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Li Hou
School of Information Engineering, Huangshan University, Huangshan 245041, China

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Journal article
Published: 22 June 2020 in Electronics
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Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

ACS Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics 2020, 9, 1028 .

AMA Style

Qi Liu, Hidayat Ullah, Wanggen Wan, Zhangyou Peng, Li Hou, Sanam Shahla Rizvi, Saqib Ali Haidery, Tong Qu, A. A. M. Muzahid. Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data. Electronics. 2020; 9 (6):1028.

Chicago/Turabian Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Sanam Shahla Rizvi; Saqib Ali Haidery; Tong Qu; A. A. M. Muzahid. 2020. "Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data." Electronics 9, no. 6: 1028.

Journal article
Published: 01 June 2020 in ISPRS International Journal of Geo-Information
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Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.

ACS Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Tong Qu; Saqib Ali Haidery. Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai. ISPRS International Journal of Geo-Information 2020, 9, 360 .

AMA Style

Qi Liu, Hidayat Ullah, Wanggen Wan, Zhangyou Peng, Li Hou, Tong Qu, Saqib Ali Haidery. Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai. ISPRS International Journal of Geo-Information. 2020; 9 (6):360.

Chicago/Turabian Style

Qi Liu; Hidayat Ullah; Wanggen Wan; Zhangyou Peng; Li Hou; Tong Qu; Saqib Ali Haidery. 2020. "Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai." ISPRS International Journal of Geo-Information 9, no. 6: 360.

Journal article
Published: 21 February 2020 in ISPRS International Journal of Geo-Information
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Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

ACS Style

Zeinab Ebrahimpour; Wanggen Wan; José Luis Velázquez García; Ofelia Cervantes; Li Hou. Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data. ISPRS International Journal of Geo-Information 2020, 9, 125 .

AMA Style

Zeinab Ebrahimpour, Wanggen Wan, José Luis Velázquez García, Ofelia Cervantes, Li Hou. Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data. ISPRS International Journal of Geo-Information. 2020; 9 (2):125.

Chicago/Turabian Style

Zeinab Ebrahimpour; Wanggen Wan; José Luis Velázquez García; Ofelia Cervantes; Li Hou. 2020. "Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data." ISPRS International Journal of Geo-Information 9, no. 2: 125.

Review
Published: 05 June 2017 in EURASIP Journal on Advances in Signal Processing
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In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

ACS Style

Li Hou; Wanggen Wan; Jenq-Neng Hwang; Muhammad Rizwan; Mingyang Yang; Kang Han. Human tracking over camera networks: a review. EURASIP Journal on Advances in Signal Processing 2017, 2017, 43 .

AMA Style

Li Hou, Wanggen Wan, Jenq-Neng Hwang, Muhammad Rizwan, Mingyang Yang, Kang Han. Human tracking over camera networks: a review. EURASIP Journal on Advances in Signal Processing. 2017; 2017 (1):43.

Chicago/Turabian Style

Li Hou; Wanggen Wan; Jenq-Neng Hwang; Muhammad Rizwan; Mingyang Yang; Kang Han. 2017. "Human tracking over camera networks: a review." EURASIP Journal on Advances in Signal Processing 2017, no. 1: 43.