Abstract Finally, an integration between the distribution of

Abstract

This
paper will focus on the distribution of hotels and attractions in Muscat
Governorate and point pattern analysis. Different tools and analysis will be
used and different examples of point pattern analysis will be mentioned.
Finally, an integration between the distribution of
hotels and attractions in Muscat Governorate and point pattern analysis will be
conducted.!!

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Introduction

Point pattern analysis is the assessment of
the pattern or distribution of a certain or set of points. It can indicate the actual
spatial or temporarily location. It’s one of the most important models in GIS
and spatial analysis since its important in population. Different methods of
point pattern will be discussed. Also, it will be integrated with the hotels
and attraction is Muscat. It is not integration, it is analysis

Literature
review

The
analysis of point patterns shows up in numerous diverse ranges of research. For
instance, you have ecology where the concentration is on defining the spatial distribution
and its causes of a tree species for which the areas have been gotten from for
the research study for example the location of it. Additionally, in case two or
more species have been recorded, it would be better to asses and evaluate whether
these species are similarly dispersed or if there is a competition exists
between them. There are other causes that dynamism each species to extent in certain
areas of the study. Also, in spatial epidemiology, the study of disease
transmission there is a common issue which is to decide whether the cases of a
certain illness are clustered. This can be evaluated by comparing the spatial
distribution of the cases to the areas of a set of controls taken at subjective
from the population.

In
general, a point process is a stochastic process in which locations of are
detected the significant locations within a restricted region. According to (Bivand., 2008) defines a point
process as a ‘stochastic mechanism which generates a countable set of events’. Also,
he provided us with an appropriate definitions and classification of the different
types of a point process and their core properties. The locations of the events
generated by a point process in the area of study will be called a point
pattern. Occasionally, supplementary covariates might need to be documented and
it will be support the locations by attaching the document in it of the perceived
events.

The
analysis of point patterns is concentrates on the spatial distribution of the
perceived procedures and create readings of the fundamental process that is
created by them. There are two primary issues of interest is particular: the
distribution of events in space and the presence of potential collaborations
between them. For only a descriptive analysis, we would represent the locations
of the point pattern in the study area. This will give us an idea of the distribution
of the points, which can lead to probable theory about the spatial distribution
of the events. Also, statistical analyses can be described and be competed.

When
examining a point process, the most fundamental test that can be performed is
that of Complete Spatial Randomness also knows and CSR. Naturally, by CSR means
that the occasions are dispersed autonomously at irregular and consistently
over the study area This infers that there are no districts where the occasions
are more likely to happen and that the presence of a given occasion does not
alter the likelihood of other occasions showing up nearby.

This
can be examined by plotting the point pattern and seeing if the points manage
to appear in clusters or if it respects a regular pattern. Anyway, the points
are not allocated homogeneously because they must be distributed filling all
the space in the study area. Usually, clustered patterns happen when there is
an attraction in the area or between points, whilst regular patterns happen
when there is a competition for example.

There
are more special analysis techniques and examples including Ripleys K Function,
the G function, The F function and Morans I point pattern analysis which are
used in the different fields of study.

Methodology

Two
tools had been used:

Average nearest
point:

The
Average Nearest Neighbor tool measures the distance between each feature
centroid and its nearest neighbor’s centroid location. Then the averages of all
the nearest neighbors distance gets conducted. Depending on the average
distance and the average for a hypothetical random distribution it defines
whether it’s considered as cluster or discrete. The average nearest neighbor
ratio is calculated as the observed average distance divided by the expected
average distance.

This
tool is used to:

Evaluate
competition of the region for example it compares a variety of pant species
within a fixed study area.It monitors
change over time since it evaluates changed in a single type of study with a
fixed study.It compares an
observed distribution to a control distribution.

 

The directional
distribution

it generates standard deviational leaps
to summarize the spatial characteristics of geographic features: central
movement, distribution, and indicator trends. Its uses are:

 

The Standard
Deviational Ellipse tool makes a new Output Feature Class that has elliptical
polygons with a coordinate for mean center, two standard distances and
rotation.  Measuring distances
using Calculations based on Euclidean or Manhattan.

 

 

Used to analyze when
a field is specified.

 

 

For the line and
polygon features, feature centroids are used in distance calculations.

 

Map layers can be
used to define the Input Feature Class.

 

 

 

 

 

 

 

 

Result:

 

 

 

 

After creating the map, it was decided that the two tools that will
be used are the directional distribution and average nearest point.

 

The
directional distribution map shows 2 outputs of an ellipses shape. The blue
ellipse represents the hotels and light yellow represent the attraction. The
map shows the that the attractions are spread more than the hotels and the
hotels are gathered in a small ellipse in the middle of the attractions. This
means hotels exist when there are attractions.

 

 

 

 

 

 

This
graph shows that the attractions in Muscat are clustered since the z score is
-9.58 and there is less than 1% that the clustered pattern could be the result
of a random chance.

the
graph show that hotels in Muscat and clustered since the z score is -10.06 and
there is less than 1% and that the clustered pattern is random.

 

 

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