Northern Ontario Air Transportation and Remote Community Resilience and Wellbeing

Pia Isabel Dimayuga

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Civil and Mineral Engineering University of Toronto

Pia Isabel Dimayuga

Master of Applied Science

Department of Civil and Mineral Engineering University of Toronto

2020 Abstract

This thesis explores the role air transportation plays in northern Ontario remote community wellbeing and resilience using mixed methods based on a modified resilience framework. Six years of cargo data and flight operations data from one airline in the region are analyzed to first, map how remote communities and their infrastructure systems are dependent on air transportation, and second, to analyze air transportation performance. Air transportation is the only mode of year- round travel in 31 northern Ontario communities and therefore plays an essential role in connecting communities to essential goods and services (e.g. food, healthcare). In terms of infrastructure interdependencies, the main cargo delivered by air is diesel fuel for energy, with a secondary good being construction materials. Performance-wise, air transportation faces both infrastructure challenges (e.g. lack of weather reporting, inadequate de-icing services) and operating challenges

(e.g. poor weather) that are exacerbated by climate change effects.

Acknowledgments

Throughout the process of developing and completing this thesis, I’ve encountered many people who have contributed not only to this research, but to my personal growth and understanding of the world.

Thank you to the University of Toronto and the XSeed funding program. Investing in inter- disciplinary research creates opportunities for meaningful research that is neither here nor there, but somewhere in between.

To Shoshanna, thank you for your guidance, mentorship, and for being a strong advocate for me. You inspire me to think critically, ask meaningful and wicked questions, and be open to different perspectives.

To Tracey, thank you for letting me be a pretend anthropologist and for being alongside me as I did so. I am in awe of your dedication to indigenous food security issues and your persistence.

To everyone at North Star Air, and especially Jeff and Luke, thank you for your willingness to collaborate and to share data. Without your help and participation in my research, I’m not sure I would even have a thesis.

To the rest of the XSeed team and the innumerable people I journeyed with and encountered in Thunder Bay, Sioux Lookout, Lac Seul, and beyond, thank you. A special thanks to the elders, chiefs, and community members for sharing with an outsider like me. Your stories and active participation in imagining a brighter tomorrow for remote communities is inspiring and necessary.

To my colleagues at U of T, in SPM, and in GB418, thank you for the engaging discussions and for the laughs. Learning and growing alongside you all has been a pleasure.

To my family and to Jad, thank you for your unwavering and continued support throughout my academic pursuits. Thank you for listening to me present my research over and over again, and for debugging my code.

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Table of Contents

ACKNOWLEDGMENTS ...... III

Table of Contents ...... iv

LIST OF TABLES ...... VI

LIST OF FIGURES ...... VII

LIST OF APPENDICES ...... X

1 INTRODUCTION ...... 1

1.1 NORTHERN ONTARIO REMOTE COMMUNITIES ...... 2

1.2 INFRASTRUCTURE AND WELLBEING ...... 5

1.3 STATE OF AIR TRANSPORTATION INFRASTRUCTURE ...... 6

2 RESILIENCE LITERATURE REVIEW ...... 8

2.1 SYSTEM AND FUNCTION DEFINITION ...... 9

2.2 INTERDEPENDENCIES ...... 9

2.3 HAZARDS, HAZARD IMPACT, AND RISK ...... 11

2.4 QUANTIFYING RESILIENCE ...... 12

2.5 DROP COMMUNITY RESILIENCE MODEL ...... 14

3 FRAMING AND METHODS ...... 17

3.1 MODIFIED RESILIENCE SCHEMATIC ...... 17

3.2 KNOWLEDGE SHARING AND DATA COLLECTION ...... 19 3.2.1 Preliminary community engagement ...... 19 3.2.2 Airlines ...... 22

3.3 DATA AND FRAMING LIMITATIONS ...... 25

4 CARGO ...... 26

4.1 DATASET DESCRIPTION ...... 26

4.2 DATA ANALYSIS ...... 30 4.2.1 Data cleaning ...... 30 4.2.2 Cargo analysis ...... 31

4.3 FINDINGS AND DISCUSSION ...... 32 4.3.1 Total cargo distributions ...... 32 4.3.2 Temporal fluctuations in cargo distribution ...... 36 iv

4.3.3 Community diesel delivery ...... 38 4.3.4 The cargo network ...... 40 4.3.5 Holding time analysis ...... 41

4.4 CARGO SUMMARY ...... 43

5 FLIGHT RELIABILITY ...... 44

5.1 DATA DESCRIPTION ...... 44

5.2 DATA ANALYSIS ...... 46 5.2.1 Data cleaning ...... 46 5.2.2 Flight data analysis ...... 47

5.3 FINDINGS AND DISCUSSION ...... 48 5.3.1 Presence of delays and cancellations ...... 48 5.3.2 Temporal variation ...... 50 5.3.3 Community distinctions ...... 52 5.3.4 Reasons behind delays and cancellations ...... 54

5.4 FLIGHT RELIABILITY SUMMARY ...... 63

6 CONCLUSION ...... 64

REFERENCES ...... 68

APPENDIX A: SEMI-STRUCTURED INTERVIEW MATERIALS ...... 77

List of Tables

Table 2-1: Five types of interdependencies (Lee, Mitchell and Wallace, 2007) ...... 10

Table 3-1: Relevant airlines contacted for this research ...... 23

Table 4-1: Data characteristic descriptions ...... 28

Table 4-2: Community data variability concerns ...... 29

Table 4-3: Example goods in each cargo category ...... 31

Table 5-1: Data characteristic descriptions for Flown and Cancelled Flights ...... 45

Table 5-2: Cancellation reason descriptions ...... 55

Table 5-3: Delay reason descriptions ...... 58

Table 5-4: Total delay distribution compared to delays with unknown reason distribution ...... 59

Table 5-5: Weather reporting infrastructure at northern Ontario airports (NAV Canada, 2019a) 61

List of Figures

Figure 1-1: Remote communities and airports in northern Ontario (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017) ...... 3

Figure 1-2: Treaties in northern Ontario (Numbered Treaties Map - Wikimedia Commons, 2011)3

Figure 1-3: First Nations medicine wheel (left) as a conceptualisation of health and wellbeing and a depiction of two-eyed seeing (right) (Guiding Principles (Two Eyed Seeing) | Integrative Science, no date) ...... 5

Figure 2-1: Bruneau et al.’s conceptualisation of resilience (the resilience triangle), adapted from (Bruneau et al., 2003) ...... 13

Figure 2-2: The Disaster Resilience of Place (DROP) model (Susan L Cutter et al., 2008) ...... 14

Figure 3-1: A modified resilience schematic for remote northern Ontario ...... 17

Figure 4-1: The NSA cargo network covers northern Ontario, northern Manitoba, Nunavut, and parts of the Northwest Territories (not pictured) (North Star Air Ltd. - Cargo Locations, no date) ...... 27

Figure 4-2: Remote Ontario cargo distribution ...... 33

Figure 4-3: Remote Ontario fuel distributions ...... 33

Figure 4-4: Modified resilience schematic ...... 34

Figure 4-5: Potential diesel electricity generation based on annual diesel shipments. 2014 and 2024 demands from (Ontario Power Authority, 2014)...... 35

Figure 4-6: Remote Ontario monthly cargo distribution ...... 36

Figure 4-7: Variation in diesel (as a percentage of total monthly cargo) across months ...... 37

Figure 4-8: Individual community and remote Ontario average monthly cargo for diesel ...... 38 vii

Figure 4-9: Community diesel deliveries compared to 2007 electricity demands ...... 39

Figure 4-10: Cargo heatmap for remote Ontario communities ...... 41

Figure 4-11: Holding time distribution for all remote Ontario cargo ...... 42

Figure 5-1: Example of how Original Delay Reasons are determined ...... 48

Figure 5-2: Breakdown of on-time performance for all flight legs in northern Ontario between November 2014 and August 2019 ...... 49

Figure 5-3: Distribution of flight leg delay order ...... 50

Figure 5-4: Flight performance over time for all Remote Ontario flights ...... 51

Figure 5-5: Variation in percent cancelled and delayed across months ...... 52

Figure 5-6: Flight performance disaggregated by community. Letters indicate remote community and B followed by a number indicate NSA base. Red asterisks (*) indicate no weather reporting, purple plus signs (+) indicate longer runways, and green tildes (~) indicate fuel availability. .... 53

Figure 5-7: Comparison of remote Ontario community and NSA base on-time performance ..... 54

Figure 5-8: Breakdown of cancellation reasons ...... 55

Figure 5-9: Breakdown of delay reasons for reported delays ...... 56

Figure 5-10: Breakdown of delay reasons for all actual delays ...... 57

Figure 5-11: De-icing materials at an NSA base (left) in comparison to at a remote airport, rolling ladder (top right) and an orange de-icing backpack in a cage (bottom right)...... 60

Figure 6-1: A modified resilience schematic for remote northern Ontario ...... 64

Figure 6-2: Updated resilience schematic that explicitly shows interdependencies between community infrastructure systems...... 65

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List of Appendices

Appendix A: Semi-structured interview materials……………………………………………77

1 Introduction

This thesis explores the role air transportation plays in remote northern Ontario community wellbeing and resilience using mixed methods based on a modified resilience framework. The first objective of this research is to map how remote communities and their infrastructure systems are dependent on air transportation and the second is to analyze air transportation performance in the region and determine underlying factors that impact air service reliability.

The focus on air transportation in this research is motivated by the central role air transportation plays in the provision of essential goods and services to remote First Nations in northern Ontario. Air transportation is the only year-round available mode of transportation in 31 First Nation communities in northern Ontario. These remote communities rely on air transportation for many essential goods and services including food, fuel, healthcare, and education. Alternative modes are seasonal and are not uniformly accessible to communities, such as summer barges and winter roads. Winter roads, which consist of a combination of packed snow on land and frozen waterways, are crucial for transporting large freight loads (e.g. diesel fuel) to communities more economically than by air. The operating season of winter roads is highly variable, however, and the disproportionate climatic warming in northern Ontario is expected to reduce the viability of winter roads (Woudsma and Towns, 2017). Consequently, the importance of air transportation is expected to increase in the future due to climate change.

Air transportation and its infrastructure face its own challenges as well. Infrastructure limitations include short, unpaved runways, inadequate lighting, and a lack of weather monitoring systems. Inclement weather often results in flights that are delayed, cancelled, or diverted, resulting in significant impacts on communities that rely on this critical infrastructure. Climate change is expected to increase inclement weather and further threaten the infrastructure (e.g. thawing permafrost deforms runways).

Critical infrastructure, as defined by Public Safety Canada, are “systems, facilities, technologies, networks, assets and services essential to the health, safety, security or economic well-being of Canadians and the effective functioning of government” (Public Safety Canada, 2009a). Transportation is one of ten critical infrastructure sectors identified in the National Strategy for Critical Infrastructure (Public Safety Canada, 2009b), and therefore air transportation is a critical mode of transportation in northern Ontario. In alignment with the National Strategy for Critical

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Infrastructure, understanding the resilience of the air transportation system is critical, and by extension, air transportation’s role in northern communities’ wellbeing and resilience.

The remainder of this chapter will provide context for the region included in the study and relevant infrastructure systems. Chapter 2 is a review of relevant resilience literature which forms the basis for the theoretical framing and methodology presented in Chapter 3. Chapter 4 presents the findings from analyzing cargo data and Chapter 5 presents the findings from analyzing flight data. Chapter 6 presents final conclusions and provides suggestions for future work.

1.1 Northern Ontario remote communities

Within northern Ontario, there are 31 remote First Nations (Figure 1-1). They are classified as remote due to the lack of permanent all-season road infrastructure leading directly into communities. Out of the 31 communities, 26 have airports near the community managed by the Ontario Ministry of Transportation (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017)1, and the focus of this research will be on these 26 communities with permanent air infrastructure. In terms of population, community size ranges from 57 to over 3600 people (Indigenous and Northern Affairs Canada, 2020).

1 Of the five remote communities with no airport, two have a helipad and ferry to a nearby municipality, two are accessible by float and ski planes, and the transportation for the fifth was unascertainable.

Figure 1-1: Remote communities and airports in northern Ontario (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017)

Figure 1-2: Treaties in northern Ontario (Numbered Treaties Map - Wikimedia Commons, 2011)

The majority of the area is covered by Treaty 9 (signed in 1905, with major additions in 1929), with parts of the area covered by Treaty 3 (signed in 1873) and Treaty 5 (signed in 1875) (Figure 1-2). The treaties, along with Canadian legislation like the Indian Act, restricted and imposed upon First Nations’ traditional way of living, from limiting where First Nations people could live to restricting their movement outside of reserves (Leslie, 2016; Nestor, 2018). The further infringement on First Nations’ rights exemplified through the residential school system and the sixties’ scoop, both projects involving the forced removal of children from their families and communities to force assimilation, reflect the intentionally harmful legacy of colonialism in Canada (Czyzewski, 2011). The lasting effects of this colonial history is intrinsically connected to the current state of remote communities and their infrastructure and therefore must be acknowledged. For example, treaties were signed to remove First Nations from valuable land that could be profitable from its natural resources and potential trade routes (Filice, 2016; Leslie, 2016). Decades later, in the 1970’s, the locations of remote airports were selected specifically to be within walking distance of reserves (Ontario Ministry of Transportation, 2018); the continuing remoteness of communities is a direct result of colonial history.

In addition to considering First Nation history in contextualizing this work, likewise a specifically Indigenous way of understanding must be acknowledged. For example, the First Nations’ conceptualization of health and wellbeing is different than the Western interpretation. Made up of four quadrants related to different aspects of health (physical, mental, emotional, and spiritual), a person’s health and wellbeing relies on the balance of all four quadrants (Figure 1-3) (Wilson, 2003). Wilson’s interviews with Odawa and Ojibway First Nations in northern Ontario reflect on their sense of wellbeing as closely linked to their connection to the earth and the community (Wilson, 2003). A holistic approach to studying First Nations wellbeing and resilience, therefore, must allow for a different worldview and specifically Indigenous perspective. The concept of integrative science and “two-eyed seeing” as put forward by Mi’kmaw Elder Albert Marshall highlights the opportunity and benefits of using Indigenous worldviews and traditional knowledge alongside Western perspectives (Bartlett, Marshall and Marshall, 2012). While the current research ultimately did not accomplish a “two-eyed seeing” approach (discussed further in Preliminary community engagement), it is acknowledged that it should be the way forward.

1.2 Infrastructure and wellbeing

Community wellbeing is dependent on community infrastructure; adequate housing, clean water, and reliable energy are examples of infrastructure services that people cannot do without. Infrastructure in First Nation communities, as is clear based on much of its media coverage over the last several decades, are not delivering all these aspects of wellbeing (e.g. long-term boil water advisories and poor housing quality) (Gerster and Hessey, 2019; Mercer, 2019). Inadequate infrastructure and remoteness have a compounding effect on community wellbeing. To compare the infrastructure conditions of Indigenous remote communities with southern Canada, the Centre for the Study of Living Standards (CSLS) developed an infrastructure index that measures 13 infrastructure indicators related to ‘economic’ infrastructure (e.g. transportation, energy, internet) and ‘quality of life’ infrastructure (e.g. healthcare, education, water, housing) (Johnston and Sharpe, 2019). Overall, out of a possible 1.0, remote First Nations in Ontario did worse than other northern Ontario municipalities (0.41 and 0.74 respectively), and much worse than other metropolitan areas in Canada (0.97) (Johnston and Sharpe, 2019).

These disparities in infrastructure contribute to health inequities and limit community access to healthcare and education. For example, high food costs in northern Canada are typically associated with high costs of transportation, which when coupled with high rates of poverty lead to food insecurity (Reading and Wien, 2009). For northern Ontario First Nations, 52 percent of households were classified as food insecure in 2012 compared to 8.2 percent for Ontario overall (Chan et al., 2014). In comparing the healthcare and education infrastructure indicators developed by the CSLS, remote First Nations communities in Canada rank significantly lower than remote non-

Indigenous communities (0.34 compared to 0.83 and 0.44 to 0.76, respectively) (Johnston and Sharpe, 2019). This difference is partially due to the lack of healthcare and education services located in communities and is sometimes overcome by air transportation. Air ambulances, for example, play a key role in emergency healthcare while regular passenger flights transport both students and non-emergency medical patients.

Barriers to improving northern infrastructure in general include the logistics and costs associated with transporting materials and equipment to remote communities. Transporting large and heavy loads are oftentimes only possible with the use of winter roads or summer barges for coastal communities (IBI Group, 2016). Winter roads are utilised for transporting materials for new schools and updated water treatment plants. In the 2016 to 2017 Ontario winter road season, winter roads were used to carry approximately 17 million litres of fuel and 1,500 full truckloads of other goods (Gee, 2020; Kitching, 2020). Any major construction or maintenance for infrastructure in community is subject to the availability and transport of material and equipment. As climatic warming threatens seasonal modes of transportation, and specifically winter roads, other modes of transportation are being discussed (e.g. all-season roads) (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017), but air transportation is the only mode that currently functions all year round.

1.3 State of air transportation infrastructure

Despite its importance for remote communities, air transportation infrastructure in northern Canada more broadly has been documented as in need of investments and improvements (Standing Senate Committee on Transport and Communications, 2013; Office of the Auditor General of Canada, 2017). Limited runway lengths, unpaved runways, inadequate lighting and lack of weather reporting infrastructure are some of the factors that differentiate northern air infrastructure from more modern air infrastructure in the South, and these factors restrict flight reliability.

Two such factors are airport runway length and whether the runway is paved or unpaved. For example, a linear regression analysis on infrastructure components and flight arrival reliability found a positive correlation between the length of the runways and flight arrival reliability (Widener, Saxe and Galloway, 2017). Runway length and material also affect the cargo weight limitations and types of planes that can safely operate at these airports (Standing Senate Committee on Transport and Communications, 2013). At the time of writing, all 26 remote airports under the

Ontario Ministry of Transportation (MTO) have unpaved gravel runways (NAV Canada, 2019b). Newer aircraft are not designed to land on gravel and other unprepared surfaces; using newer aircraft in the North requires a retrofitting with a “gravel kit”, which is rarely done due to cost and the regulatory process required to get one (Standing Senate Committee on Transport and Communications, 2013). Therefore, the majority of aircraft operating in the area are older and reliance on an older fleet has consequences of poor fuel efficiency and more regular maintenance (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017). Both of these services, fuel and maintenance, are not available at most northern airports (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017).

Another important infrastructure challenge is the lack of climate and weather monitoring systems at Northern Ontario airports. At the time of writing, only 8 out of 26 remote airports under the MTO have 24-hour Automated Weather Observation Systems (AWOS) (NAV Canada, 2019b). Community aerodrome radio stations (CARS) can provide additional weather information for aircraft operating near airports without AWOS, but these have limited hours of operation which make any emergency flights difficult to coordinate (Standing Senate Committee on Transport and Communications, 2013). Runway lighting, which facilitates landing in poor weather at airports without modern navigation technology, is an identified challenge in the Draft 2041 Northern Ontario Multimodal Transportation Strategy (Ontario Ministry of Transportation and Ontario Ministry of Northern Development and Mines, 2017). At the time of writing, all remote airports have some runway lighting available, though different light intensities and configurations are present (e.g. low intensity versus medium intensity) (NAV Canada, 2019a).

2 Resilience Literature Review

The resilience literature provides a useful lens through which to conceptualize remote communities, air transportation in the region, and their relationship to one another. Resilience, which can be defined broadly as the ability of a system to continue to function in uncertain conditions and recover rapidly from unforeseen shocks (Susan L Cutter et al., 2008), provides two main insights for this research. Firstly, resilience considers the effects of unpredictable and changing conditions on a system, and secondly, resilience requires a deep understanding of the inner workings of the system and the ways in which the system connects to and depends on external systems.

Resilience as a concept has roots in ecology, referring to the ability of a living organism to continue to function amidst changing and uncertain conditions (Holling, 1973). Since its introduction in ecology, resilience as a concept has been used and adapted by multiple disciplines, each of which has developed a domain-specific definition, resulting in no single established definition (Pendall, Foster and Cowell, 2010).

In the context of human settlements, the concept of resilience has led to the development of resilience strategies (e.g. 100 Resilient Cities) (PreventionWeb, no date) and disaster risk reduction frameworks (e.g. the United Nations Sendai Framework) (United Nations Office for Disaster Risk Reduction (UNDRR), 2020) intended to protect populations and help them recover from acute shocks like hurricanes and long-term stresses like climate change. Resilience planning is also occurring specifically for rural and remote communities (Cox and Hamlen, 2015), recognizing the different contexts of remote communities and urban centres. For the sake of this research, resilience literature across a number of disciplines and definitions is discussed (e.g. community resilience, disaster resilience, infrastructure resilience) as it pertains to the systems being studied; namely air transportation and remote communities.

The remainder of the chapter will review resilience “essentials”; concepts that describe and explain resilience; then will delve into the Disaster Resilience of Place (DROP) model of community resilience developed by Cutter et al. which serves as the foundational model of understanding resilience in this research.

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2.1 System and function definition

Understanding resilience first requires identifying a definitive system(s) or function(s) of interest; answering the question “what is being made resilient?”. Resilience can be applied to a broad range of scales, for example, single assets (e.g. wastewater treatment plants)(Karamouz et al., 2019), networks (e.g. public transit metro systems)(Chopra et al., 2016), and at the county and city level (e.g. counties and metropolitan areas in the southeastern US)(Cutter, Burton and Emrich, 2010). The selection of a system or a function leads to a clear delineation of the scope of the work. For example, Chopra et al. consider the main function of the London metro to be transportation and therefore include the walking habits of Londoners (e.g. how far they would walk if a station was closed) in their model of how the metro system recovers from station closures (Chopra et al., 2016). Cutter, Burton, and Emrich, in studying counties’ and cities’ resilience to disasters, acknowledge that multiple subsystems within the county/city play a role in responding to and recovering from disaster and therefore include these in their understanding of the system (e.g. infrastructure, society, institutions) (Cutter, Burton and Emrich, 2010).

The selection of one scale over another can lead to different conclusions about how best to increase resilience. A bridge for example, can be defined as a singular asset (i.e. its own system) or as a component of a highway network (i.e. one component of a larger system). As its own system, increasing the bridge’s resilience would likely involve infrastructure hardening. As part of a road network, however, the hardening of that particular bridge may not be the most effective solution for increasing resilience of the entire system. The added connectivity the bridge contributes to the entire network would be examined and hardening of certain essential assets may be the result.

2.2 Interdependencies

One key concept from the resilience literature that is foundational to this research is a system’s interdependencies. Interdependencies explain the ways in which components of a system interact with each other and depend upon each other as well as its environment or systems that are external to the system of interest. Table 2-1 describes the five types of interdependencies (input, mutually dependent, co-located, shared, and exclusive-or) as depicted by Lee, Mitchell, and Wallace. The differences among the five types of interdependencies highlight that the nature of connections between systems or components within systems may not always be straightforward. For example, two components within a system may be connected to different subsystems and have separate

10 functions but could be interdependent by being located close to one another. Indeed, some disasters in recent history within complex man-made systems (e.g. the accident at the Three Mile Island nuclear power generating station) are arguably a result of the unanticipated interdependencies within a system (Perrow, 1999).

Table 2-1: Five types of interdependencies (Lee, Mitchell and Wallace, 2007)

Interdependency Descriptive example Consequence Type

Input System A relies on System B for its If System B stops working, System A will be affected. function.

Mutually System A relies on System B and If either System A or B stops working, both will be dependent System B relies on System A. affected.

Co-located Where System A and System B are Hazards may affect the systems at the same time, or located within proximity of each catastrophic failure of System A or B (e.g. electrical other. fire) could prove hazardous for the other co-located system.

Shared Where System A and System B Failure of shared components will affect both System have shared components. A and System B.

Exclusive-or Where System C can only provide Competition between System A and System B for for System A or System B, but not resources from System C. Changes in demand may both. lead to reduced resources or an exceedance of what is available.

Thoroughly mapping these system interdependencies requires a detailed understanding of the system and what it relies on to function in normal conditions and under extenuating circumstances. For example, Ouyang et al. show the mutual dependence of natural gas and electricity infrastructure through mapping the interdependencies; natural gas is used as a fuel for electricity generation, but the metres and pumps required by natural gas distribution also rely on electricity (Ouyang et al., 2009). Another example is the input interdependency of a hospital on its utilities (Achour and Price, 2010). For example, while a hospital may not be located in a floodplain, if the electricity substation it relies on is in a floodplain, the hospital may experience a power outage when flooding occurs (unless it has backup power on site). Understanding the system and its interdependencies highlights vulnerabilities of the system (or the systems that it is dependent on) that may otherwise go unnoticed.

2.3 Hazards, hazard impact, and risk

The next essential aspect of conceptualizing resilience is to understand what the system should be resilient against. Three key terms clarify this aspect of resilience: hazard, hazard impact, and risk. A hazard can be defined as any possible event or situation that may impact a system and its functions. In the literature, hazards are also referred to interchangeably as threats (Cox and Hamlen, 2015), disruptions (Park, Seager and Rao, 2011), disasters (Susan L Cutter et al., 2008), shocks (Pendall, Foster and Cowell, 2010), and stresses (Pendall, Foster and Cowell, 2010). Cutter et al. further differentiate between rapid onset hazards and slow onset hazards that describe the temporal nature of how these hazards act upon the system. Examples of rapid onset hazards include flooding, earthquakes, terrorist attacks, and fire. Slow onset hazards include climate change, drought, and rising sea levels. Hazard impact describes how a specific hazard will affect a system. For example, flooding as a singular hazard may have multiple impacts on a subway station. It may cause a power outage as well as make the station inaccessible for people.

In practice and in literature, the number of hazards that are considered in resilience strategies range from “all” to none. Governments and international organizations support an all-hazards approach to resilience, including Canada, the US, and the OECD (NEA, 2018; Public Safety Canada, 2018; Department of Homeland Security, 2019). An all-hazards approach considers the broad spectrum of hazards that a system is exposed to and how strategies to enhance resilience may work across multiple hazards (e.g. both flooding and seismic activity may result in power outages and backup power may be a good strategy against both) (Public Safety Canada, 2017). Selecting specific hazards to consider in resilience planning can be motivated by recent events, political will, and/or an understanding of what hazards may have an increased likelihood of occurring and impacting the system. For example, the response following the 9/11 terrorist attacks on the World Trade Centre has been analyzed in terms of disaster resilience (Tierney, 2003) and spurned an interest in the US around resilience against human-made hazards (Flynn, 2007). Flooding in coastal cities as a result of natural disasters, such as Hurricane Sandy in New York City in 2012, has similarly sparked specific interest in studying resilience of infrastructure and cities in coastal areas (Torabi, Dedekorkut-Howes and Howes, 2017; Karamouz et al., 2019).

A key characteristic of hazard impacts is the extent to which the hazard affects the system and if it crosses a threshold of the system. The threshold can be for a particular component or subsystem

12 within the system. For example, in the event of a power outage, a grocery store may have a diesel generator to power its fridges, with enough diesel to last 24 hours. The grocery store’s threshold therefore is 24 hours without electricity, after which point, the impact of the power outage greatly increases. Thresholds of the system can be based on technical characteristics of the system but may also be affected by downstream systems or business considerations (e.g. if a supply chain is disrupted, how much resource is stored at a downstream business). A thorough understanding of the system and its interdependencies enables the mapping of how hazards will impact the system.

Resilience work which does not focus on particular hazards is often looking at a network, consisting of nodes and links, and looks at the effect of combinations of nodal and link failures in a network (Alderson, Brown and Carlyle, 2015). This networked, hazard-agnostic approach has been applied to air transportation networks (Janić, 2015, 2019; Dunn and Wilkinson, 2016), energy transmission networks (Ouyang et al., 2009), and public transit (Chopra et al., 2016). The hazard- agnostic approach provides more insight into the system’s function and its vulnerabilities. It can especially be useful in the consideration of intentional man-made hazards as it may identify the weak links in a system that may be targeted by a hostile attacker (Alderson, Brown and Carlyle, 2015).

Selection of hazards is sometimes tied to the risk associated with that hazard. Risk links a hazard and its impact with the likelihood of occurrence. Risk and risk management have been closely tied to the resilience literature and is still tied to resilience practices (Alderson, Brown and Carlyle, 2015; Public Safety Canada, 2017), but there is some critique of whether the use of likelihood in a resilience framework is appropriate (Park, Seager and Rao, 2011). Park, Seager, and Rao note that while risk-based methods are appropriate when the likelihood of certain events is known, it fails to appreciate large-scale or unlikely events that may overwhelm the system (Park, Seager and Rao, 2011). Taleb argues that “black swan” events, those that are incredibly rare but have a large potential impact, should be considered in the design of systems to not only improve resilience but bring an antifragile characteristic to systems that will thrive in a variable and unknown future (Taleb, 2014).

2.4 Quantifying resilience

Just as there is no singular definition of resilience, there is no singular way to measure resilience. The method and metrics are chosen to fit the specific context, considering the nature of the system

13 being studied, the system functions requiring resilience, and the definition of resilience being used. For example, resilience in transportation systems are typically related to passenger flows (Dijkstra et al., 2014; Mattsson and Jenelius, 2015) and the loss of function is often described in terms of the cost of passengers’ time (Janić, 2015; Chopra et al., 2016).

With respect to the definition of resilience, the distinction of resilience as an outcome or as a process also impacts how resilience is measured. Resilience as an outcome focuses on a system’s immediate response and recovery from a disruption, whereas resilience as a process often extends the temporal horizon to account for changes a system undergoes after learning post-disaster (Susan L. Cutter et al., 2008). The resilience triangle (Figure 2-1), conceptualised by Bruneau et al., is compatible with the definition of resilience as an outcome. There are clear metrics associated with the resilience triangle; the y axis denotes the quality of infrastructure, or the key system function’s performance, and the x axis denotes time. In this conceptualisation, the focus is on how fast a system can recover its functionality after a shock, and there is also a focus on minimizing the loss in functionality when a shock occurs. A great deal of resilience literature, especially in engineering, uses this conceptualisation of resilience and subsequently creates mathematical models to map a system’s resilience as a response to a loss in function (Hosseini, Barker and Ramirez-Marquez, 2016).

Figure 2-1: Bruneau et al.’s conceptualisation of resilience (the resilience triangle), adapted from (Bruneau et al., 2003)

When studying a broader system, or a system with multiple functions, measuring its resilience becomes more complex and complicated. In the case of a city, for example, selecting which

14 functions to consider in modelling resilience, and then accurately modelling the response of a city’s components (government, individuals, utilities, community organizations, businesses) individually and with respect to one another would be incredibly challenging. In that case, instead of using mathematical models, a set of indicators are developed and used (Cutter, Burton and Emrich, 2010; Cox and Hamlen, 2015). Indicators leverage existing resilience literature and the subsequent correlations and are selected to reflect a number of attributes of the system that have positive or negative effects on resilience overall. Indicator approaches do not predict technical system responses as mathematical models are apt to do but are useful in measuring many different aspects of a system that are less appropriate for modelling. For example, the Rural Resilience Index consists of 51 dimensions of resilience, each dimension having multiple indicators, where all dimensions fall under either Social Fabric, Community Resource, or Disaster and Emergency Management Preparedness (Cox and Hamlen, 2015).

2.5 DROP community resilience model

This research uses the Disaster Resilience of Place (DROP) model put forward by Cutter et al. as its foundational conceptualisation of resilience. The DROP model was selected as the most relevant conceptualisation of resilience for three main reasons: its depiction of antecedent conditions, its inclusion of resilience as a process, and its appropriateness for multiple scales. This section will present the DROP model and then elaborate on the three reasons it was selected.

Figure 2-2: The Disaster Resilience of Place (DROP) model (Susan L Cutter et al., 2008)

A schematic of the DROP model is shown in Figure 2-2 and depicts how a system responds to shocks and stresses it is exposed to. Starting from the triangle on the left, there is a system which is composed of the social systems, the built environment, and the natural system. The overall system possesses an inherent vulnerability and an inherent resilience based on the system itself. The system is then exposed to an event that has its own characteristics (e.g. a 100-year flood or a 5-year flood). The system has some immediate coping response to the event, which then influences the realized impact of the event. For example, if a disaster struck a dense urban population and required a mass evacuation, but there was no coordinated evacuation plan communicated to residents, traffic may block the throughways out of the city, worsening the impact of the disaster overall. The system may also have some tolerance to the hazard’s impact, called the absorptive capacity in the DROP model, which along with the system’s ability to improvise and adapt in the moment, will determine how well the system is able to recover from the event. For example, perhaps it is in a city’s preparedness plans for residents to have enough food and water stored in their homes for three days (e.g. Canada’s preparedness guidelines recommend enough food, water, and other supplies for a minimum of 72 hours)(Government of Canada, 2018). Three days then can be considered as a factor in the absorptive capacity of a city that may require lockdown due to an airborne biohazard. After the event, there is an opportunity for the system to increase (or decrease) both its preparedness and its mitigation of hazards. For example, imagine a city’s seawall is successful in protecting the city from a flood but is damaged in the process. If the city does not repair the sea wall, this lowers the mitigating effect of the sea wall and negatively impacts that city’s resilience for the next flooding event. The actions taken (or not taken) to improve resilience in the system between events will then affect the system’s antecedent conditions for the next hazard the system faces.

With respect to why this model was identified as foundational in this research, the first reason is because of the DROP model’s depiction of antecedent conditions. The antecedent conditions are called out specifically as an interaction of the social systems, the built environment, and the natural system, with a resultant inherent resilience and vulnerability. That is a holistic depiction of antecedent conditions and it also recognizes that the system’s antecedent conditions are not necessarily “ideal”. In comparison with the resilience triangle (Figure 2-1) that shows the theoretical quality of infrastructure as 100%, the DROP model does not presume where a system’s starting point is.

In its inclusion of time both before and after specific events, the DROP model not only shows resilience as an outcome, it reflects resilience as a process. While the outcome perspective is reflected in the immediate aftermath of an event, the process perspective is modeled in both the system’s responses to the event and in the mitigation and preparedness that occurs between events. This inclusion of actions that can be taken by people or organizations or subsystems within the larger system reflect the spontaneity and resourcefulness of those actors that impact a system’s overall resilience. By including those actions distinctly throughout the model (i.e. coping response, adaptive resilience, mitigation, and preparedness), it is also situating those actions as distinct and taken by potentially different actors (e.g. social learning occurs organically in a group of people, but mitigation and preparedness are often in the hands of decision makers).

Lastly, the DROP model is appropriate at multiple scales, meaning it can be applied to both singular assets or cities (Susan L Cutter et al., 2008), for example. Given that this research involves specific remote communities, the region of northern Ontario collectively, and the air transportation network in particular, this flexibility of the model is important.

The DROP model is thorough in its conception of resilience. It considers the dynamic nature of resilience, representing a difference between inherent resilience, adaptive resilience, preparedness and mitigation. The DROP model is highly conceptual which is appropriate for this research which is working through a resilience perspective at a high level and not adopting detailed resilience modelling methods. The next chapter demonstrates how the DROP model is used in the theoretical framing of this research.

3 Framing and Methods 3.1 Modified resilience schematic

The framing of this research is based on the DROP model within the context of northern Ontario remote communities. This research focuses specifically on community infrastructure systems and air transportation but acknowledges that factors such as colonialism, governance, and economic systems have impacts on community wellbeing and resilience. The framing, depicted in Figure 3-1, shows a high-level understanding of remote northern Ontario community resilience and the central role air transportation plays in community wellbeing and resilience.

Figure 3-1: A modified resilience schematic for remote northern Ontario

Within the schematic, there are three nested concepts: the community (light grey), community infrastructure (white), and air transportation (dark grey). Beginning with the light grey outer ellipse representing a remote community, the icons inside are examples of community wellbeing indicators. Community wellbeing and these specific indicators are directly related to and dependent on the community’s infrastructure (energy, housing, water, transportation, and telecommunications). The interconnectedness of these two components, infrastructure and community wellbeing, holds true for communities generally; infrastructure supports and enables

17 18 community function. The unique addition of this schematic is the central location of air transportation in the dark grey circle and this highlights the specific circumstances of remote communities. Air transportation being the only mode of transportation available for large parts of the year result in a singular dependence on one mode for all cargo and person transportation. Therefore, community wellbeing may be dependent on air transportation directly (e.g. air ambulances) and indirectly via community infrastructure (e.g. when water treatment chemicals are transported via air). The interdependency arrows that connect the three levels are all bidirectional, allowing for the similar dependence of air transportation on community infrastructure systems and the community more broadly. For example, air transportation may depend on telecommunications in remote communities for flight planning and on community employees as ground crew. The interdependencies between different community infrastructure systems (e.g. housing on energy) are not visually represented explicitly in the schematic for two reasons: first, to avoid cluttering the schematic with additional arrows; and second, to focus the framing on interdependencies related to air transportation.

The last component of the schematic is the black arrows representing external shocks and stresses impacting the system and its components. Depending on the shock or stress, one or more of the three subsystems can be affected, and these impacts can be felt by another subsystem. For example, poor flying weather may directly impact air transportation and result in cancelled flights. If some of the cancelled flights are carrying medical patients or students, the impact is also extending to the community’s access to education and healthcare (community wellbeing indicators). As another example, if a community is flooded, this may render the community infrastructure unusable and potentially damaged. At that point, air transportation becomes not only the evacuation mode for community members, but it could also be needed to bring materials into the community in order to repair community infrastructure after the event.

The research, guided by this schematic, aims to validate and refine the connections it makes. What are the interdependencies between air transportation and community infrastructure systems? How does this ultimately impact remote community wellbeing and resilience? Two systems to investigate become apparent: the aviation industry in the region and the remote communities themselves. Both quantitative and qualitative methods are utilised in validating the resilience schematic. Quantitatively, the amount of fuel being carried by plane each year could be found, while qualitative interviews with pilots could determine what are some common challenges in

19 flying in the region. The research intended to include remote community perspectives through interviews with First Nations living in remote communities, but these interviews did not ultimately take place (discussed further in Preliminary community engagement).

3.2 Knowledge sharing and data collection

This section lays out the sources of knowledge and data that were considered and consulted in the preliminary community engagement process through to the formal data collection. The scope of potential collaborators and sources was broad and included remote communities (e.g. local government, elders, community members), airlines, airport management, local service providers within remote communities (e.g. utility operators, nurses), and hub service providers in southern communities (e.g. healthcare providers). In acknowledgement of the historic and destructive practice of academia extracting knowledge and data from First Nation communities, this research took a cautious and no-harm approach in its process. Specifically, researchers underwent ethics training and followed the example set by the principal investigator. The principal investigator of the broader project, Dr. Tracey Galloway at the Department of Anthropology at the University of Toronto, who has experience working with Indigenous populations specifically in the region, guided the methodology, including the ethics protocols (#00037640), and community engagement process. Discussions around potential collaborations with First Nations were open and took place at the convenience and invitation of leadership in communities. The protocols developed around First Nation community engagement were open-ended, allowing for flexibility in methodology to include the wealth of knowledge in people’s lived experience (e.g. semi-structured interviews and arts-based sharing circles were considered). Additionally, funds of the project were allocated to hire a translator if deemed appropriate by community leadership and research participants were to be thanked in part with grocery vouchers.

3.2.1 Preliminary community engagement

As part of the preliminary community engagement for this research, there were two visits to the Sioux Lookout, Ontario, area and two other planned trips to remote First Nations, only one of which took place. The lessons learned from the trips that did take place will be discussed, followed by some insights that arose from the delay and cancellation of the other trips.

The two Sioux Lookout trips were a part of a broader investigation into finding opportunities for academia to collaborate with Sioux Lookout and northern First Nations to research and address First Nations concerns and so a broad range of topics were discussed. The many conversations and meetings that took place during these visits were incredibly valuable in gaining understanding of the remoteness that is experienced by First Nations as well as northern hub municipalities like Sioux Lookout (e.g. regular trips to Thunder Bay or Winnipeg for cheaper food). Because these conversations took place outside of the ethics protocol they cannot be considered as data, although they did provide valuable context for this research. A wide range of people were involved in the discussions: elders, community members, and band members from six First Nations (four of which are remote), representatives from two tribal councils, municipal and provincial government officials, healthcare providers, service providers (e.g. food, fuel), and individuals from the aviation industry.

There are five insights that arose out of the conversations that were relevant to the preliminary community engagement. Firstly, understanding transportation in remote communities is complicated by the multiple governing bodies involved and the unintended consequences of research and planning. For example, there is an understanding that funding from the federal government is related to how remote a community is. When or if a community gains any ground connection to another community, it is understood that it will experience a coinciding reduction in funds, without investigation of whether that community’s situation (e.g. ability to get goods into the community more cheaply) has actually changed. With respect to food specifically, the eligibility for Nutrition North Canada is linked to remoteness and since 2016, five communities in Canada have become ineligible due to gaining road access (Government of Canada, 2020). Also, the remote airports in communities are managed and operated by the Ontario Ministry of Transportation (MTO) and regulated by Transport Canada. Communities have little to no control over the airport’s layout, use, cleanliness, and additionally face difficulties in collaborating with the airports for security measures (e.g. bag screening).

The second insight is that air transportation is extremely costly. Communities try to utilise the winter roads as much as they can for large loads like fuel to reduce the amount of fuel that needs to be flown. One thing that was heard is that even driving trucks up with quarter loads (if the winter road cannot carry full loads due to ice capacity) is acceptable because flying is more

21 expensive. Fuel companies hire additional workers in advance of the winter road season because of the demand for fuel that is otherwise difficult to meet.

The third insight, related to the high cost of air transportation, is the high cost of living in northern communities. To combat high food costs in communities, people may do big shopping trips when they are further south in Sioux Lookout or Thunder Bay and then pay for additional bags when catching flights back home. Healthcare providers similarly pack many bags when travelling North filled with medical supplies (e.g. needle kits). Overall, the effect is that passenger flights are a mix of passengers and cargo and passengers often need to prioritize certain cargo because not all the additional bags can be taken up in one trip.

The fourth insight is the reliance of remote communities on the South and southern hub communities. This reliance is shown in the previous examples of food and goods transportation but is most clear in healthcare provision. Nursing stations in communities are not equipped for all healthcare provision, and so for many emergency and non-emergency reasons, remote community members must fly south for healthcare. In the case of the new hospital Meno Ya Win in Sioux Lookout, its design and operation are tailored to the First Nations it serves (e.g. translation services, traditional medicines and elders available), and the town itself has many accommodations and shuttle services for medical patients. Many medical patients travel with a companion, and all these folks are passengers on planes throughout the year. Similarly, fuel, construction materials, and equipment are brought from further South to northern communities.

The fifth insight is that First Nations are dealing with a number of challenges at the same time, and not necessarily conceptualizing them as individual challenges, but as a broader question of improving the future of their communities. These relatively small remote communities are dealing with multiple infrastructure challenges concurrently with mental health crises and the lasting effects of colonialism in their communities. Compartmentalizing these issues as separate from one another and addressing them as separate issues is not necessarily appropriate or desired in communities. Improving infrastructure, for example, is extremely costly and often employs southern contractors and designers, while unemployment rates in communities remain high. These infrastructure challenges, then, could also serve as an opportunity to build capacity in community to improve their infrastructure while creating jobs. This is an example of complementarity that is obvious from a community perspective but may be out of scope for academia or private industry.

Conducting research in community then, or even conducting business, requires an appreciation that communities are thinking holistically which may differ from the purview of academia or private industry. Flexibility on behalf of researchers, then, alongside with the appreciation that communities are thinking holistically, is a key component in fruitful community partnerships.

In working with two remote communities and trying to schedule community visits, this fifth insight became very clear. One community which was open to having a visit experienced mass flooding and so could not arrange a visit until a few months later. The other community visit that was planned was delayed twice due to the tragic loss of community members, the first of which was a house fire. Two of the delays (flooding and house fire) are directly related to community infrastructure resilience and the kinds of shocks and stresses that northern communities are exposed to. Unfortunately, due to another shock, the coronavirus pandemic, visiting either community and conducting interviews with residents ultimately were not possible.

3.2.2 Airlines

Airlines were identified as potential sources of data that would be able to supply both quantitative and qualitative data. Six airlines were reached out to that operate either partially or completely in northern Ontario (Table 3-1). One airline provided data for this research: North Star Air.

Table 3-1: Relevant airlines contacted for this research

Airline Name Operating region Business description Estimated market 2 share (%)

North Star Air Northern Ontario, northern Passenger, Cargo, Charter 24 Manitoba, parts of Nunavut and Northwest Territories

Wasaya Northwestern Ontario Passenger, Cargo, Charter 43

Perimeter Aviation/ Northwestern Ontario, Passenger, Cargo, Charter 19 Bearskin Aviation Manitoba

Air Creebec Northeastern Ontario, Passenger, Cargo, Charter, 14 Quebec Medical Charter

Ornge (Ornge - About, no Ontario Air Ambulance date)

SkyCare (SkyCare Northern Ontario, Manitoba Air Ambulance, Charter Charters & Air Ambulance - Home, no date)

North Star Air (NSA) was established in 1997 and was acquired in June 2017 by the North West Company. The acquisition expanded NSA operations into Manitoba, enabled the use of larger passenger aircraft, and led to the opening of an additional base in Thompson, Manitoba (North Star Air Ltd. - History, no date). The North West Company is a retailer in the North with historical presence in the area since the mid-1600s (History | The North West Company, no date). Presently, the North West Company operates Northern stores present in over 120 remote and northern communities across Canada (Locator | Northmart - NWC, no date). The roughly estimated market share of NSA is 24% in Ontario, but this may be an underestimation based on the prevalence of Northern Stores and NSA’s connection to the North West Company.

Three types of data were collected from NSA: cargo and flight datasets, operations observations, and semi-structured interviews. Descriptions of the data collection process for each are outlined below.

2 Market share estimated by the number of aircraft each airline references and the percent of destinations located in Ontario. For example, Air Creebec services 4 Ontario locations out of 13 destinations and have 18 aircraft. 18 aircraft x (4 /13 portion of Ontario locations) = 5.5 adjusted aircraft. This calculation was done similarly for each airline for a total of 39.8 adjusted aircraft. Then the estimated market share was calculated by taking the airline’s adjusted aircraft over the total adjusted aircraft, 5.5 / 39.8 = 14% market share.

Cargo and Flight Datasets

Three datasets were collected from NSA via file transfers relating to two facets of the business: cargo being transported in northern Ontario and flights taking place in northern Ontario. The cargo dataset contains all the digital cargo records from NSA from September 2014 to March 2020. The first of the two flight datasets contains data for all flight legs that have actually taken off and landed (flown flights) between April 2014 and mid-April 2020, while the second contains all cancelled flight legs between November 2014 and August 2019. The datasets are described in more detail in their respective chapters (Cargo and Flight Reliability).

Operations Observations

In the Fall of 2019, 8 days were spent shadowing NSA operations at three different bases. Two of the bases were passenger and cargo bases, and the third was a cargo-only base. As a part of these observations, nine flights were taken; seven passenger-cargo flights, and two fuel flights. The goal of shadowing NSA operations was to gain a better understanding of daily operations and contextualize collected data. Observations, written notes and pictures, were made on NSA operations and infrastructure as well as infrastructure at remote airports and what community infrastructure is visible from the airport (e.g. communities may co-locate generators and the airport for ease of fuel delivery).

Semi-structured interviews

Over the course of the 8 days shadowing NSA operations, 12 semi-structured interviews were conducted with NSA employees across a number of positions; 1 pilot, 3 employees in operations control, 2 ground crew, 2 base manager/assistant base manager, 2 Vice Presidents, 1 employee in maintenance, and 1 employee in passenger services. An additional phone interview was conducted with an employee in pilot training. The interview guides and interview consent forms were approved as part of the ethics protocol and can be found in Appendix A: Semi-structured interview materials. As semi-structured interviews, interviewees were encouraged to discuss whatever they felt was relevant in their lived experiences with respect to air transportation, flight reliability, and connections between air transportation and community wellbeing. Interviews were audio recorded as permitted by the interviewees and interview transcripts were developed. For the 4 of 13 interviews that were unrecorded, detailed notes were taken during the interview. Results of the

25 interviews were reviewed to provide context, the results of which are found in the Cargo and Flight Reliability chapters as relevant.

3.3 Data and framing limitations

Having data sourced from a single airline, where there are multiple airlines and many communities that have experiences related to this research is a significant limitation of this research. One consequence is a narrower scope in line with the available data in comparison with the theoretical scope presented in the framing. Analyzing data from a singular airline may result in an overall underestimation of flights and goods transported in the region. To the extent possible, the analysis acknowledged the possible ways in which competition may affect the findings.

Additionally, the datasets received from North Star Air cover a range of timespans between September 2014 to March 2020 (specific dates presented in Cargo and Flight Reliability respectively). Based on discussions with NSA, their methods of keeping digital records has evolved over time and the earlier data’s completeness and reliability is uneven (Sardella, 2020). In this research, the full datasets were used, but the less than complete nature of the early data is acknowledged. Additional dataset-specific limitations are discussed in the respective chapters.

A significant limitation of this research more broadly is that the initial research topic and questions were developed without collaboration with First Nations communities. Recognizing that First Nation conceptualisations of wellbeing or their definitions of dependency may differ from those presented in this research, it was an initial goal to have First Nations’ feedback on this research in a meaningful way. While the preliminary community engagement did inform the approach of this research, the regrettable lack of data collection in remote communities and interviews with community members limits the degree to which First Nation perspectives and opinions are reflected in this work.

4 Cargo

This chapter investigates the goods entering remote Ontario communities by air to map the direct dependencies between air transportation and community infrastructure systems. The infrastructure systems of interest are energy, housing, water, transportation, and telecommunications. The dependencies are mapped by analyzing what types of goods are being transported by air, how these goods support infrastructure function, and how this differs temporally and spatially.

4.1 Dataset description

The cargo dataset contains all the digital cargo records from North Star Air (NSA) from September 2014 to March 2020 across the entire NSA cargo network (Figure 4-1). The dataset was further filtered to focus on the 26 remote northern Ontario communities represented in the data.

26 27

Figure 4-1: The NSA cargo network covers northern Ontario, northern Manitoba, Nunavut, and parts of the Northwest Territories (not pictured) (North Star Air Ltd. - Cargo Locations, no date)

Within the dataset, there are 276,674 entries. Each entry represents a unique cargo good that was shipped and includes characteristics of that good. There are eight characteristics of interest: Airway Bill (AWB), Date Received, Date Shipped, Total Actual Weight, Customer Category, Entry Point, Ending Point, Category, and Remarks. Descriptions of the characteristics are presented in Table 4-1.

Table 4-1: Data characteristic descriptions

Characteristic Description

Airway Bill (AWB) Numeric code representing the manifest the good is associated with

Date Received Date and time that a good entered NSA’s system

Date Shipped Date and time that a good was put on a flight intended for its destination

Total Actual Weight Weight of the good being transported in kg

3 Customer Category Type of customer who paid for shipment

Entry Point Location where the good entered the NSA network

Ending Point Final destination of the good

Category Alphabetical code that indicates the nature of the good (e.g. FFRPR indicates fresh produce and GCNST indicates general construction)

Remark Additional information and description of the good (e.g. a remark for GCNST may be “pipes”)

A number of limitations of this dataset are present. Firstly, there are not any unique identifiers for each entry, and so duplication in the data is undetectable4. Identical entries cannot be deleted as they may represent multiples of particular cargo items (e.g. two identical mountain bikes brought into community on the same plane). Another limitation in the data is that while each airway bill indicates all the goods on a particular flight, it does not identify the flight itself. The cargo and flight datasets are separate and there are no linking identifiers between the datasets. This limitation results in the inability to directly connect flight delays or cancellations to shipping delays, though conclusions can still be inferred. For example, given that certain goods went up in January, and January had significant delays, those goods were likely delayed.

The uncertainty in the early data due to transitioning to digital records is apparent in the varying data availability for each community. For example, there are 15 communities reporting in September 2014 and 22 communities reporting in September 2015. The variation in cargo data availability can be both straightforward and complicated. An example of straightforward data

3 Customer Category is a data mask that was developed with NSA. See Data cleaning.

4 There are a possible 2,403 duplicate entries (0.9% of all entries), representing 753 tonnes (0.6% of total weight).

29 availability is a community reporting data starting in 2018, and the cargo totals vary but are more or less consistent from then on out. An example of complicated data availability is if a community that had consistent cargo shipments over several years suddenly experiences a decrease in cargo magnitude and frequency. Whether this is due to the community drastically reducing their shipments or due to competition in the aviation industry is unknown and introduces a level of uncertainty in both community-specific and Ontario observations. Table 4-2 summarizes the data availability concerns and the number of affected communities.

Table 4-2: Community data variability concerns

Data Availability Number of Communities Percentage of total communities (n=26)

Data available and consistent 17 65%

Data consistent, but only available for the 4 15% later years

Data available, but drops in magnitude 2 8% and/or frequency

Very limited data or poor consistency 3 12% (sporadic shipments only)

4.2 Data analysis

4.2.1 Data cleaning

The data was manipulated in three ways to facilitate analysis. First, to protect the privacy of NSA customers, all customers were put into one of nine categories: First Nation’s Organization, Fuel Company, Federal Medical Transport Payor, Other Medical Transport Payor, North Star Air/The North West Company, Other Business, Other Government Organization, Retailer, and Private Citizen5.

Second, new cargo type categories were developed by referencing the descriptions of the goods found in the NSA Category and Remark (full definitions in Table 4-1), and all the cargo was categorized accordingly. The 12 new cargo types are: Food, Construction, Transportation, Furniture and Appliances, General Merchandise, Water, Diesel, Gasoline, Jet Fuel, Avgas, Other Fuel, and Other. Table 4-3 provides some examples of what goods are included in each category, but these examples are not exhaustive (i.e. some categories, like Other contain too many types of goods to permit an exhaustive list).

One challenge encountered when categorizing the goods was a lack of clarity on certain goods’ intended uses. For example, some goods are identified by their hazardous goods identifier or have a generic description like “box”. Intended use was difficult to ascertain based on the provided information, and so certain judgments were made and applied to all occurrences of that description. In the two examples, hazardous goods with no easily accessible use information were ultimately categorized as Other and “box” was categorized as General Merchandise.

5 The categories were developed initially to account for individual flight bookings, hence the inclusion of two distinct medical payor categories. However, this individual flight booking dataset was ultimately not provided. An additional six categories were requested along with the re-categorization of eight out of 146 customers (5%), but this was not completed. The additional categories (Air, Construction and Maintenance, First Nation Education, Telecommunications, Utilities, and Other) would have re-categorized 57 out of 146 customers (39%) and provided additional detail.

Table 4-3: Example goods in each cargo category

Category Example Goods (not exhaustive)

Food Fresh produce, frozen food, pop, chips

Construction Lumber, insulation, doors, concrete mix, pipes

Transportation Snowmobiles, all-terrain vehicles, boats, canoes, bikes

Furniture and Appliances Mattresses, air conditioners, hot water tank, office printer, freezer

General Merchandise Toys, clothes, shoes, school supplies, cooking utensils

Water Water bottles, water treatment chemicals

Diesel Diesel

Gasoline Gasoline

Jet Fuel Jet fuel

Avgas Aviation gasoline

Other Fuel Furnace oil, propane

Other Fire extinguisher, mail, solar panels, batteries

The final data manipulation compared the Date Received and Date Shipped to create a new variable: Holding Time. Holding Time is the number of days between NSA receiving a good and shipping it to its final destination. For a small percentage of the cargo entries (0.98%), the Date Received was after the Date Shipped, which resulted in an Invalid Holding Time. Holding Time was further categorized to facilitate analysis: Same Day, Next Day, Less than 1 week, Between 1- 4 weeks, Between 4-8 weeks, and Greater than 8 weeks. These categorizations are based partially on the quartiles of the Holding Time weighted by Total Actual Weight, with additional categories introduced due to the long-tailed nature of the data.

4.2.2 Cargo analysis

The overarching goal of the cargo analysis is to understand the infrastructure dependencies of remote communities on air transportation by understanding what types of cargo are being delivered, how they are being delivered, and how this varies temporally and by community. This goal led to two paths of investigation, each of which consisted of several questions. The first path was related to understanding the nature of the goods themselves: which cargo goods support community infrastructure function and what is the relative weight of these goods compared to the

32 total cargo? How does this vary by community and over time? The second path was related to understanding the cargo network: does transportation, and subsequent dependency, go primarily South to North? Are goods shipped with equal prioritization or is there competition for airline capacity?

If community infrastructure is dependent on goods delivered by air, it follows that community infrastructure resilience is directly related to, and dependent on, air transportation reliability and resilience. Depending on the degree of dependency, a decrease in air transportation reliability could have severe consequences for community infrastructure. For example, if a remote community’s annual diesel needs are met entirely by air transportation, reduction in air service due to any shock has a negative impact on a community that can only be remediated by resuming air service.

Python scripts and MS Excel were used to analyze the data and answer the four questions from the two paths of investigation detailed above. The data were aggregated as needed by cargo type, month, year, and community. The cargo analysis overall is complemented by the operations shadowing that took place over 8 days at three different NSA bases.

4.3 Findings and discussion

4.3.1 Total cargo distributions

The total cargo distribution for northern Ontario indicates a number of interdependencies based on the goods the airline is transporting. Over the six years’ worth of cargo data representing 89,500 tonnes (Figure 4-2) delivered to 26 communities, Total Fuel accounts for over half of all goods (56.7%), followed by Food (31.1%). The remaining cargo goods are split mostly between Construction (5.2%) and General Merchandise (3.1%), with small percentages of Furniture and Appliances (0.8%), Water (0.7%), Transportation (0.5%), and Other (1.8%).

Figure 4-2: Remote Ontario cargo distribution

Considering the specific fuel types represented in Total Fuel (Figure 4-3), the majority of fuel delivered to communities is diesel (80.2%), followed by aviation gasoline (12.8%), gasoline (4.9%), jet fuel (2.1%), and minimal amounts of other types of fuel (0.1%).

Figure 4-3: Remote Ontario fuel distributions

Reflecting back on a community’s infrastructure systems (Figure 4-4), all five of the infrastructure systems (Housing, Energy, Water, Transportation, and Telecommunications) are supported by goods shipped by air to varying degrees.

Figure 4-4: Modified resilience schematic

The most substantial interdependency is in relation to the diesel fuel transported. Diesel is required by 21 of the 26 remote communities for electricity generation. The potential electricity generation6 from annual diesel totals for 2015 to 2019 range from 28% to 222% of the 2014 annual electricity demand for remote northwestern Ontario (Ontario Power Authority, 2014) (Figure 4-5). While the potential diesel generation figures do not account for efficiency losses, it is still clear that a significant amount of diesel needs are met by air transportation. Reinforcing this, the Ontario Power Authority assumed that 70% of diesel needed in remote communities would be supplied by air from 2014 to 2054 (Ontario Power Authority, 2014).

6 Using an energy density of 44 MJ/kg for diesel.

Figure 4-5: Potential diesel electricity generation based on annual diesel shipments. 2014 and 2024 demands from (Ontario Power Authority, 2014).

An additional infrastructure interdependency is evident in construction materials delivered by air. At a broad level, construction materials could be used to support any of the infrastructure in a community. To develop definitive dependencies, information on construction projects in remote communities was searched for, revealing a portfolio of projects completed by Penn-Co Construction, a Mississauga-based general contracting business. Comparing community-specific construction cargo data and project date information from Penn-Co Construction suggests that construction materials shipped by NSA were used in the construction and renovation of water treatment plants (Sachigo First Nation Water Treatment Plant | Pennco Construction, no date), civic buildings (Bear Skin Lake First Nation Police Detachment | Pennco Construction, no date; Sachigo Lake First Nation Police Detachment | Pennco Construction, no date), a utility building (Pikangikum First Nation H1RCI Work Compound | Pennco Construction, no date), and a health centre (Saggius Sainnawap Memorial Health Centre | Pennco Construction, no date). With respect to the aviation fuel transported, this result indicates that air transportation is dependent on itself. Examples of end users of the aviation fuel are NSA, a competing airline, or local helicopters and float planes for short distance travel and tourism. With respect to the possibility of the fuel being for NSA operations, NSA does have two fueling points in northern communities, but this does not account for all the jet fuel and aviation gasoline being transported. First of all, jet fuel and aviation gas is being delivered to more communities than just the two communities with NSA

36 fueling points, meaning that the fuel is being used by parties other than NSA (e.g. other airlines, local aircraft operators) across northern Ontario. Secondly, NSA aircraft only use jet fuel (Stout, 2020), and so the entirety of the aviation gas is not for NSA use.

4.3.2 Temporal fluctuations in cargo distribution

Figure 4-6 illustrates the fluctuations in the cargo distribution on a monthly basis. This view highlights the variable nature of the cargo distribution, and together with the interviews, suggests a seasonality in what goods are transported. The inclusion of the remote Ontario total weight of goods transported is to contextualize the cargo distributions. The changes in the absolute totals is in part due to number of communities reporting and the expansion of NSA’s network due to acquisition by the North West Company in June 2017(North Star Air Ltd. - History, no date).

Figure 4-6: Remote Ontario monthly cargo distribution

Seasonal fluctuations in diesel fuel specifically were described in interviews and community engagement. Firstly, communities choose the cheaper winter road delivery for diesel when it is available usually starting in late January or when the winter road opens. The winter road season varies by community and year to year. In 2014-2015, partial truck loads were permitted on winter

37 roads in mid-December to late January with full loads being permitted mid-December to February. Winter roads in 2014-2015 were officially closed by mid-March to April (IBI Group and Hemson Consulting Ltd., 2016). By storing diesel brought up to communities during the winter road season, diesel delivery in the months following the closure of the winter road is reduced but increases in the Fall when the storage runs low. Figure 4-7 shows the variation in diesel deliveries as a proportion of total monthly cargo across the year. The months with median diesel proportions above 50% are January, February, March, November and December. An ANOVA repeated measures test from the statsmodel python package indicates that the differences noted across different months is statistically significant with a p-value of 0.0013.

Figure 4-7: Variation in diesel (as a percentage of total monthly cargo) across months

Fuel demand is also impacted by seasonal temperatures. Colder temperatures correspond to additional demand for electricity and subsequent diesel. A figure that was mentioned during observations and confirmed with news reports indicates that on a cold day, a community of 2,000 needs 12,000 to 15,000 lbs of diesel fuel to run generators for heating, lighting, and other needs (Walters, 2018). For perspective, one NSA Basler aircraft can carry about 10,000 lbs of diesel fuel with no other cargo or passengers in one trip and therefore repeated flights are needed to meet a community’s diesel needs. The details of community diesel shipments and how it aligns with community needs is discussed in the next section.

4.3.3 Community diesel delivery

Focusing on diesel as the clearest infrastructure dependency for remote communities, a disaggregated view by community is taken. Figure 4-8 illustrates individual communities’ monthly cargo deliveries and highlights the variability in community diesel deliveries.

Comparing individual community’s diesel delivery spikes with the trends observed in the total diesel proportion shows that communities generally follow similar trends. For example, the observed seasonality of heightened diesel shipment in the late Fall and early in the calendar year is consistent across communities, but the details of when exactly the spike occurs and its magnitude vary year to year and by community. This variability may be the result of the geographically variable winter road season, varying population, and generally unique community contexts (e.g. population size, diesel storage available, number of homes heated by electricity versus furnace oil).

Figure 4-8: Individual community and remote Ontario average monthly cargo for diesel

To investigate the importance of the diesel deliveries, the annual deliveries were compared to the 2007 annual electricity demands for the 21 communities that are not connected to the electricity

39 grid (Government of Canada, 2011)7 (Figure 4-9). The diesel totals are expressed as a percentage of the 2007 demands so that all 21 communities could be compared. For at least three out of the five years, 11 out of 21 communities (52%) are receiving diesel supplies that meet at least 50% of their 2007 electricity demands, while 8 communities (33%) are receiving diesel that could meet 100% or more of their needs. Some communities are receiving two to fourteen times the diesel that would meet their 2007 energy demand, which could indicate vast increase in demand over time, multiple uses for diesel in communities, and/or poor generator operating efficiencies.

Figure 4-9: Community diesel deliveries compared to 2007 electricity demands

One of the 21 communities was connected to the provincial electricity grid at the end of 2018 and saw an immediate cessation of diesel deliveries (none in 2019). Outside of the 21 communities, another community that has been connected to the grid since the early 2000s received diesel deliveries ranging from 1 to 110 tonnes in each of the five years. Communities with similar populations that rely solely on diesel-generated electricity received between 70 to 500 tonnes of

7 The annual electricity demand in MWh was converted to MJ and then to equivalent tonnes of diesel using an energy density of 44 MJ/kg for diesel. This does not account for operating efficiencies.

40 diesel each year. This suggests that reliance on diesel deliveries by air may persist in communities even after connection to the grid. This may be because of electricity outages or because the grid cannot meet the growing demands of communities.

4.3.4 The cargo network

To assess the cargo network components, a heatmap visualization available in the seaborn python package was used. The heatmap creates a matrix of all the cargo entry points along one dimension, all the cargo exit points along the other dimension, and each cell is coloured according to the total weight of goods that were shipped from one location to the other (Figure 4-10). The heatmap was generated based on the criteria that either the entry point or ending point was a remote community, which expanded the heatmap to include NSA bases (noted by B and a number in the graphic). The darker the colour of the cell, the more cargo associated with that origin-destination. White cells indicate no cargo was transported between those locations.

The cargo heatmap reinforces the understanding that cargo shipments, and associated dependencies are predominantly in a South to North orientation, resulting in the reliance of remote communities on the effectiveness of southern supply chains. The cargo originating from the North and remaining in the North, a total of 4 tonnes, only represents 0.004% of all cargo by weight.

Figure 4-10: Cargo heatmap for remote Ontario communities

4.3.5 Holding time analysis

Figure 4-11 provides the breakdown of holding times for all remote Ontario goods by cargo type. Overall, 90% of cargo is stored for less than one week before being shipped to its destination. Disaggregating by cargo type, however, reveals that holding time varies by cargo type. Fuel and food both have relatively short holding times, with 81% of fuel and 69% of food being delivered the same day or the next day. On the other end, Construction and Transportation have the longest holding times. Only 26% of construction goods are delivered the same day or the next, while 6% of construction goods are held between 4 to 8 weeks or longer. Transportation has a slightly better performance, with 29% of transportation goods transported same day or next, and 2% between 4 to 8 weeks.

Figure 4-11: Holding time distribution for all remote Ontario cargo

The differences in holding time across cargo types can be explained both logically and legally. Logically, fresh food has a shorter shelf life than construction material, and so should take priority over construction goods. NSA also has service level agreements, which lay out some obligations around how fast NSA transports certain goods (e.g. mail, perishable food items), and other prioritizations set out with individual communities. Additionally, within the cargo operations, some goods are scheduled, while others are unscheduled. For example, North West Company trucks delivering groceries are expected at a base at specific dates and times. During observations, an unexpected truck full of construction materials arrived at the same time as a scheduled grocery

43 truck. The scheduled load was unloaded and prepared to be loaded onto a waiting plane, while the construction materials were unloaded and prepared to be stored until a corresponding flight could be arranged.

The main implication of different holding times by cargo type is that goods appear to be competing for finite capacity of an airline. This competition is complicated by the prioritizations and service level agreements developed between NSA and its customers. From the perspective of infrastructure dependencies, the prioritization of fuel aligns with the importance of energy and electricity in communities. The lengthened time to ship construction materials, however, may have a negative impact on community infrastructure development. Understanding that construction material may get to communities more slowly than food, communities and their residents may need to plan for the difference in delivery time. At the very least, the difference in holding times further complicates the process of shipping certain goods to remote communities, goods which may also be serving important community needs.

4.4 Cargo summary

What the analysis reveals is that there is a diverse range of goods and services that are being transported by air. For example, food and healthcare delivery by air are well-known and well- documented dependencies. The use of air transportation to deliver boats and solar panels by plane is less well-documented. The variety of goods transported demonstrate, at a minimum, partial dependency of infrastructure systems on air transportation. Further examination of diesel and construction deliveries in particular show clear dependencies of remote community energy systems and built infrastructure on cargo delivered by air. The level of dependence varies by community, by season, and year to year.

From a network perspective, the analysis confirms that the directionality of transportation, and subsequent dependence, is primarily South to North. The time it takes for goods to be transported to communities varies by cargo type, with longer holding times noted for construction materials and transportation goods. This may further complicate the process of building and maintaining infrastructure in remote communities.

5 Flight Reliability

In the last chapter, remote community infrastructure systems were shown to be dependent on goods delivered by air, most notably diesel for electricity generation. Given remote communities’ reliance on air transportation, understanding the overall performance of air transportation in the region and factors impacting flight reliability further illustrates a community’s infrastructure resilience and vulnerability. For example, a high rate of flight cancellations for one community may jeopardize its access to adequate amounts of fuel or other cargo goods. This chapter investigates flight reliability in remote northern Ontario and the contributing factors to flight reliability. Flight reliability is chiefly defined by whether a flight was on time, delayed, or cancelled. Two factors contributing to flight reliability are the air transportation infrastructure conditions (e.g. runway length) and the operating conditions (e.g. weather conditions).

5.1 Data description

Two datasets were used in this flight reliability analysis, along with the results of the semi- structured in-person interviews with airline employees. The first dataset contains all North Star Air (NSA) flight legs that have actually taken off and landed (flown flights) between April 2014 and mid-April 2020, while the second dataset contains all cancelled flight legs between November 2014 and August 2019. A flight leg can be defined as the flight segment between two separate locations, as opposed to a flight that may have multiple stops (that would be considered as consisting of multiple flight legs). The flown flight dataset contains 171,704 entries and the cancelled flight legs dataset contains 32,034 entries. Due to the different timespans for each dataset, only the overlapping time was analyzed (i.e. November 2014 to August 2019). The data were further filtered to only include flight legs that either have their origin or destination in a remote Ontario community. The two datasets share common data characteristics and so are jointly described in Table 5-1. All data fields are present in each dataset unless stated otherwise.

44 45

Table 5-1: Data characteristic descriptions for Flown and Cancelled Flights

Characteristic Description

RowID Unique identifier

Depart Scheduled departure date and time

Act Depart Tm Actual departure date and time

Origin Origin airport of that flight leg

Arrive Scheduled arrival date and time

Act Arrive Tm Actual arrival date and time

Dest Destination airport of that flight leg

Model Type of aircraft performing the flight leg

Aircraft The specific identifier of the aircraft flying that leg

Shrt Desc Shorthand code describing the reason for flight delay

Long Desc Additional information on the reason for flight delay

Reason One-word description of the reason for flight cancellation (Cancelled flights only)

Reason Detail Additional information on the reason for flight cancellation (Cancelled flights only)

While the focus of this research is on dependencies via cargo goods, NSA flights are either cargo- only or a mix of passengers and cargo, and therefore all flights were considered in the flight reliability analysis.

There are a number of data limitations present in the datasets, partially stemming from how flights are recorded and partially stemming from the nature of everyday NSA operations. Based on interviews with NSA employees and first-hand observations, the operations are flexible and in constant flux, with priorities placed on delivering service to its customers rather than recording data. For example, NSA has flex flight routes, which change as customers’ needs change (e.g. an additional stop in a nearby community may be added for one passenger). These flex routes, in combination with the fact that the flown flight dataset only records the flight as it was performed, makes detecting diversions difficult. Additionally, multiple possible schedules are sometimes prepared in anticipation of certain events (e.g. uncertainty about an aircraft’s maintenance completion), but only one is carried out and the others are cancelled. Alternatively, if Aircraft A

46 is delayed, operations may be shifted such that Aircraft B takes over the next flight on Aircraft A’s schedule, which may appear in the system as a cancellation of Aircraft A’s flight. In both situations, the result is an over-reporting of cancellations. There is also some uncertainty around the consistent use of delay and cancellation reason codes. The codes appear to change over the years and so less weight is placed on these codes as accurately reflecting why flights are delayed and cancelled.

5.2 Data analysis

5.2.1 Data cleaning

Data cleaning consisted of two main tasks: removing duplicates and streamlining delay and cancellation reason codes. With respect to removing duplicates between the flown and cancelled datasets, a duplication was designated as two (or more) flight legs that had the same origin, destination, aircraft, date, and a scheduled departure time within 15 minutes of each other. In practice, it is possible that duplications may exist that share less than these five characteristics in common, but there was not enough information in the data to confidently remove those. For example, if Community A has poor weather that would not allow a flight to land, the flight intended to A may be rescheduled to fly cargo goods to Community B, resulting in a cancelled flight to Community A and an additional flight to Community B. In the data, only the cancelled flight is noted and the unplanned flight to Community B is in the data, but without being considered an additional or replacement flight.

While the delay and cancellation reason codes change over time in the data, the broad categories for delay reasons remain somewhat common (e.g. weather is a common reason in old and new codes). A major change in delay codes was made in 2018. In total, 36 delay codes were present in the original data and these were re-categorized into 13 delay reasons: late inbound aircraft, flight crew, passenger, loading, weather, maintenance, fuel, de-icing, airport operations, OCC (operations control centre), reroute/diversion, NWC (North West Company) trucks, and other. With respect to the cancellation codes, these were streamlined from 11 to 8: administration, no “pax” (passengers), weather, customer, maintenance, duty time, runway closed, and operations. For both datasets, there are delays and cancellations that did not have a reported reason, and so an unknown reason code was added.

5.2.2 Flight data analysis

Understanding that communities and their infrastructure are dependent on air transportation warrants the investigation of flight reliability and performance. The two indicators of poor flight performance used in this research are cancellations and delays. Of these, cancellations are the clearest indication of reduction in air transportation capacity, but delays are also analyzed as indicators of stressed capacity that can lead to cancellations. As part of the analysis, new parameters were developed to identify delays and how they propagate through the network.

With respect to identifying and quantifying delays, the scheduled and actual departure time as well as the presence of delay codes were used to identify delays. The lateness threshold employed by NSA is 15 minutes, and so any flights that departed more than 15 minutes after the scheduled departure time were considered delayed. Three percent of flight legs did not have actual departure times, and so their on-time performance was listed as unknown. Delays were further categorized to quantify delay severity: 15-29 minutes, Between 30-60 minutes, Between 1-2 hours, Between 2-8 hours, and Greater than 8 hours. These categorizations are based partially on the quartiles of the distribution of delays.

Preliminary data exploration and interviews noted the possibility of delays cascading in the network, i.e. if the first flight leg is delayed, it will lead to delays in the following flight legs. Therefore, several parameters were developed to describe the propagation of delays. A column called the aircraft’s day of operation (ADOO) was developed to connect the series of flight legs one aircraft takes in a day, whereby the destination of the first leg is the origin of the second leg, and so on. Going through the flight legs of one ADOO in order, the first instance of a delay was marked as the delay origin, and any delays in the legs immediately afterwards were considered higher order delays. Delay origins were marked by a 1 and the higher order delays were marked with the corresponding order of the delay. An Original Delay Reason column was also developed to attribute higher order delays to the delay origin’s reason. Figure 5-1 shows an example wherein a second and third order delay are reported as delayed due to a late inbound aircraft. The delay origin, however, was due to weather, and so the Original Delay Reason for the second and third order delay would then be weather.

Figure 5-1: Example of how Original Delay Reasons are determined

The two questions that guide the flight analysis are: how many cancellations and delays occur in the system? And why do these cancellations and delays occur? Disaggregating by month and by community also provide indication of whether there are temporal or spatial factors in the prevalence of delays and cancellations. A combination of python scripts and MS Excel was used to aggregate and disaggregate the data according to these two questions. With respect to reasons for cancellations and delays, two broad categories of contributing factors are considered: the air transportation infrastructure conditions (e.g. runway length and conditions, weather reporting) and the operating conditions (e.g. weather conditions).

5.3 Findings and discussion

5.3.1 Presence of delays and cancellations

The breakdown of flight performance is shown in Figure 5-2. Nearly half of the flights are on time (49.0%), while the remaining half are either cancelled (17.1%) or delayed (33.9%). Delving into the delays, most are within 15 and 29 minutes late (11.1% of total flights), or between 30 to 60 minutes late (11.6% of total flights). The remaining delays are between 1 and 2 hours (6.0% of total flights), with small percentages delayed between 2 and 8 hours (2.2% of total flights) or greater than 8 hours (0.1% of total flights). Three percent of all flights do not have enough departure information to discern on-time performance.

Figure 5-2: Breakdown of on-time performance for all flight legs in northern Ontario between November 2014 and August 2019

Focusing specifically on delays reveals that most delays are less than one hour, which may be considered a small inconvenience and not a significant reduction in airline capacity, especially for non-perishable cargo. While one delay in isolation may be considered as such, the networked and interconnected nature of flights in the region mean that several smaller delays throughout the day can lead to cancellations at the end of the day. To demonstrate one way in which delays propagate, Figure 5-3 shows the distribution of delay orders among delayed flight legs, i.e. whether they are a delay origin or a higher order delay (knock on impact of earlier delay). The majority of delayed flight legs are a first order delay (82.7%) that proceed to cause the remainder of delays in the network. While less than one percent of delayed legs are beyond the fifth delay order (0.7%), the highest order delay is 12.

Figure 5-3: Distribution of flight leg delay order

Less obvious in the data is the way in which delays can accumulate and lead to cancellations. From interviews with NSA employees, it is clear that delays of all durations put a strain on air service and can lead to cancellations. This is partly due to the finite nature of a pilot’s duty time; Transport Canada regulates how long a pilot is allowed to be on active duty. In most cases, the limit on active duty time is 14 hours (Transport Canada, 2020). Whatever the reason for the delay, if one leg is delayed by half an hour in the morning, and then another leg is delayed by half an hour, the last scheduled flight for the day may be cancelled. Therefore, even smaller delays in the system are important indicators of overall capacity and performance.

5.3.2 Temporal variation

Figure 5-4 shows how flight performance changes over time. Overall, the on-time performance of flights vary widely; the proportion of on-time flights fluctuates between 30.4% and 69.9%, cancelled flights between 7.7% and 32.3%, and delayed flights between 17.2% and 49.8%.

Figure 5-4: Flight performance over time for all Remote Ontario flights

Figure 5-5 shows the variation in the proportion of flight legs that are delayed and cancelled across months. The months with median percent delayed and cancelled flight legs above 50% are January, October, November and December. The median for each month does not go below 40% delayed and cancelled flight legs. An ANOVA repeated measures test from the statsmodel python package indicates that the differences noted across different months is statistically significant with a p-value of 0.0006.

Figure 5-5: Variation in percent cancelled and delayed across months

The observed seasonality is partially aligned with results of interviews. Several interviews mentioned that the transition seasons surrounding winter, late Fall and early Spring, coincide with a higher risk of dangerous “icing conditions”; air with high moisture content and temperatures that are cold enough to result in ice forming on an aircraft’s wings during landing. Icing conditions often lead to delays and cancellations (discussed in detail in 5.3.4). Other interviews also point to summer thunderstorms as a common weather-related delay or cancellation reason. The partial alignment between the observed seasonality and expectations based on interviews suggest that there may be other contributing factors to delays and cancellations beyond weather.

5.3.3 Community distinctions

Taking a disaggregated view of flight performance by destination reveals that there are differences across the 26 remote communities and 5 NSA bases (Figure 5-6). The sixth NSA base was excluded as it had relatively few flights during the study time (77 flights compared to the next smallest of 451). Figure 5-6 was organized according to ascending percent of on-time flights, with the NSA bases remaining next to each other on the right side of the figure.

One contributing factor to the differences across communities is the differences in quality of infrastructure at the destination. Figure 5-6 specifically highlights those communities without local weather reporting, longer runways, and fuel availability. For two of the twelve communities without local weather reporting, NAV Canada lists a nearby community’s weather reporting as a proxy (NAV Canada, 2019b).

NSA bases, in general, appear to have better on-time performance than the remote communities, and Figure 5-7 demonstrates that this holds true over time. NSA bases have a higher percentage of flights departing on time and a lower percentage of cancelled flights for nearly all observed months. This may be due in part to the typically better quality infrastructure and services at NSA bases in comparison to remote communities, though this will be examined more thoroughly in the next subsection.

Figure 5-7: Comparison of remote Ontario community and NSA base on-time performance

5.3.4 Reasons behind delays and cancellations

Both the reasons provided in the data and the results of the interviews were analyzed in answering the question of why delays and cancellations occur. More emphasis is placed on the results of the interviews based on the limited and uncertain quality of the reasons provided in the data. For the cancellation reasons (Figure 5-8), the Administration reason (accounting for 74.3% of all cancellations) may be conflated with other cancellation reasons. When asked about the Administration cancellation code, interviewees included other reasons such as waiting for weather conditions to improve, which could be considered a Weather reason. It was inferred that the Administration cancellation code was a catch-all cancellation code, with more emphasis placed on delivering service rather than clearly delineating cancellation code use. The cancellation codes are explained in Table 5-2.

Figure 5-8: Breakdown of cancellation reasons

Table 5-2: Cancellation reason descriptions

Cancellation Reason Description

Administration Deemed appropriate by the administration, possibly due to weather or replacing one flight with another modified one.

No Pax No passengers booked on the flight.

Weather Poor weather, either at the departure airport or en route.

Customer Customer decision (mostly for chartered flights).

Maintenance Scheduled and unscheduled maintenance.

Duty Time Pilot’s active duty time would have been exceeded.

Runway Closed Arrival airport’s runway is closed.

Operations Due to operational reasons (may be similar to Administration code).

Within the flown flight data, there are significant differences between the reported delays (Figure 5-9) and the actual delays (Figure 5-10). Reported delays are flight legs that have a delay reason associated with it. Actual delays are flight legs that have a difference between scheduled and actual departure time greater than 15 minutes. In most cases, the reported delays are a subset of the actual delays. Note that the reported delays figure retains the Late Inbound Aircraft delay

56 reason, while the actual delays figure is based on the Original Delay Reason and therefore does not retain Late Inbound Aircraft as a reason. Table 5-3 provides a description of each delay reason.

Figure 5-9: Breakdown of delay reasons for reported delays

Figure 5-10: Breakdown of delay reasons for all actual delays

Table 5-3: Delay reason descriptions

Delay Reason Description

Late Inbound Aircraft Aircraft experienced an earlier delay.

Flight Crew Flight crew experienced an earlier delay or reached active duty time limits.

Passenger Passenger checked-in late.

Loading Loading the plane was delayed; potentially due to weather.

Weather Poor weather, either at the departure airport or en route.

Maintenance Scheduled and unscheduled maintenance.

Fueling Fueling introduced delays.

De-icing De-icing introduced delays.

OCC Deemed necessary by the Operations Control Centre (OCC).

Airport Ops Deemed necessary by either the departure or arrival airport.

Late NWC Trucks North West Company (NWC) trucks arrived late with cargo for a flight.

Reroute/diversion Rerouting or diversion introduced delays.

Other Other shipments arrived late for a flight (e.g. catering).

Unknown No information available on why the flight is delayed.

In comparing the reported and actual delays, there appears to be both an underreporting and overreporting of delays in the reported delays. The actual delays outnumber the reported delays by over 20,000 flight legs, nearly doubling the number of delays. At the same time, nearly 8,000 reported delays (31% of reported delays) are not considered actual delays, either because they are recorded as departing on time or do not have sufficient departure information. It is possible that the schedule was altered as a result of the delay (e.g. because of an anticipated 30 min delay, the departure time was changed). If that is the case, there is an overall underestimation of the number of delayed flight legs.

Focusing on the actual reasons for delays, 81.0% are Unknown. It is not clear why the majority of delays have no reason associated with them. One possible explanation is that the delays with unknown reasons were relatively short (e.g. 15 minutes) and therefore not reported, but this was disproved. Upon investigation into the duration of delays with unknown reasons, it closely follows the total distribution of delays (Table 5-4). Another possible explanation is that the process of

59 reporting delays is not streamlined across all types of flights (e.g. cargo, charter, passenger-cargo). About one third of the delayed flight legs with unknown reasons are chartered flights. Chartered flights are scheduled in accordance with the customer’s schedule and it may be that the departure times of a chartered flight are tentative compared to a scheduled flight.

Table 5-4: Total delay distribution compared to delays with unknown reason distribution

Delay Category Percent of Total Delays Percent of delays with an unknown delay reason (n = 47,711) (n = 38,636)

15-29 min 35.8 37.6

30-60 min 37.4 36.4

> 1-2 hours 19.4 19.0

> 2-8 hours 7.1 6.7

> 8 hours 0.3 0.3

While the delayed flight legs with valid reasons shed some light on why delays and cancellations occur, such as flight crew or passenger delays, this research focuses on the interviews to give a more comprehensive and holistic understanding. The contributing factors to flight reliability that arose from the interviews are organized into two broad categories: infrastructure conditions and operating conditions. While these conditions are described separately, they are acting in tandem and across the network to affect flight reliability and flight performance overall.

Infrastructure conditions refer to the air transportation infrastructure at remote airports and at NSA bases, as well as supporting infrastructure systems in communities like telecommunications. In general, the NSA bases have better air transportation infrastructure than the remote airports and the operations of the airline are largely planned around this discrepancy. Flights are scheduled with the understanding that an aircraft must have enough fuel when it leaves the base to perform a round trip flight. While there are two fueling points located in remote communities, these are much more expensive (3 to 4 times the cost of fuel at a base) and are used only under certain circumstances (e.g. emergencies or re-routing charters). De-icing capacity is also drastically different between remote airports and bases. Where scissor lifts are connected to larger barrels of heated de-icing fluid at a base, remote airports have backpack-sized containers of de-icing fluid and a rolling ladder that pilots must use themselves to de-ice a plane (Figure 5-11).

Figure 5-11: De-icing materials at an NSA base (left) in comparison to at a remote airport, rolling ladder (top right) and an orange de-icing backpack in a cage (bottom right).

Given the difficulties in effectively de-icing and the questionable safety of having a pilot de-ice a plane at a remote airport, pilots are instructed to “approach and miss” when icing conditions are identified during descent rather than risk dangerous ice accumulation on their wings (identified as the cause of a fatal crash in Fond du Lac, Saskatchewan in 2017 (Quenneville, 2018)). An “approach and miss” involves the pilot intentionally not landing at the destination and instead continuing onto the next destination, effectively cancelling that flight leg. An approach and miss impacts all passengers and goods currently on board, often returning them back to where they started their trip and also leaves behind all passengers and goods who would have boarded at that missed location. If the flight is headed to another remote community and not its original point of departure, the capacity of the aircraft to accommodate the passengers and goods at the next stop is also impacted.

Another infrastructure condition that varies widely across airports is the availability and quality of weather reporting. Across all the airports, there are three types of local weather reporting infrastructure: the first is 24-hour weather reports provided by an automated weather observing system (AWOS), next is a regularly updated weather report from an employee at the airport

(updated several times each work day), and last is a weather camera that shows a static image(s) of what the runway and airport look like (updated every 10 minutes) (NAV Canada, 2017). Each airport has anywhere from multiple of these weather reporting tools to none of them. The summary of what is available at the 26 remote communities and the 6 NSA base airports is in Table 5-5. Twelve of the 26 remote airports (46%) have no weather reporting infrastructure.

Table 5-5: Weather reporting infrastructure at northern Ontario airports (NAV Canada, 2019a)

Type of Weather Reporting Number of remote community Number of NSA bases with that Infrastructure airports with that infrastructure infrastructure (n=6) (n=26)

24-hour AWOS 8 31% 5 83%

Manually updated weather 5 19% 1 17%

Weather camera 14 54% 3 50%

None of the above 12 46% - -

While some of the airports are close to each other, enabling the rough extrapolation of existing weather forecasts to nearby airports, the lack of weather information is a major concern for pilots and for the airline’s operations more broadly. Operating without detailed weather forecasts increases the chances of unforeseen cancellations and approach and misses due to weather.

The reliability and quality of other infrastructure both at the airport and in community were brought up in relation to flight reliability and operations more broadly. The hours of the airport employee responsible for maintaining runways (typically 8am-4pm Monday to Friday) do not necessarily align with the hours at which an aircraft is arriving, making it difficult to ascertain runway conditions for certain early morning or night-time flights. Runway lighting upgrades have been made at some airports by the Ontario Ministry of Transportation (MTO) in recent years, but unreliable telecommunications in remote communities make it difficult for the operations centre in Thunder Bay to keep in contact with all operations in northern Ontario. While interviewees acknowledged that longer runways would be beneficial for operations, they also noted the geographic limitations in some communities (i.e. having a lake at both ends of the runway).

Within the realm of operating conditions, there are three main considerations: delays introduced by external factors, differences in the workforce, and weather.

Flight delays can be the result of other external delays in the transportation process, such as North West Company trucks running late, customers being late to pick up cargo, or passengers checking in late for flights. The semi-trucks carrying goods can arrive at the base later than expected, and according to interviewees, are often late when the weather has been bad. Sometimes, the community customer is late to pick up their cargo and may need to be contacted via the operations centre in Thunder Bay to come to the community airport. One interviewee estimated that out of 10 cargo flights, maybe two or three customers are late. There are no agents at the community airport for non-passenger flights, nor is there a place to store goods, and so the pilot and crew must wait for the customer to show up in order to unload the cargo. The process of unloading cargo in remote communities is also different from loading and unloading at the NSA base. At the base, there are dedicated ground crew, as well as equipment such as forklifts, to enable the loading and unloading process. In community, there are often fewer people (the pilot, first officer, one ground crew member and then the customers receiving the goods) and little to no equipment to facilitate unloading. Cold temperatures add additional delays, as “equipment doesn’t want to work in the winter” and the harsh conditions make it difficult for employees to work quickly.

Weather plays a critical role more broadly in air operations. A number of interviewees shared that the transitional seasons, during which dangerous icing conditions are likely to occur, seem to be lengthening, with very few periods of extremely cold winter. While the very cold temperatures make loading and unloading planes more difficult, they also result in ideal flying conditions with low amounts of moisture in the air. These interviewees connected this change in the seasons to climate change, to which they also attributed an increase in thunderstorms in the summer months. With limited local weather reporting, identifying or predicting where thunderstorms will occur, is more difficult for a pilot to do.

The increase in frequency and severity of natural disasters has also strained remote communities and the more northern NSA bases. One interviewee described that in summer 2019, a forest fire was threatening the city where one of the NSA bases is located. While evacuation was ultimately not necessary, the pressure on the airline to be prepared to safely evacuate the community, and questions around what would happen to the remote communities that rely on that NSA base were overwhelming. For remote communities, the need to evacuate residents by plane is a harsh reality that occurs almost annually for some communities. Kashechewan in the James Bay region, for

63 example, has evacuated due to flooding twelve times between 2004 and 2019 (Khalafzai, McGee and Parlee, 2019).

Together, these infrastructure and operating conditions create stresses on air transportation in remote northern Ontario and also result in delays and cancellations of flights in the region.

5.4 Flight reliability summary

Overall, about half of all flights between November 2014 and August 2019 departed on time, while the remaining half were delayed or cancelled. This represents both a large stress on air transportation and a decrease in capacity that is impacting the remote communities it services. It appears that the reduction in flight reliability and performance is connected to time of year, with the worst on-time performance occurring between October to January during the study period. NSA base airports experience better reliability than remote communities, which may be connected to the difference in infrastructure quality and services available at those airports. Nearly half of the 26 remote community airports do not have any weather reporting systems, and most have inadequate de-icing facilities. Delays and cancellations of flights occur for a number of reasons, many of which are interconnected (e.g. cancellations due to weather and a lack of weather reporting infrastructure). Effects of climate change are already impacting air transportation, through inclement weather and more natural disasters that both remote and NSA base communities must face. The reduction of air transportation capacity and reliability, together with the more severe environmental shocks and stresses, has the potential to negatively impact remote communities through delaying essential cargo shipments and deteriorating essential community infrastructure.

6 Conclusion

People who live in remote First Nations communities have a deep experience-based understanding of the challenges facing remote communities and the subsequent reliance on air transportation. While this work was ultimately unsuccessful in tapping into that experience-based knowledge in communities, the major contribution of this work has been the quantification of the relationship between communities and air transportation through the analysis of cargo data and flight reliability, all within the context of a modified community resilience framing. Indeed, this work is but the tip of the iceberg in understanding and mapping remote community resilience and wellbeing in relation to air transportation. In this final section the modified resilience schematic will be validated in the light of this work’s findings and opportunities for future work will be presented.

Reflecting on the modified resilience framing put forward in this work (Figure 6-1), the findings in the analyses support and validate the framing at a high-level and provide additional nuances not currently depicted in the schematic. More specifically, three aspects of the framing that are validated and clarified by the findings are the interdependencies presented, the factors that affect air transportation, and the impacts of external shocks and stresses on the system overall.

Figure 6-1: A modified resilience schematic for remote northern Ontario

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With respect to the interdependencies presented, the findings show that energy is the primary infrastructure dependency, but not the only one. Diesel fuel specifically represents nearly half of all cargo by weight transported over the study period and is used to generate a significant amount of electricity in communities in comparison to energy needs. Other dependencies were discovered in construction materials and in transportation. Construction materials delivered by air are being used in the building and renovating of community infrastructure, while transportation by water and by land are supported by air transportation (e.g. boats and snowmobiles brought in by air). Even air transportation in the north is dependent on air transportation due to the aviation gas and jet fuel being flown into remote communities. One aspect of interdependencies that is not captured thoroughly in the schematic is the interdependencies between community infrastructure. For example, the electricity generation capacity can be a limiting factor in the number of houses that can be built and connected to electricity. Therefore, interdependencies between community infrastructure also play important roles in community wellbeing and resilience that should be reflected more explicitly in the schematic.

Figure 6-2: Updated resilience schematic that explicitly shows interdependencies between community infrastructure systems.

With respect to the factors that affect air transportation, airlines face significant infrastructural and operating limitations that affect air service. From an infrastructural perspective, limitations

66 include a lack of weather reporting at 12 out of 26 remote airports (46%), inadequate de-icing facilities at remote airports, only two fueling points outside of bases, and short gravel runways that prohibit modern aircraft. North Star Air, as the main data source, relies heavily on the services available at bases for their operations, knowing that services at remote airports are insufficient. The environmental challenges that face operations, such as icing conditions and thunderstorms, can impact air service significantly, and are sometimes unforeseen due to the weather reporting inadequacies. To that end, the lack of adequate de-icing facilities and the prevalence of icing conditions compound to create “approach and miss” cancellations that additionally impact later flights. One consideration that is not obviously reflected in the framing is that air transportation resilience is not equivalent to community resilience. The framing, which contextualizes resilience from the perspective of a remote community, considers the failings of air transportation as having a direct negative impact on a community (e.g. when a flight is cancelled due to poor weather, this may impact the community’s receipt of diesel). From the perspective of airlines, however, cancellations and delays may preserve their ability to deliver service later in the day, hence increasing their resilience. In that sense, the framing focuses on community resilience which is not necessarily the same as air transportation resilience.

Lastly, the findings validate the shocks and stresses depicted in the schematic and provide additional insight into how these shocks and stresses may impact communities and air transportation in the future. Climate change in particular is expected to have numerous impacts on remote communities and air transportation. First of all, climate change is expected to impact the viability of winter roads which may increase reliance on air transportation. Climate change is also associated with an increase in severity and frequency of flooding and forest fires which will require evacuations of entire communities by plane. In addition to evacuations, such natural disasters may increase damage to community infrastructure which will then require repairs (whereby construction materials may need to be transported by air). Concurrently, climate change may increase icing conditions in the region which will negatively impact air transportation and lead to more cancelled flights.

Based on the findings, it is likely that air transportation will continue to play a key role in remote communities’ wellbeing in the future. While conversations and planning regarding the future of remote communities expand into all-season roads or community self-sufficiency that would reduce the dependence on air transportation, the dependence of remote communities on air transportation

67 will likely persist. In the case of all-season road development, air transportation will still be necessary for timely transportation, especially of medical patients. In the case of self-sufficiency, that would require developing infrastructure and systems to support community self-sufficiency, the materials for which may need to be delivered by air. Additionally, the natural disasters that have recently required mass evacuations of remote communities (e.g. flooding, forest fires) will similarly require timely responses administered by air.

With respect to future work, there are a number of opportunities. First, there is an opportunity to engage more perspectives, specifically from remote community members, remote airport management, and other service providers (e.g. air ambulances). Having perspectives from specific communities, for example, will give a more detailed understanding of the interdependencies of air transportation and community wellbeing. There is also the possibility that First Nations perspectives may be rooted in a very different conceptualisation of dependence and wellbeing. Additionally, differences in population size or geographic location may be factors that alter a community’s specific situation (e.g. very northern runways are built on permafrost and so may be facing additional infrastructural challenges). Another opportunity for future work is to more thoroughly account for competition between air transportation and winter roads.

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Appendix A: Semi-structured interview materials

All materials were approved by the University of Toronto research ethics board as a part of protocol 00037640: Prioritizing airport infrastructure upgrades to improve social sustainability and well-being in remote Northern Ontario communities.

Several materials were used as a part of conducting semi-structured interviews, namely interview guides and consent forms. Two version of each were used in this research: one for service providers and one for pilots. Each is reproduced here.

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1. Service provider consent form

2. Pilot consent form

3. Service provider interview guide

4. Pilot interview guide