Estimating Spatial Autocorrelation With Sampled Network Data

Zhou, Jing; Tu, Yundong; Chen, Yuxin; Wang, Hansheng

doi:10.1080/07350015.2015.1061437

Cited by 43 publications

(10 citation statements)

References 16 publications

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“…The area with positive spatial autocorrelation will show a cluster of similar phenomena and negative spatial autocorrelation will depict a cluster of different phenomena [Rousta, Doostkamian, Haghighi, Ghafarian Malamiri, & Yarahmadi, 2017]. In addition, the hot spot location with significant statistic will have high values with its surroundings (High-high or Low-low values) [Zhou et al, 2017].…”

Section: The Methodsmentioning

confidence: 99%

An Exploratory Analysis in Mapping of Asthma Risk in Western Australia

Febriawan

Sodre

2018

IJG

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Exploratory Analysis as one of the spatial analysis tools that has been used widely in many study fields. This tool is usually intended to obtain the spatial pattern to observe and get relationship between study variables. The exploratory analysis is usually followed by the confirmatory analysis to exhibit the hypothesis that already obtain in the exploratory analysis. This study is aimed to investigate the prevalence of asthma in Western Australia since there are many factors that cause the asthma dispersion. Many provided variables have been tested to get the best correlation with the asthma percentage variable and four variables (humidity, annual rainfall, EVI and SEIFA) were chosen and tested with high asthma percentage variable. The result of confirmatory analysis indicates that the high level of humidity and low level of SEIFA confirm with the hypothesis and means that those factors can contribute significantly in Asthma prevalence in Western Australia.

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Section: The Methodsmentioning

confidence: 99%

An Exploratory Analysis in Mapping of Asthma Risk in Western Australia

Febriawan

Sodre

2018

IJG

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“…These methods are based on spatial statistics such as Moran's I, nearest neighbor ratio (NNR) [19], and G-statistics. In recent years, new methods have been developed to deal with large data such as estimating spatial autocorrelation with large network data [20]. These methods were designed to work with datasets, in which all data have the same category or all events are considered the same.…”

Section: Spatial Pattern Of Eventsmentioning

confidence: 99%

Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

Gong

Lee

Zhou

et al. 2020

IJGI

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Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency.

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“…As a result, appropriate network sampling schemes and estimation methods should be discussed. Zhou et al [45] proposed the paired maximum likelihood estimator method for sampled data to obtain a consistent estimation for the SAR model. However, as Taylor expansion is conducted, this method requires the autocorrelation coefficient to approach zero, which can hardly be satisfied in all cases.…”

Section: Introductionmentioning

confidence: 99%

Crawling subsampling for multivariate spatial autoregression model in large-scale networks

Huang

Jing

et al. 2021

Electron. J. Statist.

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In network data analysis, multivariate spatial autoregression (MSAR) models may be used to analyze the autocorrelation among multiple responses. With large-scale networks, the estimation for MSAR on the entire network is computationally expensive. In this case, the subsampling method could be adopted. This approach selects a sample of nodes and then uses the estimate based on the sample to approximate the estimate on the full data. However, traditional sampling methods cannot obtain unbiased parameter estimates. Considering the second-order friend information of sampled nodes, we propose the crawling subsampling (CS) method for a general framework. Thereafter, based on the sampled data only, we construct the least-squares objective function. Under certain conditions, the computational complexity of optimizing the objective function is linear with the sample size ns. The identification condition for the parameters on the sampled network is theoretically provided. The sample size order requirement is provided, and the asymptotic properties of the least-squares estimators are investigated. The numerical results for the simulated and real data are presented to demonstrate the performance of the proposed CS method and least-squares estimator for the MSAR model.

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Estimating Spatial Autocorrelation With Sampled Network Data

Cited by 43 publications

References 16 publications

An Exploratory Analysis in Mapping of Asthma Risk in Western Australia

An Exploratory Analysis in Mapping of Asthma Risk in Western Australia

Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

Crawling subsampling for multivariate spatial autoregression model in large-scale networks

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