Applied Science, Faculty of
Civil Engineering, Department of
DSpace
UBCV
Lucas, Matilda
Master of Applied Science - MASc
University of British Columbia
This thesis investigates healthcare accessibility in the Lower Mainland of British Columbia, which is a geographic area consisting of the regional districts of Metro Vancouver and the Fraser Valley. The research addresses three key questions: (1) What methods and metrics can be used to measure healthcare accessibility, and how can they be adapted to the Lower Mainland 's urban context? (2) What is the current state and spatial distribution of access to healthcare services in the Lower Mainland? (3) How does accessibility change with demographic factors and under disaster scenarios? The Enhanced Two-Step Floating Catchment Area (E2SFCA) method, adapted with a distance decay function, was employed to measure accessibility across Traffic Analysis Zones (TAZs) in the Lower Mainland using network information provided by TransLink. The analysis incorporated total bed capacity in hospitals with emergency rooms as a proxy for healthcare supply, and used normalized scores to compare the results across several case studies. The scenarios evaluated include a baseline assessment, a focus on senior and employed populations, and disaster simulations such as Disaster Response Routes (DRRs) and bridge closures following earthquakes. Results reveal significant spatial disparities in healthcare accessibility. While central and well-connected areas exhibit high accessibility, peripheral regions, such as Chilliwack and areas east of Abbotsford, face notable disadvantages, particularly for seniors. Disaster scenarios highlight the vulnerability of certain regions, where restricted routes and infrastructure disruptions can severely limit access to healthcare services. Despite the limitations of the network model, which excluded local roads and public transit, the findings underscore critical gaps in healthcare and transportation planning. By providing a detailed spatial understanding of healthcare accessibility, this thesis offers valuable insights for policymakers, healthcare providers, and urban planners in developing inclusive and disaster-resilient systems.
EXPLORING HOSPITAL ACCESSIBILITY IN BRITISH COLUMBIAâS LOWER MAINLAND by Matilda Lucas B.Eng., University of Applied Science Berlin, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Matilda Lucas, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: EXPLORING HOSPITAL ACCESSIBILITY IN BRITISH COLUMBIAâS LOWER MAINLAND submitted by Matilda Lucas in partial fulfilment of the requirements for the degree of Master of Applied Science in Civil Engineering Examining Committee: Dr. Amy Kim, Professor, Department of Civil Engineering, UBC Supervisor Dr. Carlos Molina Hutt, Associate Professor, Department of Civil Engineering, UBC Supervisory Committee Member iii Abstract This thesis investigates healthcare accessibility in the Lower Mainland of British Columbia, which is a geographic area consisting of the regional districts of Metro Vancouver and the Fraser Valley. The research addresses three key questions: (1) What methods and metrics can be used to measure healthcare accessibility, and how can they be adapted to the Lower Mainland 's urban context? (2) What is the current state and spatial distribution of access to healthcare services in the Lower Mainland? (3) How does accessibility change with demographic factors and under disaster scenarios? The Enhanced Two-Step Floating Catchment Area (E2SFCA) method, adapted with a distance decay function, was employed to measure accessibility across Traffic Analysis Zones (TAZs) in the Lower Mainland using network information provided by TransLink. The analysis incorporated total bed capacity in hospitals with emergency rooms as a proxy for healthcare supply, and used normalized scores to compare the results across several case studies. The scenarios evaluated include a baseline assessment, a focus on senior and employed populations, and disaster simulations such as Disaster Response Routes (DRRs) and bridge closures following earthquakes. Results reveal significant spatial disparities in healthcare accessibility. While central and well-connected areas exhibit high accessibility, peripheral regions, such as Chilliwack and areas east of Abbotsford, face notable disadvantages, particularly for seniors. Disaster scenarios highlight the vulnerability of certain regions, where restricted routes and infrastructure disruptions can severely limit access to healthcare services. Despite the limitations of the network model, which excluded local roads and public transit, the findings underscore critical gaps in healthcare and transportation planning. By providing a detailed spatial understanding of healthcare accessibility, this thesis offers valuable insights for policymakers, healthcare providers, and urban planners in developing inclusive and disaster-resilient systems. iv Lay Summary This study explores how accessible healthcare services are to the population of the Lower Mainland region. Using a method that measures how easily people can reach hospitals, and the availability of beds at these hospitals, the research identifies healthcare access levels throughout the region of study. Results show that people living and working in core urban centres throughout the region have better access to hospitals, while those in more rural parts of the region (i.e., Chilliwack and further from urban centres), face challenges. The study also investigates how disruptions like bridge closures and emergency route restrictions can make healthcare access even more difficult, particularly for vulnerable populations. The findings highlight the need for better disaster planning, improved transportation options, and targeted healthcare services to ensure that all residents, especially those in underserved areas, can access the care they need. This research aims to support urban planning and policy decisions that make healthcare access fairer and more resilient for everyone. v Preface This thesis represents a collaborative effort involving contributions from academic advisors, industry professionals, and supporting organizations. The research program was developed under the guidance of Dr. Amy Kim, Professor and Dr. Carlos Molina Hutt, Associate Professors, both at the University of British Columbia, in collaboration with the author, Matilda Lucas. Throughout the research process, I worked closely with Ph.D. student Kiranjot Kaur, whose expertise and support were invaluable in refining the study's methodology and ensuring the robustness of the analysis. The identification and design of the research program involved close collaboration with local stakeholders, including Ryan So from TransLink, who provided access to transportation network files, and Sam Orr from Vancouver Coastal Health (VCH), who offered valuable insights into hospital capacity and healthcare accessibility metrics. These collaborations ensured that the research was aligned with regional priorities and practical applications. The research activities, including network setup and map creation, were conducted using ArcGIS, while data analysis was performed using RStudio. The author was responsible for performing these tasks, along with interpreting the results and drawing conclusions. This thesis incorporates portions of previously published work. A part of the research was presented at the Canadian Transportation Research Forum (CTRF) in Kelowna on May 13th, 2024 under the title Healthcare Accessibility in Seismic Events: A Metro Vancouver Case Study with the following co-authors: Dr. Amy Kim, Dr. Carlos Molina Hutt and Kiranjot Kaur. This paper includes content from Chapters 1, 2, and 3 of this thesis, as well as results from the baseline scenario presented in Chapter 5. vi Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface ............................................................................................................................................. v Table of Contents ........................................................................................................................... vi List of Tables .................................................................................................................................. ix List of Figures ................................................................................................................................. x Acknowledgements ....................................................................................................................... xii Chapter 1 Introduction .................................................................................................................... 1 1.1 Background and Context ................................................................................................. 1 1.2 Research Questions ......................................................................................................... 2 1.3 Context ............................................................................................................................ 5 1.3.1 Geographical Context .......................................................................................................... 6 1.3.2 Health Authority Context ....................................................................................................... 10 1.3.3 TransLink Context ............................................................................................................. 13 Chapter 2 Literature Review ......................................................................................................... 14 2.1 Accessibility .................................................................................................................. 14 2.2 Healthcare Accessibility ................................................................................................ 16 2.3 Measures of Healthcare Accessibility ........................................................................... 17 2.3.1 Rudimentary Measures .......................................................................................................... 17 2.3.2 Kernel Density Models ...................................................................................................... 17 2.3.3 Gravity Measures ................................................................................................................... 18 2.4 Critical Factors in Healthcare Accessibility .................................................................. 20 2.5 2SFCA and E2SFCA .................................................................................................... 21 vii Chapter 3 Data Sources ................................................................................................................. 25 3.1 TransLink ...................................................................................................................... 25 3.2 Hospital Capacity ......................................................................................................... 28 3.3 Disaster Response Routes ............................................................................................. 31 3.4 Bridge Data .................................................................................................................. 32 Chapter 4 Methodology ......................................................................................................... 33 4.1 Network Set-Up ............................................................................................................ 33 4.2 E2SFCA ....................................................................................................................... 37 Chapter 5 Scenarios and Results ........................................................................................... 47 5.1 Overview of Accessibility Scores ................................................................................. 47 5.2 Scenario-Specific Results ............................................................................................ 49 5.2.1 Scenario 1 Baseline ........................................................................................................... 49 5.2.2 Scenario 2 Disaster Response Routes ................................................................................ 53 5.2.3 Scenario 3 Senior Population ............................................................................................ 62 5.2.4 Scenario 4 Employment .................................................................................................... 65 5.2.5 Scenario 5 Bridges ................................................................................................................. 69 5.3 Comparative Analysis of Scenarios .............................................................................. 75 5.3.1 Comparison of Baseline and Senior Population Scenario ................................................. 75 5.3.2 Comparison of Baseline and Employment Scenario ............................................................ 78 5.4 Discussion of Findings ................................................................................................. 85 Chapter 6 Conclusions ........................................................................................................... 87 6.1 Research Questions ....................................................................................................... 87 6.1.1 Research Question 1 .......................................................................................................... 87 6.1.2 Research Question 2 .............................................................................................................. 87 6.1.3 Research Question 3 .......................................................................................................... 88 viii 6.2 Limitations .................................................................................................................... 88 References ..................................................................................................................................... 92 Appendix A ................................................................................................................................... 98 ix List of Tables Table 1 Descriptive Statistics for the TransLink Data within Study Area .................................... 25 Table 2 Hospital Capacity (also shown in Figure 6) ..................................................................... 28 Table 3 Distances and Distance Decay Factors for E2SFCA Example Calculation ..................... 44 Table 4 Descriptive Statistics for all Scenarios, Scores Normalized to Baseline Maximum ....... 48 Table 5 Sample Structure Information Used in Scenario 5 ........................................................... 69 x List of Figures Figure 1 Major Road Network in the Lower Mainland. ................................................................. 8 Figure 2 Municipalities in the Study Area ...................................................................................... 9 Figure 3 Healthcare Authorities in British Columbia ................................................................... 11 Figure 4 Healthcare Authorities in the Lower Mainland .............................................................. 12 Figure 5 TAZs in the Study Area .................................................................................................. 27 Figure 6 Hospital Capacity in the Study Area .............................................................................. 30 Figure 7 Signage Used for DRRs (Disaster Response Route Signs | TranBC, 2020) .................. 31 Figure 8 Transportation Network Used in the Network Analyst .................................................. 35 Figure 9 Study Area ...................................................................................................................... 36 Figure 10 2SFCA Method Example Step 1................................................................................... 39 Figure 11 2SFCA Method Example Step 2 ................................................................................... 41 Figure 12 E2SFCA Method Example Step 1 ................................................................................ 42 Figure 13 E2SFCA Method Example Step 2 ................................................................................ 45 Figure 14 Baseline Scenario Accessibility Scores ........................................................................ 51 Figure 15 Distribution of Baseline Scenario Scores, Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile ...................................................................................................... 53 Figure 16 DRRs in the Lower Mainland ....................................................................................... 54 Figure 17 DRR Scenario Accessibility Scores .............................................................................. 56 Figure 18 Not Connected TAZs in DRR Scenario ........................................................................ 57 Figure 19 Distribution of DRR Scenario Scores ........................................................................... 58 Figure 20 Zoomed in Map with DRR, Highways Marked with Numbers, other Bridges with Letters....................................................................................................................................................... 61 Figure 21Senior Population Accessibility Scores ......................................................................... 63 xi Figure 22 Senior Population Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile .................................................................................................................... 65 Figure 23 Employment Scenario Accessibility Scores ................................................................. 67 Figure 24 Distribution of Employment Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile ....................................................................................................... 68 Figure 25 Location of Bridges Considered âDownâ ..................................................................... 70 Figure 26 Bridges Scenario Accessibility Scores ......................................................................... 72 Figure 27 Zoomed in Map with Bridge Restrictions, Highways Marked with Numbers, other Bridges with Letters ...................................................................................................................... 73 Figure 28 Distribution of Bridges Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile ....................................................................................................... 74 Figure 29 Senior Population Scenario Scores vs. Baseline Scenario Scores ................................ 75 Figure 30 TAZs with Proportionally Lower Accessibility for Senior Population ........................ 77 Figure 31 Employment per TAZ vs. Total Population per TAZ ................................................... 78 Figure 32 Employment Scenario Scores vs. Baseline Scenario Scores ........................................ 79 Figure 33 Difference between Proportional Employment and Residential Population ................ 80 Figure 34 Example Residential Scenario Typical City Layout ..................................................... 82 Figure 35 Example Employment Scenario Typical City Layout .................................................. 83 Figure 36 Example Residential Scenario Interspersed City Layout ............................................. 84 Figure 37 Example Employment Scenario Interspersed City Layout .......................................... 85 xii Acknowledgements I would like to express my heartfelt gratitude to my advisor, Dr. Amy Kim, for her invaluable guidance and unwavering support throughout the past two years. I am also thankful to my co-advisor, Dr. Carlos Molina Hutt, for his expertise and encouragement during this journey. I am immensely grateful to my fellow cohort members, Paula Steve and Elena, whose friendship and camaraderie have profoundly shaped my experience here in Canada. Paula, my rock, the first responder for my soul, and the best friend anyone could ever ask forâI am so grateful for all our memories. I will never forget the endless road trips, late-night debriefing sessions, and our countless Starbies runs. A special thank you goes to the incredible community at the aquatic centreâAshley, Claire, Megan, Katie, Leslie, KÅlea, Tracy, Fabia, Mason, and Selaâfor their kindness and for making this chapter of my life so memorable. I am also forever thankful to my French girlies, Hannah, Remy, and Maya, for their laughter and friendship and my UmBauPause for sticking with me and being a constant presence I can always rely on. Lastly, I extend my deepest appreciation to my family, who have always supported and cheered me on from afar. To my mom and dad, who never let me doubt my potential or believe that there was anything I couldnât achieveâI owe so much of this accomplishment to you. Lybw. Thank you all for being part of this incredible journey 1 Chapter 1 Introduction 1.1 Background and Context Accessibility is a fundamental concept in transportation engineering and urban planning, applied with the aim of understanding the factors that influence equitable (or inequitable) distributions of services across a community. In the transportation context it is often defined as the ease of reaching destinations (Hansen, 1959). Of the various services we measure accessibility to, healthcare accessibility stands out as a paramount concern. It embodies the principle that everyone, regardless of personal, socio-economic, or geographic factors, should have equal and adequate access to basic healthcare services. This accessibility is crucial for enhancing public health, reducing health disparities, and reducing the risk of diseases within a population. The Lower Mainland of British Columbia, a dense urban region characterized by extensive infrastructure and a large population, presents a unique case study for examining healthcare accessibility. While the city's robust infrastructure supports a bustling urban life, it also renders the region vulnerable to disasters. Situated in a seismically active zone, the Lower Mainland faces risks from earthquakes. Additionally, the proximity to major rivers and a coastal environment exposes it to flooding, particularly during extreme weather events. Furthermore, the region's high population density includes many vulnerable individuals, amplifying the potential impact of such disasters. This thesis aims to quantify the current state of accessibility to healthcare (in the form of hospital beds available in the Lower Mainland), and analyze various scenarios to understand how different factors might influence this healthcare access. The analysis will utilize the two-step floating catchment area (2SFCA) method, a technique that measures the demand and supply of healthcare services and combines these into a single metric of accessibility. Several scenarios will be examined: 1. Baseline Scenario: An assessment of the current healthcare accessibility in the Lower Mainland without any modifications or hypothetical conditions. 2. Disaster Response Routes (DRR): This scenario will analyze the impact of blocking disaster response routes to non-emergency vehicle use on population healthcare 2 accessibility. Understanding how such restrictions affect accessibility can inform disaster preparedness and response strategies. 3. Vulnerability of the Aging Population: Given that individuals over the age of 65 are among the most vulnerable populations, this scenario will focus on their specific accessibility challenges. 4. Workday Healthcare Access: This scenario will investigate the accessibility of healthcare services during working hours, with a focus on employment locations. This analysis recognizes that people's daytime locations differ from their residential addresses, which can significantly influence healthcare accessibility. 5. Earthquake Risk: Considering the Lower Mainlandâs susceptibility to earthquakes, this scenario will use bridge and overpass year of construction as a proxy to symbolize their failure risk. Through these analyses, this thesis seeks to provide a comprehensive understanding of healthcare accessibility in the Lower Mainland, especially in the context of disaster vulnerability. The findings will not only contribute to the academic discourse on urban planning and public health but also offer practical insights for policymakers and urban planners striving to enhance healthcare accessibility and disaster preparedness in urban settings. 1.2 Research Questions Accessibility to healthcare is an important factor that is crucial to a societyâs well-being, especially during a natural disaster. To understand hospital accessibility in the Lower Mainland, this thesis will answer the following overall research question: What is the regional accessibility to healthcare facilities in the Lower Mainland region? Three research questions were developed around the above main question. Q1: What existing methods and metrics are used to measure healthcare accessibility, and how can they be adapted to the context of the Lower Mainland? This question aims to establish a foundational understanding of how healthcare accessibility is conceptualized and assessed. It seeks to examine existing literature to gather insights on existing 3 methods and metrics used in the field, providing a basis for subsequent research and analysis specific to the Lower Mainlandâs context. Objective: The primary objective is to establish a foundational understanding of how healthcare accessibility has been conceptualized and assessed in the existing literature. By comprehensively examining existing literature, this objective seeks to provide the necessary background and knowledge to assess healthcare accessibility in the Lower Mainland region. An extensive literature review was conducted by examining academic papers, reports, and case studies related to healthcare accessibility. Building upon insights gained through the literature review, this objective aims to establish specific metrics for measuring healthcare accessibility within the disaster context. These metrics will consider factors such as distance to healthcare facilities, transportation network disruptions, and demographic vulnerabilities. Relevant and simple metrics will facilitate quantitative analysis in the subsequent research study Approach: The primary method employed to address this question is a comprehensive literature review. The objective is to synthesize existing knowledge, identify gaps, contradictions, and evolving trends in the field, and ultimately, define relevant metrics for further investigation. This approach will be used to establish a robust foundation for future research, clarify how healthcare accessibility is understood within the disaster context, and define specific metrics that will be employed in subsequent research phases. By thoroughly addressing the first research question, the study aims to deepen the understanding of risks and vulnerabilities in urban healthcare systems during seismic events, thereby laying the groundwork for future efforts to enhance urban resilience. Q2: What is the current state of healthcare accessibility in the Lower Mainland, and what is the spatial distribution of healthcare facilities? This question aims to assess the current state of healthcare accessibility in the Lower Mainland by analyzing the spatial distribution of facilities. It seeks to understand the existing landscape of healthcare access and identify potential areas of disparity or insufficiency. Objective: The primary objective is to evaluate the current state of hospital accessibility in the Lower Mainland using the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. This will involve mapping and analyzing the spatial distribution of healthcare facilities across the 4 region. By assessing factors such as proximity to healthcare facilities, transportation access, and population density, this research seeks to identify areas with adequate access as well as those with potential gaps or barriers. Understanding the existing distribution and accessibility will provide a baseline for comparison in later stages of the study, especially when examining the impacts of natural disasters or demographic changes. This analysis will involve the use of geographic information systems (GIS) and spatial analysis techniques to create a comprehensive overview of healthcare accessibility in the Lower Mainland. Identifying disparities in healthcare access using the E2SFCA method can help in recommending targeted interventions to improve overall accessibility and ensure equitable healthcare distribution across the region of study. Approach: The main research question is answered using the E2SFCA Method to quantify healthcare accessibility in the Lower Mainland with a Gaussian function used as the distance decay function. The Gaussian function is used to model the gradual decrease in accessibility as travel distance increases, reflecting the realistic tendency of individuals to prefer closer healthcare facilities over those farther away. This method is employed using the ArcGIS Network Analyst extension. The accessibility will be assessed in different scenarios including a baseline scenario representing the status quo. Q3: How does healthcare accessibility in the Lower Mainland change when considering demographics and the impacts of a natural disaster like earthquakes? A. Disaster Impact: How would disaster response routes and potential disruptions (e.g., bridge failures during an earthquake) affect healthcare accessibility in the Lower Mainland? This question focuses on understanding the impact of disasters, such as earthquakes, on healthcare accessibility in the Lower Mainland. It aims to investigate how disaster response routes and infrastructure disruptions, like bridge failures, alter the accessibility of healthcare facilities across different neighbourhoods and regions within the area of study. Objective: The study aims to investigate how healthcare facility accessibility changes following natural disasters, particularly when disaster response routes are employed. It will identify areas with increased, unchanged, or decreased accessibility and assess variations across different neighbourhoods and regions. This analysis will help determine which areas are more resilient or vulnerable in terms of healthcare access during and after disasters. Additionally, the research will explore how seismic events affecting bridges or overpasses impact healthcare accessibility, 5 pinpointing regions with significant accessibility changes. By understanding these patterns, the study aims to identify vulnerable areas needing focused disaster preparedness and resource allocation, ultimately improving healthcare access after infrastructure disruptions. Approach: Accessibility will be assessed in different scenarios including the activation of the disaster response route network and the restriction of earthquake-affected links. B. Demographic Focus: How does the accessibility of healthcare facilities compare when focusing on specific population groups, such as the employed population and individuals over 65 years old, instead of the general population? This question aims to investigate how healthcare accessibility in the Lower Mainland varies for those that are employed as well as individuals over 65 years old. It recognizes that most people are away from their residence at work during the day, and considering healthcare accessibility from employment locations is important. Additionally, the study focuses on older adults, a vulnerable demographic that may have different accessibility needs compared to the general population. Objective: By conducting a demographic analysis, the research will map the distribution of these groups within the metro area. Utilizing the E2SFCA method, the study will assess the accessibility of healthcare facilities for these identified groups, considering factors such as proximity to facilities and transportation networks. The objective is to identify geographic areas with lower healthcare facility accessibility for the employed population and older adults. Additionally, the research will compare the spatial patterns of healthcare accessibility for these groups with those of the general population to highlight disparities and similarities. Approach: Accessibility will be assessed in different scenarios including the use of workplace data and age groups. 1.3 Context This chapter introduces the geographic context as well as the two key partners involved in this research. Vancouver Coastal Health Authority (VCH), one of the regional health authorities in British Columbia, is responsible for planning, managing, and delivering healthcare services across parts of the Lower Mainland (while Fraser Health Authority is responsible for the rest of the Lower Mainland). TransLink, the regional transportation authority, oversees the Lower Mainlandâs transit 6 system, major roads, and transportation demand management strategies, ensuring connectivity and mobility for the region. 1.3.1 Geographical Context The study area for this thesis is the Vancouver Lower Mainland, located in southwestern British Columbia. The name âLower Mainlandâ is unofficial but commonly used for the urbanized areas centred around the City of Vancouver. Although it refers to a geographic area of varying scope, it does consistently include the Metro Vancouver Regional District (federation of 21 municipalities, one electoral area, and one treaty First Nation) and parts of the Fraser Valley Regional District. For the purposes of this research, the definition is guided by data availability from TransLink (introduced in Section 1.3.2), focusing on Metro Vancouver and the southern portion of the Fraser Valley. The exact boundaries of the study area are shown in Figure 9. The Lower Mainland is home to around three million people and spans an area of almost three million hectares (Statistics Canada, 2023). It consists of several very densely populated cities, but also boasts some of Canada's most fertile agricultural land along the Fraser River Delta, forested mountains, and a productive coastline with active fisheries (Statistics Canada, 2022). As Canada's third-largest metropolitan area and home to the countryâs largest marine port, it is critical to both the provincial and national economies. However, the Lower Mainlandâs geographical location exposes it to several natural hazards. Situated in one of the most seismically active regions of Canada, the region faces significant earthquake risks due to the complex geologic and tectonic setting of the Cascadia Subduction Zone (Rogers et al., 1998). Additionally, the region is vulnerable to climate-related challenges. In recent years, British Columbia has experienced devastating floods, such as those in November 2021, which caused widespread damage and disrupted major transportation links, including the Trans-Canada Highway (The Canadian Press, 2024). Experts warn that events like these may become more frequent and severe due to intensifying atmospheric rivers (The Canadian Press, 2024). Moreover, the 2023 wildfire season, marked by record-breaking fires across Canada, underscores the importance of robust infrastructure and effective evacuation protocols to ensure community safety during such emergencies. Figure 1 shows the major road network in the Lower Mainland, with the main highways and freeways labeled with their respective numbers shown on the map. Figure 2 shows the 7 municipalities of the Lower Mainland Region that were included in this analysis. Although the UBC campus area is not politically designated as a municipality within the region, it is included in the study area for completeness. 8 Figure 1 Major Road Network in the Lower Mainland. 1 7 99 1 1 7 1 7 9 Figure 2 Municipalities in the Study Area 10 1.3.2 Health Authority Context Vancouver Coastal Health (VCH) is one of five regional health authorities in BC responsible for planning, managing, and delivering health services. Serving over 1.25 million people, approximately a quarter of BCâs population, VCHâs service area includes both urban and rural areas. It covers the cities of Vancouver, Richmond, North Vancouver, and West Vancouver/Bowen Island, as well as the more rural coastal regions of Coast Garibaldi and Central Coast, where rural areas account for 8% of the population. As part of British Columbia's regional health authority system, VCH plays a critical role in delivering healthcare tailored to the needs of its diverse and geographically varied population (Vancouver Coastal Health, n.d.). Within this diverse region, VCH operates and funds 13 acute care hospitals, community health centers, mental health services, long-term care facilities, and public health initiatives. Major hospitals, such as Vancouver General Hospital, Richmond Hospital, and Lions Gate Hospital in North Vancouver, are critical hubs for healthcare delivery in urban areas, while smaller facilities like Sechelt Hospital and Bella Coola General Hospital serve rural and remote communities. Fraser Health is another regional health authority in British Columbia. Serving over two million people, approximately 40% of BC's population, Fraser Health encompasses a mix of urban centers, suburban neighborhoods, and rural areas. Its service area includes the remaining areas of the Lower Mainland, including the municipalities of Surrey, Burnaby, Coquitlam, Langley, and Abbotsford, and smaller communities like Hope and Agassiz (About Fraser Health, n.d.). Fraser Health operates and funds 12 acute care hospitals and other services within this expansive region. Their hospitals include Surrey Memorial Hospital, Royal Columbian Hospital in New Westminster, Abbotsford Regional Hospital and Cancer Centre, Chilliwack General Hospital and Fraser Canyon Hospital in Hope. BCâs other healthcare authorities include Interior Health, Island Health and Northern Health. Their geographic boundaries are shown in Figure 3 and Figure 4. 11 Figure 3 Healthcare Authorities in British Columbia 12 Figure 4 Healthcare Authorities in the Lower Mainland 13 1.3.3 TransLink Context TransLink, the regional transportation authority for the South Coast of British Columbia, is unique in Canada for its integrated approach to managing various transportation systems and initiatives. Established in 1999 under the name Greater Vancouver Transportation Authority (later renamed TransLink in 2007), it oversees the planning, funding, and operation of the regionâs public transit system, including buses, SkyTrain, SeaBus, and West Coast Express commuter rail. TransLink is also responsible for managing key aspects of the region's road network, including operation and maintenance of major arterial roads and five regional bridges, Golden Ears Bridge, Pattullo Bridge, Knight Street Bridge, Westham Island Bridge and the Canada Line bike and pedestrian bridge. Additionally, it implements transportation demand management (TDM) strategies, such as carpooling, vanpooling, and bike infrastructure improvements. Beyond these operational roles, TransLink plays a strategic role in supporting regional growth, aligning transportation planning with environmental goals like improving air quality and reducing greenhouse gas emissions. This dual responsibilityâmanaging existing infrastructure while planning for sustainable growthâis crucial in ensuring the efficient movement of people and goods across the region. Funded through a mix of transit fares, fuel taxes, property taxes, and development contributions, TransLink is required to adhere to the Livable Region Strategic Plan (LRSP) and frequently consults with municipalities, the public, and other stakeholders. Its authority, granted by the BC government through the South Coast British Columbia Transportation Authority Act of 1998, enables it to address the transportation needs of the region while supporting economic development and enhancing overall livability (Legacy & Stone, 2019). 14 Chapter 2 Literature Review 2.1 Accessibility The concept of accessibility is fundamental to understanding the distribution and utilization of resources and opportunities within a given area. One of the earliest and most widely cited definitions describes accessibility as a measurement of both the ease of access and the quantity of opportunities available at different locations (Hansen, 1959). This definition underscores the dual nature of accessibility, as it includes not just the presence and quantity of resources but also the ability to reach them. Expanding on this definition, accessibility is recognized as a function of multiple factors. The measure of accessibility depends on the mobility provided by transportation systems, the spatial distribution of opportunities or destinations, and the socioeconomic characteristics of individuals (Tao et al., 2020). This definition aims to include the influence of physical infrastructure, spatial arrangement of resources, and personal attributes in determining accessibility. To further explain the concept, a classification system for accessibility has been proposed that includes four distinct categories (Tao et al., 2020): 1. Spatial Accessibility: This category focuses on the physical or geographical barriers that individuals must overcome to reach services. It considers factors such as distance, travel time, and the availability of transportation options. 2. Non-spatial Accessibility: This aspect addresses the socio-economic factors that can influence an individual's ability to access services. These include income, age, gender, and ethnicity, recognizing that personal characteristics can significantly impact accessibility beyond mere physical proximity. 3. Potential Accessibility: This refers to the theoretical possibility of people accessing services. It considers the presence and distribution of services without necessarily accounting for whether they are actually utilized. 4. Revealed Accessibility: This category focuses on the actual use of services, highlighting the role of individual preferences, perceptions, and decision-making. For instance, a nearby medical facility might go unused due to cultural perceptions, affordability, or perceived 15 quality. Revealed accessibility emphasizes that true access depends not only on service availability but also on whether services align with individualsâ needs and expectations, making it a key measure of personal accessibility. The importance of understanding accessibility, particularly spatial accessibility, has extended well beyond academic interest to influence public policy, urban planning, and healthcare service delivery. By analyzing the distribution of spatial accessibility, it becomes possible to identify areas or populations facing shortages of opportunities. This insight is crucial for evaluating equity in the distribution of resources and opportunities across a region or population (Tao et al., 2020). In conclusion, accessibility is a multidimensional concept that extends beyond mere proximity to resources. It encompasses various spatial and non-spatial factors, considering both potential and realized access, and remains a fundamental area of study that has evolved over decades (Handy, 2020). Accessibility measurement methods are broadly classified into cumulative and gravity-based approaches, which serve as the foundation for many subsequent refinements. Cumulative measures calculate the number of opportunities (such as jobs, schools, or healthcare facilities) reachable within a specific distance or travel time threshold, offering a straightforward way to evaluate accessibility. For example, a cumulative measure might assess how many hospitals are accessible within a 30-minute drive. Gravity-based measures, on the other hand, incorporate a distance decay function, reflecting the diminishing likelihood of accessing opportunities as travel costs (time, distance, or monetary) increase. These might be used to evaluate how accessibility to jobs decreases as commute times lengthen, integrating both opportunity availability and travel behavior. These conceptual approaches provide the basis for various practical applications. For instance, planners may use cumulative measures to identify underserved areas needing additional resources or apply gravity-based models to analyze the impacts of new transit systems on regional accessibility. While numerous methods to measure accessibility have been developedâand continue to be refinedâsimpler approaches endure due to their ease of application and minimal data requirements, making them well-suited for routine planning tasks (El-Geneidy & Levinson, 2007). However, standardization of accessibility measures remains crucial for advancing transportation planning practices, particularly in shifting the focus from mobility to accessibility (Handy, 2020). 16 Mobility refers to the ease of movement, while accessibility is defined as the ease of reaching destinations. While the two concepts are related, they are distinguishable: great accessibility can be achieved even with limited mobility when destinations are nearby, whereas high mobility may not ensure good accessibility if destinations are far away (Levine & Garb, 2002). Sophisticated, data-intensive modelsâsuch as those integrating socioeconomic or demographic dataâhave shown potential for detailed analyses but face challenges in adoption due to their complexity. If incorporated into user-friendly planning software, these methods could become more accessible to practitioners, supporting performance monitoring and urban policy development. As planning agencies increasingly seek adaptable tools, accessibility assessments will remain a critical component in addressing urban challenges and promoting equitable resource distribution (Miller, 2020). 2.2 Healthcare Accessibility Healthcare accessibility is an important aspect of public health and well-being. It is widely recognized that access to primary healthcare plays a significant role as a facilitator of overall population health (Guagliardo, 2004). The concept of healthcare accessibility builds upon the general principles of accessibility but focuses specifically on the healthcare sector. Several key factors influence access to healthcare (Aday & Andersen, 1974): 1. Availability of health services (supply) 2. Number of people living in an area (demand) 3. Population's health status 4. Socioeconomic and financial resources available 5. Geographical impedance between population and health services including the friction of travelling along the transportation network. Similar to the classification of general accessibility, healthcare accessibility can also be categorized into different types (Joseph & Phillips, 1984; Phillips, 1990; Thouez et al., 1988): 1. Revealed Accessibility: This refers to the actual use of healthcare services. It can be further divided into: 17 a. Spatially revealed accessibility b. Non-spatially revealed accessibility. 2. Potential Accessibility: This emphasizes the aggregate supply of medical care resources available. It can also be categorized as: a. Spatially potential accessibility b. Non-spatially potential accessibility. The distinction between revealed and potential accessibility in healthcare is important. Revealed accessibility provides insights into the actual utilization patterns of healthcare services, while potential accessibility focuses on the theoretical availability of these services. This classification allows researchers and policymakers to examine healthcare accessibility from multiple perspectives, considering both the theoretical availability of services and their actual use. Both types of measures also account for the spatial and non-spatial factors that can influence access to healthcare, providing a comprehensive framework for analyzing and improving healthcare accessibility. 2.3 Measures of Healthcare Accessibility There are numerous methods for measuring spatial healthcare accessibility, each with its own strengths and limitations. These measures range from simple, easily interpretable metrics to more complex models that account for various factors affecting accessibility (Neutens, 2015). 2.3.1 Rudimentary Measures The most basic measures of healthcare accessibility include: 1. Time to practitioner 2. Distance to practitioner 3. The practitioner-to-population ratio These simple measures are easy to implement, directly interpretable in absolute units, they can be communicated and are readily understood by policymakers. 2.3.2 Kernel Density Models 18 Kernel density models calculate medical supply and demand using a kernel function that considers the distance decay effect and cross-boundary healthcare-seeking behavior (Guagliardo, 2004; Silverman, 1986). A kernel function is a mathematical function used to weight data points based on their distance from a reference point. One common kernel function is the Gaussian (or normal) kernel, which looks like a bell curve: ð¾(ð¥) =1â2ððà¬¿à¯«à°®à¬¶ Equation 1 ð¾(ð¥) =à¬µâà¬¶à°ðà¬¿à³£à°®à°® Here, x represents the distance from the reference point, and the function assigns the highest weight to points close to the reference (where x is small) and smaller weights to points farther away (where x increases). Other kernel functions have different shapes but serve the same purpose of assigning weights based on proximity. The choice of kernel and bandwidth (the "spread" of the curve) affects the smoothness of the density estimate. Kernel functions are used to estimate the spatial distribution of healthcare services (e.g., hospitals, clinics) by smoothing their locations across an area, with higher density values indicating areas where healthcare services are more concentrated, and lower values where they are sparser. This helps visualize and analyze how accessible healthcare services are across a region (Guagliardo, 2004). However, these models face challenges in the supply side calculations which can be underestimated depending on the service area's radius (bandwidth); a smaller radius reduces this loss. Additionally, when accessibility ratios are calculated in small cells and then averaged for areas like census tracts, the results can become highly variable and inconsistent (Yang et al., 2006). 2.3.3 Gravity Measures Gravity measures, based on the work of Hansen (1959), offer a more complex approach to measuring healthcare accessibility. These measures: 1. Trade off the size and/or quality of healthcare facilities with travel impedance. 2. Assume that a population's spatial access to healthcare services depends on the availability of resources at nearby medical sites, adjusted by how far away those sites are. Specifically, this is calculated as the sum of the physician-to-population ratios at nearby facilities, with each ratio weighted by how easily or quickly the facility can be reached (travel impedance). 19 In other words, closer and larger facilities contribute more to a population's healthcare accessibility than those that are smaller or farther away. Gravity measures are based on the principle that accessibility diminishes, or "decays," with increasing distance, much like the gravitational pull between two objects weakens as they move farther apart. In this context, larger or closer service providers exert a stronger "pull," offering greater accessibility. To quantify this relationship, gravity models employ decay functions to model the relationship between distance and accessibility: 1. Piecewise decay function: Consists of constant arbitrary impedance values for a priori defined bandwidths of travel distance. 2. Rectangular function (cumulative opportunity measure): o Value = 1 if the health supply is within a predetermined distance o Value = 0 otherwise 3. Continuous travel decay function (Batty, 2009) 4. Inverse power function (Luo & Wang, 2003) 5. Gaussian function (Wan et al., 2012) These decay functions allow for more nuanced modeling of how distance affects healthcare accessibility by capturing variations in how accessibility diminishes over distance. Unlike simpler binary measures that treat all distances within a threshold as equal, these functions reflect the reality that distance impacts people differently depending on factors such as mode of transportation, travel preferences, and individual limitations. This flexibility enables gravity models to account for diverse real-world travel behaviors and accessibility challenges more effectively than rigid, one-size-fits-all approaches. Gravity models, while intuitive in their alignment with the concept of attraction, face several significant challenges. First, continuous travel decay functions can only be interpreted relatively, requiring normalization over a specific range (Batty, 2009). Additionally, these models are highly sensitive to the parameters chosen, particularly regarding distance decay; even minor adjustments can yield markedly different results, which may distort the analysis. Furthermore, there is a lack 20 of a compelling and robust theoretical foundation for measuring accessibility, raising questions about the reliability of gravity models in practical applications (Miller, 2018). Each of these measures offers different insights into healthcare accessibility, and the choice of measure often depends on the specific research question, data availability, and the scale of analysis. While simpler measures are easier to implement and communicate, more complex models like gravity measures can provide a more comprehensive understanding of healthcare accessibility by accounting for multiple factors simultaneously. 2.4 Critical Factors in Healthcare Accessibility Ensuring equitable and adequate access to basic healthcare services, regardless of personal, socio-economic, or geographic factors, is a critical public health goal. While equity in healthcare access aims to address disparities and meet the needs of underserved or vulnerable populations, spatial access specifically focuses on the physical and geographical dimensions of this challenge. Spatial access to healthcare refers to the relationship between the location of healthcare services and the populations they serve, emphasizing the importance of proximity, capacity, and connectivity in determining accessibility. Achieving equitable access is key to improving public health, reducing health disparities, and minimizing disease risk (Lee, 1991). There are three critical factors for measuring spatial access to healthcare: 1. Healthcare Capacity: This refers to the amount of supply in terms of healthcare services. Healthcare capacity provides a quantitative measure of the resources available to meet the health needs of a population. It can be measured through various indicators such as: a. Hospital beds b. Number of physicians c. Availability of specific facilities (i.e. dialysis center). 2. Population Demand: This factor considers the number of people who potentially need the service in question. Understanding the demand side of healthcare is crucial for assessing whether the available healthcare capacity is sufficient to meet the needs of the population. 21 3. Geographic Impedance: This factor measures the extent to which the distance between population demand and service location influences access. It recognizes that physical distance can be a significant barrier to healthcare access, even when services are theoretically available. Importantly, the capability of the transportation system plays a critical role in mitigating or exacerbating this barrier. Factors such as the availability of public transit, road connectivity, travel time, and the cost of transportation all influence how easily individuals can traverse physical distances to access healthcare. These three factors - healthcare capacity, population demand, and geographic impedance - form a comprehensive framework for evaluating spatial access to healthcare (Kleinman & Makuc, 1983; Wing & Reynolds, 1988; Yang et al., 2006). This approach recognizes that true accessibility is not just about the presence of healthcare services, but also about the balance between supply and demand, and the ease with which people can physically reach these services. It highlights the complex interplay between healthcare provision, population needs, and geographical constraints. 2.5 2SFCA and E2SFCA The Two-Step Floating Catchment Area (2SFCA) method is among the most commonly used methods for measuring spatial accessibility to healthcare services (Tao et al., 2020). Its development can be traced back to the gravity formulation by Joseph and Bantock (1982), followed by the spatial decomposition approach of Radke and Mu (2000), and finally formalized by Luo and Wang (2003) as the basis of the floating catchment area approach. The 2SFCA method brings together the healthcare accessibility measurement approaches (Sections 2.2-2.3) and critical factors of input to such approaches (Section 2.4) discussed. The 2SFCA method consists of two main steps: 1. First Step: This step allocates resources at each facility to the demand nodes within its catchment area by calculating supply-to-demand ratios. 2. Second Step: The supply-to-demand ratios are summed up for each demand node to calculate accessibility. 22 The 2SFCA method expresses accessibility in units of average supply per person, making it easy to interpret and convenient for policy-making (Tao et al., 2020). The 2SFCA method has several advantages (Neutens, 2015): 1. Controls for capacity restrictions: It incorporates the available capacity of healthcare facilities by weighting the number of services based on their capacity. This means that instead of treating all facilities as equal regardless of how many patients they can serve, the method adjusts accessibility scores to reflect the actual capacity. 2. Accounts for local competition: By considering the distribution of healthcare providers within a specified catchment area, the 2SFCA method takes local competition into account. This competition refers to the demand for healthcare services among individuals within the same catchment area. For example, when multiple people seek care from a limited number of providers, the availability of services per person diminishes, effectively reducing accessibility. 3. Considers cross-border healthcare-seeking behavior: The 2SFCA method allows for the analysis of healthcare access across geographic boundaries as the definitions of catchment areas are flexible and can extend beyond local jurisdictions. However, it also has some shortcomings (Neutens, 2015): 1. Like all of the measures mentioned in this section, it is a static measure, assuming static conditions, not accounting for temporal changes in accessibility due to factors like traffic patterns, service hours, or seasonal variations in service demand. 2. Local demand is often overestimated. In the 2SFCA method, populations within overlapping catchment areas are counted multiple times when calculating demand. For instance, if a neighborhood falls within the catchment zones of two different healthcare facilities, the residents of that neighborhood are included in the demand calculations for both facilities. This duplication inflates the perceived local demand for services, leading to biased accessibility ratios. 3. Demand is modeled using a single representative spatial proxy point (i.e., from the centroid of a neighborhood/region/zone/etc.). 23 While the 2SFCA method has been widely adopted, several improvements have been proposed to address its limitations: 1. Distance Decay Functions: Luo and Qi (2009) introduced discrete and continuous distance decay functions to address the limitation of uniform catchment areas, which assume equal access within a given radius. The Gaussian function in Equation 1 incorporates a gradual decline in access as distance increases, using only one parameter. This assumption replaces subjectivity and uncertainty in determining how access diminishes with distance. 2. Variable Catchment Sizes: McGrail and Humphreys (2009) proposed the use of variable catchment sizes for healthcare facilities to improve the model's realism. Fixed catchment sizes assume a uniform service area for all facilities, which fails to account for variations in service availability, population density, and demand across different regions. By allowing catchment sizes to adapt to local conditionsâsuch as larger catchments in rural areas with sparse populations and smaller ones in dense urban settingsâtheir approach captures differences in accessibility more accurately. This refinement enhances the 2SFCA's ability to address disparities in access across diverse geographic contexts while remaining consistent with the method's focus on balancing supply and demand within spatial constraints. 3. Age-Adjusted Healthcare Demand: Ngui and Apparicio (2011) addressed the limitation of uniform demand by incorporating age-adjusted healthcare demand, recognizing that healthcare needs vary significantly across age groups. By adjusting demand based on demographic variations, this improvement ensures that the accessibility estimates are more responsive to the actual healthcare needs of different population segments, providing a more equitable assessment of access to services. 4. Multiple Transport Modes: Mao and Nekorchuk (2013) identified the limitation of assuming a single mode of transportation in the 2SFCA method. Their extension to include multiple transportation options (e.g., driving, walking, public transit) enhances the method by providing a more comprehensive understanding of how different populations access healthcare services. This leads to more nuanced and accurate estimates of accessibility, particularly for individuals who rely on non-car modes of transport. 24 Many of these improvements resulted in different versions of the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. Both 2SFCA and E2SFCA have been widely used in various studies to assess healthcare accessibility, with researchers often adapting and refining these methods to suit specific contexts and research questions. In the previous sections, definitions were given for accessibility (Section 2.1) and healthcare accessibility (2.2), while measures for calculating it were given in 2.3, and the inputs needed for measurement in 2.4. Moving forward, this thesis will use spatially potential accessibility, gravity based measures, and inputs as hospital capacity (measured by the number of beds in hospitals with emergency rooms), potential hospital demand (measured by the resident population, and employment population in a later scenario), and travel impedance along the transportation network with a Gaussian decay function as per sections 2.2, 2.3, and 2.4, which are brought together in the application of the E2SFCA method. These methods and inputs were chosen to hold a balance between being detailed enough to reflect real-world patterns, while also being straightforward enough to apply with data that is commonly available for most urban regions. Gravity-based measures capture how distance influences access, and the Gaussian decay function offers a practical way to account for the gradual reduction in accessibility with increasing travel distance. Five scenarios are used to explore the different research questions identified in Section 1.2, which include a baseline scenario (Scenario 1), the impact of restricted access disaster response routes (Scenario 2), senior population hospital accessibility only (Scenario 3), employed populationâs hospital accessibility (Scenario 4), and vulnerable bridge closures (Scenario 5). This thesis presents an intuitive and easily-implementable approach to assess hospital accessibility in an urban region, which can be used for future accessibility analyses in conditions where hospital capacity and transportation network characteristics have changed. 25 Chapter 3 Data Sources Data was gathered for this study from several different sources. The sources and data provided are discussed in this Chapter. 3.1 TransLink TransLink provided information from their Regional Travel Model (RTM) âa four-step travel demand model in the Emme software. Two primary datasets from this model are utilized: data for Traffic Analysis Zones (TAZs), and the links and nodes making up the roadway transportation network. TAZs represent the spatial areas from which trips originate and are destined to, and contain various demographic factors such as population and employment. TAZs are mutually exclusive and collectively exhaustive, covering the entire urban area. Travel is aggregated to occur between TAZs. For network loading and assignment, TAZs are abstracted to single points, known as zone centroids. The TAZs used in this analysis are shown in Figure 5. Centroid connectors, artificial links that connect the centroids to the broader road and transit networks, further simplify the local street network. This spatial aggregation allows for effective analysis of regional transportation flows rather than within-zone movements (Miller, 2021). The second dataset involves the transportation network itself, including key details about roadways and intersections represented as links and nodes respectively, and roadway infrastructure features such as posted speed limits, number of lanes, and classifications (e.g., collectors and arterials).Table 1 provides an overview of the key statistics from the datasets used in this analysis. Table 1 Descriptive Statistics for the TransLink Data within Study Area Number of Links (representing collectors, arterials and highways through the study area) 16,888 Number of Nodes 6,776 Total Length of All Links 8,891 km Number of TAZs 1,700 Total Population represented in TAZs 3,145,223 However, the available dataset includes only major roads, such as collectors, arterials, and highways, as the available dataset did not include local roads. While this provides a workable 26 representation of the regional transportation network, the exclusion of local streets limits the modelâs ability to fully reflect within-zone connectivity. This limitation may overestimate the severity of restricted access in some scenarios, as local streets often provide alternative routes during disruptions, albeit with potentially longer travel times. 27 Figure 5 TAZs in the Study Area 28 3.2 Hospital Capacity Hospital capacity is another critical input to the analysis, which will be discussed in greater detail in Chapter 4. Defining hospital capacity proved more challenging than anticipated as we explored various measures in consultation with Vancouver Coastal Health. After considering several options, we ultimately decided to use the number of hospital beds as the primary measure of capacity. While emergency room capacity would have been ideal for this study, the complexity of both defining a universally-acceptable metric as well as obtaining the data for all hospitals throughout the Lower Mainland (which are also operated by two different health authorities) necessitated the use of a simpler and easily-comparable metric. Therefore, bed count offered a consistent and accessible data point. The capacity figures used in this study were sourced from publicly available databases, as outlined in Table 2. The facilities used in this study are all hospitals with an emergency room department. Table 2 Hospital Capacity (also shown in Figure 6) # Hospital Municipality Capacity Source 1 BC Childrenâs Hospital Vancouver 250 (Medical/Surgical Inpatient Units, n.d.) 2 Abbotsford Regional Hospital Abbotsford 295 (Abbotsford, n.d.) 3 Delta Hospital Delta 58 (DHCH Foundation, 2022) 4 Burnaby Hospital Burnaby 286 (Burnaby Hospital, n.d.) 5 Chilliwack General Hospital Chilliwack 336 (Chilliwack, n.d.) 6 Langley Memorial Hospital Langley 189 (Langley Memorial Hospital ECU â Seniors Advocate, n.d.) 7 Royal Columbian Hospital New Westminster 490 (Ministry of Health, 2018) 8 Peace Arch Hospital White Rock 171 (Fraser Health, 2022) 9 Mission Memorial Mission 25 (Fraser Health, n.d.) 29 # Hospital Municipality Capacity Source 10 Ridge Meadows Hospital Pitt Meadows 172 (Tucker, 2023) 11 Eagle Ridge Hospital Port Moody 165 (Shepherd, 2022) 12 Surrey Memorial Hospital Surrey 650 (Jabs, 2012) 13 Vancouver General Hospital Vancouver 955 (VGH, 2015) 14 Lions Gate Hospital North Vancouver 268 (City of North Vancouver, n.d.) 15 UBC Hospital UBC1 332 (UBC, 2020) 16 Richmond Hospital Richmond 240 (Byers, 2021) 17 Mount Saint Joseph Hospital Vancouver 240 (Mount Saint Joseph Hospital | Providence Health Care, n.d.) 18 St. Paulâs Hospital Vancouver 548 (Neill, 2021) Figure 6 shows the capacity of all hospitals used in this analysis. For the Senior Population and the Employment Scenario, hospital capacity was proportionally reduced based on the ratio of the senior/employment population to the total population, ensuring the analysis accurately captures the availability of healthcare resources relative to demographic distribution. The assumption of proportionally reducing hospital capacity simplifies healthcare demand but overlooks significant variations within each group. For example, the senior population may have diverse healthcare needs due to different health conditions or mobility limitations. This simplification risks misrepresenting the actual distribution of healthcare demand across this large demographic group. 1 The UBC University Endowment Lands is technically not a part of the City of Vancouver but rather its own unincorporated area within Metro Vancouver. 30 Figure 6 Hospital Capacity in the Study Area 31 3.3 Disaster Response Routes Disaster response routes (DRRs) are an essential component of emergency management, designed to facilitate the movement of emergency personnel and resources during critical situations such as earthquakes. DRRs are usually designated on urban highways and major arterials, and signalized by the following traffic signs and are not to be used by private vehicles when they are active. If a state of emergency is declared drivers must use the next possible exit if they find themselves on a DRR (BC Government, 2018). Figure 7 Signage Used for DRRs (Disaster Response Route Signs | TranBC, 2020) These routes are part of the larger British Columbia Disaster Response Transportation (DRT) system. While the DRRs are partly determined after a disaster to ensure the rapid deployment of emergency responders specifically to the location of the disaster, some DRRs are pre-identified to allow for fast implementation and use. DRRs are classified into short-term, medium-term, and long-term categories, based on the duration of their use and the level of travel restriction required (BC Government, 2018). The specific disaster response routes and nodes for this study area are 32 pre-identified and catalogued in the provincial digital road atlas. The data for these routes is publicly available through the BC Data Catalogue(Sinnett, 2016).2 3.4 Bridge Data In Scenario 5, bridges and overpasses are modeled as being unavailable due to earthquake impacts. To determine whether a bridge would remain available in this scenario, we used the construction year as a surrogate variable, assuming that older structures are more vulnerable. Specifically, any bridge constructed to a building code year prior to 1988 was considered unavailable, as structures built before this time are less likely to meet modern seismic standards. The data on bridge construction years was sourced from the British Columbia Ministry of Transportation and Infrastructure (BCMoTI), TransLink, and various municipalities. This data was compiled and organized by UBC researchers to enable this analysis (Shen, 2024). By applying this threshold, the scenario attempts to model the potential network disruptions caused by an earthquake, focusing on the vulnerability of older infrastructure. 2 For the purposes of clarity, I do not show the map of the original data here. The data has been cleaned and adjusted for use within the context of this thesis and is analyzed in Chapter 5. If readers wish to see the map of the DRRs in the cleaned network, it is included in Chapter 5.2.2. 33 Chapter 4 Methodology 4.1 Network Set-Up The analysis was conducted using ArcGIS, a geographic information system software widely used for spatial analysis and mapping. Specifically, the Network Analyst extension was employed to model and evaluate healthcare accessibility. Network Analyst enables the creation and analysis of transportation networks, allowing for the assessment of travel times, distances, and connectivity between demand points (population locations) and supply points (healthcare facilities). This extension is particularly suited for accessibility studies as it supports the integration of real-world constraints such as road networks, speed limits, and travel barriers, providing a more accurate representation of spatial access. Setting up the transportation network in ArcGIS involves the creation and refinement of various layers. Initially, the transportation network nodes and links, a layer representing the study area as shown in Figure 9 and TAZ layer with corresponding data are essential components. The data provided by TransLink comprises diverse link types including some for public transport, bike lanes and ferries. Since this analysis focused on car travel, some of these link types are irrelevant to the network. By filtering out links such as pedestrian links, ferries, and specific transit modes, a refined dataset is created. This involves using the "select by attribute" feature to choose links based on criteria like link type. The resultant selection is then transformed into a new layer, forming the basis of the network. The TAZ layer includes information about: 1. Population (total population and grouped by age) 2. Employment (total number of employed people working in that TAZ) 3. Education (elementary, secondary and post-secondary enrollment) 4. Household size (total number of households and grouped by size) The study area is based on TAZs, since they are confined to land space they do not include bridges and water spaces. A buffer around the selected TAZs is created to have a study area layer that includes essential features outside the immediate TAZ boundaries. The entire network is then clipped to the study area. Further adjustments involve incorporating trave time information by 34 adding a new field based on link length and speed limits. The time calculation done by dividing the length of the link by its speed limit. 35 Figure 8 Transportation Network Used in the Network Analyst 36 Figure 9 Study Area 37 The network set-up is conducted through the ArcGIS Catalog, involving the creation of a new feature dataset with a spatial reference matching that of the transportation nodes and links. The network dataset is configured by defining the travel attributes. In this case that includes the definition of the cost of traversing a link. Additional checks, include the verification of the correct number of nodes and links, ensure the network is appropriately configured. The final step involves quality assurance checks, ensuring the correct set-up of the network through various evaluations. These checks include comparing travel times from the ArcGIS network model with those obtained from Google Maps during the nighttime since the model does not account for traffic. Additionally, the accuracy of the network was verified by testing routes between various origins and destinations, ensuring that all bridges, overpasses, and intersections were correctly defined. This included checking that overpasses were not incorrectly modelled as intersections. These comprehensive checks help confirm the reliability and functionality of the network for subsequent analyses. 4.2 E2SFCA The Enhanced Two-Step Floating Catchment Area (E2SFCA) method, which was described in Chapter 3, was employed to assess healthcare accessibility, incorporating a distance decay function to reflect the diminishing influence of hospital proximity with increasing travel time. A Gaussian distance decay function was selected, Equation 3, providing a gradual reduction in accessibility over longer distances. This choice was made because it better captures how individuals perceive accessibility, acknowledging that people are likely to favor facilities that are closer, although they may also favor a facility farther away with more service than one that is closer with less service. Hospital bed count was used to represent a facilityâs service capacity. The equations for this model are described below: Step 1: Supply to Demand Ratio The first step focuses on evaluating the supply of healthcare services, by ascertaining the capacity of each healthcare facility to serve its surrounding population. Instead of defining fixed catchment areas defined by radial or network travel distance, a distance decay function is applied. 38 ð à¯ = ðà¯â ðà¯ð(ðà¯à¯)à¯¡à¯ Equation 2 where ð à¯ = supply to demand ratio for every healthcare location j Sj = number of beds at each healthcare location j Pi = population of TAZ i dij = travel time between i and j ðàµ«ðà¯à¯àµ¯ = impedance function n = number of TAZs For this analysis, a Gaussian function was used as the distance decay function: ðàµ«ðà¯à¯àµ¯ = áðà¬¿à¬µ/à¬¶Ã(à¯à³à³/à¯à°¬)à°®â ðà¬¿à¬µ/à¬¶1 â ðà¬¿à¬µ/à¬¶, ðà¯à¯ â¤ ðà¬´0, ðà¯à¯ > ðà¬´ Equation 3 Where d0 â minimum distance for all TAZs to reach at least one hospital, and all other variables as described above. Step 2: Accessibility Calculation The second step involves calculating the accessibility score for each TAZ by summing all the healthcare ratios Rj accessible to that TAZ. ð´à¯ = à· ð à¯ð(ðà¯à¯)à¯ à¯ Equation 4 Ai = accessibility at TAZ i 39 Rj = supply to demand ratio at healthcare location j dij = travel time between i and j m = number of hospitals Finally, for comparison between scenarios accessibility scores were normalized to the baseline scenario's maximum value, providing a standardized measure of healthcare accessibility. The 2-Step Floating Catchment Area (2SFCA) method can be demonstrated through a simplified example. In the scenario shown in Figure 10, each centroid, denoted by circles and numbered from 1 to 12, represents a location with one resident, and each healthcare facility, denoted by crosses and labeled a to c, has a capacity of one. Figure 10 2SFCA Method Example Step 1 Step 1: Calculating Provider-to-Population Ratios Census tract centroid Facility location Catchment around facility a Catchment around facility b 40 Catchment areas are drawn around two facilities, labelled a and b. The provider-to-population ratio for each facility is determined based on the population within its catchment area: ï· Facility ð has a catchment area that includes centroids 2 through 9, with a total population of 8. The provider-to-population ratio for facility ð is ð à¯ =ଵ଼ ï· Facility ð has a catchment area that includes centroids 5, 9, 10, 11, and 12, with a total population of 5. The provider-to-population ratio for facility ð is ð à¯ =ଵହ Step 2: Calculating Accessibility Scores Accessibility scores are calculated by drawing catchment areas around individual centroids, shown in Figure 11 and summing the ratios of the facilities located within these areas: ï· Centroid 3 lies within the catchment area of facility ð only, resulting in an accessibility score of ð´à¬· =ଵ଼= 0.125 ï· Centroid 5 lies within the catchment areas of both facilities ð and ð. Its accessibility score is ð´à¬¹ =ଵ଼+ଵହ= 0.325 41 Figure 11 2SFCA Method Example Step 2 In this example, the census tract around centroid 5 has a higher accessibility score than the census tract around centroid 3. This example illustrates how the 2SFCA method accounts for the distribution of both healthcare facilities and populations within their respective catchment areas to determine accessibility. The analysis began with creating an origin-destination (OD) matrix using ArcGIS, which provided travel times between all Transportation Analysis Zones (TAZ) and hospitals. The shortest travel time for a person in each TAZ to reach at least one hospital was identified and used as the key parameter ðà¬´ in the Gaussian decay function. The travel times were then used to determine each hospitalâs influence on surrounding TAZs, with closer hospitals exerting a stronger influence. The calculations were performed using Excel, applying the Gaussian decay function to adjust hospital bed capacities based on travel time. Census tract centroid Facility location Catchment around centroid 3 Catchment around centroid 5 42 Figure 12 E2SFCA Method Example Step 1 The distance decay function can be illustrated using the gradient above in Figure 12, where the lighter green shading (towards the centre of the circles) represents nearby facilities with a low distance decay factor, and darker green shading (towards the outer edges of the circles) indicates more distant facilities with a higher distance decay factor. The distance between facility ð and census tract centroid 9 is given as 8 units in this example. The other distances are given in Table 3. For the distance decay factor calculation d0 is set to 11 units, which is the farthest it takes for all census tracts to be connected to at least one facility, in this case the distance between census tract 1 and facility a. 43 Table 3 Distances and Distance Decay Factors for E2SFCA Example Calculation Census tract ð Distance to Facility ð ðà¯à¯ Distance to Facility ð ðà¯à¯ 2 10 0.140 - 3 5 0.751 - 4 7 0.534 - 5 7 0.534 9 0.277 6 1 0.996 - 7 5 0.751 - 8 10 0.140 - 9 8 0.409 6 0.649 10 - 7 0.534 11 - 5 0.751 12 - 8 0.409 Step 1: Calculating Provider-to-Population Ratios Catchment areas are drawn around two facilities, labelled ð and ð. The provider-to-population ratio for each facility is determined based on the population within its catchment area multiplied by the distance decay factor: ï· Facility ð has a catchment area that includes centroids 2 through 9, with a population of 1 each. The provider-to-population ratio for facility ð is ð à¯ =10.140 + 0.751 + 0.534 + 0.996 + 0.751 + 0.140 + 0.409=13.721= 0.269 Equation 5 44 ï· Facility ð has a catchment area that includes centroids 5, 9, 10, 11, and 12, with a population of 1 each. The provider-to-population ratio for facility ð is ð à¯ =10.277 + 0.649 + 0.534 + 0.751 + 0.409=12.62= 0.382 Equation 6 Figure 13 E2SFCA Method Example Step 2 Step 2: Calculating Accessibility Scores Accessibility scores are calculated by drawing catchment areas around individual centroids, shown in Figure 11 and summing the ratios of the facilities located within these areas: ï· Centroid 3 lies within the catchment area of facility ð only, resulting in an accessibility score of ð´à¬· = 0.269 â 0.751 = 0.202 Equation 7 45 ï· Centroid 5 lies within the catchment areas of both facilities ð and ð. Its accessibility score is ð´à¬¹ = 0.269 â 0.534 + 0.382 â 0.277 = 0.250 Equation 8 In this example, the census tract around centroid 5 also has a higher accessibility score than the census tract around centroid 3. This result demonstrates how the E2SFCA method, incorporating a distance decay function, refines the analysis by weighting healthcare access based on proximity. Several scenario-based modifications were made to the base model to evaluate healthcare accessibility under different conditions. The base scenario represents the standard conditions, while four additional scenarios were introduced to reflect different potential challenges or demographic variations. These are discussed in the next chapter. 46 Chapter 5 Scenarios and Results This chapter presents the hospital accessibility results for the Lower Mainland. The chapter begins with an overview of the standardized results, providing a general summary of the findings and highlighting notable trends or interesting insights. This is followed by a detailed examination of each individual scenario, where raw scores are presented and analyzed to identify patterns and significant observations. The five scenarios (including the baseline) are introduced in Section 5.1 below. The chapter concludes with two pairwise comparisons of selected scenarios, offering a focused discussion on the key findings and insights that emerge from these comparisons. The analysis draws insights to understand how various elements impact accessibility scores across the region. These findings are particularly significant in the context of healthcare accessibility in the Lower Mainland, as they provide a baseline assessment of accessibility while highlighting how specific factors influence accessibility outcomes. While this chapter provides an overview of the results for all hospital accessibility scenarios, it is crucial to note the underlying limitations in directly comparing raw scores across scenarios. Each scenario is built upon different baseline conditions and assumptions, which makes direct numerical comparisons inappropriate. Thus, the results are presented in the following order. First, in Section 5.1, an overview for all scenarios is given. This overview employs normalized values and includes descriptive statistics of the normalized accessibility scores (maximum, mean and standard deviation) , allowing for comparison across scenarios. Then, in Section 5.2, detailed analyses for each scenario using the raw (i.e., not normalized) values are presented, in order to discuss particular spatial variations in the accessibility results across the study area. It is noted that different colours are used in each scenarioâs map (of the resulting accessibility scores), as a means to further discourage direct comparisons of the raw scores across scenarios. Section 5.3 facilitates two comparisons across three different scenarios, again using the normalized values of the results, towards exploring changes in values and whether those changes are of importance. 5.1 Overview of Accessibility Scores This section provides an overview of the accessibility scores derived from the five scenarios examined in this study. A list of the scenarios to be presented is as follows: 47 ï· Scenario 1 â Baseline â Accessibility is based on the total residential population and full hospital capacities. ï· Scenario 2 â DRR (Disaster Response Routes) â Accessibility under DRR restrictions, limiting connectivity to several major arterials and key corridors. ï· Scenario 3 â Senior Population â Accessibility focuses on the population aged 65+ (and hospital capacities adjusted down accordingly). ï· Scenario 4 â Employment Population â Accessibility is based on workplace populations (instead of residential populations; hospital capacities are again adjusted down accordingly). ï· Scenario 5 â Bridges Down â Accessibility impacted by bridge closures, simulating earthquake-related disruptions. Summary statistics of all normalized accessibility scores is presented, as the raw values from each scenario cannot be compared against one another. To best facilitate comparison, all scenarios were normalized to the baseline scenario's maximum value. Table 4 presents descriptive statistics of accessibility scores across all TAZs for each scenario. In some scenarios, changes to the population distribution and transportation network result in a redistribution of catchment areas, which can lead to higher accessibility values in certain TAZs compared to the baseline. This occurs as healthcare demand and travel impedance are rebalanced across the network. These effects will be discussed in detail in Section 5.2, when the results of each scenario are discussed individually. Table 4 Descriptive Statistics for all Scenarios, Scores Normalized to Baseline Maximum Scenario Min Max Mean Median Standard Deviation 1 Baseline 0.0002 1.000 0.796 0.828 0.152 2 DRR 0.000 1.079 0.796 0.679 0.251 3 Senior Population 0.0002 1.015 0.805 0.839 0.156 48 4 Employment 0.0002 0.967 0.779 0.814 0.136 5 Bridges 0.000 1.011 0.799 0.830 0.156 The normalized values above highlight distinct patterns in healthcare accessibility within the TAZs of the Lower Mainland. The Baseline scenario, used as the normalization reference, has a mean accessibility score of 0.796, with scores ranging from 0.0002 to 1.000. The DRR scenario exhibits the same mean as the Baseline (0.796), but its scores are more dispersed, as indicated by a higher standard deviation of 0.251. Notably, the DRR scenario has the highest maximum score (1.079) among all scenarios, suggesting that some areas benefit more significantly from disaster response measures while others experience decreased accessibility (median: 0.679). The Senior Population scenario demonstrates improved accessibility, with a mean score of 0.805 and a median of 0.839, both higher than the Baseline. This scenario also shows a slightly increased maximum score of 1.015 while maintaining moderate variability (standard deviation: 0.156). Conversely, the Employment Population scenario has the lowest mean (0.779) and maximum score (0.967), reflecting reduced accessibility during workday conditions. Interestingly, it also exhibits the lowest standard deviation (0.136), indicating less variability in scores across TAZs. Finally, the Bridges scenario has a mean score of 0.799, slightly above the Baseline, and the highest median score (0.830). However, it also records a minimum score of 0.000, signifying complete inaccessibility in certain TAZs, and a maximum of 1.011, indicating some significant disparities in accessibility outcomes. In conclusion, these accessibility scores and normalized values provide some insights into the variations in healthcare access across different scenarios, but each scenario needs to be studied individually to understand the results of different challenges fully. 5.2 Scenario-Specific Results 5.2.1 Scenario 1 Baseline The key findings shown in Figure 14 reveal notable patterns of healthcare accessibility across the Lower Mainland and surrounding regions. Higher accessibility values are concentrated in central Vancouver and Burnaby, which aligns with the higher hospital capacities relative to the population in these areas. Additionally, higher accessibility scores are observed along the Trans-Canada 49 Highway (Hwy 1), including in the cities of Surrey, Abbotsford, and Chilliwack. This is likely due to the highwayâs role as a fast and direct transportation corridor, enabling faster travel times to hospitals and thus increasing accessibility for nearby areas. In contrast, regions like Mission and Maple Ridge show significantly lower accessibility despite having hospitals. This can be explained by the relatively low capacity of Mission Memorial Hospital (25 beds) and Ridge Meadows Hospital (172 beds) in these areas. Since the E2SFCA method accounts for both hospital capacity and the population size they serve, these smaller hospitals are less able to meet local demand compared to larger facilities in more densely populated regions. Additionally, the greater distances to other hospitals outside these regions further contribute to the lower accessibility values. These results highlight how healthcare accessibility is influenced by both travel times (e.g., facilitated by major highways like the Trans-Canada) and the interaction of supply-side factors (hospital capacity) and demand-side factors (population size and distribution). Accessibility tends to be higher in areas where hospitals have greater capacity and where transportation infrastructure supports faster travel to these facilities. Conversely, in outer regions like Mission and Maple Ridge, smaller hospitals and limited alternative routes reduce accessibility. 50 Figure 14 Baseline Scenario Accessibility Scores 51 The histogram in Figure 15 illustrates the distribution of accessibility scores across all TAZs under the baseline scenario. The x-axis represents the accessibility score, while the y-axis indicates the frequency, showing the number of TAZs that fall within each range of accessibility scores. The distribution of scores is right-skewed, with the majority of accessibility scores clustered toward the higher end of the range, around 0.0015 to 0.0020. This suggests that most areas enjoy relatively high accessibility. In contrast, a small number of TAZs have very low accessibility scores, below 0.0005. To facilitate interpretation of these accessibility scores, thresholds have been defined using the 25th and 75th percentiles of the data distribution. The 25th percentile represents the lower quartile, corresponding to a "low" accessibility score, while the 75th percentile marks the upper quartile, corresponding to a "high" accessibility score. These thresholds enable the classification of scores into low, medium, and high accessibility levels, making it easier to identify areas with relatively poor or excellent healthcare accessibility. Using percentiles ensures that these classifications are data-driven and reflect the specific distribution of scores, avoiding arbitrary cutoffs. In the baseline scenario, the red dashed line in Figure 15 marks the 25th percentile, while the green dashed line represents the 75th percentile. Scores below the 25th percentile reflect areas where the combination of hospital capacity, population size, and travel distance results in significantly limited access to healthcare services. For instance, zones with scores below 0.0005 likely represent sparsely populated regions located far from hospitals or areas served by hospitals with very limited capacity. On the other hand, scores above the 75th percentile reflect zones with comparatively high accessibility, highlighting areas with more favorable healthcare access conditions. This classification underscores the geographic disparities in healthcare accessibility under baseline conditions, with some zones experiencing considerably better access than others. 52 Figure 15 Distribution of Baseline Scenario Scores, Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile 5.2.2 Scenario 2 Disaster Response Routes In this scenario, DRR (Disaster Response Route) information from the BC Open Data Catalogue was utilized to model disaster-related protocols. DRRs are designated for emergency vehicles during states of emergency to ensure prioritized access for critical response efforts. The DRR dataset was incorporated into the network analyst tool in ArcGIS as barriers, simulating restricted access for non-emergency public use. By excluding DRRs from the routing module for non-emergency travel, this model attempts to represent how healthcare accessibility might be affected during emergencies when certain routes are reserved exclusively for emergency operations. The resulting dataset is shown in Figure 16. 53 Figure 16 DRRs in the Lower Mainland 54 However, it is crucial to acknowledge a significant limitation of this scenario: the TransLink network used in the analysis represents only major collectors and arterials, excluding local streets and many connectors. This simplified network model assumes no alternate routes exist once DRRs are excluded, which can overestimate the impacts of DRRs on connectivity. In reality, people in affected areas would likely rely on local roads to reach hospitals, albeit with potentially longer travel times. Nonetheless, the results also reflect the underlying issue of limited major arterials in some areas. When these key routes are designated as DRRs and closed to general traffic, residents must depend solely on local streets and collectors, significantly increasing travel challenges. This limitation is central to interpreting the results and must be considered to avoid oversimplifying conclusions about the severity of DRR impacts. The analysis of healthcare accessibility under the DRR scenario reveals complex and nuanced outcomes. The E2SFCA results shown in Figure 17 indicate that DRRs influence accessibility scores in diverse ways. Certain TAZs are disconnected from hospitals in the model, showing accessibility scores of 0. These 32 TAZs, marked in red in Figure 18, represent areas where connectivity to hospitals appears completely severed. While this outcome partly results from the limited network representation in the model, it also highlights areas with a sparse distribution of major arterials. In such regions, the closure of DRRs forces reliance on local roads and collectors, making hospital access much more difficult. This distinction is critical: the results do not imply total isolation but rather emphasize how the combination of limited arterials and DRR closures exacerbates travel difficulties and reduces healthcare accessibility. . 55 Figure 17 DRR Scenario Accessibility Scores 56 Figure 18 Not Connected TAZs in DRR Scenario 57 The DRR restrictions also create unexpected benefits for some areas. For instance, TAZs near hospitals that remain accessible under DRR conditions may experience improved accessibility scores. This occurs because other TAZs lose connectivity to those hospitals, reducing the population served and improving the provider-to-population ratio (Rj). As a result, accessibility scores increase for the nearest TAZs, as seen in clusters of high scores (Figure 19). This spatial redistribution highlights how DRRs can produce localized disparities in healthcare accessibility. Finally, the DRRs significantly influence the spatial distribution of accessibility scores, producing three main clusters: (1) TAZs with scores of 0, which appear disconnected in the model; (2) TAZs with medium scores, where DRRs had a less pronounced effect; and (3) TAZs with very high scores, where reduced competition for hospital resources led to increased accessibility. While these findings are insightful, they must be interpreted cautiously due to the limitations of the network model. The exclusion of local streets likely exaggerates the severity of connectivity loss, and in reality, the impacts of DRRs would be less extreme. This scenario underscores the importance of considering model limitations when analyzing healthcare accessibility under disaster conditions. Future work should incorporate a more complete representation of the road network to capture alternate routes and provide a more accurate picture of DRR impacts. Figure 19 Distribution of DRR Scenario Scores 58 Focusing on that area in more detail, Figure 20 provides a zoomed-in map of the major bridges linking the isolated TAZ to the rest of North Vancouver. As shown on the map, there are a total of four bridges in this area. Two of these bridges, located on Highways 1 and 99, are part of the DRR network and are therefore inaccessible for non-emergency use under the scenario's rules. The northernmost bridge, marked as A, is not part of TransLinkâs network but does exist and is passable by private vehicles, providing one potential route for residents to leave West Vancouver. The southernmost bridge, marked as B, is also passable by private vehicles but only leads to the Park Royal complex, which itself is only accessible via Highway 99, part of the DRR network. While the limitations of the TransLink network in the analysis are acknowledgedâsince it does not include all local roads and structuresâthe accessibility for these TAZs remains severely restricted. Following the scenario's rules, the only viable connection to the rest of the network for residents in this area would be via the bridge marked A. In summary, while the limitations of the TransLink network used in this analysis must be acknowledgedâparticularly the exclusion of local streets and smaller connectorsâthe results still highlight the significant impact of DRR restrictions on healthcare accessibility. Even when considering alternate routes that might exist outside the modeled network, accessibility for certain TAZs, such as those in West Vancouver, remains severely constrained. The lack of viable connections beyond a single bridge (marked A) underscores the vulnerability of these areas under disaster conditions. This scenario demonstrates that while the model likely overestimates the severity of connectivity loss, the underlying challenges faced by residents in these isolated areas would persist, emphasizing the need for disaster planning efforts to address these critical gaps in accessibility. For urban planners and policymakers, the findings from the DRR scenario emphasize the need to consider accessibility to healthcare (for citizens who may need medical care that is non-urgent or does not necessarily require emergency transport) when designing and implementing Disaster Response Routes (DRRs). While DRRs prioritize emergency vehicles during disasters, their designation can inadvertently isolate certain populations from critical services, such as hospitals. Planners should evaluate the potential impact of DRRs on non-emergency access, particularly for vulnerable or geographically isolated communities, and incorporate mitigation strategies into DRR 59 planning. This could include ensuring that alternate routes, such as local streets or non-DRR bridges, are identified and made resilient to support civilian access during emergencies. For the affected population, these constraints mean increased vulnerability and delays in accessing healthcare, particularly for those with urgent medical needs. Proactively addressing these issues during the planning phase of DRRs would help balance the needs of emergency operations with equitable access to healthcare, ultimately enhancing both disaster preparedness and community resilience. 60 Figure 20 Zoomed in Map with DRR, Highways Marked with Numbers, other Bridges with Letters 99 1 B A 61 5.2.3 Scenario 3 Senior Population This scenario shifts focus from the entire population to the oldest population group (individuals age 65+) in each TAZ in order to examine potential differences in healthcare accessibility for this more vulnerable group (Christman & Ernstmeye, 2022). For this scenario, two key modifications were made compared to the Baseline scenario. First, the demand was redefined to include only the population aged 65 and older within each TAZ, rather than the total residential population. Second, hospital capacity was adjusted proportionally by reducing it based on the ratio of the senior population to the total population. This adjustment ensures that the capacity reflects the share of hospital resources that would reasonably be expected to serve the senior population, aligning the supply side of the analysis with the specific demand group being studied. For the adjustment, the total population in the study area and the total population of individuals aged 65 and older were calculated. Dividing the latter by the former yielded an adjustment factor ðà¯ = 0.17. To calculate the new capacities, each hospital's capacity was multiplied by this factor, ensuring that the adjusted capacity reflects the proportion of resources available for the senior population. 62 Figure 21Senior Population Accessibility Scores 63 The accessibility results for the 65+ age group scenario show minimal differences from the baseline scenario, suggesting that the spatial distribution of senior residents is relatively similar to that of the general population, as shown in Figure 21. However, despite the proportional adjustment of hospital capacity based on the senior population, small differences in accessibility scores are observed. This indicates that the senior population is not distributed exactly the same as the general population across the study area. The slight variations in accessibility scores suggest that seniors may experience healthcare accessibility differently, even though they generally live in the same areas as the general population. The histogram in Figure 22 illustrates the distribution of accessibility scores for the senior population under the adjusted scenario. Similar to the baseline distribution, the scores are right-skewed, with the majority of TAZs having scores concentrated toward the higher end of the range, between approximately 0.0015 and 0.0020. The red dashed line indicates the 25th percentile threshold, marking TAZs with the lowest accessibility scores, while the green dashed line represents the 75th percentile, highlighting areas with the highest accessibility. Notably, the distribution shows that most TAZs for the senior population achieve relatively high accessibility scores, with fewer zones falling in the low-accessibility range below 0.0005. This pattern aligns broadly with the general population but reveals subtle differences in how accessibility scores are distributed, suggesting that the senior population may experience healthcare accessibility in slightly different ways compared to the general population. In the next sections, further analysis will explore regional differences in accessibility that may uniquely impact the senior population, shedding light on these subtle variations. 64 Figure 22 Senior Population Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile 5.2.4 Scenario 4 Employment In the employment scenario, the residential population data was replaced with employment data for each TAZ in order to ascertain hospital accessibility for the working population during typical work hours. Similar to the Senior Population scenario, the hospital capacity was adjusted proportionally. For this adjustment, the total employed population in the study area and the total population of individuals were calculated. By dividing the employed population by the total population, a ratio of fE = 0.51 was determined. This ratio was then applied to each hospitalâs capacity by multiplying it by fE, ensuring that the adjusted capacity reflects the proportion of healthcare resources allocated to the employed population specifically While the overall distribution of accessibility remains similar to the baseline, notable local hotspots of improved access emerged in Abbotsford and Chilliwack, which will be discussed in detail later. 65 Additionally, high accessibility levels in Burnaby extended southeast into Surrey along the Trans-Canada Highway, highlighting critical transportation corridors that facilitate access during work hours. The spatial distribution can be seen in Figure 23. 66 Figure 23 Employment Scenario Accessibility Scores 67 The histogram in Figure 24 illustrates the distribution of accessibility scores under the Employment Scenario, where healthcare accessibility was analyzed specifically for the employed population. Similar to previous scenarios, the distribution is right-skewed, with the majority of TAZs clustered around higher accessibility scores between approximately 0.0015 and 0.0020. The red dashed line represents the 25th percentile threshold, identifying TAZs with the lowest accessibility scores, while the green dashed line indicates the 75th percentile threshold, highlighting areas with the highest scores. The concentration of scores in the higher ranges suggests that most TAZs maintain relatively good accessibility for the employed population. However, some TAZs fall below 0.0005, indicating significantly limited accessibility in these areas. Figure 24 Distribution of Employment Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile 68 Figure 23 and Figure 24 suggest that the accessibility scores for this scenario and the Baseline scenario are visually similar, with both distributions displaying comparable patterns in the clustering of scores and spatial distribution across the study area. This raises the question of why the employed population's accessibility distribution aligns closely with that of the general population. A detailed exploration of this similarity, including potential factors contributing to it and any smaller differences, is provided in a later section. 5.2.5 Scenario 5 Bridges Bridge data were imported into the model, and if a specific bridge was considered "down" or unavailable (a decision which is based on the year it was built), it was introduced into the network as a barrier. This scenario was modelled to assess the impact of key bridge failures on overall healthcare accessibility, particularly in regions heavily reliant on bridge infrastructure for hospital access. The bridges that were considered âdownâ are shown in Figure 25. Details for all structures are provided in Appendix A. An example of the information for two structures is shown below. Table 5 Sample Structure Information Used in Scenario 5 Structure Name Latitude Longitude Municipality Year Built Code Code Year Down? 204 St Overpass 49.112523 -122.656738 LangleyCity 2006 CSA S6 00 2000 FALSE 208 St Nicomekl River Bridge 49.100545 -122.644121 LangleyCity 1984 CAN3-S6-M78 1978 TRUE 69 Figure 25 Location of Bridges Considered âDownâ 70 The results are shown in Figure 26. Unlike the DRR scenario, which revealed significant accessibility challenges, the impact of bridge closures appears minimal. This difference stems from the nature of the disruptions: while the DRR scenario involves the closure of corridors that span well across a city or the region, bridge closures primarily affect individual links. For instance, the Trans-Canada Highway may face barriers at specific points due to bridge closures, but its overall connectivity is maintained through alternative links and interchanges. This redundancy in the transportation network helps to preserve accessibility, as vehicles can reroute effectively even when certain links are unavailable. These findings suggest that the geographic location and functional role of the affected bridges are critical factors in mitigating the overall impact on accessibility Figure 27 provides a closer look at a specific area, the same region previously highlighted in the DRR scenario analysis. This area includes four bridge options that connect a set of TAZs to the broader network. Of these four bridges, three are modeled in the TransLink network, and two of those remain usable under this scenario. The northern bridge, marked as A, is not part of the TransLink network but exists in reality and is passable by cars, offering a potential connection. The two modeled bridges that remain accessible provide critical lifelines to healthcare facilities for residents in this part of West Vancouver. This is one example that shows how the accessibility across the study area under this network remains similar to the Baseline scenario. 71 Figure 26 Bridges Scenario Accessibility Scores 72 Figure 27 Zoomed in Map with Bridge Restrictions, Highways Marked with Numbers, other Bridges with Letters1 99 B A 73 Figure 28 Distribution of Bridges Scenario Scores Red Dashed Line â 25th Percentile, Green Dashed Line â 75th Percentile The histogram in Figure 28 illustrates the distribution of accessibility scores under the Bridges scenario. Similar to other scenarios, the distribution is right-skewed, with the majority of TAZs having accessibility scores concentrated in the higher range between approximately 0.0015 and 0.0020. The red dashed line represents the 25th percentile, identifying the TAZs with the lowest accessibility scores, while the green dashed line indicates the 75th percentile, highlighting areas with the highest scores. Notably, this scenario exhibits a wider spread of scores compared to some previous scenarios, with a larger number of TAZs falling below 0.0005. This suggests that the unavailability of certain bridges disproportionately affects some areas, significantly limiting their access to healthcare services. The clustering of scores near the upper end still indicates that a majority of TAZs maintain relatively high accessibility, but the increased frequency of lower scores highlights the localized impact of bridge disruptions on healthcare accessibility in specific regions. 74 5.3 Comparative Analysis of Scenarios 5.3.1 Comparison of Baseline and Senior Population Scenario Figure 29 compares accessibility scores between the Baseline and Senior Population scenarios. The x-axis represents the normalized accessibility scores for the Baseline scenario, while the y-axis shows the normalized scores for the Senior Population scenario, both relative to the Baseline. Most points lie along the 1:1 line, indicating that accessibility scores remain consistent between the two scenarios for the majority of TAZs. However, a small number of points fall below the 1:1 line, signifying that these TAZs have lower accessibility for the senior population compared to the total population. This suggests that certain areas may face unique challenges in accessibility for elderly individuals that are not evident in the broader population analysis. Figure 29 Senior Population Scenario Scores vs. Baseline Scenario Scores 75 The TAZs highlighted in blue in Figure 30 correspond to those included in the cluster shown in Figure 29. Figure 30 illustrates the differences in accessibility values for TAZs between the Baseline and Senior Population scenarios. Recall that in the Senior Population scenario, the demand reflects only the senior population within each TAZ, and the supply is proportionally reduced to represent the share of healthcare services proportionate to the senior population relative to the total population (i.e., total supply multiplied by the proportion of seniors). The results in Figure 30 show that the most significant differences in accessibility are observed in areas east of Abbotsford and in Chilliwack. These differences do not represent a "loss" of accessibility per se, but rather, indicate that seniors in these areas experience a disproportionate disadvantage compared to the total population. This means that healthcare accessibility for seniors in these regions is significantly lower when measured relative to their specific demand, compared to accessibility for the general population. Such disparities may arise from factors such as the geographic distribution of healthcare facilities, limited transportation options for seniors, and potential mismatches between healthcare services and the needs of an aging population. The implications of these findings for urban planning are significant. For healthcare planning, organizations such as VCH and Fraser Health could prioritize expanding services or creating outreach programs in areas where seniors face the greatest accessibility challenges. For housing planning, the results suggest the importance of integrating affordable and senior-friendly housing nearer to healthcare facilities to minimize travel distances. Transportation planning could focus on improving transit connectivity in these regions, particularly with age-friendly infrastructure such as low-floor buses, demand-responsive transit, or senior transportation programs. Addressing these accessibility disparities could help ensure that seniors in areas like Chilliwack and east Abbotsford are not left behind in terms of healthcare access. Furthermore, such findings underscore the need for coordinated efforts across healthcare, housing, and transportation sectors to create inclusive urban environments that meet the needs of aging populations. Additional research and engagement with stakeholders would provide deeper insights into effective interventions and align these findings with practical urban planning strategies. 76 Figure 30 TAZs with Proportionally Lower Accessibility for Senior Population 77 5.3.2 Comparison of Baseline and Employment Scenario In comparing the baseline scenario with the employment scenario, several interesting observations emerge regarding the general distribution of employment and population across the TAZs. Note that the total regional employment numbers are lower than population numbers, as to be expected, and thus the hospital capacities were also proportionally adjusted accordingly like in scenario 2 (older population, age 65+). First, there appears to be no clear relationship between the number of employees working in a TAZ and the number of residents living in that same TAZ, as shown in Figure 31. However, a clear linear relationship is observed between the accessibility scores for the Baseline scenario and the Employment scenario across each TAZ as seen in Figure 32. This indicates that while there appears to be no relationship between employment and residential populations for TAZs in the region, accessibility scores for the TAZs based on their residential population and employment population show similarities. Figure 31 Employment per TAZ vs. Total Population per TAZ 78 Figure 32 Employment Scenario Scores vs. Baseline Scenario Scores To analyze these contradictory results further, it is crucial to examine the spatial distribution of both the residential and the employment-population across the study area. Figure 33 illustrates the spatial differences between employment and residential populations across the study area. For each TAZ, the percentage of total employment (calculated as the number of employees in the TAZ divided by the total number of employees in the region) is compared to the percentage of total residential population (calculated as the number of residents in the TAZ divided by the total number of residents in the region). The difference between these two values indicates whether a TAZ has a stronger employment focus or a residential focus. Areas shaded in pink represent TAZs where the employment percentage exceeds the residential population percentage, indicating zones with a higher concentration of jobs relative to their population. These are typically employment hubs, such as business districts or industrial areas, e.g. the Vancouver International Airport. In contrast, areas shaded in blue indicate TAZs where the residential population percentage exceeds the employment percentage, suggesting predominantly residential neighborhoods. 79 Figure 33 Difference between Proportional Employment and Residential Population 80 Unlike the typical patterns observed in many North American citiesâwhere high-employment areas, such as downtowns or central business districts (CBDs), are surrounded by high-residential areas that extend outward into suburbsâthe Lower Mainland region does not exhibit a clear spatial relationship between employment and residential areas. While the region has multiple city centers and employment hubs, these are not consistently surrounded by high-residential neighborhoods. Instead, high-employment and high-residential areas are interspersed throughout the region, often without a discernible pattern of proximity. This spatial arrangement helps explain the similar accessibility score distributions observed between the Baseline and Employment scenarios. For instance, one TAZ might have a high concentration of employment but a low residential population, while an adjacent TAZ displays the reverse pattern. Because the E2SFCA method accounts for the proximity of demand (residential population) to supply (hospitals), the accessibility scores remain relatively stable, as the locations of demand only shift slightlyâoften by just one TAZ. This interspersed pattern of employment and residential areas diminishes the impact of employment-specific demand on healthcare accessibility, resulting in distributions that closely resemble those of the general population. To better illustrate this phenomenon a similar case study as in Chapter 4 will be used. The accessibility scores for the census tract will be calculated for both a residential and an employment scenario for two different city types. In this first example shown in Figure 34, a residential and employment scenario is modeled within a typical urban layout featuring distinct suburban (dark blue) and CBD (light blue) areas. Residential Scenario: Census tracts shaded in dark blue represent suburban areas with a high residential population density of 10. In contrast, light blue-shaded tracts, representing the CBD, have a lower residential population density of 1. ð à¯ =171 Equation 9 81 Figure 34 Example Residential Scenario Typical City Layout As this is a simplified example, the 2-step method was simplified to where census tract 3 only reached one facility. Based on that the Accessibility score for census tract 3 is: ð´à¬· =171 Equation 10 Employment Scenario: Census tracts shaded in dark pink in Figure 35 represent areas with a high employment density of 10. In contrast, light pink-shaded tracts indicate areas with a low employment density of 1. ð à¯ =117 Equation 11 82 Figure 35 Example Employment Scenario Typical City Layout The Accessibility score for census tract 3 is: ð´à¬· =117 Equation 12 In this first example, the accessibility score for the employment scenario is higher compared to the residential scenario; this is because the ratio of facility a is higher when only serving the employment population. Now, consider a model that more closely resembles the urban layout of Vancouver, where areas with high employment and high residential populations are more interspersed. The same assumptions apply, with census tracts shaded in dark blue representing areas with a high residential population density of 10, while light blue tracts represent areas with a low residential population density of 1. Residential Scenario: Facility a serves four census tracts with a high residential population (10 each) and four census tracts with a low residential population (1 each), so 44 in total. 83 ð à¯ =144 Equation 13 Residential Scenario The Accessibility score for census tract 3 is: ð´à¬· =144 Equation 14 Employment Scenario Facility ð serves four census tracts with a high employment population (10 each) and four census tracts with a low employment population (1 each), so 44 in total. ð à¯ =144 Equation 15 The Accessibility score for census tract 3 is: Figure 36 Example Residential Scenario Interspersed City Layout 84 ð´à¬· =144 Equation 16 Figure 37 Example Employment Scenario Interspersed City Layout In this second example, the accessibility scores in both scenarios remain the same, even though census tract 3 has a low residential and a high employment population. In the study area of the Lower Mainland, healthcare accessibility remains consistent across both population and employment. Assuming the residential population reflects where people are at night and employment reflects their presence during the day, the timing of a disaster, such as an earthquake, is irrelevant to the distribution of healthcare accessibility, which does not significantly vary whether it occurs during the day or night. 5.4 Discussion of Findings The findings from this study have notable implications for urban planning and policy, particularly in the context of healthcare accessibility in the disaster preparedness of the Lower Mainland. First, the analysis revealed that some TAZs appear to be completely disconnected from hospital access when DRRs are implemented, but given the modelling limitations, this simply implies that residents may only be able to travel on local roads to reach hospitals. This limitation likely overestimates the impact of DRR restrictions by not accounting for the use of local streets as 85 alternate routes. While connectivity would indeed be reduced under DRR conditions, the actual extent of disconnection may be less severe, as local roads would still provide some level of access, albeit with longer travel times. Nonetheless, these findings highlight the importance of considering the potential impacts of emergency route planning on healthcare access and ensuring that redesign efforts mitigate disruptions to essential services. Additionally, a more targeted approach to healthcare access is recommended for vulnerable populations, especially the elderly in Chilliwack, where healthcare accessibility is more severely impacted compared to the wider study area. Focusing healthcare resources and response efforts in this region could help mitigate these accessibility disparities. It was also found that in conditions where multiple bridges and overpasses are compromised due to an earthquake, the impact on healthcare accessibility generally remains relatively the same. This seems to suggest that the major transportation network within the study area demonstrates a reasonable level of redundancy, such that people have many other (similarly desirable) options to reach hospitals when some routes are unusable. Finally, comparing healthcare access by residential populations versus employment populations reveal little differences in relative accessibility. This is likely due to the balanced distribution of the population across residential and employment locations, suggesting that time-of-day variations may not significantly affect healthcare access in disaster response scenarios. 86 Chapter 6 Conclusions 6.1 Research Questions This conclusions chapter begins by revisiting the primary research questions that guided this thesis. By addressing these questions, the findings synthesize key insights into the methods, current state, and dynamic factors influencing healthcare accessibility in the Lower Mainland. This section provides a detailed response to each question, offering a foundation for subsequent discussions on the broader implications, limitations, and future directions of this research. 6.1.1 Research Question 1 What existing methods and metrics are used to measure healthcare accessibility, and how can they be adapted to the context of the Lower Mainland? This thesis has demonstrated the applicability of the E2SFCA method as a robust tool for measuring healthcare accessibility in the Lower Mainland. The method was further refined by incorporating a Gaussian distance decay function . The analysis leveraged hospital bed capacity as a proxy for emergency healthcare supply, providing a meaningful metric for evaluating the spatial relationship between population demand and healthcare services. The results revealed a baseline distribution of accessibility scores across the region, highlighting concentrations of high accessibility along major corridors, such as the Trans-Canada Highway, while identifying underserved areas in the peripheries. This baseline assessment underscores the utility of the E2SFCA method in providing a nuanced, spatially detailed understanding of healthcare accessibility in urban contexts like the Lower Mainland. 6.1.2 Research Question 2 What is the current state of healthcare accessibility in the Lower Mainland, and what is the spatial distribution of healthcare facilities? The current state of healthcare accessibility in the Lower Mainland reveals significant spatial disparities, with high-accessibility clusters primarily concentrated in central and well-connected regions, particularly near major road networks. Conversely, peripheral areas, particularly those in suburban and rural parts of the Lower Mainland, experience lower accessibility, often due to limited connectivity and fewer healthcare facilities. This uneven distribution reflects the broader 87 challenges of urban planning in a geographically constrained and rapidly growing region. While the central areas benefit from proximity to high-capacity hospitals, residents in outer regions face extended travel times and reduced access, potentially exacerbating inequalities in health outcomes. These findings highlight the importance of targeted interventions to improve access for underserved communities, particularly in outer municipalities where the demand for healthcare continues to grow. 6.1.3 Research Question 3 How does healthcare accessibility in the Lower Mainland change when considering demographics and the impacts of a natural disaster like an earthquake? When accounting for demographic factors and disaster scenarios, healthcare accessibility in the Lower Mainland exhibits notable shifts that emphasize the vulnerability of specific populations and areas. For seniors, accessibility scores slightly diverge from the baseline, reflecting subtle differences in their spatial distribution and emphasizing the need for tailored healthcare and transportation planning to address the needs of aging populations. In the employment scenario, similar distributions between workers and the general population highlight the interspersed nature of residential and employment areas in the Lower Mainland, which diminishes variations in accessibility. However, disaster scenarios like an earthquake, modeled through Disaster Response Routes (DRRs) and bridge closures, reveal severe disruptions in accessibility, particularly for peripheral areas like West Vancouver. These disruptions highlight critical gaps in resilience planning, where certain regions could face near-total isolation without targeted improvements in disaster preparedness and infrastructure. These insights underscore the urgent need for integrated planning across healthcare, transportation, and emergency management to ensure equitable and resilient access to healthcare services under both normal and disaster conditions 6.2 Limitations This study faces several limitations that should be considered when interpreting the results. First, the static metric employed does not account for real-time traffic conditions, which can significantly influence accessibility, particularly during peak hours or emergencies. Accessibility scores might differ under typical traffic congestion or during disaster scenarios when evacuation traffic patterns 88 emerge. Incorporating dynamic traffic data could provide a more accurate representation of travel times and connectivity under both normal and emergency conditions. Second, the analysis is limited to car-based transportation and does not consider other modes of transportation, such as public transit, cycling, or walking. Public transit, in particular, is a vital mode of travel for many residents, especially for seniors, low-income populations, and those without access to private vehicles. The exclusion of these modes limits the comprehensiveness of the findings, particularly in urban areas where multimodal transportation is a critical component of accessibility. Third, the transportation network model used in this study does not include local roads, focusing instead on major collectors, arterials, and highways. This omission may overestimate the severity of restricted access in some areas, particularly during disaster scenarios like the DRR scenario or bridge closures. In practice, residents would likely rely on local roads to navigate around barriers, even if such routes involve longer travel times. The incomplete representation of the full road network limits the accuracy of the results, especially in areas where local streets play a significant role in connecting residents to healthcare services. Additionally, this study uses hospital bed capacity as a proxy for healthcare supply, which does not fully capture the complexities of healthcare provision, such as the availability of specialized services, staff shortages, or differences in service quality between facilities. These factors could lead to variations in accessibility that are not reflected in the current analysis. Finally, the study assumes that healthcare demand is evenly distributed within each TAZ, which may not reflect real-world patterns of healthcare utilization. For instance, certain populations, such as seniors or individuals with chronic illnesses, may place greater demand on healthcare services than others. Incorporating more detailed population health data could help refine the analysis and improve the accuracy of accessibility measures. Despite these limitations, the study provides a valuable framework for understanding healthcare accessibility and lays the groundwork for future research that addresses these gaps. 6.3 Future Research For future research, several areas are recommended to build upon and extend the findings of this thesis. First, incorporating data on healthcare staffing levels, such as the number of medical 89 professionals or specialists at each facility, could provide a more nuanced understanding of healthcare accessibility. While this study used hospital bed capacity as a proxy for healthcare supply, the availability of personnelâparticularly during disasters or periods of high demandâplays a critical role in determining the actual capacity of a healthcare system to serve its population. Exploring this dimension would offer a more comprehensive view of healthcare accessibility that goes beyond physical infrastructure. Second, future studies could expand the scope of the model to include regions with a higher risk of structural damage during disasters, such as those situated in areas with unstable soils or near active fault lines. By integrating seismic risk maps or other geotechnical data, researchers could simulate accessibility under more severe disaster conditions and identify areas where infrastructure investments or emergency response planning are most urgently needed. This approach would provide valuable insights for urban planners and policymakers tasked with enhancing the resilience of healthcare systems. Third, the inclusion of multimodal transportation optionsâsuch as public transit, cycling, or walkingâwould improve the realism and applicability of healthcare accessibility models. Public transit, in particular, is a critical mode of travel for many urban residents, including seniors, low-income populations, and those without access to private vehicles. A multimodal approach would capture the full range of travel behaviors and provide a more equitable assessment of healthcare accessibility across different population groups. Fourth, future research could explore the integration of real-time data into accessibility models. Dynamic data, such as real-time traffic conditions, transit schedules, or road closures, would allow for a more accurate representation of accessibility under both normal and disaster scenarios. This would also enable the development of time-sensitive response strategies, such as identifying alternative routes or prioritizing resources during emergencies. Fifth, an analysis of healthcare demand patterns based on specific population characteristicsâsuch as age, socioeconomic status, or health conditionsâcould provide deeper insights into accessibility challenges for vulnerable groups. Incorporating more detailed demographic or health data could help identify areas where healthcare demand is likely to be higher and where targeted interventions are most needed. 90 Finally, future studies could expand the geographic scope of the analysis to include regions beyond the Lower Mainland, providing a broader perspective on healthcare accessibility in British Columbia or other comparable urban regions. 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Journal of Medical Systems, 30(1), 23â32. https://doi.org/10.1007/s10916-006-7400-5 97 98 Appendix A 99Sturcture Name Latitude Longtitude Municipality Year built Code Code year Old?Nordel Way BNR Overhead 49.150339 -122.934896 Delta 1986 CAN3-S6-1978&CSA-S6-88 1978 TRUE72 Ave BNR Overhead 49.133939 -122.919142 Delta 1986 CAN3-S6-M78 1978 TRUE64 Avenue BNR Overpass 49.119003 -122.923756 Delta 1986 CAN3-S6-M78 1978 TRUE51B Ave Causeway 49.097742 -122.652582 LangleyCity 1992 CSA S6 88 1988 FALSE200 St Bridge 49.096525 -122.668596 LangleyCity 1976 1966 TRUE203 St Bridge LangleyCity 2016 CSA S6 14+S6 06 2014 FALSE204 St Overpass 49.112523 -122.656738 LangleyCity 2006 CSA S6 00 2000 FALSE208 St Nicomekl River Bridge 49.100545 -122.644121 LangleyCity 1984 CAN3-S6-M78 1978 TRUEFraser Hwy Bridge 49.100517 -122.641464 LangleyCity 1960 1952 TRUEJacob Haldi Bridge(GLO-93) 49.171852 -122.5762 LangleyTwp 1991 CSA-S6-88/ATC-6 1988 FALSE88th Ave&Salmon River Bridge(88-227) 49.162835 -122.59607 LangleyTwp 1964 1952 TRUEBertrand Creek â 16th Ave Bridge(16-248) 49.030603 -122.532599 LangleyTwp 1983 1978 TRUENicomekl River - 56th Ave bridge(56-211) 49.10458 -122.637452 LangleyTwp 2013 CSA-S6-06 2006 FALSECampbell River 200th St Bridge(200-26) 49.04974 -122.669438 LangleyTwp 1950 1938 TRUESalmon River - 232nd St Bridge(232-68) 49.127354 -122.581534 LangleyTwp 2009 CSA-S6-06 2006 FALSE208 St Hwy#1 49.158071 -122.645837 LangleyTwp 2018 CSA-S6-14 2014 FALSEMufford Crescent Overpass(MUFF-GLO) 49.114163 -122.643953 LangleyTwp 2014 CSA-S6-06 2006 FALSEIoco Road at CP Overheads West 49.276924 -122.828008 PortMoody 1950 1938 1938 TRUEIoco Road at Noons Creek Bridge 49.284359 -122.831409 PortMoody 1966 CSA-S6-06 2006 FALSEMoody Street Overpass 49.278276 -122.850183 PortMoody 1969 CSA-S6-06 2006 FALSEDavid Ave at Noons Creek East 49.297072 -122.823658 PortMoody 2001 CSA-S6-88 1988 FALSEDavid Ave at Noons Creek West 49.296974 -122.825825 PortMoody 2001 CSA-6-88 1988 FALSEHBM at Noons Creek 49.286172 -122.830399 PortMoody 1978 1974 TRUEWillingdon Avenue Overpass 49.262065 -123.003281 Burnaby 1967 1966 TRUEBeaverbrook Drive Bridge 49.255775 -122.904949 Burnaby 1971 1966 TRUENorth Road Railway Overpass 49.240913 -122.892597 Burnaby 1983 CSA-S6-78 1978 TRUEOakland Street Bridge 49.228934 -122.980123 Burnaby 1984 CSA-S6-78 1978 TRUEKensington Avenue Overpass 49.260597 -122.968081 Burnaby 1985 CSA-S6-78 1978 TRUENorth Road Bridge (Burnette River) 49.238068 -122.892516 Burnaby 1986 CSA-S6-78 1978 TRUEDeer Lake Avenue Bridge 49.238656 -122.967773 Burnaby 1986 CSA-S6-78 1978 TRUEStill Creek Drive Bridge (Still Creek) 49.25976 -123.00729 Burnaby 1986 CSA-S6-78 1978 TRUERoyal Oak Avenue Bridge 49.236387 -122.988336 Burnaby 1996 CSA-S6-88 1988 FALSEByrne Road Bridge 49.204112 -122.978092 Burnaby 1999 CSA-S6-88 1988 FALSEMeadow Avenue Bridge 49.204012 -122.977055 Burnaby 1998 CSA-S6-88 1988 FALSEHenning Drive Bridge 49.264362 -123.015978 Burnaby 1998 CSA-S6-88 1988 FALSE Broadway Avenue Bridge 49.263131 -122.897663 Burnaby 1985 CSA-S6-78 1978 TRUEGaglardi Way Bridge at Eastlake Drive 49.257539 -122.912012 Burnaby 1965 CSA-S6-00 2000 FALSEGaglardi Way Bridge at Government Street 49.25038 -122.912677 Burnaby 1978 ASSHTO 1978 TRUEGaglardi Way Bridge at Cariboo Place 49.2479315 -122.9128928 Burnaby 1975 CSA-S6-06 2006 FALSELougheed Hwy at Eastlake Drive 49.252031 -122.909745 Burnaby 1959 CSA-S6-2014 2014 FALSELougheed Highway at Scott Creek 49.271951 -122.798669 Coquitlam 1974 1966 TRUECanadian Pacific Railway Overpass at Lougheed Highway 49.273281 -122.798684 Coquitlam 1975 1966 TRUEDewdney Trunk Road at Scott Creek 49.268189 -122.797705 Coquitlam 1997 CSA-S6-88 1988 FALSEMariner Way Overpass, south of Barnet Highway 49.275103 -122.801567 Coquitlam 1996 CSA-S6-88 1988 FALSEEleanor Ward Bridge-David Ave at Coquitlam River 49.294996 -122.774801 Coquitlam 2006 CSA-S6-00 2000 FALSEDavid Ave at Hyde Creek 49.293001 -122.761168 Coquitlam 2006 CSA-S6-00 2000 FALSEMarguerite Street at Hyde Creek 49.302874 -122.75855 Coquitlam 2007 2000 FALSEBurke Village Promenade at Smiling Creek 49.292044 -122.746177 Coquitlam 2007 2000 FALSENorth Road Bridge at Brunette River 49.23806 -122.892486 Coquitlam 1986 CSA-S6-78 1978 TRUENorth Road over CN and BCR Tracks 49.240937 -122.892577 Coquitlam 1983 CSA-S6-78 1978 TRUESchoolhouse Street at Booth Creek 49.234963 -122.852549 Coquitlam 1990 CSA-S6-88 1988 FALSERogers Avenue Gas Pipeline Crossing 49.224635 -122.825782 Coquitlam 1990 1978 TRUELucille Starr Drive at Booth Creek 49.236216 -122.85154 Coquitlam 2000 CSA-S6-88 1988 FALSEWoolridge Bridge,west of Schoolhouse Street 49.232117 -122.855007 Coquitlam 2009 CSA-S6-06 2006 FALSEJohnson Street at Hoy Creek 49.283152 -122.801696 Coquitlam 1986 1974 TRUEComo Lake Ave at Lougheed Highway 49.263129 -122.797826 Coquitlam 1990 CSA-S6-88 1988 FALSE100David Avenue at Scott Creek 49.294162 -122.809519 Coquitlam 2001 CSA-S6-88 1988 FALSEQuarry Road at Munro Creek 49.325607 -122.672777 Coquitlam 2007 CSA-S6-00 2000 FALSEQuarry Road at Deiner Creek 49.316848 -122.693947 Coquitlam 2008 CSA-S6-00 2000 FALSEVictoria Drive at Partington Creek 49.297387 -122.717047 Coquitlam 2003 CSA-S6-00 2000 FALSEBraid St 49.229143 -122.877304 Coquitlam 2015 CSA-S6-06 2006 FALSEGislason Avenue at Smiling Creek 49.289404 -122.744778 Coquitlam 2014 CSA-S6-06 2006 FALSEDavid Avenue at Smiling Creek 49.294102 -122.746154 Coquitlam 2007 2000 FALSEGislason Ave at Watkins Creek 49.289291 -122.755718 Coquitlam 2006 CSA-S6-00 2000 FALSEKeith Road Over Brothers Creek 49.330637 -123.126488 WestVancouver 1952 1938 TRUEInglewood Ave. Over Brothers Creek 49.334452 -123.129471 WestVancouver 1986 1974 TRUE3900 Marine Drive at Sandy Cove 49.341073 -123.225952 WestVancouver 1939 1929 TRUEAlmondel Road over Cypress Creek 49.349645 -123.237803 WestVancouver 2008 CSA-S6-06 2006 FALSE4300 Marine Drive over Cypress Creek 49.340812 -123.239849 WestVancouver 1940 1929 TRUEEagle Lake Road at Cypress Creek 49.359695 -123.232923 WestVancouver 1984 1978 TRUEKeith Place over Brothers Creek 49.330763 -123.126456 WestVancouver 1991 CSA-S6-88 1988 FALSEMillstream Road over Brothers Creek 49.355443 -123.151356 WestVancouver 1964 1952 TRUESinclair Court over Lawson Creek 49.336352 -123.16253 WestVancouver 1995 CSA-S6-88 1988 FALSEChippendale Road over Mcdonald Creek 49.350284 -123.169819 WestVancouver 2002 CSA-S6-88 1988 FALSEChippendale Road over Marr Creek 49.349624 -123.180403 WestVancouver 2004 CSA-S6-00 2000 FALSE2817 Chippendale Road 49.350996 -123.19113 WestVancouver 2010 CSA-S6-00 2000 FALSE3032 Burfield Place 49.349299 -123.202663 WestVancouver 2014 CSA-S6-06 2006 FALSEAO-1 Birch - Alder Overpass 49.266437 -123.132132 Vancouver 1976 AASHTO H20 +25% 1966 TRUEAO-2 Deering Island Bridge 49.218557 -123.183969 Vancouver 1989 CSA-S6-78 1978 TRUEAO-3 Hastings St Viaducts 49.280998 -123.081975 Vancouver 1978 AASHTO HS20-44 1974 TRUEAO-4 Marine Drive-Boundary Road Overpass 49.208121 -123.023639 Vancouver 1981 CSA-S6-78 1978 TRUEAO-5 New Brighton Road Overpass 49.28878 -123.036264 Vancouver 1998 CSA-S6-88 1988 FALSEAO-8 Powell Street Overpass 49.282526 -123.082475 Vancouver 2014 CSA-S6-06 2006 FALSED-1 Howe Street Viaduct 49.28712 -123.11326 Vancouver 1983 CSA-S6-78 1978 TRUED-2 Canada Place Viaduct 49.28767 -123.11386 Vancouver 1983 CSA-S6-78 1978 TRUED-3 Cordova 1 Viaduct 49.286374 -123.113537 Vancouver 1983 CSA-S6-78 1978 TRUED-6 Burrard Viaduct 49.287835 -123.115948 Vancouver 1983 CSA-S6-78 1978 TRUED-7 Canada Place 2 Viaduct 49.288367 -123.116421 Vancouver 2009 CSA-S6-06 2006 FALSED8-D11 Dunsmuir Georgia Viaducts Main St OP 49.277113 -123.099946 Vancouver 1969 AASHTO HS-25 1966 TRUEFC-1 Burrard Bridge 49.275268 -123.137628 Vancouver 1930 1922 TRUEFC-2 Cambie Bridge 49.271916 -123.114763 Vancouver 1985 CSA-S6-88 1988 FALSEFC-3 Granville Bridge 49.272619 -123.133317 Vancouver 1954 1938 TRUEGC-1 1st Ave Viaduct 49.269686 -123.078794 Vancouver 1937 1929 TRUEGC-2 Clark Drive Bridge 49.265715 -123.077409 Vancouver 1993 CSA-S6-88 1988 FALSEGC-3 Woodland Drive Bridge 49.264657 -123.073611 Vancouver 1991 CSA-S6-88 1988 FALSEGC-4 Commercial Drive Bridge 49.263427 -123.06977 Vancouver 1989 CSA-S6-78 1981 TRUEGC-5 Broadway Bridge 49.262295 -123.067006 Vancouver 1992 CSA-S6-88 1988 FALSEGC-6 Victoria Drive Bridge 49.26186 -123.065935 Vancouver 2001 CSA-S6-88 1988 FALSEGC-7 Lakewood Drive Bridge 49.260379 -123.062096 Vancouver 2001 CSA-S6-88 1988 FALSEGC-8 Nanaimo Street Bridge 49.258183 -123.056645 Vancouver 2000 CSA-S6-88 1988 FALSETannery Road Underpass 49.196056 -122.89327 Surrey 2014 CSA-S6-06 2006 FALSETannery Road Overhead 49.196501 -122.893885 Surrey 2014 CSA-S6-06 2006 FALSE152nd St. Underpass 49.201188 -122.797995 Surrey 2013 CSA-S6-06 2006 FALSE160th St. Underpass 49.192069 -122.77759 Surrey 2014 CSA-S6-06 2006 FALSEFraser Height Bridge Surrey 2013 CSA-S6-06 2006 FALSE156 St / Hwy 1 - Overpass 49.195652 -122.789772 Surrey 2009 CSA-S6-06 2006 FALSE96 Ave @ Serpentine River Bridge - Steel Guardrail Surrey 2012 CSA-S6-06 2006 FALSE160 St WB Off Ramp Underpass 49.190822 -122.772084 Surrey 2014 CSA-S6-06 2006 FALSE176 St Underpass @ Hwy 1 49.181654 -122.734904 Surrey 2014 CSA-S6-06 2006 FALSEBarnston Dr. Underpass @ 176 St 49.185549 -122.734415 Surrey 2014 CSA-S6-06 2006 FALSESerpentine River Bridge : 88 Ave E of 168 St 49.162912 -122.747683 Surrey 2005 CSA-S6-00 2000 FALSE152 St over Bear Creek 49.144815 -122.801086 Surrey 1981 1974 TRUESerpentine Bridges @ Hwy 15 49.142074 -122.73475 Surrey 2008 CSA-S6-00 2000 FALSE101148 St & Hyland Creek Surrey 2015 CSA-S6S1-10 2010 FALSE64 Ave Bridge Serpentine River 49.118901 -122.785023 Surrey 1993 CSA-S6-88 1988 FALSE64 Ave Serpentine Canal 49.118907 -122.773574 Surrey 1993 CSA-S6-88 1988 FALSE196 St Underpass 49.109735 -122.680036 Surrey 2014 CSA-S6-06 2006 FALSERoger Pierlet Bridge 49.100531 -122.737236 Surrey 2007 CSA-S6-00 2000 FALSE192 St. Overhead 49.097258 -122.69119 Surrey 2014 CSA-S6-06 2006 FALSE54 Ave Overhead 49.100665 -122.68629 Surrey 2014 CSA-S6-06 2006 FALSEK.G. Blvd @ Serpentine River Bridge 49.086758 -122.818697 Surrey 2010 CSA-S6-06 2006 FALSENicomekl Bridge@ Hwy 15 49.085887 -122.735355 Surrey 2008 CSA-S6-00 2000 FALSE24 Ave Underpass & Hwy 99 49.045509 -122.785353 Surrey 2005 CSA-S6-00 2000 FALSE16 Ave Underpass 49.031126 -122.769335 Surrey 2015 CSA-S6-06 2006 FALSE8 Ave @ Hwy 99 Underpass 49.016783 -122.759606 Surrey 2006 CSA-S6-00 2000 FALSEPallot 49.238084 -122.707449 PittMeadows 1949 1988 FALSECapilano Canyon 49.332417 -123.117194 NorthVanDistrict 1959 1988 FALSEMontizambert Cr 49.40728 -123.241808 WestVancouver 1984 1978 TRUE Kanaka Br 49.201539 -122.578944 MapleRidge 1955 1974 TRUEFern St UnderPass 49.311306 -123.028028 NorthVanDistrict 1958 1952 TRUELynn Creek 49.313611 -123.033056 NorthVanDistrict 1960 1998 FALSELynn Valley Rd OP 49.329052 -123.053716 NorthVanCity 1961 1952 TRUEEdgemont Blvd Underpass 49.332083 -123.093972 NorthVanDistrict 1995 1988 FALSEWestminster Hwy UP North 49.170444 -123.087111 Richmond 1959 1978 TRUEBlundell UP 49.155778 -123.086583 Richmond 1959 1952 TRUESteveston UP 49.133556 -123.086833 Richmond 1960 1952 TRUEMountain Highway UP 49.315208 -123.03746 NorthVanDistrict 1960 0 TRUEMatthews UP 49.090417 -122.959778 Delta 1961 1952 TRUEHORSESHOE BAY OH 1961 49.368405 -123.276223 WestVancouver 1961 0 TRUECPR OH #1 49.2297 -122.8355 Coquitlam 1960 1988 FALSECPR OH Cape Horn 49.232083 -122.82975 Coquitlam 1965 1978 TRUEOliver St UP 49.091361 -122.91275 Delta 1961 1952 TRUELivingstone Rd UP 49.136472 -122.581167 LangleyTwp 1965 1952 TRUE Carvolth Rd UP 49.163934 -122.668071 LangleyTwp 1965 1952 TRUE Latimer Road UP 49.170278 -122.691861 Surrey 1965 1952 TRUE Clover Valley Rd UP 49.181583 -122.734278 Surrey 1960 1952 TRUEPIKE ROAD U/P 49.19225 -122.77725 Surrey 1965 1952 TRUEJohnston Interchange 49.200972 -122.797472 Surrey 1965 1952 TRUE Lougheed OP 49.26507 -123.02626 Vancouver 1960 1952 TRUE BNR No.2 OP 49.26259 -123.02567 Vancouver 1960 1952 TRUEBOUNDARY RD O/P 49.26141 -123.02386 Vancouver 1960 1952 TRUEClydesdale St UP 49.258306 -123.019139 Burnaby 1962 1952 TRUEGilmore UP 49.257694 -123.011806 Burnaby 1962 1952 TRUEDouglas Rd UP 49.257528 -122.982611 Burnaby 1960 1952 TRUE Johnston UP HWY 99 49.0585 -122.801333 Surrey 1961 1952 TRUENorth Bluff Rd UP 49.031194 -122.769528 Surrey 1961 1952 TRUEWestview UP 49.332028 -123.087861 NorthVanCity 1997 1988 FALSERoberts Bank OH 49.051694 -123.120333 Delta 1998 1988 FALSEArthur Drive UP 49.059556 -123.077056 Delta 1998 1988 FALSE46A Street UP 49.059694 -123.096667 Delta 1998 1988 FALSE200TH STREET UP 49.164222 -122.6685 LangleyTwp 2004 1988 FALSESunset Creek 49.407958 -123.242425 WestVancouver 1961 1952 TRUESTORMONT 49.247764 -122.912992 Burnaby 1978 1966 TRUEArgyle Ave OP 49.37173 -123.273881 WestVancouver 1975 1969 TRUEHorseshoe Bay UP 49.369737 -123.27588 WestVancouver 1975 1969 TRUESquamish UP 49.366856 -123.275835 WestVancouver 1975 1969 TRUEEagleridge Drive UP 49.361272 -123.273489 WestVancouver 1975 1969 TRUEWestport OP 49.360197 -123.255594 WestVancouver 1975 1969 TRUECaulfield Dr UP 49.351397 -123.251736 WestVancouver 1975 1969 TRUEAlmondel Rd OP 49.349095 -123.236776 WestVancouver 1975 1969 TRUE102Westmount UP 49.34625 -123.216583 WestVancouver 1975 1969 TRUE15th Street OP 49.343192 -123.156047 WestVancouver 1975 1969 TRUEKnight St South 49.18928 -123.08262 Richmond 1977 1966 TRUECoquitlam UP 49.273314 -122.798733 Coquitlam 1975 0 TRUEColebrook Rd OH 49.097521 -122.818202 Surrey 1976 1969 TRUETsawwassen OH 49.059444 -123.057472 Delta 1970 1963 TRUECapilano River EB 49.326529 -123.130823 WestVancouver 1956 1952 TRUE34B Avenue UP 49.066139 -123.057278 Delta 1990 1988 FALSECypress Bowl Road UP 49.347605 -123.19508 WestVancouver 1975 1969 TRUEWestminster UP S 49.170222 -123.087028 Richmond 1975 1969 TRUEEAGLE BLUFF STRUCT 49.36172 -123.267062 WestVancouver 1975 0 TRUEBRUNETTE BNR O/H 49.23366 -122.881039 NewWestminster 1978 1974 TRUECOLONY FARM O/H 49.237 -122.8205 Coquitlam 1981 1977 TRUECOQUITLAM RIVER 49.227122 -122.805549 PortCoquitlam 1985 1978 TRUECAPE HORN FLYOVER 49.231833 -122.831083 Coquitlam 1981 1977 TRUEMUD BAY O/H 49.094326 -122.895293 Delta 1986 1978 TRUE64 AVE U/P 49.119583 -122.927139 Delta 1985 1978 TRUENORDEL U/P 49.145361 -122.937056 Delta 1988 1978 TRUECLIVEDEN AVE U/P 49.170833 -122.9515 NewWestminster 1986 1983 TRUEANNACIS CHANNEL EAST 49.176944 -122.957639 Richmond 1985 1981 TRUEBNR O/H HWY 10 49.103672 -122.899132 Delta 1985 1978 TRUEHWY 10 RAMP BNR O/H 49.104579 -122.899883 Delta 1986 1978 TRUEALOUETTE FLYOVER 49.17525 -123.093806 Richmond 1986 1981 TRUEISIS FLYOVER 49.175814 -123.091109 Richmond 1989 1981 TRUEHERMES O/P 49.177278 -123.089944 Richmond 1990 1981 TRUEJACOMBS ROAD U/P 49.177333 -123.080528 Richmond 1988 1981 TRUENO 6 ROAD O/P 49.177778 -123.069139 Richmond 1997 1981 TRUENO 7 ROAD U/P 49.177306 -123.046944 Richmond 1997 1981 TRUENO 8 ROAD U/P 49.177167 -123.024389 Richmond 1997 1981 TRUERICHMOND FLYOVER 49.174028 -123.091917 Richmond 1989 1978 TRUEANIK O/H 49.169493 -122.998327 Richmond 1989 1981 TRUEWATERSHED PARK O/P 49.105167 -122.898 Delta 1986 1978 TRUENO 5 ROAD O/P 49.177096 -123.091354 Richmond 1985 1983 TRUECASSIAR O/P SB 49.287528 -123.030639 Vancouver 1992 1988 FALSEWESTMIN HAMILTON U/P 49.169528 -122.972778 Vancouver 1993 1988 FALSEWESTMIN HAMILTON F/O 49.169333 -122.971667 Vancouver 1993 1988 FALSEKING EDWARD U/P 49.23292 -122.86179 Coquitlam 2011 2006 FALSE
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kim, Amy M.
Exploring hospital accessibility in British Columbia's Lower Mainland
Text