral color without

an outline. I also selected all the ‘high’-ranked grade AGEBs and exported them

to a shapefile named ‘AGEBS_GMHigh.’ I symbolized ‘AGEBS_GMHigh’ with

a cross-hatch fill on top of the Municipalities layer.

4. Map Example of ‘Block Level Analysis’ Using Infrastructure Variables

Map municipality for block level analysis. At 14, General Escobedo

Municipality clearly had the most number of AGEBs ranked with a ‘High’

marginalization. With the AMM_AGEBS shapefile, I selected all AGEBS within

General Escobedo and exported them to a new shapefile (‘AGEBs_Escobedo’). I

added ‘AGEBs_Escobedo’ to a new dataframe and overlayed the

‘AGEBS_GMHigh’ shapefile, clipping it to ‘AGEBs_Escobedo’ to create the

‘AGEBs_Escobedo_GMHigh’ shapefile. I then added the ‘Manzanas’ (Blocks)

shapefile and clipped it to the ‘AGEBs_Escobedo_GMHigh’ shapefile to make

the ‘Manzanas_Escobedo_GMHigh’ shapefile. The ‘Manzanas’ shapefile already

contained data on water infrastructure in the attribute table. As an example of a

specific block analysis for selected AGEBs, I symbolized the principle water

infrastructure variable ‘percent of residences with no piped water on the

property,’ using 5 classes with Natural Breaks. I highlighted two AGEBs (Study

Areas A & B) that I wanted to analyze further using the block-level water

infrastructure data.

Generate series of maps of Study Areas A & B to show availability of piped

water in the individual blocks. For each study area AGEB, I manually selected

the blocks contained within it, created a layer and exported it as a shapefile, i.e.

“Manzanas_Area_A.” Then for each study area I made four maps, using the four

variables I had data for on water access. I used Natural Breaks with 5 classes to

symbolize the percentages of homes for each variable (i.e. “Percentage of Homes

without water in the bathroom”).


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FINDINGS

List of Maps

(Series 1. Context Maps—Included in Introduction)

Series 2. Marginalization Variable-Indicator Maps

Map 2.1 People Who Speak an Indigenous Language

Map 2.2 People Who Did Not live in the State 5 years ago

Map 2.3 People Who Didn’t Complete Primaria Level of Schooling

Map 2.4 People Without Any Schooling After the Primaria Level

Map 2.5 People Age 15 and Over Who are Illiterate

Map 2.6 Residences Without Sewage Connection

Map 2.7 Residences With Only One Room

Map 2.8 Residences With Dirt Floor

Map 2.9 People Age 12 and Over Who are Unemployed

Map 2.10 People Age 12 and Over Who Receive 2 Minimum Wages or Less

Series 3. Marginalization Analysis Maps

Map 3.1 Marginalized Areas in the Metropolitan Area of Monterrey

Map 3.2 Highly Marginalized Areas by Municipality

Series 4. Example Block Analysis Maps

Map 4.1 Escobedo Municipality: Block Analysis of Highly Marginalized Areas

Map 4.2 Study Area A: Percent of Homes Without Piped Water Inside the Property

Map 4.3 Study Area A: Percent of Homes Without Piped Water Inside the Home


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Map 4.4 Study Area A: Percent of Homes Without Bathrooms

Map 4.5 Study Area A: Percent of Homes Without Water Inside the Bathroom

Map 4.6 Study Area B: Percent of Homes Without Piped Water Inside the Property

Map 4.7 Study Area B: Percent of Homes Without Piped Water Inside the Home

Map 4.8 Study Area B: Percent of Homes Without Bathrooms

Map 4.9 Study Area B: Percent of Homes Without Water Inside the Bathroom

People who speak an indigenous language

People who did not live in the state 5 years ago

1.70% or more of the population age 5 and older

Less than 1.70 % of the population age 5 and older


Less than 11.97% of the population age 5 and older

F0 5 10 15 202.5

Kilometers

Source: INEGI, 2000 General CensusCompiled by Dana Stovall on 12.5.08

MAP 2.1

MAP 2.2

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People who didn'tcomplete 'primaria' level of schooling

People without anyschooling after the'primaria' level


Less than 17.74 % of the population age 15 and older



F0 5 10 15 202.5

Kilometers


MAP 2.3

MAP 2.4

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People age 15 and over who are illiterate

Residenceswithout sewageconnection



11.84% or more of inhabited residences

Less than 11.84% of inhabited residences

F0 5 10 15 202.5

Kilometers


MAP 2.5

MAP 2.6

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Residences with only one room

Residenceswith dirt floor





F0 5 10 15 202.5

Kilometers


MAP 2.7

MAP 2.8

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People age 12 and over who are unemployed

People age 12and over who receive 2 minimumwages or less





F0 5 10 15 202.5

Kilometers


MAP 2.9

MAP 2.10

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FSource: INEGI, 2000 General CensusCompiled by Dana Stovall on 12.5.08

MAP 3.1

Marginalized Areas in theMonterrey Metropolitan Area

0 5 10 15 202.5

Kilometers

Level of Marginalization

Not Included in Study

Low

Medium

High

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MAP 3.2Highly Marginalized Areas by Municipality

0 5 10 15 202.5

Kilometers

Municipality Number of AGEBs ranked 'Highly Marginalized'

Apodaca 0Garcia 0General Escobedo 14Guadalupe 5Juarez 1Monterrey 8San Nicolas de los Garza 0San Pedro Garcia Garza 0Santa Catarina 3 AGEB= "Area Geoestadistica Basica"

High Marginalization(by AGEB*)

Monterrey

Apodaca

General Escobedo

San PedroGarcia Garza

Santa Catarina Guadalupe

Juarez

San Nicolas de los Garza

Garcia

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MAP 4.1General Escobedo Municipality:

Block Analysis of HighlyMarginalized Areas

0 2.5 5 7.5 101.25Kilometers

Study Area A

Study Area B

0% - 15%

16% - 40%

41% - 65%

66% - 90%

91% - 100%

Percent of homeswith no piped wateron the property

Percent of homes without piped water inside the property

Study Area A

0% - 5%

6% - 15%

16% - 30%

31% - 50%

51% - 85%

11% - 40%

41% - 60%

61% - 75%

76% - 85%

86% - 100%

F0 150 300 450 60075

Meters


MAP 4.2

MAP 4.3

Study Area A

Percent of homes without piped water insidethe home

General Escobedo Municipality

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Percent of homes without bathrooms

Study Area A

0%

1% - 5%

6% - 10%

11% - 12%

13% - 16%

50% - 70%

71% - 80%

81% - 90%

91% - 95%

96% - 100%

F0 150 300 450 60075

Meters


MAP 4.4

MAP 4.5

Study Area A

Percent of homes without water inside the bathroom


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Study Area B

92% - 100%

92% - 100%

F0 150 300 450 60075

Meters


MAP 4.6

MAP 4.7

Study Area B

Percent of homes without piped water inside the property

Percent of homes without piped water insidethe home


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Study Area B

0%

1% - 5%

6% - 8%

9% - 11%

12% - 15%

100%

F0 150 300 450 60075

Meters


MAP 4.8

MAP 4.9

Study Area B


Percent of homes without bathrooms

Percent of homes without water inside the bathroom

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ANALYSIS & CONCLUSIONS

Specific interpretation and analysis of the maps is organized below by series. Each series

may also include a section of additional general comments.

Series 2. Marginalization Variable-Indicator Maps

Map 2.1 ‘People Who Speak an Indigenous Language’ and Map 2.2 ‘People Who Did

Not live in the State 5 years ago’ both represent those who migrated to the MMA. Both

show a large concentration in the southwestern portion of the urban footprint, where the

Municipality of San Pedro Garcia Garza is located. San Pedro Garcia Garza is not only

the wealthiest municipality in the MMA, but the entire country. The concentration of

individuals who are recent migrants and/or indigenous language speakers is a direct

indication of those who work in domestic service in this the wealthiest zone of the city.

Many young women who work as maids and caregivers in homes are from a rural area; in

addition, it is custom that they reside in the home where they work, and they are included

in the Census count for that home. These variables are therefore an important indication

of the premise that a highly marginalized sector can and does exist in even those

municipalities that have received the lowest ratings of marginalization. The migration

variables provide important information for the provision of some types of social

services, but are less important when focusing specifically on structural settlement

improvements (since most of the population represented here is assumed to be living in

the home in which they work).

Map 2.3 ‘People Who Didn’t Complete Primaria Level of Schooling,’ Map 2.4 ‘People

Without Any Schooling After the Primaria Level,’ and Map 2.5 ‘People Age 15 and

Over Who are Illiterate’ represent the educational variables selected to indicate

marginalization. All three variables mapped in a very similar pattern, with many of the

same AGEBs being represented in all three. There is a concentration of AGEBs on the

outskirts of the urban footprint and very few AGEBs in the central core area.

Map 2.6 ‘Residences Without Sewage Connection,’ Map 2.7 ‘Residences With Only

One Room,’ and Map 2.8 ‘Residences With Dirt Floor’ are the variables indicating

substandard living conditions. As expected, they all follow a similar pattern. There is a

heavy concentration for all variables in the northwestern portion of the city. There is one

AGEB in the central region of the MMA that is mapped for all three variables (situated

just northeast of the downtown core). This is unusual, since any significant concentration

of substandard housing is expected to be located on the fringes. The particular AGEB

represented here could be a unique case of a recent informal settlement on an abandoned

territory near the core; or, it could simply be a data error. Any program targeted at

housing improvements would be advised to investigate further as to the situation in this

particular zone. Lastly, San Pedro Garcia Garza is the one municipality that does not

house any AGEBs that qualified under the housing variables.


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Map 2.9 ‘People Age 12 and Over Who are Unemployed’ and Map 2.10 ‘People Age 12

and Over Who Receive 2 Minimum Wages or Less,’ the employment variable maps,

seem to be the most sporadic. A main reason for this is the nature of the variables

themselves. ‘People Age 12 and Over Who are Unemployed’ is meant to represent those

who are economically active and find themselves currently unemployed. The 2000

Census has a section for all members of the household who are 12 and older, and this is

where the questions about employment are asked. The inherent fault here is that many

household members over the age of 12 may not even be economically active (i.e.

students, the elderly). The variable ‘People Age 12 and Over Who Receive 2 Minimum

Wages or Less’ (considered the ‘popular’ income level, even below those earning at the

‘low’ income level) also falls under the same caveat. In addition, it only indicates

individual income, and negates household factors. A single person household earning 2

minimum wages is less marginalized than a single mother of three earning 2 minimum

wages, and perhaps more marginalized than a household where four members are earning

2 minimum wages.

The thresholds for each variable were selected in order to maintain consistency by

including only the ‘worst off’ 10% of each variable. This meant that some variables

(such as ‘People who speak an indigenous language’) had extremely low thresholds

because the overall presence of that variable is extremely low. Even if some such

variables ‘appear’ to be inconsequential, their inclusion is important when creating a

model that can be applied in other areas of the country and used comparatively. For

example, the MMA may have a very low percentage of inhabitants who speak an

indigenous language simply because of its geographic location. Another city further to

the south would likely generate a higher threshold for the indigenous language variable.

In summary, all of the variables here provide their own caveats. The selection of

variables was based on similar marginalization studies conducted by the Consejo Estatal de Población de Nuevo León. With the vast options of variables available through the

2000 Census data, I determined that borrowing the variables used by a government

conducted study was the best manner to maintain consistency. I would conclude that the

10 variables used in this study are appropriate indicators of general marginalization, with

the exception of the income/employment variables, which would be better suited if

replaced with more accurate data, perhaps from a survey outside the 2000 Census.

The model generated here may also be applied using completely different variables.

While this study wanted to first determine the most marginalized areas of the city based

on a cross-section of educational, income, origin, and housing variables, the researcher

can easily conduct the analysis with a more specific set of variables geared toward one

issue, such as housing conditions.

Series 3. Marginalization Analysis Maps

Map 3.1‘Marginalized Areas in the Metropolitan Area of Monterrey’ shows the ranking

(high, medium, or low) for all AGEBs that passed the threshold for at least one variable


29

indicator. “High” are those AGEBs with marginalization values 7, 8, or 9; “Medium” are

those with values of 4, 5, or 6; and “Low” are those with values 1, 2, and 3. AGEBs that

did not pass the threshold for any variable indicators (a value of 0) are labeled ‘Not

Included in Study’ because they were not included in the ranking analysis. As expected,

the areas with highest rankings of marginalization are located on the fringes of the urban

footprint. There are a total of 31 ranked “High,” 69 ranked “Medium” and 310 “Low.”

Map 3.2 ‘Highly Marginalized Areas by Municipality’ shows the highly marginalized

areas in relation to the municipalities. In the Mexican case, this map is an essential step

in the analysis of marginalization across the metropolitan area. As outlined in the

introduction, the 9 municipalities of the MMA maintain separate administration and

programming. It is very important to note that the AGEBs included in the analysis

compose the urban footprint of the MMA. The recently added municipalities of Garcia

and Juarez contain additional AGEBs that are still considered ‘rural’ and are located

outside of the immediate reach of the MMA. Therefore, it is important to note that

although Map 3.2 marks the municipalities it is not inclusive of their reach. The map is

also limited because I removed a few AGEBs from the outer areas for which I did not

have 2000 Census data. Therefore, although Garcia and Juarez are by many indices the

poorest municipalities in the MMA, they are not represented as such by this map due to

their limited inclusion in the analysis.

Series 4. Example Block Analysis Maps

Map 4.1 ‘Escobedo Municipality: Block Analysis of Highly Marginalized Areas,’ is a

close-up map of one municipality and its 14 ‘highly marginalized’ AGEBs. I selected

Escobedo because it clearly had the largest amount of AGEBS ranked ‘highly

marginalized.’ The purpose of the Series 4 maps is to demonstrate how a smaller-scale

analysis can be conducted within the highly marginalized areas to determine which

blocks are in need of a particular service. For example, a social service agency may want

to map data on household nutrition, or the government may want to map where the

highest concentration of homes with dirt floors are located. Since I wanted to focus on

structural improvements, I chose the water access variables that I had for the block level.

Map 4.1 shows an example of how the principle water access variable (residences with

no piped water on the property) can be mapped across all the highly marginalized AGEBs

for one municipality.

I used Map 4.1 to select two AGEBs to serve as example study areas (‘A’ and ‘B’) for an

even closer-up analysis. Initially, I wanted to select an AGEB that had a ranking of 9, the

highest output in the analysis. However, I found that the few AGEBs with a ranking of 9

had too little available data at the block level. Some were AGEBs located along a river

or mountain range that had very few delineated streets and blocks with household data.

So, I selected two AGEBs that showed prominent block formations and that contained

sufficient data and a substantial number of houses.


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Maps 4.2-4.9 show how the water access variables can be mapped for a single AGEB.

These maps could be used by a municipal or private entity that is interested in water

infrastructure improvements for the most marginalized areas of the municipality. The

‘Study Area A’ maps show a diversity of access to piped-in water within one

neighborhood. In contrast, ‘Study Area B’ is an example of a neighborhood in which the

clear majority of all the blocks has no access to piped water. This is likely an area where

residents are receiving all of their water from a municipal distribution truck or from a

publically located tap. The ability to locate such areas remotely using GIS could be very

helpful to development agencies.

The purpose of these maps was to indicate how one variable could be analyzed at the

block level. Any of a number of variable types could be mapped based on the

researcher’s interest.


31

REFERENCES

Calderón Rendón, Gaby, Elvira Naranjo Priego & Gabriela Siller Pagaza (2003).

Demografía. UN DIAGNÓSTICO PARA EL DESARROLLO, Volumen 1, pp.

243-269. Instituto Tecnológico y de Estudios Superiores de Monterrey. Monterrey

N.L. México.

Fujii, Tomoki (2008). How Well Can We Target Aid with Rapidly Collected Data?

Empirical Results for Poverty Mapping from Cambodia. World Development, 36

(10), 1830–1842.

Guajardo Alatorre, Alicia Angélica, (2003). Análisis Estratégico del Área Metropolitana

de Monterrey. UN DIAGNÓSTICO PARA EL DESARROLLO, Volumen 1, pp.

460-498. Instituto Tecnológico y de Estudios Superiores de Monterrey. Monterrey

N.L. México.

Montes Avilés, Verónica (2003). Condición Socioeconómica. UN DIAGNÓSTICO

PARA EL DESARROLLO, Volumen 1, pp. 271-312. Instituto Tecnológico y de

Estudios Superiores de Monterrey. Monterrey N.L. México.


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APPENDIX

Step-by-Step Methodology

The research led me to a study conducted with 1990 data that served as my model.

Principally, I borrowed the same variables that this study used. The map generated with

the 1990 data is below, followed by my methodology steps.

Levels of Marginalization in the MMA for the year 1990

Level of Marginalization, 1990 (by AGEB)

Low

Medium

High

Very High

Not Specified

SOURCE: Consejo Estatal de Población de Nuevo León (COESPO), 1993. COMPILED BY CEDEM. Accessed from Montes Avilés, 2003.

Determine 10 variables for the analysis. The ‘model study’ of marginalization

(see above) was conducted with 1990 data. The 10 variables used were as

follows:


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1. Persons age 5 and older who speak an indigenous language.

2. Persons age 5 and older who did not reside in the state 5 years ago.

3. Persons age 15 and older who did not complete ‘primaria’ level of school.

4. Persons age 15 and older without any schooling post-‘primaria.’

5. Persons age 15 and older who are illiterate.

6. Residences without sewage connection.

7. Residences with only one room.

8. Residences with dirt floors.

9. Economically active persons who are currently unemployed.

10. Households that receive 2 or less minimum salaries.

In order to carry out a similar study with the 2000 Census data, I had to choose

the 10 variables that were closest as possible to the 1990 data variables. The 10

variables from the 2000 Census I chose were:

1. Persons age 5 and older who speak an indigenous language (HLENIND).

2. Persons age 5 and older who did not reside in the state 5 years ago

(PNORESENT95).

3. Persons age 15 and older who did not complete ‘primaria’ level of school

(PRIMIN).

4. Persons age 15 and older without any schooling post-‘primaria’

(P15MSINPOS).

5. Persons age 15 and older who are illiterate (PANALF15YM).

6. Residences without sewage connection (VIPASDRE).

7. Residences with only one room (VIPASCUARED).

8. Residences with dirt floors (VIPASPITI).

9. Persons age 12 and older who are currently unemployed (PODESOC). 10. Persons age 12 and older who receive 2 or less minimum salaries

(INGMM1A2SM).

Note that variables #9 & # 10 are distinct than variables #9 & # 10 in the 1990

study. This is likely due to a modification in the census.

Make main “Variables” data table with percentages of all 10 variables for

each spatial unit (AGEB). The data for the 10 variables were located in

numerous excel tables so I had to cut and paste all necessary 10 fields into a new

table, organized by the AGEB codes. For each variable, I used additional Census

data to calculate a percentage. I then placed all 10 variables in percentage form

(organized by AGEB code) into my final excel table. This is the ‘Variables’

table.

Prepare main ‘AMM_AGEBS” shapefile. The file I had was for all AGEBs in

the state of Nuevo León, so I wanted to reduce it to just the AGEBs in the MMA.

I did this by using the ‘Select by Attribute’ function. I selected using the field

containing the name of the municipality where each AGEB is located, and entered


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the names of the 9 MMA municipalities. Once I had the appropriate AGEBs

selected I created a layer from the selection and then exported the layer to make a

shapefile. I named this shapefile ‘AMM_AGEBS’. I added this ‘AMM_AGEBS’

shapefile to a new map and also added the newly created ‘Variables’ data table. I

joined ‘Variables’ to ‘AMM_AGEBS,’ using the AGEB code as the common

field. I then opened the attribute table and noted that some AGEBs included in

my shapefile did not have corresponding data from the ‘Variables’ table. I

decided to take out those outlying AGEBS since I didn’t have Census data for

them. I also removed some of the AGEBS in the municipality of Garcia and some

of the AGEBS in Juarez. I ‘removed’ these AGEBS by starting an editing session

in the Editing Toolbar, and deleting the fields from the attribute table. In the end, I

had an “AMM_AGEBS shapefile” with a total of 1070 AGEBS.

Choose thresholds for the variables. I had to familiarize myself with the data in

order to choose a threshold for what AGEBs would be included in the

marginalization analysis. In an excel table, I set all the variables for the 1070

AGEBs to display in descending order. I then navigated to line 107 (the top 10%

threshold for the variables) and noted the values at the line. They were as

follows:

1. HLENIND—1.70%

2. PNORESENT95—11.97%

3. PANALF15YM—5.89%

4. PRIMIN –17.74%

5. P15MSINPOS—49.13%

6. VIPAPITI—6.17%

7. VIPACUARED—13.71%

8. VIPASDRE –11.84%

9. PODESOC –1.02%

10. INGMM1A2SM—38.46%

By setting the above values as the threshold, I was including in the analysis the

top 10% most marginalized for each variable.

Run query and create layer for each variable. In ArcMap, I added the

AMM_AGEBS shapefile. Starting with HLENIND, I used the ‘Select by

Attribute’ function to run a query. I entered “HLENIND” “greater than or equal

to” “1.70.” (Note: 1.70 is the threshold value for HLENIND as indicated above.)

Once I had the selection (of all AGEBs with a HLENIND value greater than or

equal to 1.70), I created a layer from that selection. Once I had the new layer, I

exported it as a shapefile to my data folder. I gave this first layer the name

‘HLENIND,’ for the variable it indicated. I added the required map elements


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(title, north arrow, scale, source, legend, author, and date). I then repeated these

steps with the remaining nine variables, finishing with ten new shapefiles.

Generate a map for each variable. I added the AMM_AGEBS shapefile to a

new dataframe and then added the new HLENIND shapefile on top of it. I

symbolized the HLENDIND layer with a bright color and the AMM_AGEBS

layer with a neutral gray to serve as a background. I did this for each variable in

it’s own separate data frame. I formatted a layout with two dataframes on one

sheet, in order to end with 5 pages. I made sure to create the first sheet with the

desired layout, and then used it as a template for the remaining.

Combine variables to generate ‘marginalization ranking.’ From ArcMap, I

opened the attribute table and choose the Options button. I chose the option to

export the attribute table for AMM_AGEBS as a .dbf file, and then I opened it in

Excel. I deleted all of the rows of AGEB variables that I did not need for my

analysis. Starting with the first marginalization variable column (HLENIND), I

sorted the column in descending order. I then proceeded to replace all values

equal to and over the threshold with a 1, and all values under the threshold with a

0. I repeated this for each of the 9 remaining variable columns. Then I created a

new column and titled it ‘GM’ (Grade of Marginalization), and entered in it the

sum of all the variables columns (now made up of 1’s and 0’s) for each AGEB

row. The GM column contained values for the ranking ranging from 0 to 9. I

saved this excel table as “AMM_AGEBS_grados.”

Generate map to represent ranking of “Marginalized Areas in the AMM.”

In ArcMap, I added the AMM_AGEBS shapefile, and then added the

AMM_AGEB_grados Excel table; then I joined the two together. I wanted to

symbolize the newly created ‘GM’ column. The column contained values from 0

to 9 and I wanted to group them into my own categories (low, medium, and high).

In the symbology tab I selected ‘Quantities’ and selected “AMM AGEBS GM”

for the value field and ‘none’ for the normalization field. In the Classification

box, I set 4 classes and chose the ‘Manual’ breaks method. This allowed me to

set the breaks to 0, 3, 6, and 9. I then chose colors for each class and changed the

labels for each (0=Not included in study; 0.000001-3= Low; 3.000001-6=

Medium; 6.000001-9= High.) I added the required map elements.

Generate Map of Highly Marginalized Areas by Municipality. I used the

previous map as a template to begin this map. First I used the ‘Select by

Attribute’ tool and entered: “AMM_AGEBS_GM=7 OR AMM_AGEBS_GM=8

OR AMM_AGEBS_GM=9;” this gave me a selection of all the AGEBs I

determined to have a ‘high’ grade of marginalization. I created a layer of the


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selection and exported it to create the shapefile ‘AGEBS_GMHigh.’ I added the

new shapfile to the map. I adjusted the symbology for the AMM_AGEBS layer;

in ‘Categories’ I selected the field for the names of the municipality where each

AGEB is located and I then selected neutral fill colors with no outline for each

Municipality. In the Display tab, I adjusted the transparency of the layer to fade

the colors out a bit. In the layout, I added text labels over each Municipality.

Finally, I symbolized the ‘AGEBS_GMHigh’ layer with a narrow crosshatch over

the municipalities, to ensure the reader could see both layers. Finally I studied the

attribute table of ‘AGEBS_GMHigh’ to determine how many highly marginalized

AGEBs were located in each municipality. I added this information to a table in

Excel and placed it in the map layout.

Map one municipality for block-level analysis. Based on the previous map, I

determined the municipality with the highest number of highly marginalized

AGEBs (it was General Escobedo, at 14). I started a new ArcMap document and

added the AMM_AGEBs shapefile in a new dataframe. I used the ‘Select by

Attributes’ tool to select all AGEBs located in Escobedo, and I then made a layer

out of them and exported them to a shapefile, ‘AGEBS_Escobedo.’ I added it to

the dataframe and removed AMM_AGEBS. I also added the ‘AGEBS_GMHigh’

shapefile. I used the Clip tool to clip ‘AGEBS_GMHigh’ to ‘AGEBS_Escobedo,’

creating the ‘AGEBs_Escobedo_GMHigh’ shapefile. I then added the

‘Manzanas’ shapefile, which contained the block shapefiles for the entire state. I

clipped it to the ‘AGEBs_Escobedo’ layer to create the Manzanas_Escobedo’ shapefile. I clipped Manzanas_Escobedo’ to ‘AGEBs_Escobedo_GMHigh’ make another layer, Manzanas_Escobedo_GMHigh.’ I removed the ‘AGEBs_Escobedo’ layer and symbolized the Manzanas_Escobedo’ layer with a faded gray color. I made sure ‘Manzanas_Escobedo_GMHigh’ was on top and I symbolized it as a ‘Quantity’ using data already contained in the attribute table. I

entered as the value the variable ‘homes with no piped water on the property’ and

entered as the normalization ‘number of homes.’ This allowed me to symbolize

‘percent of homes with no piped water on the property.’ I used 5 classes with

Natural Breaks. I then examined the attribute table for

‘Manzanas_Escobedo_GMHigh’ to select two AGEBs with sufficient homes and population to provide good examples of block level analysis. I used graphic elements to highlight these areas on the map, labeling them ‘Study Area A’ and ‘Study Area B.’ I added the appropriate map elements.

Generate series of maps of Study Areas A & B to show availability of piped

water in the individual blocks. In a new dataframe, I added the

‘Manzanas_Escobedo_GMHigh’ shapefile. I manually selected the blocks that composed the AGEB I had chosen for ‘Study Area A.’ I created a layer from the


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selection and exported it to the shapefile ‘Manzanas_Area_A.’ I had four variables concerning water access that I wanted to represent with this shapefile, so I created four small maps, borrowing the layout from my 10 variable maps. The first variable was ‘homes with no piped water on the property,’ and I normalized

it by ‘number of homes’ to get percentage values. I followed the same steps in

new data frames for the remaining 3 variables ‘homes with no piped water in the

house;’ ‘homes with no bathroom;’ ‘homes with no piped water in the bathroom.’

I normalized all values by number of homes, and used Natural Breaks with 5

classes when possible (some had too few differentiation). I used a different color

gradation for each variable since the breaks were different. Upon completing the

4 maps for Study Area A, I used them as templates and following the same steps

for Study Area B. I made sure to include all necessary map elements, finishing

with 8 block-level analysis maps.

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