assessing the suitability of airship ecotourism routes in ......the viability of radical tourism...

Assessing the Suitability of Airship Ecotourism Routes in

Coastal Washington

A GIS6100 Final Project- April 17, 2019

Authored by Marc Santos

Santos- Suitable Airship Ecotour Routes

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Table of Contents

Abstract (278 words) .................................................................................................................. 3

Key Words: ................................................................................................................................. 4

Introduction ................................................................................................................................ 4

Literature Review ....................................................................................................................... 5

Tourism Innovation Testing for Novel Scenic Airship Travel Demand: A New Zealand

Case Study .................................................................................................................................. 5

Mapping Species Richness with GIS ..................................................................................... 7

Incidental Tourism of British Airplane Travel in the 1930’s & Effective GIS

Communication Scales................................................................................................................ 8

Figure 1. Imperial Airways Routes in 1930s ..................................................................... 8

Influences of Isolation and Landscape Diversity in Western Ecotourism ............................. 9

Table 1. Estimation Results for La Palma, Spain............................................................. 10

GIS-Based Site Suitability Analysis .................................................................................... 11

Synthesis of the Literature Review ...................................................................................... 12

Metadata & Methods ................................................................................................................ 13

Table 2. Coastal WA Counties of Interest ....................................................................... 14

Table 3. Proposed Work Table ........................................................................................ 15

Figure 2. USGS BISON Data Representation ................................................................. 17


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Figure 3. Cities Within Counties of Interest- Coastal Washington State ......................... 18

Table 4. Tourism Spending by County in 2017 ............................................................... 19

Results ...................................................................................................................................... 20

Figure 4. Potential A-Sites) Coastal Cities with Airport Associated ............................... 20

Figure 5. Potential B-Sites) Coastal WA Cities Isolated from Other Cities .................... 21

Figure 6. Potential Airship Routes, Biodiversity, & Comparative Tourism Spending .... 22

Figure 7. Suitability Components a) Biodiversity Access & b) 2017 Tourism Spending 23

Figure 8. Suitable Airship Service Routes for Coastal Washington ................................ 25

Figure 9. Elevation Test for Suitable Airship Routes ...................................................... 26

Table 5. Summary of Suitable Airship Routes................................................................. 26

Discussion and Conclusions ..................................................................................................... 27

Acknowledgements .................................................................................................................. 28

Works Cited ............................................................................................................................. 29

Appendix .................................................................................................................................. 32

Table A ............................................................................................................................. 32


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GIS Determined Route Suitability for

Airship Ecotourism in Coastal Washington

Marc Santos

GIS 6100- Geographic Information Systems

University of South Florida

April 17, 2019

Abstract (278 words)

Airship technology has modernized and been used as a model business for ecotourism, or

nature-based environmentally friendly tourism. Low-elevation air travel from the 1930s has been

documented to increase demand for conspicuous consumption of travel amenities. Fuel saving

technologies encouraged higher altitude flights which limited the ability of passengers to act as

‘incidental tourists,’ thus hindering the growth of low-elevation air travel tourism.

New innovative tourism strategies may provide tremendous opportunities for growth in the

local tourism economy. However, assessing novel tourism ideas, also termed ‘discontinuous

innovations,’ has proven difficult for conventional product assessment strategies. New

assessment strategies offer insights to possible consumer demand for discontinuous innovations

such as an ecotourism-orientated airship service.

Additionally, GIS-based suitability assessments have provided data-backed guidance on

investment strategies and may offer guidance on formulating a suitability model for proposing

such novel tourism innovations. Using previous data on biodiversity potential of a given area and

the potential access to existing tourism hotspots via expenditure data may provide variables that

can feed into such a suitability index. Those paths found to have a greater potential for


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opportunity to educate passengers on local nature as well as have greater proximity to tourist

hotspots may be more successful than those considered elsewhere.

In this study, I propose using a suitability analysis to prioritize potential service routes based

on existing biodiversity data and previous tourism expenditures en-route. Coastal Washington,

known for its scenic views and emphasis on nature-based tourism provides an excellent focal

area to test such a model. The resulting suitability index and assessment suggest that there are at

least six routes worth studying further, the majority of which use Bremerton, WA as a major

terminal.

Key Words:

GIS, route optimization, scenic airship services, ecotourism

Introduction

This study intends to analyze available literature to develop a model for assessing suitability

of potential airship service routes orientated towards an ecotourism business structure where

passengers are encouraged to learn about the local environment. The hope is that the resulting

route suggestions will provide ample opportunity to both educate passengers as well as tap into a

wealthy tourism industry.

Biodiversity must first be assessed in order to surmise areas which provide ample opportunity

to immerse potential passengers in a nature-based experience at an elevation of roughly 1,000

feet. Tourism demand can be assessed through proxy of tourism expenditures within proximity

of the flight path. Airship routes passing through counties with high tourism expenditure rates


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will likely service passengers already willing to spend more for such an experience while routes

passing through counties with little existing tourism economy should discourage investment in

such an expensive venture. Finally, testing for parameters limiting to airship designs (such as

elevation requirements) can be used in congruence with a suitability index to select and prioritize

route options which investors may then consider.

I hypothesize that such a model for assessing suitability can provide such a list of routes that

seem plausible as potential opportunities to begin an ecotourism-orientated airship service. The

following literature review provides the basis for this hypothesis and subsequent analysis and

results demonstrate how they operate.

Literature Review

Tourism Innovation Testing for Novel Scenic Airship Travel Demand:

A New Zealand Case Study

In the article “Testing discontinuous innovations in the tourism industry: The case of scenic

airship services,” Henderson, Avis, and Tsui (2018) put forth a multi-phase process for assessing

the viability of radical tourism innovations, dubbed ‘discontinuous innovation[s]’ (2018, p. 167).

The authors find that tourism is an essential driver of economic growth and in turn, innovation

can serve the same role for the tourism industry, however available literature has not provided a

sufficient means for assessing the viability of radically new ideas. Their paper proposes and tests

a sequential, five-phase, mixed-methods means of assessing potential consumer interest and

service preferences regarding the novel use of scenic airship travel proposed for six locations

with New Zealand. Modern airship designs operate using incombustible helium as a buoyant


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medium and can cost upwards of $25 Million USD, thereby warranting careful examination of

interest prior to investment (Henderson et al., 2018).

Students, totaling at 28, from New Zealand’s Massey University were recruited to participate

in the study and were comprised of 13 males and 15 females, ranged in age from 18 to 66 years

with a mean age of 27.7 years, and hailed primarily from New Zealand (39.3%) and China (25%)

while the rest (35.7%) hailed from other countries (Henderson et al., 2018, pp. 170–171).

Participants in the study formed three different focus groups, were given a brief information

sheet covering some general ideas and expectations associated with airships (e.g. cabin designs

and general operationality of airships, etc.), and then were given a series of iterative qualitative

and quantitative interviews and surveys. They were inquired about preferences, expectations, and

considerations regarding scenic airship services based at any of the six identified New Zealand

cities (2018).

Results from the Henderson et al. study demonstrated a number of service considerations that

were almost unanimously favored including: the provision of “Food and Drink” (95%

agreement), “Educational Activities” (95.3%), a “Transparent Floor” and “Viewing Devices”

(91.3% and 91% respectively), as well as the desire for a “Nature-Based” experience (mode

answer= Strongly Agree; 2018). Overall, over 90% of participants were found to have substantial

interest in the proposed scenic airship services averaging at $400 NZD (roughly $268 USD;

“CurrencyConverter,” n.d.) for a 3.5 hour long trip with snacks and beverages (2018, pp. 174–

175). The authors found their discontinuous tourism innovation testing methodology insightful

for assessing a discontinuous innovations such as airship travel but caution that other areas may

need to modify methodologies to more accurately analyze local interest (2018, p. 177).


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Despite the obvious bias in participants being primarily students, this paper provides a

reasonable justification for exploring route options aimed at maximizing the potential to assess

areas that are biologically diverse and thus, allow for optimum opportunity to educate and

explore nature-based resources en-route. I find the enthusiasm of the participants in desiring

educational activities and for having a nature-based experience to be informative in establishing

ideal biodiversity criteria for assessing optimum route paths for an airship service in coastal

Washington.

Mapping Species Richness with GIS

Conroy and Noon (Conroy & Noon, 1996) compare and contrast different techniques to

model species richness, and thus biodiversity, ranging from simplistic observation counts to

vegetative modeling. Framed for informing decision-making guidelines, the authors discuss the

dangers with relying on simplistic observational surveys at inappropriate scales in not being able

to provide enough statistical robustness regarding implications about diversity. This is to say,

poor survey designs with this type of data may lead to poor decision-making (Conroy & Noon,

1996).

This paper illustrates a concern regarding light-bodied biodiversity datasets. However, in this

study, I use the USGS Biodiversity Information Serving Our Nation (BISON) data repository,

regarded as a “model portal” (Pizzigatti Corrêa et al., 2018, p. 224), to pull over 5.5 million

observation points across coastal Washington. These data points would seem to provide the

robustness which Conroy and Noon (1996) emphasize in their findings as valuable to decision-

making.


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Incidental Tourism of British Airplane Travel in the 1930’s

& Effective GIS Communication Scales

In his paper “Incidental tourism: British Imperial air travel in the 1930s,” Pirie (2009)

explores the unique characteristics and evolving form of tourism that grew out of an infantile

aeronautic travel industry during an inter-war time period. What was available of the sparse

reports and data of the time period led Pirie to hypothesize that thousands of air travelers making

use of Imperial Airways flight options in the 1930s were largely business-orientated or

government-orientated arrangements during a time when Britain’s Empire stretched from

Vancouver Canada across much of the African Continent, India and into Australia and what is

now New Zealand (see Figure 1 below).

Figure 1. Imperial Airways Routes in 1930s

Source: Pirie, 2009, p. 51

Air travel technology allowed pilots to achieve a maximum elevation of 3000 meters (roughly

9,840 feet) only by the end of the 1930s, which encouraged passengers to become ‘incidental


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tourists’ both in the air as they passed by beautiful scenery and on the ground during frequent

routine and emergency overnight landings made for refueling or repairs (Pirie, 2009). While

Pirie writes that numerous cheaper options for travel (both leisure and business) existed in the

forms of steamers and railcars, namely, at fractions of the cost of an airline ticket, conspicuous

consumption and an amassing gallery of in-flight photography added to the demand of air travel.

Pirie (2009) provides data and information that at elevations where ground features are

discernable and aesthetically pleasing to experience, there is an associated demand which

justifies a higher value for destination-orientated trips associated with business and other-wise

obligatory travel. Given the option to travel to a destination already serviced by more affordable

means, there were a substantial number of business people and government officials willing to

pay more to arrive at the same destination. I use this paper to derive a means to prioritize airship

route options using financial indicators in addition to those concerning biodiversity.

Additionally, Pirie (2009) uses an effective example of GIS mapping technique to

demonstrate networked flight routes which convey a sense of breadth and understanding of his

paper’s findings. I plan to thus frame my mapping results and analyses in such scale and

orientation demonstrated by Pirie as appropriate for the scope of my study.

Influences of Isolation and Landscape Diversity in Western Ecotourism

In the publication, “The Influence of Remoteness and Isolation in the Rural Accommodation

Rental Price among Eastern and Western Destinations” (Santana-Jiménez, Sun, Hernández, &

Suárez-Vega, 2015), the authors examine the influence which remoteness (measured by the

population with 10 minutes of driving) and other variables have on the price per night for two


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people in the cities of La Palma, Spain and Penghu, Taiwan. The other variables in the statistical

analysis that the authors used included the number of various amenities present in a given hotel,

remoteness from distinct service zones measured in travel time to reach those services (such as

airports, beaches, health centers, etc.), and landscape diversity.

Landscape diversity was measured as an index between 0 and 1 and, with regards to the

Western city of La Palma, Spain, used the proportion of land uses within a 1 km radius of the

observed hotel (Santana-Jiménez et al., 2015). Of all the independent variables modelled in the

authors’ multivariate statistical analysis, landscape diversity had the largest effect size that was

statistically significant while isolation was statistically significant but had a very small effect size

(see Table 1).

Table 1. Estimation Results for La Palma, Spain

Source: Santana-Jiménez et al., 2015, p. 389

Variables other than landscape diversity and isolation were regarded as less conclusive to

influencing pricing demand for accommodations in La Palma, Spain as well as Penghu, Taiwan

(not shown in Figure 1; Santana-Jiménez et al., 2015). The authors more generally claim that


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“[v]isitor’s motivation, destination familiarity, and regional characteristics and imagines are

proposed as key factors in influencing market preference for rurality” (2015, p. 393).

This paper provides guidance for selecting criteria to determine potential site hosting as a

destination-orientated leisure travel product. While inherent remoteness would seem a poor

indicator of desirability in a travel destination, the opportunity to provide a diverse array of land

uses near a tourism destination may produce upwards pressure on demand for a tourism product

such as airship travel. Data for accumulating the type of information used in this study is not

feasible to collect within the scope of this study. As a proxy variable, it may be reasonable to

assume that cities which are more isolated from other cities may allow for a more diverse array

of land uses which may otherwise be priced out of congested metropolises. Synthesis of this and

above studies is provided below to further elaborate.

GIS-Based Site Suitability Analysis

Jain and Subbaiah (2007) examine urban development site suitability analysis using weighted

attributes to provide a suitability score. The four variables of existing land use rate, flood hazard

preference, ground water quality, and road accessibility were scored to impose a higher score

value on more desirable features for urban development (i.e. less used up land, lower flood

hazards, higher quality ground water, and greater road accessibility) and lower scores for

opposite spectrum qualities (2007).

The authors then used these calculated values to derive tiers of ranking ranging from Highly

Suitable to Moderate to Unsuitable corresponding to a score ranging from 10 to 550. They find


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that such a type of weighted analysis is helpful for planners to “plan development of the city”

(Jain & Subbaiah, 2007, p. 2583).

I plan use the same suitability structure into ranking access to biodiversity and access to

existing tourism spending as they overlay onto buffers within potential viewing range of an

airship.

Synthesis of the Literature Review

While technological limitations of 1930s flight operations seemingly coerced non-tourists to

become ‘incidental tourists,’ scenic air-travel based tourism did grow as a percentage of

passengers near the end of the decade (Pirie, 2009). Conspicuous consumption and growing

publicity of the scenic quality of low-elevation air travel was associated with higher travel fairs

in the 1930s (Pirie, 2009) while nature-orientated airship routes and opportunity to partake in an

educational processes was correlated with substantial demand for scenic airship services in

Twenty First Century New Zealand (Henderson et al., 2018).

Thusly, contemporary business and leisure travel between cities with existing levels of varied

travel infrastructure provide a base level of service which may be augmented through more

expensive, conspicuous consumption options. Airship travel has been couched as an ecologically

minded means of travel by conservationists in past (Parsons & Bauer, 2013) and may sometimes

be associated with isolated destinations that are pleasing for tourists to vacation in. Santana-

Jiménez et al. (2015) provide guiding criteria which may be helpful in evaluating the desirability

of a city in servicing distance to a scenic airship service route.

This study proposes examining route viability for an innovative airship service company

orientating itself as an eco-tourism aligned product intent on providing environmental education


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for its passengers that is immersivity relevant to the flight path of the airship. Washington State,

known for its many environmentally friendly tours (Carreiro, n.d.), provides an ideal scenario to

analyze potential route viability to consider proposing a scenic airship service. Assuming cities

that reside in counties are already promoting themselves, or are considered, tourist and travel

destinations, I use available biodiversity indicator data to compare route viability between cities

with existing airport infrastructure (A- Sites) and other such cities. Additionally, if a city can be

determined to be desirable through its associated isolation and landscape diversity, then this

would provide a viable destination city (B-Sites) to consider as route terminals connected to A-

Sites. Based on Jain and Subbaiah’s work(2007), observed biodiversity along flight paths

(provided through BISON data following suggested considerations over data quality; Conroy &

Noon, 1996) and the access to recorded tourism spending dollars in 2017 will inform the

suitability of such flight paths to provide passenger traffic that is more likely to help such a novel

tourism company to survive as a for-profit business. Mapping these findings and analysis at

appropriate scales should help to convey the information effectively as demonstrated by Pirie

(2009).

Metadata & Methods

The methods chosen for this study were informed by the above literature. The study area

focuses on Washington State. In particular, I focus on the major coastlines of Washington which

touch either the Pacific Ocean or the major shorelines of Puget Sound, both of which provide

habitat to high desirable whale watching opportunities (Cominelli et al., 2018; Houghton et al.,

2015; Southern Resident Orca Task Force, 2018). These sorts of environments provide ample

opportunity to convey a nature-based tourism experience who may be enticed by educational


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opportunities as indicated above. Exploratory examination of major shoreline data that

intersected with county data revealed a list of 14 counties which are listed in Table 2 below.

Table 2. Coastal WA Counties of Interest

Kitsap

Skagit

Whatcom

Clallam

San Juan

Jefferson

King

Mason

Snohomish

Grays Harbor

Pierce

Pacific

Thurston

Island

I propose to first collect potential route terminals using cities that already have an operating

airport, thus lowering the upfront cost of a novel tourism business. These airport-associated sites

will be marked as A-Sites.

Moving forward from above discourse on use of city-to-city proximity as a proxy to isolation

and landscape diversity, I plan to examine potential route terminals by setting the threshold

within 1-hour of airship flight to be less than 20 cities. These more isolated and potentially more

landscape diverse terminal candidates will be marked as B-Sites.

Based on previous airship-based tourism operations in San Francisco in operation for several

years just prior to the 2008 economic recession (“Airship Rides in San Francisco,” 2009), I will

propose a set of characteristics which we can assume will likely reflect the type of vessel

purchased for the proposed novel tourism business discussed in this paper. These characteristics

may then be used to determine route suitability for an ecotourism-orientated airship service. Such

an airship would have a net length of 246 feet, carry up to 12 passengers at a time, travel at a

maximum cruise speed of 40 miles per hour at a height of roughly 1,000 feet in the air, and

charge around $500 per passenger every hour (“Airship Rides in San Francisco,” 2009).

Route suitability will therefore be expected to weigh access to normalized biodiversity and

tourism spending data in the form of a suitability index. Furthermore, routes should be a


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minimum of half-an-hour long (20 miles) but capped at a maximum of one hour (40 miles) to

begin analyzing. Longer trips cost more and reduce the number of passengers able to be fit into a

day and shorter trips would likely make it difficult to render enough revenue to justify long term

costs.

Additionally, all routes deemed suitable will need to be screened through an altitude layer to

determine if an airship can traverse the distance without needing to alter preferred operating

elevation (1,000 ft above sea level). The following work table (Table 3) is indicative to the

general operations needed to conduct the analysis:

Table 3. Proposed Work Table

# Requirement Defined As Spatial

Data

Attribute

Data

Dataset Preparation

Coastal area Intersecting w/

major shoreline

County GU Or

County

Equivalent

Intersection

Potential sites Within Counties of

interest

Cities City Limits

A-Sites City with airport Cities City name Potential

sites

Edit attribute

data

B-Sites < 20 cities within

40 miles

Cities City name Potential

sites

Buffer and

overlay

Potential

Routes

20<=length<=40

miles

New Feature

class- Lines

Edit Features

Access to

biodiversity

Net exposure to

biodiversity along

route

Observation

data

USGS-

BISON

Convert .csv

to discrete

GIS and

raster GIS

data

Access to

tourism

spending

Net exposure to

county associated

tourism spending

2017

Tourism

Spending by

county

Washington

Travel

Impacts

Table

Edit attribute

data

Suitability

Index

0-2 Index Score Calculate

Prioritized

Routes

Routes

Final map Routes Routes

Final map Elevation

zones

WA DEM

layer

Final map Terrain Basemap


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The datasets included in the work table above have been collected through various

institutions. I restricted data collection to be bound by the county areas listed in Table 2 in order

to keep datasets both manageable and relevant to the study parameters.

The largest dataset (2.7 gigabyte in .csv format) collected was from the USGS-BISON

(USGS, 2015) data repository and comprised of over 5.5 million data observations from over 120

institutions, universities, and research centers. The data uses the WGS84 Datum and was

sequentially modified into a usable and manageable format. The .csv file was first imported into

ArcPro as a table using “XY to Point” operation using WGS84 as the projection Datum. After

that, the file was rasterized using the “Point to Raster” operation set to a cell size of .001 and cell

values set to count the number of points within its boundaries. Figure 2 shows the difference

between the vector representation of the data (left) and rasterized data (right). The rasterized data

is represented as a stretched value of the number of observations calculated in that cell’s range.

Since the scope of this data set was the most data rich and was constructed using WGS84 as a

Datum, subsequent datasets were re-projected as necessary to maintain the accuracy of the

BISON dataset in subsequent analysis.


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Figure 2. USGS BISON Data Representation

Source: U.S. Geological Survey, 2015

City limits data were collected through the Washington State Dept. of Transportation data

portal (“WSDOT GIS Data Download,” n.d.) and published in February of 2019 (WSDOT,

2019). Since these were collected in NAD83 HARN projection system, the data for these city

limits were re-projected to WGS84 to conform with map analysis. The polygons associated with

city limits were then converted into centroids for use in route analysis.

Major shorelines data were likewise collected from the WSDOT data portal (GIS

Implementation Team, WSDOT, 1995). This “major shores” polygon-based feature class was

used in congruence with the Government Unit- County or Equivalent dataset published by the

US Geological Survey (USGS, 2014). Both datasets were originally constructed using the


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NAD83 Datum and were thus re-projected into WGS84. Additionally, the first attempt to use

“Select by Location” to identify counties of interest yielded results that were accurate but not

reasonable with regards to human interpretation of what is meant by ‘coastal Washington’

counties due to the extent of river shorelines included in the major shorelines dataset. Thus, a

subset of counties was hand selected by the author as a reasonable interpretation of the study’s

intent and resulted in the list of 14 counties listed in Table 2 and illustrated below in Figure 3.

Figure 3. Cities Within Counties of Interest- Coastal Washington State

Source: (USGS, 2014; WSDOT, 2019)

Tourism spending data was collected for the 2017 fiscal year on a county-wide basis for the

14 identified counties listed in Table 2. The following results were added to the attribute table of

the Counties of Interest depicted in Figure 3 and used to determine the access to tourism

spending on any proposed airship route (Table 4).


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Table 4. Tourism Spending by County in 2017

County Spending at Destination 2017 ($ Million USD)

Kitsap 362.9

Skagit 333.3

Whatcom 571.2

Clallam 279.7

San Juan 233.8

Jefferson 152.6

King 7833.4

Mason 118.3

Snohomish 1018.1

Grays Harbor 368.0

Pierce 1122.8

Pacific 175.2

Thurston 321.2

Island 202.4

Source: Dean Runyon Associates, Inc., n.d.

Using the above datasets, I was able to identify potential route terminal cities for an airship

ecotourism service route, determine the cumulative number of species observations made along

its path, calculate the net amount of tourism dollars that were spent within the counties the route

passes through, and determine a prioritized array of routes to consider. As a final step, I needed

to test whether or not a flight path was reasonable given the elevation constraints expected of the

airship design being considered in this study: a cruise altitude of 1,000 feet on average (“Airship

Rides in San Francisco,” 2009).

I used DEM datasets to verify the suitability of the potential routes. Because of the typically

large size of the DEM files, I opted to use them last in an effort to maintain a smaller file

geodatabase size that is more manageable for processing. The DEM files used were collected by

USGS in 2001 using UTM Zone 10N NAD27 projection with resolution set to 10-meter squares

per cell (USGS, 2001). Since this introduced another data-rich dataset into the GIS analysis, all

verifications of route suitability concerning elevation limits were conducted in UTM Zone 10N

NAD27 projected coordinate system. This was especially justifiable given that any route


20

determinations resulting from earlier analyses were likely to be approximations given the nature

of air travel in an airship (i.e. the airship is likely to drift off course to counter wind and other

weather forces at play on any given day).

Results

After the above initial preparative steps were made, my first step in analysis was to surmise

which cities identified with the counties of interest were associated with an airport. These would

comprise my collection of A-Sites, defined as cities that are likely to have existing amenities for

which to base an airship service route from. The following figure demonstrates which cities were

likely candidates to serve as primary route terminals.

Figure 4. Potential A-Sites) Coastal Cities with Airport Associated

Source: USGS, 2014; WSDOT, 2019


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B-Sites were derived by first generating 40-mile buffer zones (geodesic orientation) around

all coastal cities illustrated in both Figures 3 & 4. I next used the “Tabulate Intersection”

operation to tabulate the total number of city features within each established buffer zone by

each of the 281 cities (see Appendix Table A). Next, by joining the newly formed table to the

centroid feature class I was able to use an SQL expression to Make a New Feature Class using

tabulated counts denoting cities had had a maximum of 20 cities within its 40-mile buffer. The

resulting array of 22 cities comprised the B-Sites used to produce potential flight routes (see

Figure 5).

Figure 5. Potential B-Sites) Coastal WA Cities Isolated from Other Cities

Source: USGS, 2014; WSDOT, 2019


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Suitable airship routes were deemed to be between 20 miles and 40 miles in length,

suggesting a travel time of roughly half-an-hour to one full hour. Routes were additionally not

allowed to travel over international boundaries (i.e. stay within county boundaries), had to cross

a major coastline at least once (to take advantage of marine-based nature ecotourism

opportunities) and could either connect two A-Sites or connect an A-Site to a B-Site. The

resulting route options stemming from the A-Sites (necessary in either scenario) totaled in at 16

routes. When overlaid biodiversity data from BISON and comparative tourism spending in 2017

by county, the resulting visualization is fairly complicated though a bit indicative of likely

desirable paths (see Figure 6).

Figure 6. Potential Airship Routes, Biodiversity, & Comparative Tourism Spending

Source: Dean Runyon Associates, Inc., n.d.; U.S. Geological Survey, 2015; USGS, 2014;

WSDOT, 2019


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The suitability of each potential route is not made clear in Figure 6. To provide suitability

analysis of the routes, a .5-mile buffer zone was generated for each path. The “Zonal Statistics”

operation was used to generate a raster layer of the buffer paths as they relate to the cumulative

BISON observation counts. By using the cell values of the resulting raster layer (via the “Get

Cell Value” tool) I was able to add an attribute field to the potential routes feature class which

corresponded to the potential access to biodiversity within its flight path. Results are

demonstrated in Figure 7a. The maximum cell value for BISON observations on any route was

74,527 and is used to normalize data for use in assessing suitability.

Figure 7. Suitability Components a) Biodiversity Access & b) 2017 Tourism Spending


WSDOT, 2019


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Figure 7b. illustrates the results of the “Identity” tool used to analyze the intersection of the

potential routes layer and tourism spending by county layer. By adding attribute fields that

correspond to the cumulative tourism dollars spent in 2017, I was able to associate a potential

flight route with access to tourism spending data. The maximum tourism spending sum for any

one route was $9,214,400,000 USD (or 9,214,400 in $ Thousands USD) and is likewise used to

normalize 2017 tourism spending data for use in the suitability assessment below.

To devise a suitability index, I as able to use the “Calculate Field” function using Python3

coding language for the following function: ! 𝐴𝑐𝑐𝑒𝑠𝑠𝐵𝑖𝑜𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦! / 74527 +

! 𝐴𝑐𝑐𝑒𝑠𝑠𝑇𝑜𝑢𝑟𝑖𝑠𝑚𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔! / 9214400. By dividing each input by the highest value recorded,

I was able to normalize the data and provide a suitability index ranging from 0 to 2 associated

with each potential route. Routes valued at 1 or greater were utilized at top potential routes and

those rounding up to 2 were delineated visually. Routes falling below 1 were symbolized in red

while routes below 0.5 were excluded from the results entirely. A total of 7 routes were mapped

and renamed to include the start and end city to facilitate enhanced understanding. The results

are illustrated in Figure 8 below.


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Figure 8. Suitable Airship Service Routes for Coastal Washington


WSDOT, 2019

Finally, with a prioritized list of suitable airship service routes, I was able to use 10-meter

resolution DEM data to test whether or not any of the flight paths will pass the elevation

expectations devised for the airship- 1,000 feet above sea level. Despite previous analysis being

conducted in WGS84 projected coordinate system, I decided to maintain the NAD27 projected

coordinate system as the analytical environment for the following map and re-project the suitable

route options illustrated in Figure 8 from WGS84 to UTM Zone 10N, NAD27 Datum. The result

is the following depicted in Figure 9.


26

Figure 9. Elevation Test for Suitable Airship Routes

Source: USGS, 2001

The results of the elevation test reveal that all but one (Bellingham-Friday Harbor) of the

routes illustrated in Figure 8. The following Table 5 summarizes the results of the above

analysis.

Table 5. Summary of Suitable Airship Routes

Rank Route Name Suitability Index Score Elevation Test

1 Bremerton-to-Everett 2.00 Pass

2 Bremerton-to-Snohomish 1.53 Pass

3a Bremerton-to-Kenmore 1.23 Pass

3b Everett-to-Seattle 1.23 Pass

4 Snohomish-to-Seattle 1.12 Pass

5 Bremerton-to-Tukwila 1.00 Pass

6 Bellingham-to-Friday Harbor 0.83 Fail


WSDOT, 2019


27

Discussion and Conclusions

While it may be interesting to look at Bremerton-to-Everett as an ideal service route, what

stands out more is the prevalence of Bremerton as a suitable route terminal in this analysis. Four

out of the six prioritized airship routes have Bremerton listed as a terminal. Seattle, Everett and

Snohomish are the next most frequently cited cities. Assessing potential consumer demand for a

discontinuous tourism innovation as exemplified in New Zealand (Henderson et al., 2018) in

these cities may provide investment-worthy insights which this study cannot claim to reveal.

Additionally, it is important to note the assumption that historic biodiversity observations are

indicative of observable biodiversity when put into practice. While there is sampling bias likely

associated with BISON data (because more populated areas will likely provide more opportunity

for observing species for example) this may serve to help formulate an environmental education

activity, which was one of the features in high demand in the New Zealand case study

(Henderson et al., 2018).

Also, tourism spending by county as a proxy assumes that just by flying through a county,

airship operators can gain tap into the consumer demand apparent in the 2017 travel spending

data (Dean Runyon Associates, Inc., n.d.). A more thorough economic assessment of consumer

draw should be conducted in order to more thoroughly understand relationships between low-

elevation traveling and nearby tourism spending.

In conclusion, a total of 6 routes were deemed suitable for consideration of establishing an

ecotourism-orientated airship service route. Despite modeling two different types of potential

sites for airship service, only cities selected as A-Sites (denoting presence of an airport) were


28

deemed suitable terminal points in the routes using the derived suitability index. The hypothesis

that some airship routes would be evaluated as more suitable than others based on biodiversity

observation differences and tourism-based economics data seems to be affirmed, though the

degree of accuracy needs to be further assessed. Incidental tourism implications of the 1930s

(Pirie, 2009) combined with a suitability index modeled after previous examples (Jain &

Subbaiah, 2007) additionally help to provide a means of assessing airship service demand. This

can likely be used in conjunction with discontinuous tourism innovation assessments proposed

by Henderson et al. (2018).

Acknowledgements

I would like to acknowledge my wife, Jessa Madosky, PhD for her support as well as my

Professor Dr. Elizabeth Walton for her support in providing this project opportunity and

guidance.


29

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Appendix

Table A. Washington Cities- Number of cities within 40 (Geodesic) miles

ID City Name Count ID City Name Count ID City Name Count

1 Aberdeen 13 93 Granger 18 186 Pomeroy 16

2 Airway Heights 24 94 Granite Falls 55 187 Port Angeles 6

3 Albion 21 95 Hamilton 25 188 Port Orchard 75

4 Algona 75 96 Harrah 19 189 Port Townsend 37

5 Almira 19 97 Harrington 18 190 Poulsbo 70

6 Anacortes 24 98 Hartline 18 191 Prescott 14

7 Arlington 50 99 Hatton 16 192 Prosser 16

8 Asotin 9 100 Hoquiam 13 193 Pullman 19

9 Auburn 74 101 Hunts Point 79 194 Puyallup 69

10 Bainbridge Island 79 102 Ilwaco 7 195 Quincy 18

11 Battle Ground 14 103 Index 46 196 Rainier 49

12 Beaux Arts Village 79 104 Ione 9 197 Raymond 19

13 Bellevue 79 105 Issaquah 77 198 Reardan 22

14 Bellingham 19 106 Kahlotus 15 199 Redmond 79

15 Benton City 14 107 Kalama 18 200 Renton 76

16 Bingen 5 108 Kelso 18 201 Republic 9

17 Black Diamond 71 109 Kenmore 74 202 Richland 13

18 Blaine 11 110 Kennewick 13 203 Ridgefield 14

19 Bonney Lake 67 111 Kent 77 204 Ritzville 17

20 Bothell 70 112 Kettle Falls 11 205 Riverside 13

21 Bremerton 71 113 Kirkland 79 206 Rock Island 18

22 Brewster 19 114 Kittitas 22 207 Rockford 25

23 Bridgeport 21 115 Krupp 20 208 Rosalia 28

24 Brier 70 116 La Center 15 209 Roslyn 14

25 Buckley 57 117 La Conner 30 210 Roy 57

26 Bucoda 37 118 Lacey 57 211 Royal City 14

27 Burien 80 119 LaCrosse 23 212 Ruston 76

28 Burlington 29 120 Lake Forest Park 74 213 Sammamish 78

29 Camas 11 121 Lake Stevens 60 214 SeaTac 79

30 Carbonado 57 122 Lakewood 67 215 Seattle 79

31 Carnation 75 123 Lamont 27 216 Sedro-Woolley 28

32 Cashmere 14 124 Langley 57 217 Selah 17

33 Castle Rock 21 125 Latah 26 218 Sequim 17

34 Cathlamet 17 126 Leavenworth 12 219 Shelton 37

35 Centralia 34 127 Liberty Lake 21 220 Shoreline 74

36 Chehalis 32 128 Lind 18 221 Skykomish 30

37 Chelan 15 129 Long Beach 7 222 Snohomish 65

38 Cheney 27 130 Longview 19 223 Snoqualmie 74

39 Chewelah 9 131 Lyman 25 224 Soap Lake 20

40 Clarkston 10 132 Lynden 15 225 South Bend 17

41 Cle Elum 13 133 Lynnwood 71 226 South Cle Elum 13

42 Clyde Hill 79 134 Mabton 18 227 South Prairie 62

43 Colfax 24 135 Malden 29 228 Spangle 27


33


44 College Place 8 136 Mansfield 22 229 Spokane 25

45 Colton 14 137 Maple Valley 76 230 Spokane Valley 21

46 Colville 11 138 Marcus 9 231 Sprague 25

47 Conconully 11 139 Marysville 54 232 Springdale 16

48 Concrete 18 140 Mattawa 25 233 St. John 25

49 Connell 17 141 McCleary 30 234 Stanwood 42

50 Cosmopolis 14 142 Medical Lake 25 235 Starbuck 14

51 Coulee City 23 143 Medina 79 236 Steilacoom 65

52 Coulee Dam 17 144 Mercer Island 77 237 Stevenson 11

53 Coupeville 37 145 Mesa 16 238 Sultan 55

54 Covington 76 146 Metaline 8 239 Sumas 14

55 Creston 18 147 Metaline Falls 8 240 Sumner 73

56 Cusick 10 148 Mill Creek 69 241 Sunnyside 18

57 Darrington 23 149 Millwood 21 242 Tacoma 76

58 Davenport 18 150 Milton 74 243 Tekoa 25

59 Dayton 10 151 Monroe 61 244 Tenino 41

60 Deer Park 16 152 Montesano 20 245 Tieton 17

61 Des Moines 79 153 Morton 19 246 Toledo 23

62 DuPont 60 154 Moses Lake 18 247 Tonasket 9

63 Duvall 73 155 Mossyrock 22 248 Toppenish 17

64 East Wenatchee 18 156 Mount Vernon 30 249 Tukwila 77

65 Eatonville 47 157 Mountlake Terrace 70 250 Tumwater 46

66 Edgewood 75 158 Moxee 18 251 Twisp 11

67 Edmonds 73 159 Mukilteo 62 252 Union Gap 17

68 Electric City 18 160 Naches 17 253 Uniontown 12

69 Ellensburg 21 161 Napavine 28 254 University Place 69

70 Elma 25 162 Nespelem 17 255 Vader 22

71 Elmer City 17 163 Newcastle 79 256 Vancouver 13

72 Endicott 26 164 Newport 9 257 Waitsburg 10

73 Entiat 15 165 Nooksack 15 258 Walla Walla 8

74 Enumclaw 64 166 Normandy Park 81 259 Wapato 18

75 Ephrata 20 167 North Bend 71 260 Warden 17

76 Everett 64 168 North Bonneville 11 261 Washougal 11

77 Everson 15 169 Northport 7 262 Washtucna 17

78 Fairfield 26 170 Oak Harbor 33 263 Waterville 17

79 Farmington 22 171 Oakesdale 28 264 Waverly 26

80 Federal Way 75 172 Oakville 28 265 Wenatchee 17

81 Ferndale 16 173 Ocean Shores 9 266 West Richland 14

82 Fife 75 174 Odessa 19 267 Westport 11

83 Fircrest 75 175 Okanogan 17 268 White Salmon 5

84 Forks 1 176 Olympia 49 269 Wilbur 16

85 Friday Harbor 16 177 Omak 17 270 Wilkeson 57

86 Garfield 23 178 Oroville 7 271 Wilson Creek 22

87 George 16 179 Orting 62 272 Winlock 26

88 Gig Harbor 77 180 Othello 16 273 Winthrop 10


34


89 Gold Bar 52 181 Pacific 75 274 Woodinville 73

90 Goldendale 4 182 Palouse 23 275 Woodland 16

91 Grand Coulee 17 183 Pasco 15 276 Woodway 73

92 Grandview 19 184 Pateros 14 277 Yacolt 14

185 Pe Ell 30 278 Yakima 17

279 Yarrow Point 79

280 Yelm 56

281 Zillah 18

assessing the suitability of airship ecotourism routes in ......the viability of radical tourism...

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