See publication - University of Texas at El Paso
Transcripción
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 156 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. *** 157 158 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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 159 160 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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- 161 162 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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. 163 164 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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 165 166 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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 167 168 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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 169 170 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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 171 174 D INÁ MICA S LOCAL ES DE L CAMBIO AMBIEN TA L GLO BAL 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. 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Hazards., 52:369-389. Universidad Autónoma de Av. del Charro 450 N. 32310. Ciudad Juárez, Chihuahua, México [email protected] 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