# Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Tourist Trajectory

#### 2.1.1. Constructing Travel Trajectory

#### 2.1.2. Differentiating Natives and Tourists

#### 2.1.3. Segmenting Individual Travel Journey

- Criterion I: It is intuitive that a tourist seldom takes no photos in ${\u03f5}_{t}$ consecutive days during a trip. If this situation happens, it is highly possible that the tourist pays another visit to the target city. In this sense, we segment tourists’ travel trajectories into distinct journeys if the time interval between two consecutive semantic photos exceeds a predefined ${\u03f5}_{t}$ days. Mathematically, if $({\overline{p}}_{i+1}.time-{\overline{p}}_{i}.time)\ge {\u03f5}_{t}$, we break the travel path into different parts.
- Criterion II: Journeys are abandoned if the total duration of staying at the tourism destination is less than ${\u03f5}_{l}$ minutes. Mathematically, if $({\overline{p}}_{end}.time-{\overline{p}}_{start}.time)\le {\u03f5}_{l}$, we drop this journey from further analysis.

#### 2.2. Travel Motif

#### 2.2.1. Topological Travel Motif

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

#### 2.2.2. Temporal Travel Motif

- If the given topological motif $\mathbb{S}$ contains less than K distinct locations (i.e., $\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|<K$), its corresponding temporal travel sequence is:$$\begin{array}{c}\hfill \mathbb{T}=\{lo{c}_{1}.duration,\cdots ,lo{c}_{\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|}.duration,0,\cdots ,0\}\end{array}$$
- If the given topological motif $\mathbb{S}$ contains exactly K distinct locations (i.e., $\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|=K$), its corresponding temporal travel sequence is:$$\begin{array}{c}\hfill \mathbb{T}=\{lo{c}_{1}.duration,\cdots ,lo{c}_{\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|}.duration\}\end{array}$$
- If the given topological motif $\mathbb{S}$ contains more than K distinct locations (i.e., $\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|>K$), its corresponding temporal travel sequence is:$$\begin{array}{c}\hfill \mathbb{T}=\{lo{c}_{1}.duration,\cdots ,lo{c}_{K-1}.duration,\frac{lo{c}_{K}.duration+\cdots +lo{c}_{\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|}.duration}{\left|\right|uniq\left(\mathsf{\Lambda}\right(\mathbb{S}\left)\right)\left|\right|-K+1}\}\end{array}$$

#### 2.2.3. Semantic Travel Motif

- Regarding the first factor, landmarks are classified into L distinct categories, where landmarks in each category are semantically homogenous on the basis of common sense [31]. In this way, a semantic topological travel motif can be transformed into an L-type semantic-category sequence. For instance, “The Museum of Modern Art → Bryant Park → Times Square” is represented as “Cultural → Business → Natural”. In this sense, a semantic travel sequence $\mathbb{C}$ is formally defined as:$$\begin{array}{}\mathrm{(14)}& \mathbb{C}=\{{c}_{1},{c}_{2},\cdots ,{c}_{k}\}\hfill \mathrm{(15)}& =\{lo{c}_{1}.type,lo{c}_{2}.type,\cdots ,lo{c}_{k}.type\}\hfill \end{array}$$
- With regard to the alignment of different semantic types of landmarks, the semantic travel motif is generated by the relative value of each category of attractions. Here, we define the value as the average of subscripts of categories that are identical to the target category in a semantic travel sequence and denote it by the operator $rank(\ast )$ as:$$\begin{array}{c}\hfill rank\left(c\right)=\left\{\begin{array}{cc}\frac{\sum rank\left({c}_{i}\right)}{n},\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}{c}_{i}=lo{c}_{i}.type=c\phantom{\rule{4.pt}{0ex}}\mathrm{and}\phantom{\rule{4.pt}{0ex}}\mathrm{there}\phantom{\rule{4.pt}{0ex}}\mathrm{exists}\phantom{\rule{4.pt}{0ex}}n\phantom{\rule{4.pt}{0ex}}\mathrm{such}\phantom{\rule{4.pt}{0ex}}\mathrm{locations}\phantom{\rule{4.pt}{0ex}}\mathrm{in}\phantom{\rule{4.pt}{0ex}}\mathbb{C}\phantom{\rule{4.pt}{0ex}}\hfill \\ +\infty ,\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}\mathrm{there}\phantom{\rule{4.pt}{0ex}}\mathrm{exists}\phantom{\rule{4.pt}{0ex}}\mathrm{no}\phantom{\rule{4.pt}{0ex}}\mathrm{locations}\phantom{\rule{4.pt}{0ex}}\mathrm{associated}\phantom{\rule{4.pt}{0ex}}\mathrm{with}\phantom{\rule{4.pt}{0ex}}\mathrm{category}\phantom{\rule{4.pt}{0ex}}c\hfill \end{array}\right.\end{array}$$

#### 2.3. Motif-Based Clustering

## 3. Data and Results

#### 3.1. Yahoo Flickr Creative Commons 100M Dataset

#### 3.2. Typical Travel Motifs in Manhattan

#### 3.2.1. Characteristics of Topological Travel Motifs

#### 3.2.2. Characteristics of Temporal Travel Motifs

#### 3.2.3. Characteristics of Semantic Travel Motifs

#### 3.3. Tourists’ Distinct Travel Patterns

- Cluster 1 denotes tourists who relate to uncommon topological travel motifs, i.e., “cycle”, “downlinked mutual dyad”, “central linked dyad” and “uplinked dyad”. Take Tourist 781 and 291 as examples. Tourist 781 traveled to “Empire State Building → Ground Zero → Brooklyn Bridge → Empire State Building” in a “circle” pattern. Tourist 291 has two trips to New York. The first trip “Time Square → Greenwich Village → Time Square” pertains to the “mutual dyad” topological pattern, and the second trip “Grand Central Terminal → Time Square” is a “chain”. The same characteristic of their trips is that “mutual dyad” exists in both of them.
- Unlike Cluster 1, Cluster 2 represents tourists following the common “chain” topological travel motif. Furthermore, this group of tourists is more interested in “business” than “cultural” and has the least interest in “natural” attractions. For instance, Tourists 66 and 1201 visited “business → business → cultural → cultural” and “business → cultural”, respectively. They both take “Empire State Building” (a “business” attraction) as the first choice and “cultural” attractions as later targets. Besides, they did not travel to “natural” attractions.
- Tourists in Cluster 3 follow the “chain” topological pattern like those in Cluster 2, but they have a preference for “cultural” rather than “business”. For example, Tourists 1261 and 207 traveled to “Greenwich Village → Rockefeller Center” and “Time Square → Rockefeller Center → Central Park → Central Park Zoo”, respectively. They both firstly visited a “cultural” attraction followed by a “business” place (i.e., “Rockefeller Center”).

- Cluster 1 contains tourists who like to stay in the first and second place for a similar duration. Taking Tourists 835 and 469 as examples, following “chain” topological travel motifs with three and eight places respectively, they both spent little time in the first and second places and more time in their remaining visitations.
- Cluster 2 represents tourists who prefer to visit “natural” attractions firstly. Taking Tourists 563 and 0 as examples, Tourist 563 visited “Central Park → Brooklyn Bridge → Rockefeller Center”, and Tourist 0 visited “Central Park → Ground Zero → Greenwich Village → Empire State Building”. They both take the “natural” attraction “Central Park” as the first target.
- Cluster 3 stands for a group of tourists who are more interested in both “cultural” and “natural” than “business”. For example, Tourist 37 follows a semantic travel motif “cultural → cultural → natural → natural → cultural → natural → cultural”, without “business” attractions visited. Tourist 868 followed the semantic travel motif “natural → cultural → business → natural”, traveling to a “business” attraction after “cultural” and “natural” attractions.

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**One million photo sample of the 48 million geo-tagged photos from the Yahoo Flickr Creative Commons 100M (YFCC100m) dataset plotted around the globe (image source: The Competence Center Multimedia Analysis and Data Mining (MADM) at the DFKI).

**Figure 3.**Dendrogram of topological travel motifs based on (

**a**) discrete sequence and (

**b**) consecutive sequence. Note that N denotes the number of attractions within the identified motif; the value above each motif indicates the threshold ${\u03f5}_{m}\%$ for detecting it as a significant motif; and the “double-linked mutual dyad” motif is excluded for analysis in that its corresponding threshold is lower than $0.05$.

**Figure 4.**Statistical characteristics of temporal travel motifs based on (

**a**) discrete sequence and (

**b**) consecutive sequence.

**Figure 5.**Statistical characteristics of semantic travel motifs based on (

**a**) discrete sequence and (

**b**) consecutive sequence.

**Figure 6.**Travel behavior patterns of distinct tourist groups in terms of (

**a**) discrete sequence and (

**b**) consecutive sequence based motifs. Note that each value in the matrix indicates the similarity between a pair of users based on their topological, temporal and semantic travel motifs.

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**MDPI and ACS Style**

Yang, L.; Wu, L.; Liu, Y.; Kang, C.
Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr. *ISPRS Int. J. Geo-Inf.* **2017**, *6*, 345.
https://doi.org/10.3390/ijgi6110345

**AMA Style**

Yang L, Wu L, Liu Y, Kang C.
Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr. *ISPRS International Journal of Geo-Information*. 2017; 6(11):345.
https://doi.org/10.3390/ijgi6110345

**Chicago/Turabian Style**

Yang, Liu, Lun Wu, Yu Liu, and Chaogui Kang.
2017. "Quantifying Tourist Behavior Patterns by Travel Motifs and Geo-Tagged Photos from Flickr" *ISPRS International Journal of Geo-Information* 6, no. 11: 345.
https://doi.org/10.3390/ijgi6110345