Muscat have been successfully pre-processed and a spatial point

Muscat is a a well known for itshotels and attractions. Tourism in Oman is growing and more hotels andattractions are being built. with a number of tourists visiting the country.

therefore this report shows you the study of “The distribution of hotels andattractions in Muscat Governorate” alongwith the literature review,tools used,methodology,results and conclusion. Thiswas donw to analyze the distribution of hotels and attractions and findout therelationship between them. Also talking about point pattern analysis in thelitrature review. IntroductionOman tourism has been growing more andmore each year and it has become one of the most popular destinations in thegulf with more thn 2.5 million tourists last year. Most travellers prefer tostay in Muscat because of the number of attractions and hotels. Dhofor is thesecond place most visitors go to especially in “Khareef” season. this report helps to show you the number of hotelsand attractions in Willayat Muscat.

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               Literature review Analysis of Spatial Point Patterns inNuclear Biology There is considerable interest in cell biology in determiningwhether, and to what extent, the spatial arrangement of nuclear objects affectsnuclear function. A common approach to address this issue involves analyzing acollection of images produced using some form of fluorescence microscopy. Weassume that these images have been successfully pre-processed and a spatialpoint pattern representation of the objects of interest within the nuclearboundary is available.

Typically in these scenarios, the number of objects pernucleus is low, which has consequences on the ability of standardanalysis procedures to demonstrate the existence of spatial preference in thepattern. There are broadly two common approaches to look for structure in thesespatial point patterns. First a spatial point pattern for each image isanalyzed individually, or second a simple normalization is performed and thepatterns are aggregated. In this paper we demonstrate using synthetic spatialpoint patterns drawn from predefined point processes how difficult it is todistinguish a pattern from complete spatial randomness using thesetechniques and hence how easy it is to miss interesting spatial preferences inthe arrangement of nuclear objects. The impact of this problem is alsoillustrated on data related to the configuration of PML nuclearbodies in mammalian fibroblast cells.

 Figure 4. Demonstrating Boundary Shape Heterogeneity. Thefigures show how the cube of the maximum inter-point distance within a boundaryvaries with the volume enclosed by the boundary. If all the boundaries had thesame 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. Itdemonstrates that the scatter within each shape class is much smaller thanbetween shape classes. B) Nucleus envelopes from MRC5 dataset demonstratingscatter. .

C) shows the same data as A) but on a different scale, so that itcan be compared with D), where D) shows 50 instances from each of the 4 thicksynthetic boundary shapes shown in Figure 3.  Problems Associated with Spatial Point Pattern Analysisanoverview of quantitative reasoning about nuclear architecture and thedifficulties that can arise. This is primarily concerned with the inadequacy ofsimple procedures to reveal complicated spatial preferences in replicatenuclear compartment point patterns. Thus it is assumed that the dataacquisition and pre-processing steps mentioned above have been successfullyapplied, yielding a collection of processed images. In this case, we presumethe processing has provided 2 things: First, a representation of the shape ofthe nucleus boundary; second, the 3D spatial coordinates (the point pattern)of the target compartment. Note that the compartments themselves also haveextent meaning that they will be represented by more than one pixel locationwithin the image stack. We are concerned with compartments that can reasonablybe represented as a spatial point pattern, which means that, for example, thecentroids of the compartment of interest yield the point locations.

Givensuch processed data, the main problem is making inferential statements given acollection of point patterns derived from replicate nuclei images, with theobjective of identifying interesting and potentially informative spatialarrangements.  Anoverview of the Spatial Statistics toolboxTheSpatial Statistics toolbox contains statistical tools for analyzing spatialdistributions, patterns, processes, and relationships. While there may besimilarities between spatial and nonspatial (traditional) statistics in termsof concepts and objectives, spatial statistics are unique in that they weredeveloped specifically for use with geographic data. Unlike traditionalnonspatial statistical methods, they incorporate space (proximity, area,connectivity, and/or other spatial relationships) directly into theirmathematics.Thetools in the Spatial Statistics toolbox allow you to summarize the salientcharacteristics of a spatial distribution (determine the mean center oroverarching directional trend, for example), identify statistically significantspatial clusters (hot spots/cold spots) or spatial outliers, assess overallpatterns of clustering or dispersion, group features based on attributesimilarities, identify an appropriate scale of analysis, and explore spatialrelationships.

In addition, for those tools written with Python, the sourcecode is available to encourage you to learn from, modify, extend, and/or sharethese and other analysis tools with others. Spatial Distribution Characteristicsof Healthcare Facilities in Nanjing: Network Point Pattern Analysis andCorrelation AnalysisSpatialpattern analysis has been examined widely to explore global or local spatialdistribution patterns ofurban activities. It can be classified into first-order and second-order effects of a spatialprocess. Kernel density estimation (KDE) and Ripley’s K-function aretwo of the most popular methods for analyzing the first-order and second-orderproperties 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 accidentanalysis, and road accident hot-spot analysis and classification. Ripley’s K-function hasbeen used to test whether any pair of events is spatially dependent or uniformby distance measure.

In fact, these studies are based on the assumption ofinfinitely continuous planar space in which distances are measured as astraight-line (Euclidean) distance.However,many kinds of point events associated with urban activities are constrained byroad networks in the real world. Those events can be classified into on-networkevents and alongside-networks events. Almost all facilities in urbanized areasare regarded as alongside-networkevents. The use of a planar point pattern analysis (PPA) method over a Euclidean space haslimitations for analyzing these events because they are often constrained to onlythe network portion of the 2-D Euclidean space, the so-called network space.

Therefore, the traditional planar PPA methods lead to false analysis resultsfor the network-constrained point events. Methodology used in the analysisFirstof all the data of the distrution of hotels and attraction from ministry ofTourism was used and  observed withseveral tools that was listed below , second of all the area was analayzed andtook assumption and note into consideration then i looked at the details of themaps and where the attractions and hotels are located. followiing that i lookedat the relationship between the hotels and attractions based on the map andalso most common areas for hotels and attractions and finally the results ofwhat was shown on the map.Tools Used:1.Program used ; ArcMap with data thatincluded “willayat muscat map”  2.AverageNearest Neighbour : Calculates a nearest neighbor index based on the averagedistance from each feature to its nearest neighboring feature.

 3.High/LowClustering : Measures the degree of clustering for either high values or lowvalues using the Getis-Ord General G statistic. 4.Datafrom ministry of Tourism.

 5.Mean Center tool returns a point at the average Xand average Y coordinate for all feature centroids, the median center uses aniterative algorithm to find the point that minimizes Euclidean distance to allfeatures in the dataset. 6.DirectionalDistribution.            Study area : “Willayat Muscat map” and differenthotels and attractions in that area.          ResultsThe first two maps show thedistribution of hotels and attractions in the Governorate of Muscat.

theattractions are mostly in the Wilayat of Mutrah and Bawshar. However, There arenot attractions in Al Amrat but suprisingly they are hotels there.they are alotof attractions in seeb along with several hotels. Most hotels are located onthe coastline near to the attractions mostly.

Bausher is located in seeb and itis shown that it also has a number of attractions whichinclude huge malls like Avenues and GrandMall.The Average Nearest Neighbour shows that both data sets areclustered that there is a relationship between the two of them. However, thedirectional distribution and the median center of hotels and attractions provethat the relationship is not that strong.The directional distribution eclipsesare not overlapping much and the median centres are far apart.      Conclusion In conclusion, attractions and hotelsare 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 countryand there are only hotels and attractions that are distributed randomly.

Thereis a strong relationship between hotels and attractions because they supporteachother in terms of attracting tourists thats why you only find attractionsand hotels close to eachother.