VISUALISATION OF SOCIO-SPATIAL ISOLATION BASED ON HUMAN ACTIVITY PATTERNS AND SOCIAL NETWORKS IN SPACE-TIME

2011 ◽  
Vol 102 (4) ◽  
pp. 468-485 ◽  
Author(s):  
JAE YONG LEE ◽  
MEI-PO KWAN
Keyword(s):  
Social Networks ◽  
Human Activity ◽  
Activity Patterns ◽  
Space Time ◽  
Spatial Isolation ◽  
Human Activity Patterns

scholarly journals A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

2016 ◽  
Vol XLI-B2 ◽  
pp. 549-552 ◽  
Author(s):  
W. Huang ◽  
S. Li ◽  
S. Xu
Keyword(s):  
Human Activity ◽  
Activity Pattern ◽  
Pattern Analysis ◽  
Human Mobility ◽  
Activity Patterns ◽  
Space Time ◽  
Clustering Methods ◽  
Clustering Method ◽  
Activity Data ◽  
Human Activity Patterns

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. <br><br> In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. <br><br> Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.

scholarly journals A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

2016 ◽  
Vol XLI-B2 ◽  
pp. 549-552 ◽  
Author(s):  
W. Huang ◽  
S. Li ◽  
S. Xu
Keyword(s):  
Human Activity ◽  
Activity Pattern ◽  
Pattern Analysis ◽  
Human Mobility ◽  
Activity Patterns ◽  
Space Time ◽  
Clustering Methods ◽  
Clustering Method ◽  
Activity Data ◽  
Human Activity Patterns

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. <br><br> In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. <br><br> Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.

scholarly journals A Multiscale Survival Process for Modeling Human Activity Patterns

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151473 ◽  
Author(s):  
Tianyang Zhang ◽  
Peng Cui ◽  
Chaoming Song ◽  
Wenwu Zhu ◽  
Shiqiang Yang
Keyword(s):  
Human Activity ◽  
Activity Patterns ◽  
Human Activity Patterns

scholarly journals Deep Neural Networks and Kernel Density Estimation for Detecting Human Activity Patterns from Geo-Tagged Images: A Case Study of Birdwatching on Flickr

2019 ◽  
Vol 8 (1) ◽  
pp. 45 ◽  
Author(s):  
Caglar Koylu ◽  
Chang Zhao ◽  
Wei Shao
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Social Media ◽  
Spatial Analysis ◽  
Citizen Science ◽  
Human Activity ◽  
Activity Patterns ◽  
Kernel Density ◽  
Human Activity Patterns ◽  
Vision Algorithm

Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.

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