Muscat have been successfully pre-processed and a spatial point

Muscat is a a well known for its
hotels and attractions. Tourism in Oman is growing and more hotels and
attractions are being built. with a number of tourists visiting the country.
therefore this report shows you the study of “The distribution of hotels and
attractions in Muscat Governorate” along
with the literature review,tools used,methodology,results and conclusion. This
was donw to analyze the distribution of hotels and attractions and findout the
relationship between them. Also talking about point pattern analysis in the
litrature review.

 

Introduction

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Oman tourism has been growing more and
more each year and it has become one of the most popular destinations in the
gulf with more thn 2.5 million tourists last year. Most travellers prefer to
stay in Muscat because of the number of attractions and hotels. Dhofor is the
second place most visitors go to especially in “Khareef” season. this report helps to show you the number of hotels
and attractions in Willayat Muscat.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Literature review

Analysis of Spatial Point Patterns in
Nuclear Biology

There is considerable interest in cell biology in determining
whether, and to what extent, the spatial arrangement of nuclear objects affects
nuclear function. A common approach to address this issue involves analyzing a
collection of images produced using some form of fluorescence microscopy. We
assume that these images have been successfully pre-processed and a spatial
point pattern representation of the objects of interest within the nuclear
boundary is available. Typically in these scenarios, the number of objects per
nucleus is low, which has consequences on the ability of standard
analysis procedures to demonstrate the existence of spatial preference in the
pattern. There are broadly two common approaches to look for structure in these
spatial point patterns. First a spatial point pattern for each image is
analyzed individually, or second a simple normalization is performed and the
patterns are aggregated. In this paper we demonstrate using synthetic spatial
point patterns drawn from predefined point processes how difficult it is to
distinguish a pattern from complete spatial randomness using these
techniques and hence how easy it is to miss interesting spatial preferences in
the arrangement of nuclear objects. The impact of this problem is also
illustrated on data related to the configuration of PML nuclear
bodies in mammalian fibroblast cells.

 

Figure 4. Demonstrating Boundary Shape Heterogeneity.

 

The
figures show how the cube of the maximum inter-point distance within a boundary
varies with the volume enclosed by the boundary. If all the boundaries had the
same shape then all the points on each graph should fall on a straight line. A)
shows 50 instances from each of the 4 thin synthetic boundary shapes. It
demonstrates that the scatter within each shape class is much smaller than
between shape classes. B) Nucleus envelopes from MRC5 dataset demonstrating
scatter. . C) shows the same data as A) but on a different scale, so that it
can be compared with D), where D) shows 50 instances from each of the 4 thick
synthetic boundary shapes shown in Figure 3.

 

 

Problems Associated with Spatial Point Pattern Analysis

an
overview of quantitative reasoning about nuclear architecture and the
difficulties that can arise. This is primarily concerned with the inadequacy of
simple procedures to reveal complicated spatial preferences in replicate
nuclear compartment point patterns. Thus it is assumed that the data
acquisition and pre-processing steps mentioned above have been successfully
applied, yielding a collection of processed images. In this case, we presume
the processing has provided 2 things: First, a representation of the shape of
the nucleus boundary; second, the 3D spatial coordinates (the point pattern)
of the target compartment. Note that the compartments themselves also have
extent meaning that they will be represented by more than one pixel location
within the image stack. We are concerned with compartments that can reasonably
be represented as a spatial point pattern, which means that, for example, the
centroids of the compartment of interest yield the point locations.

Given
such processed data, the main problem is making inferential statements given a
collection of point patterns derived from replicate nuclei images, with the
objective of identifying interesting and potentially informative spatial
arrangements.

 

 

An
overview of the Spatial Statistics toolbox

The
Spatial Statistics toolbox contains statistical tools for analyzing spatial
distributions, patterns, processes, and relationships. While there may be
similarities between spatial and nonspatial (traditional) statistics in terms
of concepts and objectives, spatial statistics are unique in that they were
developed specifically for use with geographic data. Unlike traditional
nonspatial statistical methods, they incorporate space (proximity, area,
connectivity, and/or other spatial relationships) directly into their
mathematics.

The
tools in the Spatial Statistics toolbox allow you to summarize the salient
characteristics of a spatial distribution (determine the mean center or
overarching directional trend, for example), identify statistically significant
spatial clusters (hot spots/cold spots) or spatial outliers, assess overall
patterns of clustering or dispersion, group features based on attribute
similarities, identify an appropriate scale of analysis, and explore spatial
relationships. In addition, for those tools written with Python, the source
code is available to encourage you to learn from, modify, extend, and/or share
these and other analysis tools with others.

 

Spatial Distribution Characteristics
of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and
Correlation Analysis

Spatial
pattern analysis has been examined widely to explore global or local spatial
distribution patterns of
urban activities. It can be classified into first-order and second-order effects of a spatial
process. Kernel density estimation (KDE) and Ripley’s K-function are
two of the most popular methods for analyzing the first-order and second-order
properties of a point event distribution. KDE has been used to analyze “hot spots” of point events,
such as the traffic hazard intensity of bicycles, wildlife–vehicle accident
analysis, and road accident hot-spot analysis and classification. Ripley’s K-function has
been used to test whether any pair of events is spatially dependent or uniform
by distance measure. In fact, these studies are based on the assumption of
infinitely continuous planar space in which distances are measured as a
straight-line (Euclidean) distance.

However,
many kinds of point events associated with urban activities are constrained by
road networks in the real world. Those events can be classified into on-network
events and alongside-networks events. Almost all facilities in urbanized areas
are regarded as alongside-network
events. The use of a planar point pattern analysis (PPA) method over a Euclidean space has
limitations for analyzing these events because they are often constrained to only
the network portion of the 2-D Euclidean space, the so-called network space.
Therefore, the traditional planar PPA methods lead to false analysis results
for the network-constrained point events.

 

Methodology used in the analysis

First
of all the data of the distrution of hotels and attraction from ministry of
Tourism was used and  observed with
several tools that was listed below , second of all the area was analayzed and
took assumption and note into consideration then i looked at the details of the
maps and where the attractions and hotels are located. followiing that i looked
at the relationship between the hotels and attractions based on the map and
also most common areas for hotels and attractions and finally the results of
what was shown on the map.

Tools Used:

1.Program used ; ArcMap with data that
included “willayat muscat map” 

2.Average
Nearest Neighbour : Calculates a nearest neighbor index based on the average
distance from each feature to its nearest neighboring feature.

 

3.High/Low
Clustering : Measures the degree of clustering for either high values or low
values using the Getis-Ord General G statistic.

 

4.Data
from ministry of Tourism.

 

5.
Mean Center tool returns a point at the average X
and average Y coordinate for all feature centroids, the median center uses an
iterative algorithm to find the point that minimizes Euclidean distance to all
features in the dataset.

 

6.Directional
Distribution.

           

Study area : “Willayat Muscat map” and different
hotels and attractions in that area.

 

 

 

 

 

 

 

 

 

 

Results

The first two maps show the
distribution of hotels and attractions in the Governorate of Muscat. the
attractions are mostly in the Wilayat of Mutrah and Bawshar. However, There are
not attractions in Al Amrat but suprisingly they are hotels there.they are alot
of attractions in seeb along with several hotels. Most hotels are located on
the coastline near to the attractions mostly. Bausher is located in seeb and it
is shown that it also has a number of

attractions which
include huge malls like Avenues and GrandMall.

The Average Nearest Neighbour shows that both data sets are
clustered that there is a relationship between the two of them. However, the
directional distribution and the median center of hotels and attractions prove
that the relationship is not that strong.The directional distribution eclipses
are not overlapping much and the median centres are far apart.

 

 

 

 

 

 

Conclusion

In conclusion, attractions and hotels
are shown to be very close to eachother and the point pattern map proves it
.Most hotels and attractions are clustured along the coastline of the country
and there are only hotels and attractions that are distributed randomly. There
is a strong relationship between hotels and attractions because they support
eachother in terms of attracting tourists thats why you only find attractions
and hotels close to eachother.