Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis
2020; National Institute of Environmental Health Sciences; Volume: 128; Issue: 9 Linguagem: Inglês
10.1289/ehp4030
ISSN1552-9924
AutoresKathryn C. Conlon, Evan Mallen, Carina J. Gronlund, Veronica J. Berrocal, Larissa Larsen, Marie S. O’Neill,
Tópico(s)Thermoregulation and physiological responses
ResumoVol. 128, No. 9 ResearchOpen AccessMapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis Kathryn C. Conlon, Evan Mallen, Carina J. Gronlund, Veronica J. Berrocal, Larissa Larsen, and Marie S. O’Neill Kathryn C. Conlon Address correspondence to Kathryn C. Conlon, One Shields Ave., MedSci 1, Davis, CA 95616 USA. Telephone: (530) 754-0689. Email: E-mail Address: [email protected] University of Michigan School of Public Health, Ann Arbor, Michigan, USA School of Medicine, University of California Davis, Davis, California, USA Search for more papers by this author , Evan Mallen University of Michigan Taubman College of Architecture and Urban Planning, Ann Arbor, Michigan, USA Georgia Institute of Technology School of City and Regional Planning, Atlanta, Georgia, USA Search for more papers by this author , Carina J. Gronlund University of Michigan School of Public Health, Ann Arbor, Michigan, USA University of Michigan Institute for Social Research, Ann Arbor, Michigan, USA Search for more papers by this author , Veronica J. Berrocal School of Information and Computer Science, University of California Irvine, Irvine, California, USA Search for more papers by this author , Larissa Larsen University of Michigan Taubman College of Architecture and Urban Planning, Ann Arbor, Michigan, USA Search for more papers by this author , and Marie S. O’Neill University of Michigan School of Public Health, Ann Arbor, Michigan, USA Search for more papers by this author Published:2 September 2020CID: 097001https://doi.org/10.1289/EHP4030AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Extreme heat poses current and future risks to human health. Heat vulnerability indices (HVIs), commonly developed using principal components analysis (PCA), are mapped to identify populations vulnerable to extreme heat. Few studies critically assess implications of analytic choices made when employing this methodology for fine-scale vulnerability mapping.Objective:We investigated sensitivity of HVIs created by applying PCA to input variables and whether training input variables on heat–health data produced HVIs with similar spatial vulnerability patterns for Detroit, Michigan, USA.Methods:We acquired 2010 Census tract and block group level data, land cover data, daily ambient apparent temperature, and all-cause mortality during May–September, 2000–2009. We used PCA to construct HVIs using: a) “unsupervised”—PCA applied to variables selected a priori as risk factors for heat-related health outcomes; b) “supervised”—PCA applied only to variables significantly correlated with proportion of all-cause mortality occurring on extreme heat days (i.e., days with 2-d mean apparent temperature above month-specific 95th percentiles).Results:Unsupervised and supervised HVIs yielded differing spatial vulnerability patterns, depending on selected land cover input variables. Supervised PCA explained 62% of variance in the input variables and was applied on half the variables used in the unsupervised method. Census tract–level supervised HVI values were positively associated with increased proportion of mortality occurring on extreme heat days; supervised PCA could not be applied to block group data. Unsupervised HVI values were not associated with extreme heat mortality for either tracts or block groups.Discussion:HVIs calculated using PCA are sensitive to input data and scale. Supervised HVIs may provide marginally more specific indicators of heat vulnerability than unsupervised HVIs. PCA-derived HVIs address correlation among vulnerability indicators, although the resulting output requires careful contextual interpretation beyond generating epidemiological research questions. Methods with reliably stable outputs should be leveraged for prioritizing heat interventions. https://doi.org/10.1289/EHP4030IntroductionExtreme heat poses a current and future threat to human health (Crimmins et al. 2016). In response to this threat, public health practitioners and researchers are tasked with developing preparedness, response, and mitigation plans and policies that protect those who are experiencing and who will experience most of the health burden related to extreme temperatures.Considerable progress has been made in recent years to understand the relationship between extreme heat and human health (Anderson and Bell 2011; Anderson et al. 2013), and findings from epidemiological studies have laid the groundwork for identifying population characteristics associated with adverse effects of extreme heat on human health (Basu and Samet 2002; Curriero et al. 2002; Gronlund 2014; Ostro et al. 2009; Schwartz 2005). Socioeconomic and demographic factors such as older age (Bouchama and Knochel 2002; Ostro et al. 2009), racial and ethnic minority status, low income, having less than a high school education (Hajat et al. 2005; Semenza et al. 1996; Stafoggia et al. 2006), being unmarried (Jones et al. 1982), air conditioning prevalence (O’Neill et al. 2005), and social factors such as living alone or having access to transportation (Semenza et al. 1996) have been associated with increased risk of mortality during extreme heat events. Measures of green space such as the percent impervious surface (Barnett et al. 2006; Hass et al. 2016) and having access to green space (Dadvand et al. 2016; Medina-Ramon and Schwartz 2007; Yeager et al. 2018) have garnered attention as protective area-level characteristics. Further, there is widespread agreement that the distribution of these heat-related risks varies across populations and communities (Ebi et al. 2018). With numerous variables to consider when determining a population’s risk of health effects from extreme heat, public health practitioners who are looking to translate research into actionable preventive programs are challenged to simplify a complex relationship.A commonly used approach to assess human health risk during hot weather is to characterize it in terms of measurable vulnerability. Vulnerability research grew in popularity in the context of social vulnerability, a unitless measure of the extent to which a population is resilient to natural disasters and hazards (Cutter et al. 2003; Flanagan et al. 2011). Although vulnerability can be defined in numerous ways (Cutter et al. 2003; Fussel 2007; Harlan et al. 2006; NRC 2010), it broadly consists of environmental, demographic, and population-specific health and societal characteristics. One definition of vulnerability is presented by Wilhelmi and Hayden (2010), who define vulnerability within a multifaceted top-down and bottom-up framework that draws on populations’ exposure, sensitivity, and adaptive capacity. The dynamic interactions between exposure, sensitivity, and adaptive capacity make characterizing vulnerability—a fluid, population- and locale-specific concept—challenging.In the United States, state and local public health practitioners are identifying populations and locations most vulnerable to environmental hazards such as extreme heat to design and implement protective interventions (Manangan et al. 2014). Public health departments in Michigan (Seroka et al. 2011), Minnesota (Minnesota Climate and Health Program 2012), New York State (Nayak et al. 2018), and San Francisco, California (San Francisco Department of Public Health 2013), for instance, have drawn from and used the methods put forth in Reid et al. (2009) to develop heat vulnerability indices and maps that consider current and future climate conditions. These methods have become the conventional approaches for incorporating environmental, demographic, and socioeconomic data to capture population-level heat vulnerability. Individually, characteristics associated with vulnerability can be quantitatively and qualitatively measured. Because of data limitations, vulnerability is often represented via proxy indicators rather than a direct measure. For example, a researcher may choose to represent heat exposure indirectly through estimating prevalence of impervious surfaces or lack of vegetation, both of which are associated with the urban heat island, or more directly through remotely sensed land surface temperatures (Bao et al. 2015; Wolf and McGregor 2013). These measures are then aggregated to create a single index of vulnerability. Characterizing vulnerability as a single measure is either often discussed or actually used as a tool for translating research into action via vulnerability maps (Harlan et al. 2013; Johnson et al. 2012; Reid et al. 2009; Wolf and McGregor 2013) that can inform policy and planning (Bradford et al. 2015; Hoppe et al. 2018; Johnson et al. 2012; Nayak et al. 2018).Principal components analysis (PCA) is a technique commonly used to construct heat vulnerability indices (Harlan et al. 2013; Reid et al. 2009). PCA is a dimension-reduction technique that can distill multiple, potentially correlated variables into new, independent constructs/factors; typically, the number of constructs is much smaller than the number of variables in the original data set. This technique can be an appealing approach for handling heat vulnerability data sets. The growing use of PCA to construct vulnerability indices for heat could extend to other climate-related exposures, such as floods, aeroallergens, and wildfires. Despite the increasing trend in developing single measures of vulnerability via indices (Bao et al. 2015), there have been few studies that have assessed the appropriateness of the methods or reliability of the products themselves (Reid et al. 2012; Tate 2012).Social vulnerability indices that have been constructed using PCA and non-PCA methods (Cutter et al. 2003; Flanagan et al. 2011) have been assessed. Validation studies of social vulnerability indices have indicated that the mapped products are sensitive to input data, suggesting that they should be interpreted with caution (Schmidtlein et al. 2008; Tate 2012). Although different methodologies for constructing vulnerability indices exist, here we focus on a methodology that is commonly used to construct HVIs—PCA—and conduct a critical assessment of indices produced with input data that were intended to capture similar constructs relevant to heat exposure (i.e., vegetated land cover or lack thereof) but were derived from different publicly available data sources.In recognition of the growing interest in identifying intraurban patterns of heat-related vulnerability, we explore three questions regarding PCA-derived heat vulnerability indices, using Detroit, Michigan, USA, as a case study. First, how and to what extent are heat vulnerability indices sensitive to physical environment input variables, specifically land cover measures, when describing spatial patterns of heat vulnerability? Second, what is the relationship between a heat vulnerability index (HVI) and all-cause mortality (2000–2009) on extreme heat days at both fine (i.e., block group) and neighborhood (i.e., tract) levels? Third, does screening for which variables are used when creating a heat vulnerability index based on their association with the health outcome (i.e., a supervised HVI) produce the same spatial patterns?Materials and MethodsStudy LocationDetroit, which covers 142 square miles, is home to roughly 670,000 residents, more than 80% of whom are African American; roughly 35% live below the poverty line; and about 14% are over the age of 65 ( www.census.gov/acs). Although located in the northern United States, it is common for Detroit to experience prolonged periods of heat and high humidity during the summer months. The City of Detroit and neighboring Southeast Michigan municipalities have been planning for heat events via an established network of cooling centers, outreach and education, utility assistance programs, and community emergency response teams (Sampson et al. 2013). The demographic and socioeconomic profiles of the resident population reflect a high level of sensitivity to high temperatures, suggesting this population is particularly at risk during extreme heat events (Gronlund et al. 2015).Data Sources and Variable SelectionWe created HVIs that represent the period between 2000 and 2009 in Detroit, Michigan. The Cities of Highland Park and Hamtramck, which are located within the boundaries of the City of Detroit, were treated as part of the City of Detroit for this analysis. Following the methodology established by Reid et al. (2009), we determined a priori the variables to include in the calculation of the HVI, with the addition of four different variables to represent nongreen space: nontree canopy, nonvegetation including water, nontrees, and distance to water (Table 1). Variables were defined so that an increase in value would correspond to a hypothesized increase in heat vulnerability. Demographic variables were extracted from the American Community Survey (ACS) 5-y estimates for 2006–2010, for the census tract and block group geographies for the City of Detroit, Michigan ( www.census.gov/acs). Our analysis was conducted at both the census tract levels and the block group levels given the interest in understanding intraurban patterns of heat vulnerability (Christenson et al. 2017; Johnson et al. 2012). Variables included proportions of the following groups in each tract or block group: over the age of 65, living alone, over the age of 65 and living alone, less than a high school education, living at or below the poverty level, and of race/ethnic minority status. Minority status (in relation to the Metropolitan Statistical Area; www.census.gov/acs) was defined as being not white and not Hispanic.Table 1 Descriptive statistics of tract (N=308) and block group (N=913) variables used in calculating HVIs; Detroit, Michigan, USA (2000–2009).Table 1, in three columns, lists heat vulnerability indices, tract (uppercase n equals 308), and block group (uppercase n equals 913).VariableTract (N=308)Block group (N=913)Mean (±SD)Min.Max.Mean (±SD)Min.Max.Over age 65a0.12 (0.06)0.000.400.12 (0.08)0.000.51Living alonea0.15 (0.11)0.001.000.14 (0.11)0.001.00Over age 65, living alonea0.04 (0.03)0.000.290.04 (0.05)0.000.43Minoritya0.91 (0.13)0.271.000.91 (0.14)0.031.00Less than HS educationa0.14 (0.07)0.000.410.14 (0.09)0.000.49Under poverty levela0.35 (0.14)0.000.730.35 (0.19)0.000.88Impervious surfaceb0.60 (0.11)0.000.880.60 (0.11)0.000.92Nontree canopyc0.95 (0.08)0.000.990.95 (0.07)0.000.99Nonvegetated, including waterd0.49 (0.14)0.150.990.48 (0.14)0.100.99Nontreesd0.68 (0.14)0.271.000.67 (0.15)0.181.00Distance to watere0.39 (0.23)0.001.000.41 (0.23)0.001.00Note: HS, high school; HVIs, heat vulnerability indices; Max., maximum; Min., minimum.aAmerican Community Survey (ACS), 5-y estimate (2006–2010).bNational Land Cover Database (NLCD), Impervious layer, 30m (2006).cNational Land Cover Database (NLCD), Tree canopy layer, 30m (2001).dSoutheastern Michigan Council of Governments (SEMCOG) Aerial photograph, 1m (2005).eESRI 10.4, River shapefile (2010).Variables to represent heat exposure were obtained from different sources and iteratively included in the calculation of the HVI to assess the sensitivity of PCA to input variables. Land cover, including the prevalence of impervious surface and nonvegetated land cover, is highly associated with heterogeneous intraurban heat exposure due to the urban heat island (Weng et al. 2004). We tested different variables for inclusion in the HVI to represent the proportion of nonvegetative land cover in each census tract or block group, specifically the proportion of impervious surfaces, nontree canopy, nonvegetation, and nontree areas. Although correlated (Figure 1), these variables estimate vegetative land cover differently. For instance, nontree canopy coverage is not equivalent to a measure of percentage of nontrees; the tree canopy could cover more area than the percentage of trees. Tree canopy coverage, however, is not always available for the geography and time period of interest. Vegetation variables in heat–health analyses are not always represented using the same metric (Yeager et al. 2018). Because vegetative land cover can be modified within a city—it is possible to change the amount, location, and type of vegetation—we consider vegetative land cover an index variable amenable to intervention by a given municipality (e.g., 10% increase in vegetation by geographic unit).Figure 1. Correlation heat map of variables used to calculate unsupervised HVIs. Data obtained from the U.S. Census Bureau (2010), U.S. Geological Survey (2001, 2006), and the Southeastern Michigan Council of Governments (2005). Note: HVIs, heat vulnerability indices.Land cover data was derived from three products. Each variable was defined to reflect the hypothesis that less vegetated land cover increases heat vulnerability. First, we extracted the impervious surface layer from the 2006 National Land Cover Database (NLCD) ( http://www.mrlc.gov/nlcd06_leg.php). NLCD is available for the conterminous United States and is often used for characterizing vegetative and impervious land cover (Pearsall 2017). The 30-m resolution product has been used in heat–health studies to characterize nonvegetated land cover. In this analysis, the impervious surface layer (“Impervious”), was calculated as a proportion of tract and block group and represented the commonly used nongreen space characterization. Second, we used the 30-m 2001 NLCD tree canopy layer (“Nontree canopy”) to calculate the proportion of a tract or block group that is not covered by tree canopy. The 2001 NLCD data set was the only publicly available tree canopy assessment for the City of Detroit for the study period (Homer et al. 2007). The NLCD tree canopy layer represents a snapshot of the tree canopy for the study area.We developed HVIs using fine-scale land cover data to estimate fine-scale vulnerability to heat, because some analyses indicate that NLCD underestimates vegetation (Nowak and Greenfield 2010). To do this, we used 1-m resolution aerial photography of the metropolitan Detroit area from late spring 2005, which we acquired from the Southeast Michigan Council of Governments (SEMCOG) Imagery product (SEMCOG 2005). Land cover classifications from this source included proportions of impervious surface, bare earth, open space, trees, and water. We defined “Nonvegetation” (Equation 1) at the block group and tract levels as: 1−∑(Open Space+Trees+Water). [1] We developed a final vegetative land cover variable, which was aerial photograph–derived “nontrees” (Equation 2) to represent the sole contribution of vegetation that is not trees, calculated as: 1−(Trees). [2] All land cover variables were averaged and assigned to census tracts and block groups in ArcMap using the Zonal Statistics tool on 2010 Census TIGER shapefiles.Distance to water, which has been demonstrated to have a cooling effect in urban microclimates (Steeneveld et al. 2014), was calculated as a straight-line distance from the Detroit River to the centroid of each tract and block group in ArcGIS (ArcMap, version 10.6). The measurements were scaled by dividing the largest distance to have a value between 0 and 1, so that 1 indicated the furthest distance from the river, with further distances hypothesized to confer higher vulnerability to heat exposure.Characterizing Extreme HeatIt is well established that mortality increases significantly at higher temperatures (Anderson and Bell 2009) and that apparent temperature on the day prior to and the day of death (AT01) captures the acute effect of heat (Barnett and Åström 2012). Hourly daily mean temperature and dew-point data were extracted from the National Centers for Environmental Information for airport weather stations in Detroit and were used to calculate apparent temperature (Global Surface Summary of the Day 2012). The 2-d mean apparent temperature (AT01) captures the acute effect of heat by averaging the apparent temperature for the day of and day prior (Barnett and Åström 2012). We defined extreme heat days as days during the study period (2000–2009) on which AT01 exceeded the month-specific 95th percentile for Detroit during the summer months (May–September).Unsupervised PCA and HVI CalculationThe first method for calculating the HVI applied PCA (PROC FACTOR, SAS version 9.3; SAS Institute Inc.) to demographic variables that have been associated with heat-related mortality (proportion>65 years of age, living alone, >age 65 and living alone, less than high school education, at or below poverty level, and of race/ethnic minority status) [i.e., “unsupervised” (Bair et al. 2006)] plus one of the four measures of nonvegetative land cover. Following Reid et al. (2009), we rotated the factor pattern, retained factors whose eigenvalues >1, normalized factor scores, summed the scores to calculate a final HVI value, and classified them by standard deviation. A total of eight unsupervised HVIs—four at the census tract level and four at the block group level, respectively—were calculated and mapped.Agreement MapsIn addition to mapping scores for each of the four individual HVIs for each census tract and block group, we present maps that illustrate the agreement between each of the HVIs. Specifically, we present maps that show, for each tract or block group, a) the difference between highest HVI value and the lowest HVI value obtained for the given tract or block group; and b) the number of individual HVIs with scores in the highest quartile for each census tract or block group (range 0–4). The agreement maps offer an alternative perspective that may be useful for determining areas with higher vulnerability relative to other locations in a given area.Supervised PCA and HVI calculation.We next applied a supervised PCA (Bair et al. 2006) approach, with variables selected based on associations with heat-related mortality. For this purpose, we obtained daily, geocoded mortality data from the Michigan Department of Community Health (MDCH) for the years 2000–2009. Institutional Review Boards (IRBs) for the University of Michigan (UM) and MDCH approved this study (UM IRB: HUM00067448). Daily nonaccidental deaths [International Classification of Diseases 10th revision (ICD-10): A00-R99, T67, X30] were assigned a census tract identifier and a block group identifier [in ArcGIS (ArcMap, version 10.6)], and subsequently aggregated at the census tract level and block group level. We limited the analysis data set to May–September, when extreme heat days (two consecutive days with apparent temperature above the month-specific 95th percentile for Detroit) were most likely to occur.To determine which variables to use in the supervised HVI, we first estimated the proportion of all-cause mortality that occurred on extreme heat days vs. other days during the May–September period. Then, we regressed the proportion of all-cause mortality that occurred on an extreme heat day on each variable used in the creation of the unsupervised HVI. We examined assumptions of independence and normality, finding that the errors were approximately normally distributed. Variables that were moderately significantly associated (p 0.99, Table S1), and our primary interest was in examining how well HVI explained differences among census tracts.Because the number of all-cause deaths on an extreme heat day is a variable that is likely to be spatially correlated, we assessed the assumption of independence of the residuals in the linear regression models. Particularly, we wanted to evaluate whether the residuals displayed spatial correlation. For this purpose, we fitted simple linear regressions with the proportion of all-cause deaths occurring on an extreme heat day per tract and block group as the outcome and each individual variable as the sole covariate. We used the OLS tool in ArcMap. We derived the residuals of each linear regression model and computed Moran’s I to assess the presence of residual spatial correlation and, thus, a need to account for spatial correlation in the error terms of the linear regression model. As the Moran’s I for the residuals corresponding to each simple linear regression were nonsignificant (data not shown), we did not perform spatial regression.Comparison of Unsupervised and Supervised HVIsTo evaluate the robustness of our findings across the different approaches used to derive HVI, we conducted simple OLS regression analyses, regressing the proportion of all-cause mortality occurring on extreme heat days on the tract- and block group-specific HVI values obtained using both unsupervised and supervised PCA. In the regression analyses, we modeled HVI both in its continuous form and categorized based on absolute scores (0–3, 4–6, 7–9, 10–12, 13–15). We performed trend tests by modeling ordinal variables with integer scores (1, 2, …, 5) assigned to each category.ResultsDuring the period of our analysis, Detroit contained 913 populated census block groups and 308 populated census tracts (Table 1). Population characteristics were similar between census tract and block group calculations. City residents were primarily African American and lived in areas with low vegetated land cover. Land cover measures differed from each other, with most of the city, on average, having almost no tree canopy coverage and about half of the city covered with nonvegetation, including water (Table 1). Land cover measures were highly correlated with each other, as were age and living alone status (Figure 1).The first factor in all eight unsupervised HVIs, at both geographic scales, was composed of three variables: over the age of 65, living alone, and over the age of 65 and living alone (Table 2). The first factor represents variables that describe age/isolation. The remaining five variables loaded onto the second and third factors; across all iterations, minority status loaded separately from education and income variables, which consistently loaded together. Variables that indicated lack of vegetation either loaded with minority status or onto the factor containing education/income. Land cover variables did not indicate the same direction. Impervious surface, nonvegetation, and nontrees all loaded in the negative direction with minority status, indicating that tracts and block groups with higher percent minority populations had higher vegetated land cover, whereas the nontree canopy coverage variable loaded with education and income variables, indicating that locations with higher proportions of residents with low income and low educational attainment occurred in areas where there was less tree canopy coverage. The directions of the factor loadings were consistent between census tract and block group analyses. On average, the three factors for tract HVIs accounted for 67% of the variance in the data; the three factors for block group HVIs accounted for about 64% of the variance (Table 2).Table 2 Variance explained and factor loadings for PCA outputs for tract level and block group level unsupervised HVIs calculated by including impervious surface (NLCD-derived), nontree canopy (NLCD-derived), nonvegetation including water (aerial-derived), and nontrees (aerial-derived), respectively, for Detroit, Michigan, USA.Table 2, in three columns, lists categories, tract, and block group. Tract and block group is sub divided into three columns, namely, factor 1, factor 2, and factor 3.TractBlock groupFactor 1Factor 2Factor 3Factor 1Factor 2Factor 3With impervious surface (NLCD) Factor loading Over age 650.810.030.200.83−0.040.10 Living alone0.76−0.11−0.260.760.08−0.11 Over age 65, living alone0.91−0.07−0.020.910.060.01 Minority0.300.080.720.150.110.79 Less than HS education−0.010.78−0.260.230.66−0.19 Living under poverty level−0.020.85−0.04−0.190.810.15 Distance to water−0.22−0.340.67−0.09−0.560.45 Impervious coverage0.110.20−0.610.12−0.28−0.61 Variance explaineda Eigenvalue2.261.961.022.301.701.02 % Variance explained28.224.612.728.821.212.0With nontree canopy (NLCD) Factor loading Over age 650.80−0.050.190.83−0.050.14 Living alone0.780.08−0.030.770.08−0.08 Over age 65, living alone0.920.080.030.910.040.04 Minority0.13−0.110.920.07−0.070.90 Less than HS education0.020.68−0.330.270.58−0.30 Living under poverty level−0.070.670.00−0.160.54−0.14 Distance to water−0.20−0.740.16−0.07−0.790.04 Nontree canopy0.030.720.410.050.730.34 Variance explaineda Eigenvalue2.311.961.082.311.741.04 % Variance explained28.924.513.528.921.713.0With nonvegetation including water (aerial) Factor loading Over age 650.800.210.010.830.12−0.03 Living alone0.77−0.23−0.070.76−0.130.07 Over age 65, living alone0.91−0.020.070.910.000.05 Minority0.280.760.010.150.760.13 Less than HS education−0.01−0.210.800.24−0.190.67 Living under poverty level−0.030.010.85−0.180.110.82 Distance to water−0.230.58−0.42−0.100.52−0.52 Nonvegetation, including water0.15
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