A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data
Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and correspo...
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doaj-aef7ee86fc3e4d21970d66b30ccb3c9a2021-02-07T00:00:45ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-02-0110666610.3390/ijgi10020066A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial DataJiawei Zhu0Chao Tao1Xin Lin2Jian Peng3Haozhe Huang4Li Chen5Qiongjie Wang6School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, ChinaCentral and Southern China Municipal Engineering Design & Research Institute Co., Ltd, Wuhan 430010, ChinaSchool of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, ChinaChina Center for Information Industry Development, Beijing 100083, ChinaAnalyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model.https://www.mdpi.com/2220-9964/10/2/66urban functional regionsmultiple subspacesbig geospatial dataurban spatial structuresparse subspace clustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiawei Zhu Chao Tao Xin Lin Jian Peng Haozhe Huang Li Chen Qiongjie Wang |
spellingShingle |
Jiawei Zhu Chao Tao Xin Lin Jian Peng Haozhe Huang Li Chen Qiongjie Wang A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data ISPRS International Journal of Geo-Information urban functional regions multiple subspaces big geospatial data urban spatial structure sparse subspace clustering |
author_facet |
Jiawei Zhu Chao Tao Xin Lin Jian Peng Haozhe Huang Li Chen Qiongjie Wang |
author_sort |
Jiawei Zhu |
title |
A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data |
title_short |
A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data |
title_full |
A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data |
title_fullStr |
A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data |
title_full_unstemmed |
A Multiple Subspaces-Based Model: Interpreting Urban Functional Regions with Big Geospatial Data |
title_sort |
multiple subspaces-based model: interpreting urban functional regions with big geospatial data |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2021-02-01 |
description |
Analyzing the urban spatial structure of a city is a core topic within urban geographical information science that has the ability to assist urban planning, site selection, location recommendation, etc. Among previous studies, comprehending the functionality of places is a central topic and corresponds to understanding how people use places. With the help of big geospatial data which contain affluent information about human mobility and activity, we propose a novel multiple subspaces-based model to interpret the urban functional regions. This model is based on the assumption that the temporal activity patterns of places lie in a high-dimensional space and can be represented by a union of low-dimensional subspaces. These subspaces are obtained through finding sparse representations using the data science method known as sparse subspace clustering (SSC). The paper details how to use this method in the context of detecting functional regions. With these subspaces, we can detect the functionality of urban regions in a designated study area and further explore the characteristics of functional regions. We conducted experiments using real data in Shanghai. The experimental results and outperformance of our proposed model against the single subspace-based method prove the efficacy and feasibility of our model. |
topic |
urban functional regions multiple subspaces big geospatial data urban spatial structure sparse subspace clustering |
url |
https://www.mdpi.com/2220-9964/10/2/66 |
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