The distribution of hotels and attractions areimportant in each area on the world because it considered the most significantfacilities and locations that shows the level of tourism sector on thatcountry, region or city. In this report, the distribution of hotels andattractions will focus on Muscat governorate which is the capital city ofSultanate of Oman. Also, Muscat governorate includes several Wilayt which areMuscat, Mutrah, Qurayyat, Al Amirat, Bawshar and Al Seeb.
in addition, todefine the most important data of any location is point pattern analysis (PPA) whichcan be understudied by different concepts. Also, point pattern analysissupports countries to present their cities, town or villages easily and Itstarts during the late of 1950s and early of 1960s. Thisreport will analyze both of Muscat governorate and the usage of PPA. Notclear 1. literature review 1.1 Spatial Point Pattern Analysis and Its Application inGeographical Epidemiology Onthis article, the researcher borrowed from applying plant ecology literatureand adopting different techniques which can be used in the description ofspatial patterns.
For instance, “studiesof settlement distributions” (Dacey and King, 1962). To explain, settlementdistribution depends on the population distribution dataset to get thecharacteristics of the settlement patterns which helps on defining the pointpattern analysis. In addition, it can be calculated regarding to the spatialdistribution of the detailed input data of the population sample and can beused to measure the ordering or clustering of point pattern. 1.
2 Statistical Analysis and Modelling of Spatial PointPatterns According to (Dr. Antti Penttinen and Dr. Janine Illian,2008), “the aim of point process statistics is to analyse the geometricalstructure of patterns formed by objects that are distributed randomly in one-,two- or three-dimensional space. Examples include locations of trees in aforest stand, blood particles on a glass plate, galaxies in the universe, andparticle centres in samples of materials”. An explanation of the article, theelements or objectives can be described by points and marks in elegant ornatural way.
These points represented the location of an area and the marks canillustrate more information or data which has several characteristics such as,type, shape and size. In addition, the analysis of spatial pattern representedby simple graphical of point pattern on maps. 1.3 Quantifying inhomogeneity of spatial point patterns (Udo Schilcher, Günther Brandner and ChristianBettstetter ,2017), “Thisarticle compares measures for quantifying the level of inhomogeneity of a givenspatial point pattern. Comparisons are based on two main metrics: first, weevaluate the performance of the measures on both certain stochastic pointprocesses and on very specific point patterns designed to expose potentialweaknesses of the inhomogeneity measures. Second, we evaluate the computationalcomplexity of the measures. Results show that choosing a measure is a tradeoffbetween accurate assessment of inhomogeneity and computational complexity.
Theonly exception is the proposed extended wrap-around measure, which isperforming well in terms of both metrics.” To explain, the inhomogeneity can measureby different functions to clarify the distance of each point patterns. Inaddition, inhomogeneity distribution has some issues that effect on spatialpoint pattern for example, it makes analysis of performance more complex andthat can be solved by offering standard software tools. 2.
Data used The data used on figuring out the results are hotelsand attractions in Muscat governorate. The data belongs to ministry of tourismin Sultanate of Oman and it provides specific details of hotels and attractionin Muscat governorate. Also, helping on representing the features of eachWilyat on several maps.
3. Study area The study area is Muscat governorate which consist ofdifferent Wilayt, Muscat, Mutrah, Qurayyat, Al Amirat, Bawshar and Al Seeb. Inaddition, Muscat governorate has cultural and heritage aspects which made it anattractive place.
Also, consist of many modern and old markets, shops,buildings, museums, nature, mountains, parks, forts and castles. 4. Methodology 5.1 Directionaldistribution The directional distribution defined as tool thatcreates the deviational ellipses and can summarize the spatial characteristicsof geographical features. It can illustrate its data on different two ways thefirst one is the input feature class as (points) and the second is the outputfeature class as (circle). The input feature class known as a feature class ofdistribution that can calculate the standard deviational ellipses.
The outputclass defined as a feature class with polygons that will show the outputellipses features. 5.2 Average nearest neighbor The average nearest neighbor can calculate the nearestneighbor index which measure the average distance from each feature to thefeature of the nearest neighbor. To explain, if the distance of the averagenearest neighbor is less than the average of hypothetical random distribution,the distribution considered as clustered.
if the distance of the average nearest neighbor is greater than theaverage of hypothetical random distribution, the distribution considered as dispersed. 5. Results The results are illustrated on different tools whichare the directional distribution and the average nearest neighbor.
