Mapping Vulnerability to Climate Change and Variability in Hamilton, Ontario Using Geographical Information Systems 1 McMaster Institute of Environment and Health, McMaster University, Ontario June Cheng, MD, MPH Bruce Newbold, Director, MIEH and Professor, School of Geography & Earth Sciences
August 2010
Abstract Climate change and variability are known influence human health outcomes, particularly in certain vulnerable subpopulations. This study uses Geographical Information Systems to help visualize the geographical locations of such vulnerable groups within Hamilton, Ontario. Several variables (by census tracts) were selected to highlight vulnerable groups based on previous literature. Principal component analysis was then applied to these variables to find two main underlying components, “elderly living alone” and “low-income immigrants”. A vulnerability index was then created for each census tract based on the two main components, and the results visualized using GIS. Local indicators of spatial association analysis were used as a final step to find statistically significant clusters in the vulnerability indices.
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This paper was completed while the 1st author was working in the McMaster Institute of Environment & Health (MIEH, http://www.mcmaster.ca/mieh) as a rotation in her Community Medicine Residence Program studies (winter 2010). She acknowledges the support of MIEH. The authors also acknowledge the GIS support of Irene Tang.
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Two areas in Hamilton were found to be most vulnerable as defined by our study, the Hamilton Core and East Hamilton. Policies relating to preventing adverse health outcomes from climate change, especially heat waves, should be targeted to these areas.
Introduction Although the term “climate change” is often used synonymously with "global warming," climate change implies a variety of climactic effects that threaten to cause significant economic, environmental and social changes, in addition to global warming. It is now widely accepted in the scientific community that climate change is occurring primarily as a result of human dependence on fossil fuels (Flannery, 2005). With the effects of climate change growing more obvious, human beings have begun to experience negative consequences resulting from fossil fuel consumption. Visible consequences include polar ice cap and glacier melting, and the intensification of floods, droughts, and cyclones (Shiva, 2008).
A 2007 report from the Intergovernmental Panel on Climate Change has shown that many consequences of climate change – such as increasing intensity of heat waves, extreme weather events, social and economic disruption, changing infectious disease patterns – impact human morbidity and mortality (Confalonieri et al., 2007). Haines and Patz (2004) have summarized the health effects currently known to relate to climate change. Climate change and variability can, through differing mechanisms, lead to heat-related illnesses and deaths, extreme weather-related health effects, air pollution-related health effects, allergic diseases, infectious
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diseases, water-borne and food-borne diseases, vector-borne and rodent-borne diseases, malnutrition, storm surge-related drowning and injuries, and health problems associated with displaced populations.
It is ironic that most of the adverse health impacts due to climate change will be seen in the less developed countries (LDCs) (Shiva, 2008). By emission records, LDCs have not traditionally been leading contributors to greenhouse gas emissions (EPA, 2009). Although China surpassed the United States as the top emitter of greenhouse gases in 2006, and although India’s emissions are also growing, America remains the largest per capita emitter of these gases, producing them at roughly 5 times the per capita rate of China, and approximately 20 times the per capita rate of India (Union of Concerned Scientists, 2009). Despite the international disparities, however, the global nature of the impact of climate change means that we must work internationally to mitigate climate change and to ensure a livable future. Though the 2009 United Nations Climate Change Conference in Copenhagen concluded with no substantial agreements, concerned parties must continue their efforts to ensure international cooperation. However, while global mitigation is the key to the future public health of human communities, we must deal with current climate change consequences through adaptation.
Much as the negative effects of climate change disproportionately affect populations in LDCs, vulnerable populations in developed countries (DCs) are also more likely to experience disproportionately worse health outcomes due to climate change. The IPCC has defined vulnerability as the sum of risk factors minus the totality of
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protective factors which ultimately determine whether an individual, population or subpopulation experiences adverse health outcomes (IPCC, 1995). For example, in the case of heat-related health effects, vulnerable populations are those whose temperature-mortality relationship is modified by medical, social, environmental and other factors (Basu & Samet, 2002; Koppe et al., 2004; Larrieu et al., 2008; Rey et al., 2009). As seen in heat waves experienced by American and European cities during the past century, local factors such as climate, topography, heat-island magnitude, income, and the proportion of elderly people are important in determining the underlying temperature–mortality relationship in a population (Curriero et al., 2002; Hajat, 2006; McGeehin & Mirabelli, 2001; O’Neill & Ebi, 2009).
In the case of the August 2003 European heat wave, patterns of vulnerability were noted by researchers, and these patterns correlated with increased negative health effects. By observing excesses in mortality rates, Fouillet et al. (2006) found that the elderly and people living alone are particularly vulnerable to heat waves. Similarly, in a study looking at all major heat waves in France from 1971 to 2003, Rey et al (2007) found that mortality ratios increased with age for subjects aged over 55 years. Also using the measure of excess mortality, it was concluded that the elderly, women, and people with specific diseases were particularly vulnerable to heat waves. Both papers note, however, that while vulnerable populations suffer from higher rates of excess mortality, no segment of the population was considered protected from the risks associated with heat waves (Fouillet et al, 2006, Rey et al, 2007). Researchers have found vulnerable groups also by studying Chicago heat
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waves. Kaiser et al (2007) concluded that during the 1995 heat wave in Chicago, Blacks were disproportionally affected when they examined mortality risk and displacement. Naughton et al (2002) found in their study of the 1999 Chicago heat wave, that those living alone and those not leaving home daily had the highest odds ratios of heat-related deaths.
Balbus and Malina (2009) have summarized research to date on the relationship between climate and population vulnerability; they have shown that vulnerable subpopulations including children, pregnant women, older adults, impoverished populations, and outdoor workers are disproportionately affected by specific climate change-related events including heat stress, air pollution, extreme weather events, water and food-borne illness, and vector-borne illness. The purpose of this report is to map populations that are vulnerable to the above effects of climate change in Hamilton, Ontario using Geographic Information Systems (GIS). In particular, the paper focuses on those populations, such as the old or those living alone, that may be more vulnerable to extreme heat events. Such information is valuable from a planning perspective because, in times of need, it will help public health professionals to target interventions effectively according to the geographical distribution of vulnerable populations. A visual depiction of population with vulnerable characteristics will help with this goal.
Literature Review: Population Vulnerability and Climate Change IPCC has produced a useful typology for conceptualizing vulnerability to climate change; this typology consists of 3 parts (IPCC 3rd Assessment Report, 2001):
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1. Adaptive capacity: the ability of a system to adjust to actual or expected climate stresses or to cope with the consequences of climate stresses. Adaptive capacity is considered a function of wealth, access to technology, education, access to information, skills, access to infrastructure, access to resources, and stability and management capabilities. Because adaptive capacity is often distributed unevenly across and within societies, our study focuses on the adaptive capacity of specific subpopulations in Hamilton, Ontario. 2. Sensitivity: the degree to which a system will be affected by a change in climate, either positively or negatively. 3. Exposure: the degree of climate stress upon a particular unit of analysis; it may be represented as either long-term change in climate conditions, or by changes in climate variability, including the magnitude and frequency of extreme events.
The City of Hamilton (2006 population = 504,559) (Statistics Canada, 2006), is located in Southern Ontario on the western end of the Niagara Peninsula. It is 70 km from Toronto’s city center. Its population consists of the third highest percentage in Canada of foreign-born residents at 20%, after Toronto and Vancouver. The population is 84.8% white, 3.0% South Asian/East Indian, 2.8% Black, 1.9% Chinese, 1.5% Aboriginal, 1.2% Southeast Asian, 1.1% Latin American, 1.1% Arab, 0.8% Filipino, and 1.8% other (Statistics Canada, 2006). Individuals aged 65 years and older make up 14.9% of the population (Statistics Canada, 2006).
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Traditionally, Hamilton’s economy had been based around the steel and heavy manufacturing industries. Within the last decade, the decline of these industries and the recession of 2009 meant that the city has been affected by rising unemployment and poverty, with unemployment hovering around 8.0-8.5% in late 2009 (Arnold, 2009). Hamilton shares Ontario’s highest poverty rate with Toronto, with 20% of its population living in low-income households. The poverty rate for children under 12 is 25%. Similarly, the poverty rate is higher than average for seniors over 75 at 29%, and it is also higher than average for recent immigrants at 52%. Comparing Hamilton’s rates of poverty to Canada’s poverty rate of around 15-17% over the past three decades, Hamilton stands out for the size of its population (Johnson, 2006).
Because the focus of this paper is on vulnerable populations in Hamilton which will be most affected by climate change, it will help to identify the precise spaces where these vulnerable populations are located. At the conclusion of this report, there are policy suggestions to improve Hamilton’s capacity to respond to the needs of vulnerable populations affected by climate change.
Existing Vulnerability Studies Toronto Public Health has done similar vulnerability mapping of Toronto to include at-risk populations. Its social vulnerability index looked at ambulance calls, heatrelated hospitalizations, and deaths in given neighbourhoods, and it also looked at at-risk places in terms of temperature (indoor & outdoor), housing quality, and the
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presence of urban heat islands (Monica Campbell, Environmental Protection Office, 2008 Presentation). City-wide vulnerability studies have also been undertaken in California, with researchers constructing a social vulnerability index that focuses on three main risk factors: the proportion of population that is elderly, the measure of social isolation (>65 yrs and living alone), and the proportion of population below poverty line (H. Margolis & P. English, California Department of Health Services). Cutter et al. (2003) developed a Social Vulnerability Index that included 11 factors and attempted to describe vulnerability of American counties to environmental hazards, employing a method called principle component analysis. To date, there does not appear to be considerable similarity among the methods of these analyses. There does appear to be some overlapping variables used in each study, however there are very limited numbers. A consensus on identifying vulnerable populations to climate change has not yet been reached within the published literature, but more than likely needs to vary depending on the context and question. Given, for example, Hamilton’s large immigrant populations and the large proportion of its population in poverty, any analysis of population vulnerability must include such covariates, in addition to commonly accepted ones based on age or living arrangements, two correlates often associated with population vulnerability and extreme heat effects.
Methods and Data For our study, we performed a principal component analysis of the factors we have identified from the literature as factors related to climate change vulnerability. We then used a variety of mapping methods within GIS to map the vulnerable
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characteristics we identified and to identify the geography of community vulnerability.
For the purpose of the study, the boundaries of City of Hamilton are as outlined in Figure 1. The study area includes the downtown core, suburban areas including Dundas, Stoney Creek, Ancaster, Waterdown, and some rural areas.
Figure 1. Hamilton, Ontario.
Population variables were defined at the census tract scale, with data obtained from Statistics Canada based on the 2006 census. The City of Hamilton data by census tract extracted for the study include: population density; percent of the population older than 85 years; percent of the population living alone; proportion of the
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population without formal education; percent immigrant/refugee population; he proportion of the population without English or French as a first language; and percent low family income population (See Appendix 1 for further details). ArcGIS 9.0 was used for visualization of each variable in the Hamilton area (See Appendix 1 for individual maps representing the variables selected in this study).
Following the initial selection of variables, correlation analysis was performed to determine the degree (strength) of correlation between individual variables, with the intent of removing highly correlated effects between any two variables by removing one of the two from our analysis. Correlation analysis was performed using SPSS 16.0 (See Appendix 2). Variables highly correlated with each other were identified using a threshold of r greater than 0.6. No two variables were found to be highly correlated as defined by this threshold. As such, no variables were removed from our study after correlation analysis. However, we did note that most variables did correlate with other variables to some degree.
Principal component analysis (PCA) was then used to find underlying components that might explain the correlations and variability among the variables we have chosen. PCA is a mathematical method that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible (Joliffe, 2002). PCA allows us to find the underlying processes among all variables. Varimax rotation was performed on the results of
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the principal component analysis to maximize the variance accounted for by the components. The components were then visualized with ArcGIS 9.0. Components were added together to create the vulnerability index, which was mapped by census tract, and the Local Indicator of Spatial Association (LISA) was used to evaluate the existence of clusters in the spatial arrangement of the vulnerability index. In our analysis, the application of LISA enhanced our existing map of vulnerability by grouping the vulnerable populations into statistically significant geographic clusters. This spatial clustering was illustrated in a summative map of vulnerable populations. High-high clustering relationships were found using p<0.005. High-high relationships in this analysis refers to census tracts with high values for the vulnerability index that also have neighbouring census tract with high values for the vulnerability index, therefore identifying those census tracts with higher vulnerability index which cluster together, with p<0.005 defined as statistically significant.
Results and Discussion Table 1 is the Varimax rotated component matrix. Two components were extracted. Component 1 loaded highly on percentage population with no English or French, percentage with no formal education, and percentage with low family income; this component accounted for 40% of the variation in the data. We chose to name this component â&#x20AC;&#x153;low-income immigrants.â&#x20AC;? Component 2 loaded highly on population density, percentage of population older than age 85, and percentage living alone; this component accounted for 21% of the variation in the data. This component was
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named â&#x20AC;&#x153;elderly living alone.â&#x20AC;? Together, the two components accounted for 61% of all the data variance. Table 1. Varimax Rotated Component Matrixa Component 1 Pop_Den 0.365 Pr_A85 -0.290 Pr_LL 0.120 Pr_NoEF 0.902 Pr_IMM 0.790 Pr_NoEdu 0.636 Pr_L_FamInc 0.318 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations.
2 0.675 0.564 0.886 0.139 0.016 0.167 0.696
The two components were combined into one map for visualization purposes. The values presented in Figure 2 were weighted by the eigenvalues of each variable in the principal component analysis. A simple addition was performed to achieve the summed values represented in the map, named the Vulnerability Index.
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Figure 2. Map of Vulnerability in Hamilton Based on Main Components.
This vulnerability map of Hamilton clearly visualizes which areas contain those individuals who are most likely to be vulnerable to heat waves. That is, these census tracts capture areas in the city that have larger proportions of “low-income immigrants” and “elderly living alone.” In order to provide these data with statistical significance, we created a LISA map (Figure 3). It higlights the areas of spatial clustering that have high-high relationships with neighbouring areas, showing the concentration of areas of vulnerability, with a significance level of p<0.005.
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Figure 3. Local Indicators of Spatial Association Map of Vulnerability in Hamilton.
The LISA (Figure 3.) map demonstrates that two areas are likely more vulnerable to climate change, specifically heat-related morbidity and mortality. The larger of the two neighbourhoods is near the downtown area, arbitrarily named ‘core neighbhourhoods’. The smaller area of vulnerability is located in east Hamilton, sonamed. Core neighbourhoods include the neighbourhoods of Strathcona, Kirkendall North, Central, Beasley, Landsdale, Gibson, Durand, Corktown and Stinson, as defined by the City of Hamilton (see Appendix 4). East Hamilton neighbourhoods include the neighbourhoods of Kentley and Riverdale West. The groups were highlighted based on the components from our principal component analysis – “low income immigrants” and “elderly living alone.”
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Figure 4. Surface Temperature in Hamilton
As highlighted in Figure 4, which represents average summer surface temperature, both the downtown core, as well as east Hamilton experience higher temperatures than surrounding areas, this is more pronounced in the downtown area. The combination of higher surface temperatures and presence of vulnerable groups in these areas reinforce the fact that these groups of neighbourhoods should be targeted for intervention during future heat waves.
Policy Implications As the effects of climate change become a reality in North American cities, climate change adaptation strategies should be established in every jurisdiction, as well as at every level of government. As illustrated in this paper, these adaptation
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strategies might begin with an assessment of the community, and they can begin by identifying those at most risk of harm. Efforts at adaptation should start in these neighbourhoods. Each community will need strategies tailored to its unique populations and needs. The following are some suggestions of potential policy areas.
Adaptation (short-term policy) options include: o
Heat warning systems for the city
o
Outreach to immigrant populations, as well as elderly populations living in isolation â&#x20AC;&#x201C; warning systems and programs
o
Increasing air conditioning in private and public spaces
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Cooling houses â&#x20AC;&#x201C; especially in neighbourhoods highlighted by this paper
o
Have a disaster response plan
Adaptation (long-term policy) options include: o
Tree planting
o
Weatherization of homes
o
Improving building ventilation
o
Poverty reduction
o
Increasing access to health care
In implementing above suggested or other related policies, policy makers would benefit from having community partners who support like ideas and causes. Such potential partners include, Clean Air Hamilton, Environment Hamilton, Hamilton Area Eco-Network, Hamilton Eat Local Project, Hamilton Street Railway, MACgreen,
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Green Venture, Climate Change Champions, Hamilton Community Garden Network, Hamilton.reuses.com, Hamilton Cycling Committee, and many others. By working together towards a common goal, Hamilton can help protect its most vulnerable from the effects of climate change.
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