Urban activity pattern classification using topic models from online geo-location data

Hasan, Samiul; Ukkusuri, Satish V.

doi:10.1016/j.trc.2014.04.003

Cited by 249 publications

(131 citation statements)

References 19 publications

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“…From an urban planning perspective, researchers have begun to focus on urban area characterization and dynamic patterns associated with various human activities and behaviors [16][17][18][19]. Liu et al (2012) [20] used a seven-day taxi trajectory data to study the relationship between the urban land uses and traffic patterns.…”

Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

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Section: Introductionmentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Wang

Tsou

et al. 2016

Sustainability

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“…With regards to urban planning, the unpacking of big data has led to a reduction in time spent to respond to service delivery grievances, as the community can easily inform council of any grievances via mobile applications, thus bridging the gap between the ordinary citizen and local authorities (Hasan & Ukkusuri, 2014). As mobile devices have become more advanced within built sensors, it is now possible to trace and create a digital foot print showing the movement of people, through the collection of big data from mobile network towers, social media platforms and wifi feeds (Lancey, 2001;Yang, et al, 2012;Chatzimilioudis & Zeinalipour-Yazti, 2013).…”

Section: 2mentioning

confidence: 99%

“…The general consensus amongst scholars is that it is now possible to model the spatial dependence of commuters using geographical location data to predict areas of clusters and outliners ( (Wolf, et al, 2003;Stopher & Greaves, 2009;Gao & Liu, 2013;Hasan & Ukkusuri, 2014). …”

Section: Introductionmentioning

confidence: 99%

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Moyo¹,

Musakwa²

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The study of commuters' origins and destinations (O_D) promises to assist transportation planners with prediction models to inform decision making. Conventionally O_D surveys are undertaken through travel surveys and traffic counts, however data collection for these surveys has historically proven to be time consuming and having a strain on human resources, thus a need for an alternative data source arises. This study combines the use social media data and geographic information systems in the creation of a model for origin and destination surveys. The model tests the potential of using big data from Echo echo software which contains Twitter and Facebook data obtained from social media users in Gauteng. This data contains geolocation and it is used to determine origin and destination as well as concentration levels of Gautrain commuters. A kriging analysis was performed on the data to determine the O-D and concentration levels of Gautrain users. The results reveal the concentration of Gautrain commuters at various points of interest that is where they work, live or socialise. The results from the study highlight which nodes attract the most commuters and also possible locations for the expansion for Gautrain. Lastly, the study also highlights some weakness of crowdsourced data for informing transportation planning. (208)

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“…As such, there are many opportunities to gain fundamental knowledge about user behavior analyzing these data at various levels of spatiotemporal resolution. Researchers are realizing the potential to harness the rich information provided by the location-based data, which have already enabled many novel applications, such as recommendation system for physical locations (or activity) (Zheng et al, 2010;Chang and Sun, 2011;Bao et al, 2012), recommending potential customers or friend (Zheng, 2011;Saez-Trumper et al, 2012), determining popular travel routes in a city (Wei et al, 2012), discovering mobility and activity choice behavior (Cheng et al, 2011;Noulas et al, 2012;Hasan et al, 2013;Pianese et al, 2013), activity recognition and classification (Lian and Xie, 2011;Hasan and Ukkusuri, 2014), estimating urban travel demand and traffic flow (Hasan, 2013;Liu et al, 2014;Wu et al, 2014), and modeling the influence of friendship on mobility patterns (Cho et al, 2011;Wang et al, 2011). In this paper, we analyze a dataset from a social media check-in service to understand the extent of social influence on individual activity behavior.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, we investigated how individual activity participation (Hasan and Ukkusuri, 2014) and lifestyle choices (Hasan and Ukkusuri, 2015) have structural patterns. These patterns may depend on individual choices subjected to individual needs and geographic constraints.…”

Section: Introductionmentioning

confidence: 99%

Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

2016

Self Cite

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Social media check-in services have enabled people to share their activity-related choices, providing a new source of human activity and social networks data. Geolocation data from these services offer us information, in new ways, to understand social influence on individual choices. In this paper, we investigate the extent of social influence on individual activity and lifestyle choices from social media check-in data. We first collect user check-ins and their social network information by linking two social media systems (Twitter and Foursquare) and analyze the structure of the underlying social network. We next infer user check-in and geo lifestyle patterns using topic models. We analyze the correlation between the social relationships and individual-level patterns. We investigate whether or not two individuals have similar activity choice and geo lifestyle patterns, if they are socially connected. We find that the similarity between two users, in their check-in behavior and lifestyle patterns, increases with the increase of the friendship probability.

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Urban activity pattern classification using topic models from online geo-location data

Cited by 249 publications

References 19 publications

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

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Understanding Social Influence in Activity Location Choice and Lifestyle Patterns Using Geolocation Data from Social Media

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