LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities
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 th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/7/6/128 |
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| author | Somaye Moghari Mohammad K. Fallah Saeid Gorgin Seokjoo Shin |
| author_facet | Somaye Moghari Mohammad K. Fallah Saeid Gorgin Seokjoo Shin |
| author_sort | Somaye Moghari |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.167</mn></mrow></semantics></math></inline-formula> 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. |
| format | Article |
| id | doaj-art-014b1946c30e47cfacb15fb778a278aa |
| institution | Kabale University |
| issn | 2624-6511 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-014b1946c30e47cfacb15fb778a278aa2024-12-27T14:53:46ZengMDPI AGSmart Cities2624-65112024-11-01763315333310.3390/smartcities7060128LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart CitiesSomaye Moghari0Mohammad K. Fallah1Saeid Gorgin2Seokjoo Shin3Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood 3619995161, IranDepartment of Computer Engineering, Chosun University, Gwangju 61453, Republic of KoreaDepartment of Computer Engineering, Chosun University, Gwangju 61453, Republic of KoreaDepartment of Computer Engineering, Chosun University, Gwangju 61453, Republic of KoreaThe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.167</mn></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2624-6511/7/6/128smart citylifestyle approximationmobility data analysisvector space model |
| spellingShingle | Somaye Moghari Mohammad K. Fallah Saeid Gorgin Seokjoo Shin LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities Smart Cities smart city lifestyle approximation mobility data analysis vector space model |
| title | LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities |
| title_full | LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities |
| title_fullStr | LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities |
| title_full_unstemmed | LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities |
| title_short | LEAF: A Lifestyle Approximation Framework Based on Analysis of Mobile Network Data in Smart Cities |
| title_sort | leaf a lifestyle approximation framework based on analysis of mobile network data in smart cities |
| topic | smart city lifestyle approximation mobility data analysis vector space model |
| url | https://www.mdpi.com/2624-6511/7/6/128 |
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