Dataset Information

Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

ABSTRACT: A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.

SUBMITTER: Soliman A

PROVIDER: S-EPMC5517059 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

Soliman Aiman A Soltani Kiumars K Yin Junjun J Padmanabhan Anand A Wang Shaowen S

PloS one 20170719 7

A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when ...[more]

PMID: 28723936

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Project description:This study evaluated the geospatial distribution of fecal indicator bacteria (FIB) (i.e., Escherichia coli, Enterococcus spp.) and the alternative fecal indicator pepper mild mottle virus (PMMoV) in tropical freshwater environments under different land use patterns. Results show that the occurrence and concentration of microbial fecal indicators were higher for urban than for parkland-dominated areas, consistent with land use weightage. Significant positive correlations with traditional FIB indicate that PMMoV is a suitable indicator of fecal contamination in tropical catchments waters (0.549 ≤ rho ≤ 0.612; P < 0.01). PMMoV exhibited a strong significant correlation with land use weightage (rho = 0.728; P < 0.01) compared to traditional FIB (rho = 0.583; P < 0.01). In addition, chemical tracers were also added to evaluate the potential relationships with microbial fecal indicators. The relationships between diverse variables (e.g., environmental parameters, land use coverage, and chemical tracers) and the occurrence of FIB and PMMoV were evaluated. By using stepwise multiple linear regression (MLR), the empirical experimental models substantiate the impact of land use patterns and anthropogenic activities on microbial water quality, and the output results of the empirical models may be able to predict the sources and transportation of human fecal pollution or sewage contamination. In addition, the high correlation between PMMoV data obtained from quantitative real-time PCR (qPCR) and viral metagenomics data supports the possibility of using viral metagenomics to relatively quantify specific microbial indicators for monitoring microbial water quality (0.588 ≤ rho ≤ 0.879; P < 0.05).IMPORTANCE The results of this study may support the hypothesis of using PMMoV as an alternative indicator of human fecal contamination in tropical surface waters from the perspective of land use patterns. The predictive result of the occurrence of human fecal indicators with high accuracy may reflect the source and transportation of human fecal pollution, which are directly related to the risk to human health, and thereafter, steps can be taken to mitigate these risks.

Dataset Information

Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

Publications

Social sensing of urban land use based on analysis of Twitter users' mobility patterns.

Similar Datasets

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