Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business

Kim, Jonghyuk; Hwangbo, Hyunwoo; Kim, Sung Jun; Kim, Soyean

doi:10.3390/su11226209

Cited by 5 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 11 illustrates the ISTSM matrix of the six indoor semantic trajectories and the dendrogram resulting from the agglomerative hierarchical clustering with the ISTSM. In Figure 11, the cell value of the grid is the ISTSM value, and the numbers (3,6,1,2,4,5) on the left and top of the grid are the IDs of the six trajectories. The dendrogram is on top of the grid.…”

Section: Trajectory Comparisonmentioning

confidence: 99%

“…Indoor positioning devices, such as WiFi, Bluetooth, and RFID (radio frequency identification) devices, generate an extensive number of indoor trajectories for objects moving indoors. With indoor trajectories, insightful indoor movement patterns reflecting complex human spatial behavior can be discovered by adopting various methods, such as clustering analysis [1][2][3][4]. These methods are frequently based on an indoor trajectory similarity that measures the similarity degree of two indoor trajectories.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

Zhu

Cheng

Zhang

et al. 2021

IJGI

View full text Add to dashboard Cite

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

show abstract

Section: Trajectory Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

Zhu

Cheng

Zhang

et al. 2021

IJGI

View full text Add to dashboard Cite

show abstract

“…Yang et al [51] combined questionnaire data about customers' purchasing behavior and their movement trajectories tracked by the UWB indoor positioning system, and found that the spatial layout of the supermarket significantly affect people's impulse purchasing behavior. Kim et al [52] conducted three field experiments with different visual merchandising displays in shopping mall to compare customer movement patterns based on location-based tracking data. Their results confirmed that effective store rearrangement could change customer movement patterns and improve the overall sales of store zones.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Finally, the influencing factors were quantified via a linear multivariable regression model, where customer flow density on the weekend was the dependent variable and the independent variables included locational and social factors. To determine the factors that may influence customer flows to stores, we needed to choose the influencing factor first, which was based on previous research [3,4,13,14,18,50,52] and online comment information (dianping.com). Here, we selected 9 factors, among which the first eight were locational variables and the last one was a social one.…”

Section: Analysis Proceduresmentioning

confidence: 99%

What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data

Pei

Liu

Shu

et al. 2020

IJGI

View full text Add to dashboard Cite

Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations.

show abstract

“…The fifth paper, with the title "Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business" [5], provides results that might be useful for managers trying to improve the sustainability of their businesses. It describes three field experiments with different visual merchandising displays in stores in a shopping mall in South Korea to analyse customer movement patterns based on indoor location-based tracking data.…”

mentioning

confidence: 99%