Sensing multiple semantics of urban space from crowdsourcing positioning data
Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urba...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Ling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field - Gorfer, Markus ELSEVIER, 2022, the international journal of urban policy and planning, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:93 ; year:2019 ; pages:31-42 ; extent:12 |
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| DOI / URN: |
10.1016/j.cities.2019.04.011 |
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| 520 | |a Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. | ||
| 520 | |a Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. | ||
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| 700 | 1 | |a Liu, Ju |4 oth | |
| 700 | 1 | |a Ma, Ting |4 oth | |
| 700 | 1 | |a Pei, Tao |4 oth | |
| 700 | 1 | |a Zhou, Chenghu |4 oth | |
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10.1016/j.cities.2019.04.011 doi GBV00000000000744.pica (DE-627)ELV047874686 (ELSEVIER)S0264-2751(18)31808-0 DE-627 ger DE-627 rakwb eng 630 640 VZ BIODIV DE-30 fid 42.90 bkl 48.32 bkl Cai, Ling verfasserin aut Sensing multiple semantics of urban space from crowdsourcing positioning data 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Xu, Jun oth Liu, Ju oth Ma, Ting oth Pei, Tao oth Zhou, Chenghu oth Enthalten in Elsevier Science Gorfer, Markus ELSEVIER The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field 2022 the international journal of urban policy and planning Amsterdam [u.a.] (DE-627)ELV007911351 volume:93 year:2019 pages:31-42 extent:12 https://doi.org/10.1016/j.cities.2019.04.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OPC-GGO SSG-OPC-FOR 42.90 Ökologie: Allgemeines VZ 48.32 Bodenkunde Bodenbewertung Land- und Forstwirtschaft VZ AR 93 2019 31-42 12 |
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10.1016/j.cities.2019.04.011 doi GBV00000000000744.pica (DE-627)ELV047874686 (ELSEVIER)S0264-2751(18)31808-0 DE-627 ger DE-627 rakwb eng 630 640 VZ BIODIV DE-30 fid 42.90 bkl 48.32 bkl Cai, Ling verfasserin aut Sensing multiple semantics of urban space from crowdsourcing positioning data 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Xu, Jun oth Liu, Ju oth Ma, Ting oth Pei, Tao oth Zhou, Chenghu oth Enthalten in Elsevier Science Gorfer, Markus ELSEVIER The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field 2022 the international journal of urban policy and planning Amsterdam [u.a.] (DE-627)ELV007911351 volume:93 year:2019 pages:31-42 extent:12 https://doi.org/10.1016/j.cities.2019.04.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OPC-GGO SSG-OPC-FOR 42.90 Ökologie: Allgemeines VZ 48.32 Bodenkunde Bodenbewertung Land- und Forstwirtschaft VZ AR 93 2019 31-42 12 |
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10.1016/j.cities.2019.04.011 doi GBV00000000000744.pica (DE-627)ELV047874686 (ELSEVIER)S0264-2751(18)31808-0 DE-627 ger DE-627 rakwb eng 630 640 VZ BIODIV DE-30 fid 42.90 bkl 48.32 bkl Cai, Ling verfasserin aut Sensing multiple semantics of urban space from crowdsourcing positioning data 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Xu, Jun oth Liu, Ju oth Ma, Ting oth Pei, Tao oth Zhou, Chenghu oth Enthalten in Elsevier Science Gorfer, Markus ELSEVIER The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field 2022 the international journal of urban policy and planning Amsterdam [u.a.] (DE-627)ELV007911351 volume:93 year:2019 pages:31-42 extent:12 https://doi.org/10.1016/j.cities.2019.04.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OPC-GGO SSG-OPC-FOR 42.90 Ökologie: Allgemeines VZ 48.32 Bodenkunde Bodenbewertung Land- und Forstwirtschaft VZ AR 93 2019 31-42 12 |
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10.1016/j.cities.2019.04.011 doi GBV00000000000744.pica (DE-627)ELV047874686 (ELSEVIER)S0264-2751(18)31808-0 DE-627 ger DE-627 rakwb eng 630 640 VZ BIODIV DE-30 fid 42.90 bkl 48.32 bkl Cai, Ling verfasserin aut Sensing multiple semantics of urban space from crowdsourcing positioning data 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Xu, Jun oth Liu, Ju oth Ma, Ting oth Pei, Tao oth Zhou, Chenghu oth Enthalten in Elsevier Science Gorfer, Markus ELSEVIER The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field 2022 the international journal of urban policy and planning Amsterdam [u.a.] (DE-627)ELV007911351 volume:93 year:2019 pages:31-42 extent:12 https://doi.org/10.1016/j.cities.2019.04.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OPC-GGO SSG-OPC-FOR 42.90 Ökologie: Allgemeines VZ 48.32 Bodenkunde Bodenbewertung Land- und Forstwirtschaft VZ AR 93 2019 31-42 12 |
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10.1016/j.cities.2019.04.011 doi GBV00000000000744.pica (DE-627)ELV047874686 (ELSEVIER)S0264-2751(18)31808-0 DE-627 ger DE-627 rakwb eng 630 640 VZ BIODIV DE-30 fid 42.90 bkl 48.32 bkl Cai, Ling verfasserin aut Sensing multiple semantics of urban space from crowdsourcing positioning data 2019transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. Xu, Jun oth Liu, Ju oth Ma, Ting oth Pei, Tao oth Zhou, Chenghu oth Enthalten in Elsevier Science Gorfer, Markus ELSEVIER The effect of environmental parameters and fertilization practices on yield and soil microbial diversity in a Kenyan paddy rice field 2022 the international journal of urban policy and planning Amsterdam [u.a.] (DE-627)ELV007911351 volume:93 year:2019 pages:31-42 extent:12 https://doi.org/10.1016/j.cities.2019.04.011 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OPC-GGO SSG-OPC-FOR 42.90 Ökologie: Allgemeines VZ 48.32 Bodenkunde Bodenbewertung Land- und Forstwirtschaft VZ AR 93 2019 31-42 12 |
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Sensing multiple semantics of urban space from crowdsourcing positioning data |
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Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. |
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Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. |
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Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. |
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