AbstractThisreport is a study of different tools in ArcMap for hotels and attractions inthe Muscat Governorate which illustrate the different output of the analysis oftwo tools TheAverage Nearest Neighbor and The Directional Distribution, in different sectionin the report there will be a details information about the tools anddescription of the result of using these tools in ArcMap. IntroductionGIS is a system links geographicinformation (where things are) with descriptive information (what things are)and allow to visualize, analyze and interpret data to understand relationships,patterns, and trends (ESRI). And the use of GIS helps to Improveddecision making – decisions are made easier because specific and detailedinformation is presented about one or more locations.
Reducecosts and increase efficiency – especially regarding maintenance schedules,fleet movements or scheduling timetables.Improvedcommunication between any involved organizations or departments as the visualformat is easily understood by all.Easyrecordkeeping – geographical changes are easily recorded by GIS for thoseresponsible of recording the changes.
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Managing geographically – knowingwhat is and will be occurring in a geographic space in order to plan a courseof action ArcMap is an application used in ArcGIS inwhich the user can analyze GIS dataset in an area and create map layers. Some Typical tasks performed in ArcMap used by users: (“What is ArcMap?—Help | ArcGIS forDesktop”, 2018): Work with maps Print maps Compile and edit GIS datasets Use geoprocessing to automate work and perform analysis Publish map documents as map services using ArcGIS for Server Share maps, layers, geoprocessing models, and geodatabases with other users Document user’s geographic information Customize the user experience Literature reviewDuringthe late 1950s and early 1960s, the spatial point patterns analysis becomeimportant in geography, and therefore researchers started to adopt newtechnique used to describe the spatial patterns, these techniques aredistance-based techniques by which it can be using information and data todescribe pattern by points spacing, the second technique is area-based whichbased on different features of the observed number’s distributed frequency ofselected points in the sub-regions of the study area(‘quadrats’) (Gatrell, Bailey, Diggle & Rowlingson, 1996).Pointpattern analysis is a distribution of a group of point on a surface or patternevolution, which indicate to the temporal location or the actual spatial ofthese points or to contain outsource point. Analyzing Point pattern is suitablefor different studies such as population science in term of human statisticsthat usually appear in single point or event form, also it appropriate for distributionacross space by analyzing distribution across time and therefore it can bereplacing ‘study area’ with ‘study time’ and the distance of a point I spacewith distance from a point in time (Ervin, 2016).Furthermore the Point patterns can be categorized as random,uniform, clustered or dispersed based on two elements (Upton & Fingleton,1995):• Random vs.
Uniform(stratified, regular)• Clustered vs.Dispersed These two elements doesn’t require to be relatedbut the distribution of Point patterns can be randomly clustered (right imagein the graph below) or it can be distributed dispersed (middle image in thegraph below). Also the attribute of one of these elements doesn’t affect theother element but the difference is easily figured. In addition there aredifferent techniques to express the amount of clustering or stratification. Data usedThedata used in the practical section it the date of the Muscat GovernorateWillyats and the distribution data of the hotels and attraction based ondifferent characteristics in the Governorate, these data are the man aspect ofthe analysis because these data will show the result of different methods inthe ArcMap application.Study areaThestudy area was the area of Muscat Governorate in The Sultanate Of Oman, whereMuscat is capital city of Oman and considered the largest in The Sultanate inwhere actually the high numbers of hotels and human made attraction are,therefore the data will be analyzed easily and more accurately.
Methodology used in theanalysisFor the data analysis of Muscat’sHotels and attraction, two tools have been used the first one is The AverageNearest Neighbor which used to measure the distance betweeneach center point of mass geometricobject and the neighbor nearest center location, and then calculate the averageof all distances of the nearest neighbor points. The feature’s distributionanalyzed as cluster if the result shows the average distance is less than therandom distribution and if the feature’s distribution is greater than randomdistribution then it analyzedas dispersed (“How AverageNearest Neighbor works—ArcGIS Pro | ArcGIS Desktop”, 2018) The second toolis Directional Distribution itused to summarize the geographic feature’s spatial characteristics in term of dispersion,directional trends and central tendency. It works by analyzing input intoOutput Feature Class displays in elliptical polygons for each field.
Thesepolygons has the orientation of the ellipse , an attribute value in X and Ycoordinates for the mean center, and two standard distances (long and shortaxes) (“DirectionalDistribution (Standard Deviational Ellipse)—Help | ArcGIS for Desktop”,2018). ResultIn this section the results of thepractical part will be shown for both tools Directional Distribution and the AverageNearest Neighbor with appropriate graphs to approve the result with deepexplanation. The graph belowshows the map after using DirectionalDistribution tool for the hotels and attractions in Muscat and from the map itshows that the blue circle is where actually the highest number of hotels arelocated, same with the green circle which shows the more attractions in Muscat,and from the result of the two circles it means that there is a high linkagebetween the hotels and the attractions in Muscat where most of the hotelslocated at the same area of the attraction and this might be happened becausethe tourist prefer to stay in a hotel that near to everything. The Average Nearest NeighborThe graph below shows the distribution of hotelsin Muscat, as it seen after the analyzing the hotels feature distribution isclustered which means they distributed the hotels based on commoncharacteristics not randomly, these characteristics can be number of rooms orthe star level. Average NearestNeighbor Summary Observed Mean Distance: 846.5057 Meters Expected Mean Distance: 1621.
3899 Meters Nearest Neighbor Ratio: 0.522086 z-score: -10.057104 p-value: 0.000000 Dataset Information Input Feature Class: mct_hotels Distance Method: EUCLIDEAN Study Area: 1272390138.551651 Selection Set: False And the second graph below illustrate the sameresult of the hotels in Muscat but for the attraction and it shows the sameanswer which is distributing the attraction in clustered instead of randomlymeans that they distributed the attraction in same area. Average NearestNeighbor Summary Observed Mean Distance: 1626.3196 Meters Expected Mean Distance: 3609.1346 Meters Nearest Neighbor Ratio: 0.
450612 z-score: -9.575226 p-value: 0.000000 Dataset Information Input Feature Class: mct_attractions Distance Method: EUCLIDEAN Study Area: 4324583048.564745 Selection Set: False Conclusion To conclude the data used in the analysis are the hotels and the attraction in Muscat and result basically describe how both features are distributed using two point pattern analyses which are The Average Nearest Neighbor and the Directional Distribution. These results are actually useful for the future planning in the tourism sector by helping them deciding where to improve in the governorate and to keep the area out of crowd.