Spatiotemporal Patterns of the Use of Urban Green Spaces and External Factors Contributing to Their  Use in Central Beijing

Li, Fangzheng; Zhang, Fen; Li, Xiong; Wang, Peng; Liang, Junhui; Mei, Yuting; Cheng, Wenwen; Qian, Yun

doi:10.3390/ijerph14030237

Cited by 53 publications

(22 citation statements)

References 42 publications

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“…Green spaces provide city residents with access to nature, thus improving their physical and mental well-being. They promote the development of recreational activities, filter out harmful pollution, offer a refuge from noise and hot weather and enhance the aesthetic appeal of cities (Li et al, 2017;Sun, Chen, 2017). Green spaces are functional components of urban ecosystems, and they feature a combination of carefully selected plant species.…”

Section: Introductionmentioning

confidence: 99%

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

Senetra

Krzywnicka²,

Mielke³

2018

Bulletin of Geography. Socio-Economic Series

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Rapid urban growth can exert negative effects on the natural environment due to the loss of naturally vegetated areas, loss of biological diversity, deforestation and soil erosion. The condition of cities is inherently linked with the natural environment which has a positive influence on health, social relations, human welfare and economic activity. Urban areas should abound in green spaces, and should also be easily accessible to the general public. The aim of this study was to determine the spatial distribution, influence and quality of urban green spaces on the example of the city of Tczew in northern Poland. The proposed methodology can be applied in cities of a similar size and urban structure to promote rational management of urban green space in line with the principles of sustainable development and spatial order. The Green-Space Record, a useful tool for inventorying urban green spaces, was developed to pursue the main research goal. The information accumulated in the Record constitutes valuable input data for further analysis, including the determination of the area, distribution, influence and quality of urban green spaces. The results of the analysis revealed that urban green spaces occupy more than 19% of Tczew’s territory, which is equivalent to 70.6 m2 per resident. Managed green spaces span the area of only 66.75 ha (11.31 m2 per resident) and are unevenly distributed in the city. More than half of these areas are found in the Stare Miasto (Old Town) district, whereas two residential districts (Gdańska, Prątnica) are completely devoid of public greens. The quality of urban green spaces is generally satisfactory in Tczew; however, not all residents have equal access to high-quality public greens.

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Section: Introductionmentioning

confidence: 99%

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

Senetra

Krzywnicka²,

Mielke³

2018

Bulletin of Geography. Socio-Economic Series

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“…KDE could be used to fulfill the frequent need to analyze health data from fields of different sizes and populations [29], [31]. Authors in [32] used social media data and KDE to examine the spatial and temporal patterns of use of urban green parks and the external factors that contribute to their use. The density of food shops was measured differently in order to assess the availability or accessibility of food stores or fast food outlets [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

et al. 2020

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Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010-2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the wellbeing of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens. INDEX TERMS Urban green parks, big data, social networks, spatiotemporal, KDE, data mining.

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“…The "heat" of the map represents the density (humans per area) per cell of the map, and the density is presented as different colors and continuously updated every 15 min. Early applications of these data for spatially explicit urban activity have used mobile phone data to examine how people move within and between cities [21][22][23][24][25], while others utilized user-generated geographic information to calculate core density and analyze the distribution and supply of infrastructure [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

Zhang

Wang

et al. 2020

Sustainability

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Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.

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Spatiotemporal Patterns of the Use of Urban Green Spaces and External Factors Contributing to Their Use in Central Beijing

Cited by 53 publications

References 42 publications

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

An analysis of the spatial distribution, influence and quality of urban green space – a case study of the Polish city of Tczew

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

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