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Smart city article details
| Title | Leaf: A Lifestyle Approximation Framework Based On Analysis Of Mobile Network Data In Smart Cities |
|---|---|
| ID_Doc | 34846 |
| Authors | Moghari S.; Fallah M.K.; Gorgin S.; Shin S. |
| Year | 2024 |
| Published | Smart Cities, 7, 6 |
| DOI | http://dx.doi.org/10.3390/smartcities7060128 |
| Abstract | Highlights: What are the main findings? The proposed framework offers a robust and accurate method for modeling and understanding urban residents’ lifestyles by analyzing anonymized mobile network data. We have defined a set of analytical patterns designed to extract key insights and valuable knowledge from the input data. By fusing this information with ontological data and geographical maps, our approach uncovers significant and straightforward perspectives that enhance understanding of urban lifestyles. What is the implication of the main finding? The framework allows for identifying lifestyle patterns and epidemic behaviors enabling more informed decision-making and strategic planning, ensuring that services and infrastructure can be tailored to meet the specific needs of different urban areas. This approach provides valuable insights into daily routines, preferences, and behaviors, which can be crucial for urban planners, policymakers, and businesses. The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of (Formula presented.) between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics. © 2024 by the authors. |
| Author Keywords | lifestyle approximation; mobility data analysis; smart city; vector space model |
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