Explore Popular Tourist Spots through GIS and Geotagging Photos

Scenario

Nowadays, traveling has been increasingly popular and diverse in Taiwan and more and more scenic spots are explored as well. Therefore, tourism departments now also pay attention to how to get information of popular tourism spots and routes. Most investigations on tourism are conducted with questionnaires and interviews. However, these surveys might be at higher cost. Furthermore, frequency of surveys and sample collection are limited, and new tourist spots might not be included in the data employed by surveyors. Thus, surveyors need an objective and scientific way to figure out popular scenic spots for tourists.

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Goals

Researchers (Kisilevich & Keim 2010; Hollenstein 2009; Arase et.al 2010) said that photos people took show their love to the spots. People now take photos often by cameras equipped on smart phones and tablets. These photos may record photographers’ bearings through GPS built in smart devices. After that, people can upload photos with bearing information to the Internet. This kind of photo is called Geotagging photos.

In this research, Geotagging photos taken on Penghu Island and shared by tourists on the Internet are collected and analyzed by spatial analyses with Geographic Information System to explore popular tourism spots on the island.

Solutions

1. Collect Geotagging photos that are taken on Penghu Island in different years on Flickr, select photo providers at random to estimate the minimum and maximum stay time and tell if the provider is an inhabitant or a tourist.

2. Filter out duplicate photos, export XY coordinates of photos and build a new point layer with SuperGIS Desktop 3

3. Employ Kernel Density Estimation within SuperGIS Spatial Analyst to analyze popular tourism spots and distribution of tourists.

4. Conduct correlation analysis on visiting frequency of invested tourism spots and the result of this research.

Results

1. The collection results of Geotagging photos:
 After filtering out the duplicate photos and those taken by inhabitants, researchers found that 5724 coordinates of tourism spots, and photos with these coordinates were taken by 553 tourists.

2. Spatial distribution of the Geotagging Photos: Employ “Add XY Data” function within SuperGIS Desktop 3 to build coordinates of a new point layer according to the collected XY data.

3. Kernel Density Estimation:
Kernel Density Estimation within SuperGIS Spatial Analyst is employed to estimate spatial density of point coordinates into density surface:
a. Search Radius: The distance a tourist moves in the region of a tourism spot is usually defined as the search radius. In this research, the research radius is defined as 500 meters.
b. Output Cell: Divide the minimum distance between scenic spots by 2 and use the result as cell width. In this research, the cell width is 150 meters.
c. Distance Function: Employ distance decay function built in SuperGIS Desktop 3.2.

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Calculation Process
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Parameter Setting

4. Kernel Density Result: 
Employ “Extract by Shape” function within SuperGIS Spatial Analyst to define the research zone.

Subsequently, the researchers modify cell colors to visualize and perform the Kernel Density Estimation result. For example, the cells in red in the picture below contain higher Kernel Density value and denote that the spots have more possibilities of being visited by tourists. Besides, Multiple Map Frame layout is adopted to display the significant spots.

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5. Comparison:
The measurement unit employed in questionnaire is different from the one that Kernel Density uses. Therefore, the researchers utilize correlation analysis to compaire if that the correlation coefficient is close to 1. If so, the result of popular tourism spots investigation got through Kernel Density Estimation is consistent with the one from questionnaires. After comparing the two results, reaserchers found that correlation coefficient is 0.72 and highly correlated.

Effects

1. By collecting Geotagging photos and utilizing Kernel Density Estimation within SuperGIS Spatial Analyst, tourists’ preference of tourism spots can be analyzed more precisely. Besides, after comparing the results from two investigation surveys, the researchers found that the correlation coefficient is highly coorelated, which means that the results from the two investigation surveys are similar to each other. Therefore, Kernel Density Estimation within SuperGIS Spatial Analyst enables surveyors to explore the spots that can’t be investigated by questionnaires, and help users to shorten the time on data collection.

2. Geotagging Photos provides a new and effective way to collect relevant data but requires smart devices and the Internet to use it. Therefore, the samples collected so far cannot cover all the tourists’ data. If there are more people using smart devices and Internet, the accuracy will be higher in the future estimation.

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