Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos
Abstract
:1. Introduction
- An efficient method of eliminating noises among collected footprints and selecting attractive footprints with a local maximum for delineating POIs and ROIs is proposed.
- An effective clustering toward pattern discovery that involves spatial and temporal properties and attributes, such as tags, with a spatial overlap (SO) algorithm is exploited. The discovered ROIs are particularly spatial overlap available that the satisfied region of ROIs can be shared for appreciating attractions.
- A POI and an ROI with peak value that indicate the most popular location and range for appreciating attractions, respectively, are uncovered.
2. Related Work
3. Method
3.1. Attractive Footprints Discovery
3.2. Clustering
3.2.1. Pattern Discovery
Algorithm 1: Pattern discovery. |
def validation(Available_Points): #area validation S=[] for n in Available_Points: m = month of n S[m].append(n) area=0 for s in S area+=s.area() return area>0 def pattern(p,r): #pattern calculation N=Neighbours(p,r) Available_Points=[] for n in N: #find nearest unique users if n.user_id not in Available_Points: Available_Points[n.user_id]=n; else: if distance(p,n)<distance(p,Available_Points[n.user_id]): Available_Points[n.user_id]=n; if validation(Available_Points): X = [] for n in Available_Points: m = month of n X[m]++ #pattern calculation based on set time return vector_normalization(X) #pattern normalization else: return null def patternDiff(a,b): #pattern difference calculation Pdiff=[] for m in range(1,12) Pdiff[m]=|a.XN[m]-b.XN[m]| return vector_normalization(Pdiff) #pattern difference normalization def findSimlarPatternPoints(p,afs): #find points with similar pattern S=[] for q in afs: if q.XN!=null: PdiffN=patternDiff(p.XN,q.XN) if PdiffN<=T2: S.append(q) return S def main: #main function for p in afs: p.XN=pattern(p,r) for p in afs: if p.XN !=null: p.S=findSimlarPatternPoints(p,afs) |
3.2.2. Clustering with a Spatial Overlap Algorithm (SO Algorithm)
Algorithm 2: Clustering with a SO algorithm. |
def clustering: sortByPeakValue(afs) #order by peakValue DESC of afs unmark_all(afs) #unmark all point of afs clusters=[] for p in afs if p.XN!=null and is_unmark(p): cluster=[] #create new cluster cluster.append(p) for q in p.S #PdiffN<=T2 if is_unmark(q): cluster.append(q) PdiffN=patternDiff(p.XN,q.XN) if PdiffN<=T2-T3: mark(q) clusters.append(cluster) return clusters |
3.2.3. Naming
3.2.4. Merge
3.3. POI and ROI Determination
4. Implementation
4.1. Study Areas and Materials
4.2. Result
4.3. Discussion and Evaluation
5. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Item | Study Area A | Study Area B |
---|---|---|
Total number of photos | 276,018 | 1,956,980 |
Percentage of photos in Taiwan | 3.44% | 24.36% |
Distinct contributed users | 6,749 | 22,886 |
User tags (total/distinct) | 925,761/34,140 | 2,918,749/97,803 |
Photos with user tags | 144,249 | 406,461 |
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Kuo, C.-L.; Chan, T.-C.; Fan, I.-C.; Zipf, A. Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS Int. J. Geo-Inf. 2018, 7, 121. https://doi.org/10.3390/ijgi7030121
Kuo C-L, Chan T-C, Fan I-C, Zipf A. Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS International Journal of Geo-Information. 2018; 7(3):121. https://doi.org/10.3390/ijgi7030121
Chicago/Turabian StyleKuo, Chiao-Ling, Ta-Chien Chan, I-Chun Fan, and Alexander Zipf. 2018. "Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos" ISPRS International Journal of Geo-Information 7, no. 3: 121. https://doi.org/10.3390/ijgi7030121
APA StyleKuo, C. -L., Chan, T. -C., Fan, I. -C., & Zipf, A. (2018). Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos. ISPRS International Journal of Geo-Information, 7(3), 121. https://doi.org/10.3390/ijgi7030121