Analyze the usage of urban greenways through social media images and computer vision

2022 ◽  
pp. 239980832110646
Author(s):  
Yang Song ◽  
Huan Ning ◽  
Xinyue Ye ◽  
Divya Chandana ◽  
Shaohua Wang
Keyword(s):  
New York ◽  
Computer Vision ◽  
Social Media ◽  
Urban Landscape ◽  
Health Economy ◽  
User Engagement ◽  
Media Images ◽  
Detection Analysis ◽  
High Line ◽  
Urban Greenway

Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV) technology to analyze and compare how the general public uses two typical greenway parks, The High Line in New York City and the Atlanta Beltline in Atlanta. Face and object detection analysis were conducted to infer user composition, activities, and key experiences. We presented the temporal patterns of Instagram postings as well as the group gatherings, smiling, and representative objects detected from photos. Our results have shown high user engagement levels for both parks while teens are significantly underrepresented. The High Line had more group activities and was more active during weekdays than the Atlanta Beltline. Stronger sense of escape and physical activities can be found in Atlanta Beltline. In summary, social media images like Instagram can provide strong empirical evidence for urban greenway usage when combined with artificial intelligence technologies, which can support the future practice of landscape architecture and urban design.

scholarly journals Tweeting the High Line Life: A Social Media Lens on Urban Green Spaces

2020 ◽  
Vol 12 (21) ◽  
pp. 8895
Author(s):  
Jisoo Sim ◽  
Patrick Miller ◽  
Samarth Swarup
Keyword(s):  
New York ◽  
Social Media ◽  
Housing Prices ◽  
Green Spaces ◽  
Social Media Analytics ◽  
Urban Green ◽  
Urban Green Spaces ◽  
High Line ◽  
Study Sites ◽  
Media Data

The objective of this study is to investigate elevated parks as urban green spaces using social media data analytics. Two popular elevated parks, the High Line Park in New York and the 606 in Chicago, were selected as the study sites. Tweets mentioning the two parks were collected from 2015 to 2019. By using text mining, social media users’ sentiments and conveyed perceptions about the elevated parks were studied. In addition, users’ activities and their satisfaction were analyzed. For the 606, users mainly enjoyed the free events at the park and worried about possible increases in housing prices and taxes because of the 606. They tended to participate in physical activities such as biking and walking. Although the 606 provides scenic observation points, users did not seem to enjoy these. Regarding the High Line, users frequently mentioned New York City, which is an important aspect of the identity of the park. The High Line users also frequently mentioned arts and relaxation. Overall, this study supports the idea that social media analytics can be used to gain an understanding of the public’s use of urban green spaces and their attitudes and concerns.

scholarly journals Social media as a tool for developing learning spaces of city universities around the world

2021 ◽  
Vol 98 ◽  
pp. 05014
Author(s):  
Elena Petryaeva ◽  
Daria Milyaeva ◽  
Deirdre Wynter ◽  
Natalia Ageeva
Keyword(s):  
New York ◽  
Social Media ◽  
Teaching And Learning ◽  
Evidence Base ◽  
High Potential ◽  
User Engagement ◽  
Learning Spaces ◽  
The World ◽  
The University ◽  
The City

In the context of the digital transformation of education, this study focuses on the analysis of social media as an efficient tool for developing learning spaces of universities. The goal of this study is to explore the use of Instagram by city universities, highlight the existing trends, and determine best practices and high-potential directions of online development for universities. The evidence base of this study included the Instagram accounts of six city universities of the world, specifically: the Moscow City University, the Dublin City University, the University of Taipei, the City, University of London, the City University of New York, and the City University of Hong Kong. The research was conducted using the method of content analysis with the use of Google Data Studio services. The analysis uncovered the following topic-based groups of content featured in the Instagram accounts of city universities: Personalities, University, Applicants, Learning and career, Science and technologies, City, Society and politics, Art, and Atmosphere. Subsequently, four high-potential directions of online development were identified for universities: first, more active user engagement and support; second, development of new forms of teaching and learning activities; third, popularisation of research; fourth, branding the university as a partner of the city. The novel contribution of this paper consists in engaging modern analytical tools to visualize a university profile from its published online content. The findings can be used by universities as recommendations on developing and adjusting their content strategies to adapt to the ever-changing realities and ensure effective online promotion and realization of their teaching potential.

Exploring public space through social media: an exploratory case study on the High Line New York City

2017 ◽  
Vol 23 (2) ◽  
pp. 69-85 ◽  
Author(s):  
Hyung Jin Kim ◽  
Bongsug Kevin Chae ◽  
Seunghyun Brian Park
Keyword(s):  
New York ◽  
Social Media ◽  
New York City ◽  
Public Space ◽  
York City ◽  
Exploratory Case Study ◽  
High Line

scholarly journals Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning

2021 ◽  
Vol 10 (11) ◽  
pp. 734
Author(s):  
Ling Zhao ◽  
Li Luo ◽  
Bo Li ◽  
Liyan Xu ◽  
Jiawei Zhu ◽  
...  
Keyword(s):  
Social Media ◽  
Urban Landscape ◽  
Media Images ◽  
Feature Maps ◽  
Landscape Features ◽  
Fine Grained ◽  
Cultural Character ◽  
The One ◽  
The City ◽  
Urban Characteristics

The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.

scholarly journals Real-time spatio-temporal event detection on geotagged social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yasmeen George ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Kwan Hui Lim
Keyword(s):  
New York ◽  
Social Media ◽  
Event Detection ◽  
Detection System ◽  
Time And Space ◽  
Social Media Data ◽  
Event Time ◽  
Spatio Temporal ◽  
Geographical Space ◽  
Media Data

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

Infobotting COVID-19: A case study of Ask Nameesa in Egypt

2021 ◽  
Vol 13 (3) ◽  
pp. 451-476 ◽  
Author(s):  
Mona Khattab
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Information Literacy ◽  
The Internet ◽  
User Engagement ◽  
Interactive Engagement ◽  
Award Winning ◽  
Credible Information ◽  
Public Demand

With the outbreak of the COVID-19 pandemic, misinformation and unscientific interpretations flooded the internet. Seeking credible information in Egypt was paramount at the time. An answer to this quest was ‘Ask Nameesa’, an award-winning Egyptian-focused chatbot that utilizes Facebook Messenger to communicate with social media users in an individualized response engagement. It relies on information validated by WHO and the Egyptian Ministry of Health. This article examines the structure of Ask Nameesa as an example of infobots and studies the interactive engagement it offers users to provide health information. The study analyses data gathered by interviewing the founder and CEO of DXwand, the company that developed Ask Nameesa as well as content analysis of conversations with Ask Nameesa to assess its user engagement. The study aims at understanding the potential Ask Nameesa has in providing information literacy and tackling public demand for information.

Formation Process of the Proposed Renewal Plan Utilizing Historic Viaduct in the High Line, New York

2010 ◽  
Vol 45 (0) ◽  
pp. 34-34
Author(s):  
Yusuke Kimura ◽  
Keita Yamaguchi ◽  
Yoshiaki Kubota ◽  
Masashi Kawasaki
Keyword(s):  
New York ◽  
Formation Process ◽  
High Line
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