Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Wang, Yandong; Wang, Teng; Ye, Xinyue; Zhu, Jianqi; Lee, Jay

doi:10.3390/su8010025

Cited by 98 publications

(75 citation statements)

References 39 publications

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“…The development of mobile communication technology provides new opportunities to investigate problems in the real world through social media data [39][40][41][42][43]. Based on a Huff model, we obtained different sample sets for delimitating trade areas from social media data.…”

Section: Resultsmentioning

confidence: 99%

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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Abstract:Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.

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Section: Resultsmentioning

confidence: 99%

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Wang

Jiang

Liu

et al. 2016

IJGI

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“…This can broaden the literature of human mobility, origin-destination estimation, emerging data and public transit analysis [37][38][39][40][41][42]. The findings would provide an objective bottom-up view to depict human mobility as well as new insights for traffic optimization and urban transport planning policy.…”

Section: Resultsmentioning

confidence: 99%

Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization

Dong

Shen

et al. 2017

Sustainability

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A transit ridership study is an essential part of sustainability, and can provide a deep understanding of people's travel patterns for efficient transportation development and urbanization. However, there is a lack of empirical studies comparing subway and taxi services, and their interactions within a city, that is to say, the interdependent transportation networks. Incorporating new data, this study aims to examine the spatial variation of urban taxi ridership due to the impacts of a new subway line operation opened in 2014 in Wuxi, China. We examine the spatial patterns and interactions of ridership in Wuxi by integrating taxi trajectory from GPS data and subway data from continuously collected fare transactions. The results indicated that the demand for taxi and subway usage is quite elastic with respect to both location and time, and the new subway's opening had more influence on areas adjacent to subway stations and urban center-suburban travel. Furthermore, increases in travel time and distance would increase the demand for subway, while taxi trips largely represented movements for those locations that the subway could not reach. This paper betters the understanding of travel patterns through large volumes of transportation data for sustainable urbanization policy design.

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“…Rigorous spatial analysis and modeling of socioeconomic and environmental dynamics opens up a rich empirical context for scientific research and policy interventions [40]. A robust spatial join can serve as a fundamental analytical power to understand how everything is related and to what extent, such as how a firm's location is related to a specific site in a large national dataset [41] or disaster risk related to multiple spatially-related factors [42,43]. In this study, we proposed and described SJS, a high-performance spatial join algorithm with Spark.…”

Section: Discussionmentioning

confidence: 99%

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

et al. 2016

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Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relationship detection at a faster pace. Spatial join is a fundamental method for making data more informative with respect to spatial relations. The dramatic growth of data volumes has led to increased focus on high-performance large-scale spatial join. In this paper, we present Spatial Join with Spark (SJS), a proposed high-performance algorithm, that uses a simple, but efficient, uniform spatial grid to partition datasets and joins the partitions with the built-in join transformation of Spark. SJS utilizes the distributed in-memory iterative computation of Spark, then introduces a calculation-evaluating model and in-memory spatial repartition technology, which optimize the initial partition by evaluating the calculation amount of local join algorithms without any disk access. We compare four in-memory spatial join algorithms in SJS for further performance improvement. Based on extensive experiments with real-world data, we conclude that SJS outperforms the Spark and MapReduce implementations of earlier spatial join approaches. This study demonstrates that it is promising to leverage high-performance computing for large-scale spatial join analysis. The availability of large-sized geo-referenced datasets along with the high-performance computing technology can raise great opportunities for sustainability research on whether and how these new trends in data and technology can be utilized to help detect the associated trends and patterns in the human-environment dynamics.

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Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm

Cited by 98 publications

References 39 publications

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

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