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.!!

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.