Spectral clustering for sensing urban land use using Twitter activity

Frías-Martínez, Vanessa; Frı́as-Martı́nez, Enrique

doi:10.1016/j.engappai.2014.06.019

Cited by 178 publications

(113 citation statements)

References 22 publications

(21 reference statements)

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“…They include: the development of new area/neighborhood profiles using social media data [88][89][90]; estimates of the mobile population at risk of crime [91]; the identification of "important" places in peoples' lives from mobile telephone data [92]; the detection and delineating of events [93,94]; analysis of regular mobility patterns [95,96]; classification of areas based on their Twitter temporal profile [97]; and a wealth of others. However, examples applied in the context of urban modeling, let alone ABM specifically, are much scarcer.…”

Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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Section: Big Datamentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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“…Their study shows that human mobility data from smartphones can provide a good estimation for urban land use patterns in a timely fashion, which can help urban planners design better routes for mitigating traffic and improving public services. Frias-Martinez et al (2014) [21] presented another good case study of land use pattern detection by using location-based Twitter data in Manhattan, London, and Madrid, showing that geo-located tweets can constitute a complementary data source for urban planners. However, there may be a certain bias when using single-source data to detect the urban land use types of the city, especially for large cities in developing countries.…”

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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“…However, as our proposed method is not limited to utilising POIs, multi-sourced data including commercial Internet maps and OpenStreetMap can also be utilised to fill the gap between the uneven distribution of POIs and continuous land cover regions. (4) Since most POIs are simply classified as artificial surfaces, detailed information that is related to human activities is ignored, such as land use information [27]. For example, POIs with "Restaurant" and "Apartment" categories represent commercial areas and residential areas, respectively.…”

Section: Discussionmentioning

confidence: 99%

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

2017

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Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010-2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

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Spectral clustering for sensing urban land use using Twitter activity

Cited by 178 publications

References 22 publications

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

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

Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

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