Data-driven behavioral analysis and applications: A case study in Changchun, China
The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xianghua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study - Dai, Jiamiao ELSEVIER, 2022, europhysics journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:596 ; year:2022 ; day:15 ; month:06 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.physa.2022.127164 |
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| 520 | |a The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. | ||
| 520 | |a The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. | ||
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10.1016/j.physa.2022.127164 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001729.pica (DE-627)ELV057341931 (ELSEVIER)S0378-4371(22)00173-X DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Li, Xianghua verfasserin aut Data-driven behavioral analysis and applications: A case study in Changchun, China 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. Mobile data Elsevier Functional area identification Elsevier Student behaviors Elsevier Deng, Yue oth Yuan, Xuesong oth Wang, Zhen oth Gao, Chao oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:596 year:2022 day:15 month:06 pages:0 https://doi.org/10.1016/j.physa.2022.127164 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 596 2022 15 0615 0 |
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10.1016/j.physa.2022.127164 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001729.pica (DE-627)ELV057341931 (ELSEVIER)S0378-4371(22)00173-X DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Li, Xianghua verfasserin aut Data-driven behavioral analysis and applications: A case study in Changchun, China 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. Mobile data Elsevier Functional area identification Elsevier Student behaviors Elsevier Deng, Yue oth Yuan, Xuesong oth Wang, Zhen oth Gao, Chao oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:596 year:2022 day:15 month:06 pages:0 https://doi.org/10.1016/j.physa.2022.127164 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 596 2022 15 0615 0 |
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10.1016/j.physa.2022.127164 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001729.pica (DE-627)ELV057341931 (ELSEVIER)S0378-4371(22)00173-X DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Li, Xianghua verfasserin aut Data-driven behavioral analysis and applications: A case study in Changchun, China 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. Mobile data Elsevier Functional area identification Elsevier Student behaviors Elsevier Deng, Yue oth Yuan, Xuesong oth Wang, Zhen oth Gao, Chao oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:596 year:2022 day:15 month:06 pages:0 https://doi.org/10.1016/j.physa.2022.127164 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 596 2022 15 0615 0 |
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10.1016/j.physa.2022.127164 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001729.pica (DE-627)ELV057341931 (ELSEVIER)S0378-4371(22)00173-X DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Li, Xianghua verfasserin aut Data-driven behavioral analysis and applications: A case study in Changchun, China 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. Mobile data Elsevier Functional area identification Elsevier Student behaviors Elsevier Deng, Yue oth Yuan, Xuesong oth Wang, Zhen oth Gao, Chao oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:596 year:2022 day:15 month:06 pages:0 https://doi.org/10.1016/j.physa.2022.127164 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 596 2022 15 0615 0 |
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10.1016/j.physa.2022.127164 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001729.pica (DE-627)ELV057341931 (ELSEVIER)S0378-4371(22)00173-X DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Li, Xianghua verfasserin aut Data-driven behavioral analysis and applications: A case study in Changchun, China 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. Mobile data Elsevier Functional area identification Elsevier Student behaviors Elsevier Deng, Yue oth Yuan, Xuesong oth Wang, Zhen oth Gao, Chao oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:596 year:2022 day:15 month:06 pages:0 https://doi.org/10.1016/j.physa.2022.127164 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 596 2022 15 0615 0 |
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The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. |
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The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. |
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The mobile phone data have become crucial in behavioral analysis to detect habits of human mobility and reveal rules of behaviors. Previously, questionnaires were often used to identify urban functional areas, with vast labor and poor timeliness. To address the issue, this paper applies data-driven behavioral analysis to identify functional areas for governments to construct urban design, offer site selection and manage transportation. Moreover, data-driven behavioral analysis can also be applied in student behaviors to help schools adjust facility arrangements, develop learning efficiency and provide high-quality services. Therefore, based on mobile phone data in Changchun, this paper utilizes a two-stage clustering method combining human mobility to identify urban functional areas, including business, working, residential and low passenger-flow areas. The interesting finding is that local prosperity in Changchun is prominent and the proportion of low passenger-flow areas can reflect the development level. Furthermore, this paper compares student behaviors in three schools, which shows each school varies in distribution features of students. Experiments provide enlightening insights to reveal the spatial structure of cities and comprehend the living state of students. |
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