Geographic Information

Systems (GIS), a diverse and fascinating aspect of the study of geographies,

and a field in its own right. GIS is often employed to aid researches,

planners, and geographers better understand the relationships between various

factors within regional or the global environment.

A specific application

of GIS is Spatial Analysis. This is undertaken by geographic analysists who are

in need of specific and detailed information concerning the link between two or

more features within a given territory, and want to create conclusions or

identify patterns from the results which may hold current or future uses or

applications.

Literature

Review

A tool within the

arsenal of a Spatial Analyst is the Point Pattern Analysis. Daniel Erven (2018)

states that Point Pattern Analysis is an integral function of GIS and Spatial

Analysis, which evaluates the patterns and distributions of a set of points

within a predetermined space.

Daniel Erven (2018)

reveals some commonly used terminology that is often employed by Spatial

Analysts to discuss the phenomenon’s in the results of a Point Pattern

Analysis.

If it is understood,

in its more simplistic definition, that Point Pattern Analysis is the

determination of the number of points within a space, than it can be further

understood that the most basic Point Pattern Analysis terminology will include

the use of Descriptive Statistics.

Descriptive Statistics

are the common statistical tools often employed in any research containing

numerical data, and are used to count the number of points, identify the mean

and medians of the set of points, and to discover the standard deviation of the

set of points.

Considering how Point

Pattern Analysis uses points, the terms Frequency and Density are critical in

any spatial study. Frequency refers to the occurrence or occurrences of a point

within a time period, and Density shows the number of points within a certain

space.

Typically mentioned

when discussing Point Pattern Analysis are the terms Random, Uniform, Clustered,

and Dispersed.

Sets of points which

appear to be distributed across a surface according to a pattern or logic are

understood as being Uniform. Random sets of points are those which appear to be

distributed with a given area without pattern or logic.

Tony E. Smith (nd) illustrates

the contrast between a set of points which is Clustered, and another which is

Dispersed. Clustered points are those which are crowded around small areas

within a certain environment. Basically, if it can be observed that in a

surface area, the points are clumped together in concentrations with empty

space between them, then the points are Clustered.

The opposite is true

for Dispersed sets of points, implying that if the points are evenly

distributed across the surface area, with no apparent concentrations, then the

set of points can be understood as Dispersed.

Manuel Gimond (2017)

informs that density can be measured using Quadrants. This is when a surface

area is divided into smaller regions. Then the density of each Quadrant is

calculated to reveal the average density of each region. The rational of using

this technique is to simplify the information for the observer, and make it

more easily understood.

Phonetically and

practically similar to Descriptive Statistics, Descriptive Spatial Statistics

are often used in Point Pattern Analysis. Manuel Gimond (2017) explains that

Descriptive Spatial Statistics focus on the Mean Center, Median Center, and the

Standard Distance.

The Mean Center is the

average of all the X coordinates of a set of points, and the average of all the

Y coordinates of a set points, which indicates the location of the center of

the entire set. In similar fashion to the Mean Center, the Median Center will also

notify the Analyst of which point is the most in the center, but with the use

of Euclidian distances.

The Standard Distance

measures how far away all the points are from the Mean Center and informs the

researcher of the average difference of these distances. This allows for an

understanding of how clustered or dispersed the points are from the center.

Research

Objectives

The purpose of this

research is to ascertain the existence of any relationships between the

locations of hotels with the locations of attractions in the Muscat Governorate

by using Point Pattern Analysis.

Observing the data

without conducting any analysis, the author of this research hypothesizes that

within the entire research area, that there is no correlation between the

placement of hotels and the locations of attractions.

However, there is a

high likelihood that certain Willayats will demonstrate a definite tendency for

the placement of hotels near the locations of attractions, such as in Muscat

and Mutrah, which are highly desirable places for tourists.

It should be noted

that the author of this paper is working under the assumption that an

attraction is defined as “Something that attracts or is intended to attract

people by appealing to their desires and tastes” (Merriam-Webster, 2018).

Study Area

The concerned area of

Study is the Muscat Governorate, which is one of eleven governorates in the

Sultanate of Oman, and is the Governorate in which the Capital City of the

Country, Muscat, is located. The Muscat Governorate is divided into six smaller

administrative regions known locally as Willayats. These Willayats are, Seeb,

Bowshar, Al Amerat, Quriyat, Mutrah, and Muscat.

Data Used

The data was provided

by the Ministry of Tourism, which is the government body responsible for the

administration, regulation, and growth of the tourism industry within the

Sultanate of Oman. The Data was acquired through Mrs. Ruqaiya Al Habsi who was

teaching the GIS and Spatial Analysis university module at the time. Please see

data below.

Methodology

Used in the Analysis

It will be critical

for the purpose of this paper to use a Point Pattern Analysis technique labeled

the Average Nearest Neighbor, as well as the Directional Distribution, and the

Mean Center methods of analysis. To do this, the author has selected to use the

ArcGIS software.

Applying these

techniques to the Hotel and Attraction sets of points, the observance of any

correlations will be facilitated. For instance, if the hotel set of points is

clustered, that may serve as evidence for the possibility of a relationship

between the hotels and attractions (as it would be logical that they would be

clustered around the attractions).

The possibility can

further be investigated by observing if the Mean Centers of the two data sets

are in close proximity of each other. Moreover, by using the Directional

Distribution on both data sets, it will be clear if the graphics are

overlapping in a way that could indicate a link between the two data sets.

For the purpose of

clarification:

The Average Nearest Neighbor as described by Ruqaiya Al Habsi (2017), calculates

the distances between each point (for one set of points), and the point which

is closest to it. All these distances are averaged to reveal the Average

Nearest Neighbor. This tool indicates whether the points are clustered,

dispersed, random, or uniform, which is useful in discovering the behavior of

one set points. ArcGIS (2002) explains that the Directional Distribution tool creates a

polygon which reveals the spatial characteristics of the set of points.

To be able to

determine the validity of the hypotheses, it is necessary to conduct the

research as such. Firstly, the author of this paper will apply these techniques

to the entire Muscat Governorate, in an attempt to discover if the first

hypothesis can be concluded as truthful or not.

Subsequently, it will

be necessary to apply the same principles to each individual Willayat to

discern the validity of the second hypothesis, which predicted that specific

Willayats display a pattern in the relationship between hotels and attractions.

Results

Muscat Governorate

For the entire

Governorate of Muscat, the Average Nearest Neighbor analysis reveals that the

Attractions are Clustered, with less than

1 percent chance that the result is due to random chance.

For the entire

Governorate of Muscat, the Average Nearest Neighbor analysis reveals that the

Hotels are also Clustered, with less than

1 percent chance that the result is due to random chance.

This provides a strong

possibility that both the Hotels and Attractions may be clustered in the same

locations. To determine the validity of this, it will be necessary to perform

the other spatial analysis functions.

Looking at the image

above, it can be seen that there is no apparent evidence to suggest that there

is a relationship between the Hotels and the Attractions in the Governorate of

Muscat. The Directional Distributions only overlap minimally, and the Mean

Centers are far from one another.

With this information,

it can be understood that Hotels and Attractions may share no apparent

relationships within the Governorate, and may both be clustered for other

reasons, such as being the result of transport networks, infrastructure,

natural phenomena, and so on.

There may however also

be some issue in the way the data has been analysed, for example, there are

many Attractions in the Wilayat of Quriyat but no Hotels, and these may serve

as outliers which strongly affect the analysis.

For this reason, it is

the authors firm belief that the individual Wilayats must also be analyzed to

reveal the possibility of any correlations between data sets.

Mutrah

From simple

observation, it appears that there may be some relationship between the Hotels

and Attractions in the Mutrah Wilayat.

The Average Nearest

Neighbor analysis reveals that the Attractions in the Mutrah Wilayat are Clustered, with a less than one percent chance

that this is the result of random chance.

The Average Nearest

Neighbor analysis reveals that the Hotels in the Mutrah Wilayat are also Clustered, with a less than one percent chance

that this is the result of random chance.

This serves as strong

evidence to support the hypothesis that certain Wilayats display strong

relationships between the Attractions and Hotels data sets. In the case of

Mutrah, with observation and the Average Nearest Neighbor analysis, it seems to

be clear that there is a pattern in the Wilayat.

Evaluating the

Directional Distribution and Mean Centers of the of the two data sets, it

becomes clearer that the relationship is not as strong as previously thought.

Although there is some overlap, and the Mean Centers are not so distant from

each other, the difference is considerable enough to come to the conclusion

that there is not a definite pattern between the Hotels and Attractions in the

WIlayat of Mutrah. This may be because hotels in the Ruwi area of the Wilayat,

which is a business and commercial hub, are established to cater to people who

want to do business in the area and not to visit attractions, as such there is

a lesser relationship from the two sets of points.

Bowshar

Upon first impression,

there appears to be a clear relationship between the Hotels and Attractions in

the Bowshar Wilayat, especially near the coastline.

The Attractions in Bowshar,

according to the Average Nearest Neighbor, are

Clustered. The analysis also reveals that the Hotels in the Wilayat

of Bowshar are also Clustered. This could

imply that there is a strong relationship between the data sets.

Looking at the Mean

Centers of the two data sets, it can be understood that there is a strong

correlation in the distribution of Hotels and Attractions in this Wilayat.

Perhaps the attraction far inland of Bowshar behaves as an outlier and

stretches the difference in distance between the Mean Centers, however it could

be concluded that the hotels are distributed in relationship the coastal

Attractions.

The Directional

Distributions of the two data sets show that the distribution of Hotels is very

similar to the distribution of the Attractions, only over a smaller surface

area. This could be, as before, due to the outlying Attraction in inland

Bowshar.

As such, the data infers a

positive response to the hypothesis, in which the conclusion that within the

WIlayat of Bowshar, there is a relationship between the Hotels and Attractions.

This heavily implies

that the Hotels in the coastal area of the Wialyat may be distributed around

the Attractions. This could further indicate that there is much visitor and

tourist interest in the area, and that any tourism development should

investigate the potential of this area.

Seeb

Looking at the distribution of

the two data sets, it appears that, as with the Bowshar Wilayat, there is a

relationship between the Hotels and the Attractions along the coastal areas of

the Seeb Wilayat.

The Average Nearest

Neighbor analysis reveals that both the Hotels and the Attractions within the

Wilayat are Clustered. This could be

indicative of a correlation between the two data sets, and serve as evidence

for a positive hypothesis.

Examining the

Directional Distribution and Mean Centers of both data sets, it is clear, that

there is without a doubt a relationship between the distribution of Hotels and

Attractions in the Wilayat of Seeb. The Directional Distributions overlap

almost exactly and are nearly the same shape and size. The Mean Centers are

within one kilometer of each other only.

With this evidence, it can be

confidently concluded that the majority of Hotels in the Wilayat of Seeb are

distributed in relation to the location of the Attractions.

Al Amerat

In the Wilayat of Al

Amerat there are two Hotels and no Attractions, as such it is impossible to

deduce any relationship between the Hotels and Attractions of this Wilayat, as

there can be no such relationship resulting from the fact that there are no

attractions.

Performing the Average

Nearest Neighbor analysis on the hotels of this Wilayat reveal that they are

spaced Randomly. As such, it can be

conclusively argued that the Hotels in Al Amerat are certainly not located

according to any pattern, and most definitely not spaced according to the

attractions of the Wilayat.

Muscat

Observing the

distribution of Hotels and Attractions in the Wilayat of Muscat, there seems to

be no clear correlation between the two data sets, with the exception of Sifa,

in the southern area of the Muscat Wilayat.

The Hotels and

Attractions in Muscat are both Randomly

dispersed, which reduces the likelihood that they are located in relation with

each other. This is not surprising for the Hotels, which can clearly be seen to

be located in a random pattern along the coast line of the Wilayat. However,

this is a strange revelation concerning the Attractions, which upon observation

would strongly suggest clustering, especially in the North of the Wilayat, in

the “City of Muscat”. This may be due to the outlying Attractions that have

distorted the analysis.

It appears that there is very

little similarity between the distribution of the two data sets. The only

common feature appears to be the direction of the Hotels and Attractions, which

are all in the coastal areas. There is a brief overlap in the Bandar A’Rowda

area, which may be due to the touristic or practical value of the area.

This lack of relation

between the location of Hotels and Attractions may also be due to the nature of

the Attractions. Most of the Attractions are located in the Old City of Muscat,

which houses many government buildings and the residence of His Majesty Sultan

Qaboos bin Said. As such, it may not be permitted to build or operate hotels in

that area, and as such, the hotels have been opened further south, or in the

adjacent Wilayat of Mutrah.

As far as the spatial

relation between the two data set, it can be said that there is very little or

no correlation between the Hotels and Attractions in Wilayat of Muscat.

Quriyat

As can be clearly

seen, there are Attractions in the Wilayat of Quriyat, but no Hotels. This

ensures that there is no relation between the Hotels and Attractions in this

Wilayat. In addition, the Average Nearest Neighbor analysis proves that the

Attractions are dispersed randomly, thus

reiterating the point that there can be no correlation between the two data

sets in this Wilayat.

Conclusion

This research provided

evidence that the first and second Hypotheses were positive. It could be seen,

in the Muscat Governorate, that although both data sets were clustered, there

was little justification to assume that they were clustered in the same locations.

Moreover, the observations supported the notion that certain Wilyats had a

stronger link between the Attractions and Hotels data sets.

The Wilayats where a

strong relationship between the Attractions and Hotels could be seen were Seeb

and Bowshar, and the Wilayats that displayed a lesser relationship between the

two data sets were Muscat and Mutrah. The Wilayats which displayed no

correlation were Quriyat and Al Amerat, as they only featured one of the data

sets.

The question may

arise, as a result of this research, as to why certain Wilayats display a

geographical relationship between Hotels and Attractions.

This may be the case

as a result of several reasons, such as the predisposition of certain Wilayats

towards the attraction of tourists or visitors, administrative, political, and

legal considerations, the availability of other nearby services, facilities, or

transportation networks (such as roads, ports, or airports), or simply the

difference in the level of development between areas.

To discover the truth

behind this question with absolute certainty, much more data collection would

be needed, and the relevant authorities would need to be interviewed to

determine what constitutes an attraction and how they are classified, what are

the limitations imposed by the Ministry of Housing to give away land, and what

are the restrictions imposed by the Ministry of Commerce and the Ministry of

Tourism for those interested in building hotels in certain geographic areas.

However, this paper

has been successful in presenting an interesting introduction on the

distribution of Hotels and Attractions in the Governorate of Muscat, and

gathering enough evidence to support the Hypotheses made at the formulation of

the research.