scholarly journals Urban function connectivity: Characterisation of functional urban streets with social media check-in data

Cities ◽  
2016 ◽  
Vol 55 ◽  
pp. 9-21 ◽  
Author(s):  
Yao Shen ◽  
Kayvan Karimi
Keyword(s):  
Social Media ◽  
Urban Streets ◽  
Urban Function ◽  
Function Connectivity

Urban Region Function Mining Service Based on Social Media Text Analysis

2021 ◽  
Vol 31 (04) ◽  
pp. 563-586
Author(s):  
Yanchun Sun ◽  
Hang Yin ◽  
Jiu Wen ◽  
Zhiyu Sun
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Human Activities ◽  
Web Application ◽  
Classification Model ◽  
Urban Region ◽  
Urban Regions ◽  
Social Media Text ◽  
Urban Function ◽  
High Level

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions’ functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region’s main functions, we design a service which extracts human activities using Sina Weibo ( www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region’s main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions’ function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method.

scholarly journals Urban Function as a New Perspective for Adaptive Street Quality Assessment

2020 ◽  
Vol 12 (4) ◽  
pp. 1296 ◽  
Author(s):  
Feng Hu ◽  
Wei Liu ◽  
Junyu Lu ◽  
Chengpeng Song ◽  
Yuan Meng ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Land Use Planning ◽  
Urban Land Use ◽  
Urban Streets ◽  
Detection Model ◽  
Urban Structures ◽  
Public Resources ◽  
Urban Function ◽  
New Perspective ◽  
Urban Functions

Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.

Urban function recognition by integrating social media and street-level imagery

2020 ◽  
pp. 239980832093546
Author(s):  
Chao Ye ◽  
Fan Zhang ◽  
Lan Mu ◽  
Yong Gao ◽  
Yu Liu
Keyword(s):  
Social Media ◽  
Physical Environment ◽  
Human Activities ◽  
Urban Structure ◽  
Street Level ◽  
Data Source ◽  
Urban Function ◽  
Single Data ◽  
Beijing China ◽  
Urban Functions

Recognizing urban functions is crucial for understanding urban spatial structures and urban planning. Previous work has investigated urban functions based on human activities that were derived from mobile phone positioning data, check-in data, taxi data, etc. However, urban functions can only be comprehensively sensed from both human activities and the physical environment together. To do so, a deep learning method was proposed to predict urban functions by integrating social media data and street-level imagery. The verbs extracted from social media posts were taken as the proxy for human activities, and we identified urban physical environmental information from street-level imagery. Then urban functions were uncovered from both the verbs in terms of human activities and street-level imagery from the perspective of the physical environment. Twelve types of urban function were recognized by verbs in social media posts, which were then improved by integrating street-level imagery within the 5th Ring Road of Beijing, China. The experiment demonstrated that verbs as direct proxies for human activities can avoid noise, and the multi-source data integration eliminated biases caused by a single data source. This work provides a comprehensive understanding of urban structure and dynamics for urban management and planning.

Where Social Media Meets AAC

ASHA Leader ◽  
2015 ◽  
Vol 20 (7) ◽  
Author(s):  
Vicki Clarke
Keyword(s):  
Social Media

Making Twitter Work For You

ASHA Leader ◽  
2013 ◽  
Vol 18 (5) ◽  
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Speech Language Pathologists

As professionals who recognize and value the power and important of communications, audiologists and speech-language pathologists are perfectly positioned to leverage social media for public relations.

Doctors employ social media

2013 ◽  
Vol 44 (1) ◽  
pp. 4
Author(s):  
Jane Anderson
Keyword(s):  
Social Media

A Lesson in Taking Social Media to the Medical Level

2011 ◽  
Vol 44 (7) ◽  
pp. 75
Author(s):  
SALLY KOCH KUBETIN
Keyword(s):  
Social Media
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