The new data landscape for regional and urban analysis

Ye, Xinyue; He, Canfei

doi:10.1007/s10708-016-9737-8

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…The large volumes of such user-generated locational and contextual information are especially beneficial to studies relevant to the evolution of population size and human settlement structure as well as highly topical subjects such as traffic and epidemiological forecasting. For instance, real-time customer shopping behaviors might be rapidly identified by searching specific keywords in tweets, which allows for urban researchers and business analysts to monitor the fine-scale dynamics of economic geography and market outcome (Ye and He 2016). This new data landscape might not directly provide an ultimate solution to longstanding social or economic issues, but can increasingly shed light on many societal characteristics that are otherwise difficult to discover using traditional questionnaires or surveys.…”

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confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

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The human behaviors and interactions on social media have maintained themselves as highly dynamic real-time social systems representing individual social awareness at fine spatial, temporal, and digital resolutions. In this chapter, we introduce the opportunities and challenges that human dynamics-centered social media bring to Digital Earth. We review the information diffusion of social media, the multi-faced implications of social media, and some real-world cases. Social media, on one hand, has facilitated the prediction of human dynamics in a wide spectrum of aspects, including public health, emergency response, decision making, and social equity promotion, and will also bring unintended challenges for Digital Earth, such as rumors and location spoofing on the other. Considering the multifaceted implications, this chapter calls for GIScientists to raise their awareness of the complex impacts of social media, to model the geographies of social media, and to understand ourselves as a unique species living both on the Earth and in Digital Earth. Keywords Social media • Human dynamics • Social awareness • Location spoofing 12.1 Introduction: Electronic Footprints on Digital Earth Geo-positioning system-enabled instruments can record and reveal personal awareness at fine spatial, temporal, and digital resolutions (Siła-Nowicka et al. 2016; Li et al. 2017; Ye and Liu 2019). With an exponential growth, human dynamics data are retrieved from location-aware devices, leading to a revolutionary research agenda regarding what happens where and when in the everyday lives of people in both real and virtual worlds (Batty 2013; Yao et al. 2019). Many location-based social media

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confidence: 99%

Social Media and Social Awareness

Zhao

Nguyen

et al. 2019

Manual of Digital Earth

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“…This work contributes to the extension of regionalization problems into the network space. A city is a large and complex system (Ye and He 2016). The presence of streets and roads could have different types of influence over the nearby areas; a ring road would produce a clear cut between rural and urban areas, while an urban street would attract areas on both sides.…”

Section: Discussionmentioning

confidence: 99%

Exploring Regionalization in the Network Urban Space

She

Benya

2018

J geovis spat anal

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Isotropic homogeneity does not hold in urban areas. Street networks exert a great influence on human mobility. As a result, city structure is largely shaped by this network, especially the streets that carry a higher volume of traffic. In practice, small areas along network edges often need to be grouped into regions for management purposes. This work formalizes the extension to the P-regions problem that takes the network as the underlying constraint and proposes a heuristic-based approach to solve the problem to near optimality. The network is subdivided into aggregator edges that attract regions and separator regions that divide areas apart. Two types of regions emerge in the region formation process: regions that grow along a certain network edge (network regions) and regions that grow from areas that are far away from all the network edges (planar regions). The heuristic solution effectively uses pre-computed spatial contiguity and distance matrices. The global objective function consists of the original heterogeneity factor and the discounted network proximity factor. This approach is elaborated with both a simulated and a real-world dataset. The regionalization results help design, study, and service regions that explicitly consider the network configuration with flexible parameter controls.

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“…In traditional regional analysis, cities should interact with adjacent big cities. Although researchers have been concerned with flow space, the urban system based on scale has gradually transformed into a network paradigm [5][6][7]. On the basis of this transformation, relationships among cities have been continuously reconstructed and redefined, and the development of intercity relationships has emerged as an important issue that needs to be studied in depth.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks

Xue

2019

Sustainability

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Globalization and the spread of information have made city networks more complex. The existing research on city network structures has usually focused on discussions of regional integration. With the development of interconnections among cities, however, the characterization of city network structures on a regional scale is limited in the ability to capture a network’s complexity. To improve this characterization, this study focused on network structures at both regional and local scales. Through the lens of function and innovation, we characterized the city network structure of the Economic Zone of the West Side of the Taiwan Strait through a social network analysis and a Fast Unfolding Community Detection algorithm. We found a significant imbalance in the innovation cooperation among cities in the region. When considering people flow, a multilevel spatial network structure had taken shape. Among cities with strong centrality, Xiamen, Fuzhou, and Whenzhou had a significant spillover effect, which meant the region was depolarizing. Quanzhou and Ganzhou had a significant siphon effect, which was unsustainable. Generally, urbanization in small and midsize cities was common. These findings provide support for government policy making.

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The new data landscape for regional and urban analysis

Cited by 20 publications

References 14 publications

Social Media and Social Awareness

Social Media and Social Awareness

Exploring Regionalization in the Network Urban Space

Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks

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