Identification people's regions of interest and ranking them based on their popularity level have many applications such as tourism development, traffic management and urban planning. Most of interesting places from tourism's view-points are parks, museums, historical places and scenic areas. To identify these places, it is necessary to access people's perception about environment. Accessing to this environmental perception isn't possible from traditional maps or satellite images. One approach to achieve this kind of people's environmental perception is VEP. In this method, people are asked to take photos about subject of research and then the content of these photos are analyzed. So in the VEP method, the content of images is very important part. However, todays with the development of web 2, people share their taken photos from scenic places and interesting subjects. Each photo has metadata about its spatial location, identity and name of its up-loader, title and more. Therefore, researches in the field of geo-tagged photos, analyzed metadata of these photos. However, in this research, we investigated both of photos' content and metadata to rank popular places and it can be said that the integration of VEP and VGI methods is our main contribution.
Tehran region 6 was considered as the case study and its geo-tagged photos are extracted from Panoramio site. Then DBSCAN method was applied to extract regions of interest. The DBSCAN method has many advantages that are 1) it is density-base and the place that have dense data points is identified as cluster. Therefore, it is appropriate for our research that density of photos is an indicator of place's popularity 2) Moreover, in this method, there is no need to know the number of clusters previously and 3) the shape of clusters can be arbitrary.
Two mandatory parameters of the DBSCAN method are Eps (Neighborhood’s radius) and MinPts (at-least number of points) but there isn't any ideal method to obtain optimal values of these parameters for all applications. Therefore, to find appropriate values, we ran DBSCAN with different parameters and finally we set Eps as 1000 and MinPts as 10, and in result, 17 clusters were identified. The concept of each cluster was identified based on GeoNames POI and unrelated clusters of tourism were removed. Then the popularity score of each region of interest was computed based on its photos' contents, number of photos and number of up-loaders. One of the scene-recognition algorithms was applied to investigate photos' content. The Laleh Park, Saei Park and Ferdosi square achieved high popularity scores. In the next step, popularity of these places in different months, seasons and years were investigated. Totally it can be said that most of the photos were taken in the April and May, or in other words, in the spring. Moreover, the relation between regions of interest and their land-use types were investigated that shows that green-space was significantly more than other land- use types. The detected places were compared with tourist attractions in Tehran region 6 and this comparison showed that natural attractiveness such as parks and gardens have appeared more than other attractiveness in geo-tagged photos. Comparing the computed popularity score of each region of interest, with sum of its scores on GoogleMap and FourSquare, showed that more coincidence exists in the class of very popular places.
Density Based Spatial Clustering With Noise