Multi-scale spatial analysis of household car ownership using distance-based Moran's eigenvector maps: Case study in Loire-Atlantique (France)

Hankach, Pierre; Gastineau, Pascal; Vandanjon, Pierre Olivier

doi:10.1016/j.jtrangeo.2021.103223

Cited by 11 publications

(4 citation statements)

References 67 publications

(83 reference statements)

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“…Moran's I values, ranging from −1 to 1, widely used to test spatial autocorrelation (Hankach et al, 2022). If the value is >0, it indicates positive spatial dependence; if the value is <0, it indicates negative spatial dependence; if the value is equal to 0, it represents the spatial randomness of the target variables (Wu et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

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Effect of the Grain‐for‐Green Program on forest fragmentation

Zhang

et al. 2023

Land Degrad Dev

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Fragmentation profoundly alters the character and pattern of forests, influencing biodiversity in the context of global climate change. Forest area density (FAD) is currently considered the most effective method for characterizing forest fragmentation. Therefore, reasonable and accurate quantification of FAD and its relationship with forest cover changes are essential for evaluating forest fragmentation in the Loess Plateau under the implementation of the Grain‐for‐Green Program (GGP). Based on land use/cover (LULC) in 1998–2022, we integrated the Random forest (RF) algorithm and the conversion of land use and its effects at small regional extent (CLUE‐S) model to derive LULC in 2030. Subsequently, we proposed a multi‐scale window threshold to evaluate forest fragmentation by constructing nonlinear models. Furthermore, we explored the relationship between FAD and forest cover changes by spatial regression models. The results are as follows: (1) in 1998–2030, the main conversion of LULC was of the cropland to broad‐leaf forests and grassland. (2) The 43 × 43 window served as the optimal multi‐scale threshold to assess forest fragmentation. (3) The GGP slowed down forest fragmentation. (4) The degree of fragmentation in broad‐leaf forests was higher than that in coniferous forests. (5) Spatial error model (SEM) was the most appropriate for establishing the spatial relationship between forest fragmentation and forest cover changes. In conclusion, since the implementation of the GGP, forest cover changes had a significant impact on forest fragmentation and its multi‐scale threshold. This research provides a basis for the reasonable configuration of land resources in the Loess Plateau and novel methods for studying forest cover changes and fragmentation.

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Section: Methodsmentioning

confidence: 99%

“…Moran's I values, ranging from À1 to 1, widely used to test spatial autocorrelation (Hankach et al, 2022). (Figure S2), indicating that the fitted model could be used to predict LULC.…”

Section: Model Selection and Evaluationmentioning

confidence: 99%

Effect of the Grain‐for‐Green Program on forest fragmentation

Zhang

et al. 2023

Land Degrad Dev

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“…Moran's I is mainly used to test whether there are similarities or differences between neighboring areas in the whole region ( 49 ). The value of Moran's I is distributed between [−1, 1] ( 50 ). Moran's I >0 indicates a positive correlation, which refers to the agglomeration of high value and high value or low value and low value.…”

Section: Methodsmentioning

confidence: 99%

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Zhang

Liao²,

Zhao³

2022

Front. Public Health

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An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 109kg and 4.28 × 106kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.

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“…It also generates maps that help visualize the spatial variations with the application of different spatial operators on the input layers, which ultimately assist in improving problem management and decision-making. Spatial analysis has been used in Land 2022, 11, 2211 2 of 20 many ways in several fields, such as soil sciences [21], groundwater studies [22,23], medical sciences [24][25][26], transportation [27,28], e-commerce, and business [29,30]. The emerging hotspot analysis is an innovative spatial analysis tool that can detect spatial-temporal trends and varying patterns of different phases in each period; it has been progressively utilized in multiple scientific disciplines [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis

Younas

Khan

Qasim

et al. 2022

Land

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Land subsidence is an ongoing problem negatively affecting Victoria County along the Gulf Coast. Groundwater withdrawal and hydrocarbon extraction in the County are some of the known factors behind this geological hazard. In this study, we have used geospatial analysis and a conceptual model to evaluate land subsidence. A significant decline in the groundwater level in this area was noted from 2006 to 2016. The decline in the water level correlates with the major drought events along the Gulf Coast reported in earlier studies. These results are further corroborated by the emerging hotspot analysis performed on the groundwater data. This analysis divides the study area into intensifying, sporadic, and persistent hotspots in the northwest region and intensifying, persistent coldspots in the southeast region of Victoria County. Hydrocarbon production data show high oil and gas extraction from 2017 to 2021. There are a higher number of hydrocarbon production wells in the central and southern regions of the County than elsewhere. The conceptual models relate these events and suggest the existence of subsidence in the County, through which the water and hydrocarbon reservoirs in the study area may lose their reservoir characteristics due to sediment compaction.

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Multi-scale spatial analysis of household car ownership using distance-based Moran's eigenvector maps: Case study in Loire-Atlantique (France)

Cited by 11 publications

References 67 publications

Effect of the Grain‐for‐Green Program on forest fragmentation

Effect of the Grain‐for‐Green Program on forest fragmentation

Vector maps and spatial autocorrelation of carbon emissions at land patch level based on multi-source data

Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis

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