A novel Spatio-temporal principal component analysis based on Geary's contiguity ratio

Krzyśko, Mirosław; Nijkamp, Peter; Ratajczak, Waldemar; Wołyński, Waldemar; Wojtyła, Andrzej; Wenerska, Beata

doi:10.1016/j.compenvurbsys.2023.101980

Cited by 2 publications

(1 citation statement)

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“…In terms of other indicators, there are aerosol optical depth (AOD) (Yang et al, 2020), precipitation (Zhao et al, 2023), etc. For the determination of index weights, the principal component analysis method (Hong et al, 2020), analytic hierarchy process method (Gashaw et al, 2022), entropy method (Gao et al, 2014), and information method (Bopche and Rege, 2022) are recognized.The most commonly used methods for calculating the spatial autocorrelation between indicators are Moran's I (Wang et al, 2023), Geary's contiguity ratio (Krzyśko et al, 2023), Getis (Wang and Lam, 2020), etc. These existing indicator selection and calculation methods do provide a good research path.…”

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confidence: 99%

Ecological environment quality assessment and spatial autocorrelation of northern Shaanxi mining area in China based-on improved remote sensing ecological index

Zhu,

Cao,

Yang

et al. 2024

Front. Environ. Sci.

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The northern Shaanxi coal mining area is an important coal production base in China and an ecologically fragile area, and it is of great significance to explore the ecological environment quality and spatial evolution trend of the mining area. Based on Sentinel-2 and MODIS image data, an improved remote sensing ecological index was constructed by principal component analysis method: normalized difference vegetation index, normalized difference water index, normalized differential buildup and bare soil index, and net primary productivity. The ecological environment quality of the northern Shaanxi coal mining area and its influencing factors, as well as the spatial autocorrelation analysis of ecological environment quality, were discussed. The results showed that: 1) The vegetation coverage in the study area showed an overall increasing trend, but it was greatly affected by the average annual temperature. The NDVI index at the mine area is higher than the NDVI index at the non-mine area. 2) The water area gradually decreases with the year, which has a certain negative correlation with the total raw coal production. 3) The NDBB index showed a decreasing trend with the year, and compared with the non-mining area. 4) The coupling of year-by-year precipitation and temperature leads to interannual fluctuation of NPP value. 5) The change of ecological environment quality in the study area is the result of the comprehensive effect of natural factors and human factors. The implementation of ecological protection projects such as geological environmental protection and land reclamation in mining areas also has a certain impact on the trend of ecological environment quality. 6) There was a significant spatial autocorrelation in the quality of the ecological environment in the study area. There are significant “High-High” gathering areas of ecological environment quality within the coal mining area.

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