scholarly journals Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

2021 ◽  
Vol 10 (12) ◽  
pp. 834
Author(s):  
Feng Gao ◽  
Guanping Huang ◽  
Shaoying Li ◽  
Ziwei Huang ◽  
Lei Chai
Keyword(s):  
Social Media ◽  
Spatial Structure ◽  
Human Activities ◽  
Temporal Structure ◽  
Activity Patterns ◽  
Urban Spatial Structure ◽  
Social Media Data ◽  
Function Identification ◽  
Effective Principal ◽  
Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.

Variations in the Temporal Structure of Sociability across American Cities

Sociology ◽  
2020 ◽  
pp. 003803852095694
Author(s):  
Dhiraj Murthy ◽  
John D O’Brien ◽  
Alexander Gross ◽  
Nathan Meyers
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Temporal Structure ◽  
Geographic Proximity ◽  
Social Media Data ◽  
Highly Educated ◽  
Per Capita ◽  
Urban Contexts ◽  
American Cities ◽  
Media Data

Though sociologists have been interested in how temporal patterns of sociability vary in urban contexts, the study of city-level dynamics at short timescales has been challenging historically. Social media and new computational methods provide a solution. Our study clusters cities using sociality as a metric. We collected three months of social media data to investigate variation in the temporal structure of sociability across American cities. We find that cities cluster into three distinct types (‘Coastal’, ‘Transitional’ and ‘Heartland’) and that geographic proximity together with race, education and language associate with this clustering. Specifically, we found that clusters of Blacker cities tend to tweet more per capita, but also that more highly educated cities tend to tweet less per capita. These findings provide evidence that social media may be facilitating new opportunities to empower traditionally marginalized urban groups, a conclusion relevant to #BlackLivesMatter, the George Floyd protests and other social movements.

scholarly journals Capturing and Characterizing Human Activities Using Building Locations in America

2019 ◽  
Vol 8 (5) ◽  
pp. 200 ◽  
Author(s):  
Ren ◽  
Jiang ◽  
Seipel
Keyword(s):  
Social Media ◽  
Big Data ◽  
Power Law ◽  
Human Activities ◽  
Statistical Distribution ◽  
Human Beings ◽  
Spatial Clusters ◽  
Social Media Data ◽  
Positioning Systems ◽  
Media Data

Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

scholarly journals From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yuan Liao ◽  
Sonia Yeh ◽  
Gustavo S. Jeuken
Keyword(s):  
Social Media ◽  
Multiple Scales ◽  
Human Mobility ◽  
Activity Patterns ◽  
Population Heterogeneity ◽  
Easy Access ◽  
Long Distance ◽  
Transport Modelling ◽  
Social Media Data ◽  
Media Data

AbstractThis paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data
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