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See publication - University of Texas at El Paso
Dinámicas locales
del cambio ambiental
global
Aplicaciones de
percepción remota y
análisis espacial en la
evaluación del territorio
Universidad Autónoma
de Ciudad Juárez
Ricardo Duarte Jáquez
Rector
David Ramírez Perea
Secretario General
Dinámicas locales
del cambio ambiental
global
Coordinadores
Aplicaciones de
percepción remota y
análisis espacial en la
evaluación del territorio
Erick Sánchez Flores
Rolando E. Díaz Caravantes
Manuel Loera de la Rosa
Secretario Académico
Erick Sánchez Flores
Director del Instituto de Arquitectura, Diseño y Arte
Ramón Chavira Chavira
Director General de Difusión Cultural
y Divulgación Científica
Universidad Autónoma de Ciudad Juárez
Universidad Autónoma de Ciudad Juárez
CONTENIDO
© 2012 Universidad Autónoma de Ciudad Juárez,
Avenida Plutarco Elías Calles #1210,
Fovissste Chamizal, C.P. 32310
Ciudad Juárez, Chihuahua, México
Tel : +52 (656) 688 2100 al 09
Primera edición, 2012
Impreso en México / Printed in Mexico
http://www2.uacj.mx/publicaciones/
ISBN: 978-607-9224-80-6
7
Introducción
SECCIÓN I. DINÁMICA DE USO Y
COBERTURA DEL SUELO
17
FICHA CATALOGRAFICA
Percepción remota para el
análisis de la distribución y
cambios de uso de suelo en
zonas áridas y semiáridas
José Raúl Romo-Leon, Willem
J. D. van Leeuwen y Alejandro
Castellanos Villegas
49
A fragmentação natural de
uma paisagem em mosaico:
Campos Gerais do Paraná,
sul do Brasil
Rosemeri Segecin Moro, Valquiria
Martins Nanuncio y Karine
Dalazoana
77
La edición, diseño y producción editorial de este
documento estuvo a cargo de la Dirección General de
Difusión Cultural y Divulgación Científica, a través de
la Subdirección de Publicaciones
Proyección tendencial de
cambio 2010 y 2030 en la
cobertura de suelo de la
región de Burgos mediante
cadenas de Markov
M. Patricia Vela Coiffier y Diego
Fabián Lozano García
111 Análisis espacial de la
Corrección: Jesús José Silveyra
Cuidado de la edición: Subdirección de Publicaciones
Diseño de cubierta y diagramación:
dinámica de cambio de
uso de suelo y vegetación
en el Municipio de Juárez,
Chihuahua
Ma. De Lourdes Romo Aguilar y
José Reyes Díaz Gallegos
SECCIÓN II. MONITOREO DEL
MEDIO AMBIENTE URBANO
155 Characterizing climate
change risks and informing
adaptation strategies in
the Ciudad Juárez-El Paso
metropolitan region based
on spatial analyses of
extreme heat-vegetation
abundance-population
vulnerability relationships
Gilberto Velázquez-Angulo, Raed
Aldouri, Timothy Collins, Sara
Grineski, María de Lourdes Romo
Aguilar, Faraj Aboargob, Abdelatif
Eldeb, Yolanda McDonald, and
Felipe Poblano–Amparán
179 La demanda social de agua
potable en la dinámica de la
cobertura vegetal del área
peri-urbana: estudio de caso
en el norte de México
Rolando Enrique Díaz Caravantes,
Erick Sánchez Flores y Lara Wiebe
Quintana
205 Mapeo de ciudades con
datos LIDAR
Fabiola D. Yépez Rincón y D.
Fabián Lozano García
Gilberto Velázquez-Angulo,1
Raed Aldouri,2
Timothy Collins,2
Sara Grineski,2
María de Lourdes Romo
Aguilar,3
Faraj Aboargob,2
Abdelatif Eldeb,2
Yolanda McDonald2 y
Felipe Poblano-Amparán1
1
Universidad Autónoma de
Ciudad Juárez. Departamento
de Ingeniería Civil y Ambiental
Av. del Charro 450 Nte. 32310.
Ciudad Juárez, Chihuahua,
México
[email protected]
[email protected]
2
University of Texas at El Paso
500 W University Ave., El Paso,
TX. 79968, USA
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
3
El Colegio de la Frontera
Norte-Ciudad Juárez,
Av. Insurgentes 3708. 32350.
Ciudad Juárez, Chihuahua,
México
[email protected]
Characterizing climate
change risks and informing
adaptation strategies in
the Ciudad Juárez-El Paso
metropolitan region based
on spatial analyses of
extreme heat-vegetation
abundance-population
vulnerability relationships
Abstract
There are significant human impacts associated with climate change. Extreme heat
events are expected to increase in frequency
and magnitude, causing significant health
impacts for exposed human populations.
Certain population subgroups are expected
to be particularly vulnerable to the impacts
of extreme heat events, such as people of
lower socioeconomic status and the elderly.
Urban greening has been proposed as a climate change adaptation strategy, since vegetation cover creates cooler microclimates.
This paper presents a methodology for integrating remotely-sensed
imagery and socio-demographic data to
facilitate geographic information system
(GIS)-based spatial examinations of population vulnerability to extreme heat
and urban greening adaptation options
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D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL
at the neighborhood level. Extreme heat exposure (based on land surface temperature [LST] estimates), vegetation abundance (based on the Normalized Difference
Vegetation Index [NDVI]), and cumulative heat vulnerability (based on Mexican and U.S. census data) were
spatially characterized for neighborhoods throughout
the Ciudad Juárez-El Paso metropolitan region at the
U.S.-Mexico border. GIS-based integration of these data
enabled two types of analyses: (1) extreme heat, vegetation and cumulative heat vulnerability mapping;
and (2) spatial statistical analyses of LST – vegetation
abundance – cumulative heat vulnerability relationships. This study identified 10 neighborhoods in each
city with the strongest relationships between high heat
vulnerability, high heat, and low vegetation. Results
clarify relationships between urban vegetation, cumulative heat vulnerability and extreme heat exposure at
the neighborhood level, and can be used to help inform
effective and efficient urban “greening” programs.
Keywords: climate change, extreme heat, NDVI, vulnerability, U.S.-Mexico border.
Resumen
El cambio climático tiene impactos significativos en los seres humanos. Se espera que los eventos de calor extremo
se incrementen en frecuencia y magnitud, causando importantes impactos en la salud de las poblaciones humanas expuestas a dichos eventos. Ciertos subgrupos de la
población son, particularmente, vulnerables a los impactos
de los eventos de calor extremo, como las personas de nivel
socioeconómico bajo y los adultos mayores. El incremento
en la cobertura de vegetación urbana (urban greening en
inglés) ha sido propuesto como una estrategia de adaptación al cambio climático, ya que las plantas crean microclimas más frescos.
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
Este artículo presenta una metodología para
integrar imágenes de sensores remotos y datos sociodemográficos utilizando sistemas de información geográfica (SIG) para facilitar el análisis espacial de vulnerabilidad de la población a extremos de calor y opciones
de adaptación por medio del incremento en la cobertura
de vegetación urbana a nivel de colonia o vecindario. La
exposición a calor extremo (basada en estimaciones de
temperatura de la superficie [LST, por sus siglas en inglés]), la abundancia de vegetación (basada en el Índice
Normalizado de Diferencia de Vegetación [NDVI, por
sus siglas en inglés]) y la vulnerabilidad por acumulación de calor (basada en datos de los censos de México y
EE. UU.) fueron caracterizados espacialmente para vecindarios en la región metropolitana de Ciudad JuárezEl Paso en la frontera México-EE. UU. La integración
de estos datos en SIG permitió dos tipos de análisis:
1) mapeo de calor extremo, vegetación y vulnerabilidad
por acumulación de calor; y 2) análisis estadístico espacial de las relaciones entre temperatura de la superficie,
abundancia de vegetación y vulnerabilidad por acumulación de calor. Este estudio identificó 10 vecindarios en
cada ciudad con la relación más fuerte entre alta vulnerabilidad al calor, altas temperaturas y baja vegetación.
Los resultados clarifican las relaciones entre vegetación
urbana, vulnerabilidad por acumulación de calor y exposición a altas temperaturas a nivel de vecindario, y
pueden ser usados para facilitar el informar sobre programas efectivos y eficientes sobre el incremento en la
cobertura de vegetación urbana.
Palabras clave: cambio climático, calor extremo, NDVI,
vulnerabilidad, frontera México-EE. UU.
***
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Introduction
Global climate change is an increasingly pressing environmental
and public health concern. Major human health impacts by climate change are anticipated to occur in the coming decades due
to changes in the environment, including increased heat, changes
in precipitation regimes, and degraded air quality. For the western
U.S.-Mexico border region, the Intergovernmental Panel on Climate
Change (IPCC, 2007a) projects temperature increases of 3 to 5 oC
by 2100, with possible decreases of 5 to 8% in precipitation. Since
heat waves have already increased towards the end of the 20th
century, and will continue to increase in the 21st century (IPCC,
2007a, b), it is imperative that adaptation measures be taken in order to better protect populations expected to be more vulnerable
to extreme heat due to climate change, including the elderly and
people of low socioeconomic status (IWGCCH, 2010). This chapter
presents a methodology for integrating remotely-sensed imagery
and socio-demographic data to facilitate geographic information
system (GIS)-based spatial examinations of extreme heat, cumulative heat vulnerability and urban greening adaptation at the
neighborhood level. Extreme heat exposure, vegetation abundance and cumulative heat vulnerability, were mapped for neighborhoods throughout the Ciudad Juárez-El Paso metropolitan region
at the U.S.-Mexico border, and spatial analysis techniques were
used to identify a subset of neighborhoods where urban greening
programs could be most effectively and efficiently implemented.
Population vulnerability and extreme heat
The IPCC (2007b) projects that the environmental effects of climate change will impact the health and wellbeing of people around
the world, especially vulnerable populations with reduced ability to
cope (English et al., 2009). Because of the differential spatial impacts
associated with climate change, an expanding body of research focuses on describing, analyzing, and modeling risks. Reducing risks
requires spatial assessment – involving identification of environmental stressors likely to affect a given place and the vulnerability
of the people likely to be affected – and the development of inter-
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
ventions that are effective across a range of climate change-related
hazards (Cutter et al., 2000). Spatial approaches for the assessment
of societal vulnerability to climate change-related exposures have
been developed and applied to countries at global scales (Brooks
et al., 2005) as well as to smaller areas at finer national and subnational scales (Collins and Bolin, 2007; Reid et al., 2009). We are
aware of just a few fine-scale spatial assessment models in settings
where climate change has the potential to generate transnational
impacts (Grineski et al., 2012; Collins et al., 2012).
The IPCC (2007a, b) reported that heat waves increased
toward the end of the 20th century and projected that climate
change will cause more frequent, more intense, and longer heat
waves worldwide. Pockets along the interior U.S.-Mexican border
(including the El Paso-Ciudad Juárez area) have been identified as
heat vulnerability “hot spots” (Reid et al., 2009). The acute effects of
extreme heat events on human health outcomes (e.g., heat stress,
mortality) have been well-documented (Basu and Samet, 2002;
Curriero et al., 2002; McMichael et al., 2003). Extreme heat causes
more deaths annually than all other extreme weather events combined in the U.S., and is a leading cause of weather related mortality worldwide (Kalkstein and Greene, 1997; Luber and McGeehin,
2008). A study of temperature related mortality in 12 cities in lowand middle-income countries concluded that urban populations in
the less developed world are adversely affected by high temperatures and may be especially vulnerable in the future to the direct
impacts of extreme heat under climate change (McMichael et al.,
2008). The interior northern Mexican city of Monterrey (with social
and biophysical characteristics comparable to Juárez) displayed a
particularly impressive increase in mortality in association with
temperature extremes (McMichael et al., 2008).
Urban greening
Urban greening has been proposed as a climate change adaptation strategy since vegetation cover creates cooler microclimates.
The effects of extreme heat can be mitigated through targeted
efforts to reduce the heat island effect created by the lack of vegetation and high thermal absorbance. Initiatives to reduce the heat
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S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
island effect are gaining in importance because of their potential
to address several urban problems, including heat-related illness,
energy demand, greenhouse emissions, air pollution, and storm
water management (Luber & McGeehin, 2008).
Urban greening strategies include installing green roofs,
planting trees and vegetation in parking strips or abandoned lots,
green alleys, and implementing urban agriculture (Ebi et al., 2008;
Foster et al., 2011). Urban greening helps to mitigate urban heat as
vegetation reduces air temperatures through the direct shading
of dark surfaces and by evapotranspiration; these processes can
create an “oasis” effect (Kurn et al., 1994).
The benefits of urban greening extend beyond of microclimate effects. For example, a study carried out in 2003 in Portland,
Oregon (Semenza et al., 2007; Ebi et al., 2008), showed an increased sense of community, improvements in mental health, and the
expansion of social interaction through the implementation of
a neighborhood-based strategy that included installing hanging
gardens green roofs and planter boxes.
Study area
Ciudad Juárez and El Paso form one conurbation in the Rio Grande/
Bravo basin in the Chihuahuan Desert. El Paso has a population of
approximately 740 000, while Juárez has nearly 1.5 million residents.
Ciudad Juárez, the 5th largest city in México, was rapidly growing
with an economy dominated by industrial production (Grineski and
Collins, 2010), until drug-related violence and the recession slowed
in-migration and commerce beginning in 2008. Juárez has a reputation for being a relatively high-wage Mexican city, but an unpublished study conducted by El Colegio de la Frontera Norte in 2006
found that the median annual household income for Juárez was
$9890, while the statistic for Mexico was $11 460 (pesos converted
to 2007 American dollars). El Paso County is a majority-minority
context, with 82% of residents being Hispanic, and a median income of $36 333 (based on 2006-2010 estimates), which is well below
the U.S. national average (U.S. Census Bureau, 2012). Figure 1 provides a contextual perspective on the study area.
Figure 1. The Ciudad Juárez and El Paso County study area at the U.S.-Mexico border
Methodology
Unit of analysis
While matching geographical units across country boundaries can
be problematic, we attempted to address this issue by selecting
the most equivalent geographic units in area/population size for
analysis between the U.S. and Mexican censuses (as per Collins et
al., 2009; Collins et al., 2012). These units are census block groups
(BGs) in the U.S. and Áreas Geoestadísticas Básicas (AGEBs) in
Mexico. The analysis includes 518 AGEBs in Ciudad Juárez and 513
BGs in El Paso County (with population data for 2000 aggregated
based on 2010 census boundaries) (figure 1). We refer to AGEBs
and BGs as “neighborhoods” for the sake of representational par-
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simony, recognizing that they do not conform to residents’ varied
constructions of neighborhood boundaries.
Analysis approach
The analysis was conducted in three linked phases, with Ciudad
Juárez and El Paso County visualized as one urban area, but
analyzed separately to identify the neighborhoods in each city
most suitable for the implementation of urban greening programs. In the first phase we used remotely sensed imagery to
develop neighborhood-level measures of extreme heat exposure (mean land surface temperature) and vegetation cover (mean
normalized differential vegetation index –NDVI–) for each city. In
the second phase, we conducted principal components analyses
(PCA) of eight heat vulnerability indicators for each city to limit
the number of variables and create independent components
for inclusion in cumulative heat vulnerability indices for Ciudad
Juárez and El Paso. Third, to determine the neighborhoods most
in need of greening in each city we employed a two-step spatial
analysis approach using biLISA (bivariate local indicators of spatial autocorrelation) tests. We first analyzed the spatial relationship between vegetation and heat in order to identify clusters of
neighborhoods characterized by high heat and low vegetation
where greening programs have the greatest biophysical potential
to reduce temperatures. As a second step we analyzed the spatial relationship between biophysical suitability and cumulative
heat vulnerability in order to identify neighborhoods where greening programs are most needed as a means to reduce vulnerable
people’s exposure to extreme heat.
Characterizing extreme heat
High land surface temperature (LST) during summertime is a major health risk factor. In large metropolitan regions urban heat islands can be determined using the thermal band from Landsat
images. In order to determine LST, Landsat images (Landsat 4, 5,
and 7) were selected for days with average high temperatures above 100 degree Fahrenheit.
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
Five summer daytime images from Landsat 7 (with zero
cloud cover and of high quality) were selected so that stable LST
values could be calculated. The images were selected to center
on the year 2000 (2000-06-10, 2000-07-28, 2000-08-13, 2001-06-13
and 2001-07-15), when census data were collected by the U.S.
and Mexican governments, in order to synchronize data used in
analyses.
ENVI version 4.8 and ESRI ArcGIS 10 were used for image
processing. Thermal band pixel values for the images were calibrated and converted to radiance values; parameters required for
the calibration like gain, offset, sun elevation and acquisition day
were read from the header file attached to each image. The radiance values were then converted using the normalized emissivity function that applies the inverse of the Planck function to derive Kelvin temperature values. Then, using a band math function,
Kelvin temperatures were converted to Fahrenheit in order to facilitate interpretation.
Once temperature values in degrees Fahrenheit were obtained in raster format, the mean LST values for each neighborhood (BG/AGEB) in vector format were created. The resulting shapefiles were loaded on the raster image and converted
to individual regions of interest (ROI), and the statistical parameters for LST were calculated and exported to text files. The
main parameter needed for our analysis (mean LST) was then
extracted from the text file and saved to a CSV file that was later joined with BG/AGEB shapefile. Since multiple images were
used, the data were combined and the average of all images for
mean LST was calculated. Figure 2 depicts mean LST values for
Ciudad Juárez and El Paso County neighborhoods; it shows that
the neighborhoods have a mean LST of 94 – 116 °F. Table 1 provides descriptive statistics for mean LST, separately for Ciudad
Juárez and El Paso County.
Characterizing vegetation cover
To characterize vegetation cover for the El Paso-Ciudad Juárez
area the Normalized Difference Vegetation Index (NDVI) was determined for the five selected images used to calculate LST.
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S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
were then combined to create mean NDVI values for all BGs/AGEBs in the study area. Figure 3 depicts mean NDVI values for Ciudad Juárez and El Paso County neighborhoods. Table 1 provides
descriptive statistics for mean NDVI separately for both cities.
Figure 2. Neighborhood level heat (based on mean LST) for Ciudad Juárez and El Paso
County.
The NDVI is an index measuring vegetation presence relating near infrared (NIR) and visible band (RED):
Figure 3. Neighborhood level vegetation (based on mean NDVI) for Ciudad Juárez and El
Paso County.
Valid values range between -1 and 1, and common range for green
vegetation is 0.2 – 0.8 (Rouse et. al., 1973).
To calculate NDVI values for pixels in each image the
original images were clipped to smaller images covering the El
Paso-Ciudad Juárez area, which facilitated processing. Then, NDVI
values were calculated with the resulting images having values
ranging between 0 and 0.8. In order to calculate the mean NDVI
for each BG/AGEB, any pixel values outside of the vegetation range
were masked, and assigned a value of zero.
The neighborhood shapefiles were overlaid on each NDVI
image and converted to ROI; then the statistical parameter of interest was calculated (mean NDVI). The data for multiple images
Characterizing heat vulnerability
The literature on social vulnerability in the contexts of health and
environmental hazards clarifies characteristics associated with increased risk to extreme heat exposures, and provides a foundation
for selecting heat vulnerability measures and constructing cumulative heat vulnerability indices. We used the heat vulnerability index construction approach of Reid et al. (2009) as a general guide.
Heat vulnerability was measured with a suite of eight indicators, chosen because they relate to documented dimensions of
social vulnerability to extreme heat and because they are compatible between the two countries (as per Collins et al., 2009; Collins
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et al., 2012). Heat vulnerability indicators were constructed from
2000 census data obtained from the U.S. Bureau of the Census
and the Mexican Instituto Nacional de Estadística y Geografía
(INEGI). Our heat vulnerability indicators correspond with those
used by Reid et al. (2009) and include two measures of socioeconomic status (mean income and mean education –in years of formal schooling–), percent elderly (age ≥ 65 years), total population
density, percent disabled (a proxy for pre-existing health problems
and reduced capacity to mitigate heat exposure), percent lacking
telephone (a proxy for social isolation and reduced emergency
response capacity), and two measures of housing-related vulnerability (percentages lacking kitchen and plumbing, both of which
are proxies for a lack of air conditioning). Descriptive statistics for
each of the social vulnerability indicator variables are included in
table 1.
Principle component analyses: An inventory of neighborhood
socio-demographic attributes can help identify isolated variables
related to heat vulnerability (e.g., an elderly population needing assistance during an extreme heat event), but it cannot address how
multiple characteristics of neighborhoods interact to amplify heat
vulnerability. For example, the level of vulnerability in a neighborhood that contains high concentrations of the elderly and infirm,
low-income residents, poorly-educated adults, and people lacking
air conditioning may be substantially greater than the result of
each attribute assessed in isolation. Therefore, to characterize cumulative heat vulnerability, we reduced the social vulnerability
indicator variables using principal component analysis (PCA) (as
per Reid et al., 2009). PCA identifies the underlying dimensions of
a large set of variables and mathematically transforms data into
a smaller set of components based on variable intercorrelations.
Varimax rotation maximizes the loadings on each factor, especially when factors exhibit correlation. Prior to running the PCA for
each city, the mean income and mean education variables were
reversed (by multiplying each by -1), since these variables are inversely related to heat vulnerability. For both Ciudad Juárez and El
Paso County the three components retained in the analysis were
extracted using PCA with a varimax rotation, and each had an
Eigenvalue above 1.0. We treated component loadings for an in-
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
dividual social vulnerability variable as being significant at ≥ 0.5
and ≤ -0.5.
Heat vulnerability: components and construction of indices:
The principal components analysis (PCA) of neighborhoods resulted in three broad components that explain 75.1% and 67.1% of
the cumulative variance in Ciudad Juárez (CJ) and El Paso County
(EP) respectively. For each city these three components and the
neighborhood variables they represent are summarized under
three overarching themes –housing-related risk, socioeconomic
disadvantage, and concentrations of the elderly and infirm–.
The first component represents 40.5% (CJ) and 25.6% (EP)
of total variance in the two datasets (Eigenvalue = 3.243 [CJ], 2.048
[EP]) and includes the following three variables associated with
housing-related vulnerability: percent lacking plumbing (.787 [CJ],
.840 [EP]), percent lacking a kitchen (.881 [CJ], .793 [EP]), and percent lacking a telephone (.881 [CJ], .731 [EP]). While the analysis
lacks an air conditioning variable (important for coping with extreme heat), homes without plumbing or kitchens are typically of
poor quality and do not have access to evaporative cooling, the
most common form of air conditioning in both Ciudad Juárez and
El Paso County. A lack of home air conditioning has been shown to
be a key risk factor for heat-related mortality in numerous studies
(Braga et al., 2001; Curriero et al., 2002; Kaiser et al., 2001; Naughton
et al., 2002; Semenza et al., 1996). While also reflecting poor quality
housing, the percent lacking telephone variable captures constrained access to information resources via communications technologies, which can amplify risk during extreme environmental
events (Guha-Sapir and Lechat, 1986). Additionally, lacking a telephone indicates social isolation, a well-known heat-related mortality risk factor (Klinenberg, 2003, Naughton et al., 2002, Semenza
et al., 1996).
The second component represents 19.8% (CJ) and 24.5% (EP)
of total variance (Eigenvalue = 1.580 [CJ], 1.963 [EP]) and includes
two variables associated with socioeconomic disadvantage: mean
income-reversed (.931 [CJ], .949 [EP]) and mean education-reversed
(.878 [CJ], .955 [EP]). These relationships align with empirical research, which demonstrates that heat-related vulnerability is shaped by low socioeconomic status (SES), including low incomes and
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education levels (Harlan et al., 2006; Künzli, 2010). Higher rates of
heat-related mortality have been found for people of low SES (Chan
et al., 2010; Medina-Ramon et al., 2006; IWGCCH, 2010). In sum, for
neighborhoods with concentrations of people of low SES, mutually
reinforcing disadvantages provide a relatively narrow range of choice in preparing for and responding to extreme heat exposures.
The third component represents 14.8% (CJ) and 17.0% (EP)
of total variance (Eigenvalue = 1.186 [CJ], 1.363 [EP]) and includes
three variables associated with concentrations of the elderly and
infirm: population density (.748 [CJ], .796 [EP]), percent age ≥ 65
years (.602 [CJ], .558 [EP]), and percent disabled (.804 [CJ], .589 [EP]).
Numerous studies have shown that the elderly and infirm (reflected in the percent disabled variable) are two particularly vulnerable groups to heat-related morbidity and mortality (Naughton
et al., 2002; Semenza et al., 1999; Stafoggia et al., 2006; Stafoggia
et al., 2008). Empirical research confirms that population density
represents an important dimension of vulnerability to extreme
heat. The density of people relates to heat vulnerability in an aggregate sense. All things being equal, the more densely populated
a neighborhood exposed to extreme heat, the greater the vulnerability of people in the area. Aside from helping identify vulnerable population concentrations, in the context of extreme heat,
high population densities and concentrations of buildings have
been associated with increased population exposure and sensitivity (Harlan et al., 2006; Jenerette et al., 2007; Medina-Ramon et al.,
2006; IWGCCH, 2010; Smoyer, 1998).
After using PCA to create the three independent components
for each city described above, we constructed two separate cumulative heat vulnerability indices: one for Ciudad Juárez and one for El
Paso County. To more accurately represent those components that
contribute the most to variability in heat vulnerability across each
city’s neighborhoods (based on the variables included in the PCA),
we weighted each component score by the proportion of variance it explained, thereby forcing components to contribute to the
cumulative heat vulnerability indices based on their explanatory
power (Schmidtlein et al., 2008; Wood et al., 2010). After componentweighted cumulative heat vulnerability scores were calculated for
all neighborhoods within each city, those social vulnerability scores
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
were standardized, classified by standard deviations from the study
area mean, and mapped. Figure 4 depicts the spatial distribution
of the cumulative heat vulnerability indices for neighborhoods in
Ciudad Juárez and El Paso County. Results are discussed below.
Figure 4. Neighborhood level heat vulnerability for Ciudad Juárez and El Paso County.
Identifying neighborhoods most suitable for urban greening programs
To understand the role of vegetation in cooling a desert environment and study area neighborhoods most in need of vegetation
(to reduce vulnerability to heat stress), we employed a three step
process: 1) visualization; 2) aspatial correlations; and 3) spatial
correlations. For visualization we created and examined maps of
extreme heat, vegetation abundance, and cumulative heat vulnerability. Then we ran (aspatial) Pearson correlations between heat,
vegetation, and heat vulnerability. Finally, to determine the neighborhoods most in need to greening in both cities, we employed a
two-step spatial analysis approach using the biLISA (bivariate local indicators of spatial autocorrelation) test, available in the open
source GeoDa software. This test describes the linear association
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S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
between a variable value in a particular location and the values for
other variables at neighboring locations (Uthman, 2008). For each
neighborhood the test reports the Moran’s I coefficient, its p-value,
and which of the four classes of spatial autocorrelation –High-High,
High-Low, Low-Low, and Low-High– each of the neighborhoods with
a significant Moran’s I (p < .05) falls into. Using the biLISA we first
explored the overlap between vegetation and heat. Second, we ran
biLISA for the heat vulnerability index and a variable representing
the spatial clusters of high heat-low vegetation determined in the
first step (neighborhoods received a score of 1 if they had a significant high heat-low vegetation relationship and a 0 if they did not).
coefficients for heat vulnerability with heat (-0.012, p = 0.793) and
vegetation (0.072, p = 0.102) were not significant. In Juárez, vegetation and heat were also significantly (p < .001) correlated with each
other (corr = -0.62). Unlike in El Paso, heat vulnerability in Juárez
was significantly correlated with higher heat (0.195, p < 0.001) and
less vegetation (-0.165, p < 0.001); more vulnerable neighborhoods
tended to be hotter and less vegetated. However, it is also important
to examine these relationships at a local level, to determine if there
are correlations in specific parts of the study areas.
Results
After running the biLISA for heat and vegetation (figure 5) we
found significant clustering of high heat and low vegetation in
the urban cores of both cities. In total, 25% of neighborhoods in
Juárez and 48% of neighborhoods in El Paso fell within this category. Significant clustering of low heat and high vegetation (see
figure 5) was also found in the river valley areas near the international border in west El Paso, east El Paso and east Juárez; fewer
neighborhoods were found in this category: 6% of neighborhoods
in Juárez and 11% in El Paso.
Then, to determine areas most in-need of greening, we ran
the second biLISA; results are shown in figure 6. In both cities, the
urban core emerged as an area with high heat, low vegetation and
high heat vulnerability (see figure 6). Approximately 16-17% of
neighborhoods in both cities fell within this urban greening candidate category, indicating strong overlap between hotter, less vegetated, and highly vulnerable neighborhoods in the study areas.
Given the practical difficulties in greening 100 neighborhoods in
both cities, we used the Moran’s I statistic to identify the 10 neighborhoods in each city with the strongest relationships between
the heat vulnerability and “high heat-low vegetation” variables:
those at “extreme need for greening”. These are indicated with
a darker shade in figure 6. They are located in the central city of
El Paso and in the western arroyo neighborhoods just west of the
central city in Juárez (Anapra).
Visualization
The hottest neighborhoods in the study area are those containing
open desert and those in heavily urbanized areas (figure 2). The coolest
areas are found in the Rio Grande/Bravo river valley on the El Paso
side of the border where agriculture (e.g., pecan farms, cotton fields)
predominates. In terms of vegetation (figure 3), the highest densities
are found in the river valley and in large urban parks in both cities.
For heat vulnerability (figure 4), El Paso’s downtown and semi-rural
colonias emerge as particularly at-risk; the more affluent areas on the
east and west side of town are notably less vulnerable. In Juárez, the
fringes of the city house the most vulnerable residents, including those who live just outside of the downtown area in the western arroyos
near the mountains; those living in the urbanized, formally developed
neighborhoods east of the urban core are the least vulnerable.
Aspatial correlations
In contrast with the local spatial correlations to be presented next,
aspatial Pearson correlations can be considered global indicators
of relationships across the study areas. In El Paso, vegetation and
heat were significantly (p < 0.001) correlated with each other (corr
= -0.71), indicating substantial overlap between hotter temperatures and less vegetation throughout the study area. The correlation
Spatial relationships
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fabric, the implementation of community-based urban greening
programs can be further justified through the social regeneration
co-benefits they would likely provide (Semenza et al., 2007). Second,
achieving the goals of climate adaptation strategies, like urban
greening, demands the cooperation and participation of multiple
stakeholders, including business leaders, community leaders, civil
society organizations (CSO’s), the general public, and government
agencies (Ebi et al., 2008). Binational joint strategies should also be
considered. Third, it is necessary to consider that the study region
is at risk to water scarcity and that the use of low water demand,
desert-adapted shade tree species should be encouraged, since
they have been shown to mitigate micro-to-local scale heat island
effects (Chow and Brazel, 2011). Fourth, additional co-benefits of the
implementation of urban greening can be considered, for example,
the use of green alleys as a stormwater management strategy are
estimated to be 3-6 times more effective than conventional methods (Foster et al., 2011). In the case of Ciudad Juárez, where urban
flooding is a common problem in the rainy season, urban greening
can be used as a climate change adaptation strategy not only for
extreme heat events, but for extreme precipitation events as well.
Acknowledgments
S ECCI Ó N I I . M o n i t o re o d e l m e d i o a m b i e n t e u r b a n o
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Por décadas la disponibilidad de suelo se
ha considerado como uno de los principales
limitantes del crecimiento de las ciudades.
Sin embargo, en las ciudades del norte de
México, usualmente ubicadas en regiones
áridas o semiáridas, la disponibilidad de
agua se ha convertido también en un factor crítico para su crecimiento y desarrollo
sustentable. Como se ha demostrado en diversos estudios sobre el tema, en la actualidad una planeación integral de las ciudades está obligada no sólo a considerar sus
alrededores o área peri-urbana en función
del suelo, sino también de la disponibilidad
de recursos críticos, por ejemplo, el agua.
La consecuencia de que haya mayor
interés en el uso del suelo que en el uso del
agua es que el análisis de cómo estos dos
recursos naturales interactúan, ha sido pocas veces abordado. Sin embargo, entender
mejor esta relación es un requisito para
avanzar al desarrollo urbano sustentable,
ya que la demanda social de agua para uso
urbano produce severas alteraciones al medio natural, tales como el agotamiento de
acuíferos y una disminución en las coberturas vegetales. Dada la importancia del

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