Spatial clusters in a global-dependence model

Wang, Tai-Chi; Yue, Ching-Syang Jack

doi:10.1016/j.sste.2013.03.003

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…In the case of geotagged tweets, previous studies have proved that geotagged tweets are affected by spatial autocorrelation, meaning that tweets are usually surrounded by similar ones, and nearby tweets are dealing with the same topics (Steiger et al 2015). Consequently, to perform a more robust and accurate clustering of geotagged tweets, spatial autocorrelation should be taken into account (Wang and Yue 2013).…”

Section: Background and Related Workmentioning

confidence: 99%

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Ghaemi

Farnaghi

2021

Journal of Spatial Science

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Twitter, as the most popular social media platform, has made a great revolution in producing real-time user-generated data. This research aims to propose a method to extract the latent spatial pattern from geotagged tweets. We take both spatial autocorrelation and spatial heterogeneity into account while revealing the underlying pattern from geotagged tweets. Moreover, the textual similarity is considered to extract spatial-textual clusters. The method was implemented and tested during hurricane Dorian on the east coast of the U.S. The results proved the superiority of the proposed method against Moran's Index and VDBSCAN algorithms in extracting clusters with various densities.

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Section: Background and Related Workmentioning

confidence: 99%

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Ghaemi

Farnaghi

2021

Journal of Spatial Science

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“…Spatial regression is a popular tool for analysing the spatial data [ 2 , 35 ] and the first-order stationarity is a common assumption, which means that the expected (mean) values are fixed at different locations. The error terms of spatial regression are usually not independent and, like in time series analysis, their covariance is assumed to follow some spatial models, such as the simultaneous autoregressive (SAR) and moving average (MA) models [ 12 , 30 , 34 ]. However, the first-order stationarity is a questionable assumption in practice and the modifiable areal unit problem (MAUP) often occurs [ 5 , 13 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

A modification to geographically weighted regression

Leong

Yue

2017

Int J Health Geogr

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BackgroundGeographically weighted regression (GWR) is a modelling technique designed to deal with spatial non-stationarity, e.g., the mean values vary by locations. It has been widely used as a visualization tool to explore the patterns of spatial data. However, the GWR tends to produce unsmooth surfaces when the mean parameters have considerable variations, partly due to that all parameter estimates are derived from a fixed- range (bandwidth) of observations. In order to deal with the varying bandwidth problem, this paper proposes an alternative approach, namely Conditional geographically weighted regression (CGWR).MethodsThe estimation of CGWR is based on an iterative procedure, analogy to the numerical optimization problem. Computer simulation, under realistic settings, is used to compare the performance between the traditional GWR, CGWR, and a local linear modification of GWR. Furthermore, this study also applies the CGWR to two empirical datasets for evaluating the model performance. The first dataset consists of disability status of Taiwan’s elderly, along with some social-economic variables and the other is Ohio’s crime dataset.ResultsUnder the positively correlated scenario, we found that the CGWR produces a better fit for the response surface. Both the computer simulation and empirical analysis support the proposed approach since it significantly reduces the bias and variance of data fitting. In addition, the response surface from the CGWR reviews local spatial characteristics according to the corresponded variables.ConclusionsAs an explanatory tool for spatial data, producing accurate surface is essential in order to provide a first look at the data. Any distorted outcomes would likely mislead the following analysis. Since the CGWR can generate more accurate surface, it is more appropriate to use it exploring data that contain suspicious variables with varying characteristics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-017-0085-9) contains supplementary material, which is available to authorized users.

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Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Wang

2018

Nat Hazards

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Spatial clusters in a global-dependence model

Cited by 5 publications

References 22 publications

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

A modification to geographically weighted regression

Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

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