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利用地理标签数据感知城市活力

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利用地理标签数据感知城市活力. / Zhu, Tingting; Tu, Wei; Yue, Yang; Zhong, Chen; Zhao, Tianhong; Li, Qiuping; Li, Qingquan.

In: Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, Vol. 49, No. 3, 01.03.2020, p. 365-374.

Research output: Contribution to journalArticlepeer-review

Harvard

Zhu, T, Tu, W, Yue, Y, Zhong, C, Zhao, T, Li, Q & Li, Q 2020, '利用地理标签数据感知城市活力', Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, vol. 49, no. 3, pp. 365-374. https://doi.org/10.11947/j.AGCS.2020.20190051

APA

Zhu, T., Tu, W., Yue, Y., Zhong, C., Zhao, T., Li, Q., & Li, Q. (2020). 利用地理标签数据感知城市活力. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica, 49(3), 365-374. https://doi.org/10.11947/j.AGCS.2020.20190051

Vancouver

Zhu T, Tu W, Yue Y, Zhong C, Zhao T, Li Q et al. 利用地理标签数据感知城市活力. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica. 2020 Mar 1;49(3):365-374. https://doi.org/10.11947/j.AGCS.2020.20190051

Author

Zhu, Tingting ; Tu, Wei ; Yue, Yang ; Zhong, Chen ; Zhao, Tianhong ; Li, Qiuping ; Li, Qingquan. / 利用地理标签数据感知城市活力. In: Cehui Xuebao/Acta Geodaetica et Cartographica Sinica. 2020 ; Vol. 49, No. 3. pp. 365-374.

Bibtex Download

@article{7236e5faa01343ad9dcc6c7892e7a059,
title = "利用地理标签数据感知城市活力",
abstract = "Promoting neighborhood vibrancy is vital for urban development. Recently, geotagged data provide unprecedented opportunities for discovering urban vibrancy patterns and their affecting mechanism. However, traditional urban vibrancy studies rely on fields survey therefore are time-consuming and highly-cost. This study constructs two urban vibrancy indices using point-of-interest and social media check in data. The spatial patterns of urban vibrancy are explored with spatial auto-regression analytic. Ordinary regression models (OLS) and spatial autoregression models (SAM) are established for revealing the influences of built environment on urban vibrancy by using geospatial data such as land use, roads and buildings. An empirical study in Shenzhen was implemented. The results show that: commercial land, industry land, mixed land use, the road density, and metro stations are five main factors highly influencing Shenzhen vibrancy. Residential land use and building footprints only have significant effects on vibrancy exhibited by POI. These exploratory findings demonstrate that urban vibrancy should be assessed and improved for different consideration.",
keywords = "Geotagged check in data, POI, Spatial auto-regression, Urban vibrancy",
author = "Tingting Zhu and Wei Tu and Yang Yue and Chen Zhong and Tianhong Zhao and Qiuping Li and Qingquan Li",
year = "2020",
month = mar,
day = "1",
doi = "10.11947/j.AGCS.2020.20190051",
language = "Chinese",
volume = "49",
pages = "365--374",
journal = "Cehui Xuebao/Acta Geodaetica et Cartographica Sinica",
issn = "1001-1595",
publisher = "Editorial Department of Acta Geodaetica et Cartographica Sinica",
number = "3",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - 利用地理标签数据感知城市活力

AU - Zhu, Tingting

AU - Tu, Wei

AU - Yue, Yang

AU - Zhong, Chen

AU - Zhao, Tianhong

AU - Li, Qiuping

AU - Li, Qingquan

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Promoting neighborhood vibrancy is vital for urban development. Recently, geotagged data provide unprecedented opportunities for discovering urban vibrancy patterns and their affecting mechanism. However, traditional urban vibrancy studies rely on fields survey therefore are time-consuming and highly-cost. This study constructs two urban vibrancy indices using point-of-interest and social media check in data. The spatial patterns of urban vibrancy are explored with spatial auto-regression analytic. Ordinary regression models (OLS) and spatial autoregression models (SAM) are established for revealing the influences of built environment on urban vibrancy by using geospatial data such as land use, roads and buildings. An empirical study in Shenzhen was implemented. The results show that: commercial land, industry land, mixed land use, the road density, and metro stations are five main factors highly influencing Shenzhen vibrancy. Residential land use and building footprints only have significant effects on vibrancy exhibited by POI. These exploratory findings demonstrate that urban vibrancy should be assessed and improved for different consideration.

AB - Promoting neighborhood vibrancy is vital for urban development. Recently, geotagged data provide unprecedented opportunities for discovering urban vibrancy patterns and their affecting mechanism. However, traditional urban vibrancy studies rely on fields survey therefore are time-consuming and highly-cost. This study constructs two urban vibrancy indices using point-of-interest and social media check in data. The spatial patterns of urban vibrancy are explored with spatial auto-regression analytic. Ordinary regression models (OLS) and spatial autoregression models (SAM) are established for revealing the influences of built environment on urban vibrancy by using geospatial data such as land use, roads and buildings. An empirical study in Shenzhen was implemented. The results show that: commercial land, industry land, mixed land use, the road density, and metro stations are five main factors highly influencing Shenzhen vibrancy. Residential land use and building footprints only have significant effects on vibrancy exhibited by POI. These exploratory findings demonstrate that urban vibrancy should be assessed and improved for different consideration.

KW - Geotagged check in data

KW - POI

KW - Spatial auto-regression

KW - Urban vibrancy

UR - http://www.scopus.com/inward/record.url?scp=85082830011&partnerID=8YFLogxK

U2 - 10.11947/j.AGCS.2020.20190051

DO - 10.11947/j.AGCS.2020.20190051

M3 - Article

AN - SCOPUS:85082830011

VL - 49

SP - 365

EP - 374

JO - Cehui Xuebao/Acta Geodaetica et Cartographica Sinica

JF - Cehui Xuebao/Acta Geodaetica et Cartographica Sinica

SN - 1001-1595

IS - 3

ER -

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