New city landscape

Neuhaus, Fabian

doi:10.1386/tear.9.1.31_1

Cited by 8 publications

(2 citation statements)

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“…Check-in data record a user’s activity information, which is represented by words, photos and expressions, at specific spots or POIs at a given time. Neuhaus [28] used Twitter data and presented preliminary descriptions of urban landscapes. Wakamiya [29] utilized social networks as a mirror of the public’s perceptions of the real world and measured crowd activity on Twitter, and he used these data to describe urban areas.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China

Ren

et al. 2016

PLoS ONE

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Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

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Section: Introductionmentioning

confidence: 99%

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China

Ren

et al. 2016

PLoS ONE

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“…Once identified these critical nodes could permit direct military action or covert operations.By applying a geospatial perspective, common to the management of international commercial communication networks, an intelligence analyst can develop a map of exploitable networks outside the US. The intelligence value of mapping this new digital data has been seen in commercial industries that mapped the urban landscape, commonly referred to as the new city landscape 12. An example of this new city landscape can be seen with Twitter, a shared instant messaging system where users post messages and images on a public message board.…”

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confidence: 99%