scholarly journals Multi-scale patterns of spatial variability in sessile assemblage structure do not alter predictably with development time

2013 ◽  
Vol 482 ◽  
pp. 29-41 ◽  
Author(s):  
DA Smale
2019 ◽  
Vol 8 (2) ◽  
pp. 50 ◽  
Author(s):  
Tara Smith ◽  
J. Sandoval

The current study spatially examines the local variability of robbery rates in the City of Saint Louis, Missouri using both census tract and block group data disaggregated and standardized to the 250- and 500-m raster grid spatial scale. The Spatial Lag Model (SLM) indicated measures of race and stability as globally influencing robbery rates. To explore these relationships further, Geographically Weighted Regression (GWR) was used to determine the local spatial variability. We found that the standardized census tract data appeared to be more powerful in the models, while standardized block group data were more precise. Similarly, the 250-m grid offered greater accuracy, while the 500-m grid was more robust. The GWR models explained the local varying spatial relationships between race and stability and robbery rates in St. Louis better than the global models. The local models indicated that social characteristics occurring at higher-order geographies may influence robbery rates in St. Louis.


2018 ◽  
Vol 61 (2) ◽  
pp. 591-601
Author(s):  
Jilong Liu ◽  
Lingling Zhang ◽  
Qiang Fu ◽  
Gaoqi Ren ◽  
Lu Liu ◽  
...  

Abstract. The objective of this research was to reveal the spatial variability of soil particle-size distribution heterogeneity. The farmland (48 m × 48 m) used in this study is located in the black soil region of northeast China and was divided into sixty-four 6 m × 6 m squares for sampling. The soil particle-size distribution was measured with a Mastersizer 2000. Soil particle-size distribution heterogeneity, the spatial variability of soil particle-size distribution heterogeneity, and the relationships between soil particle-size distribution heterogeneity and the clay, silt, and sand contents were studied by applying multifractal, geostatistical, and joint multifractal methods, respectively. The soil particle-size distribution had multifractal characteristics. Local information causing soil particle-size distribution heterogeneities were mainly low values of soil particle-size distribution; heterogeneities from the low-value side of the particle-size distribution were larger than those from the high-value side of the particle-size distribution. In the different soil layers, the degree of variation in soil particle-size distribution heterogeneities was moderate, with spatial correlation ranges of 37.82 m and moderate spatial dependences. At the single scale and multi-scale, the impacts of the clay, silt, and sand contents on the soil particle-size distribution heterogeneity changed with soil layer depth. The clay, silt, and sand contents had different degrees of influence on the spatial variability of soil particle-size distribution heterogeneity at the single scale and multi-scale. Multi-scale analysis could better reveal the degrees of influence of the above soil properties on the spatial variability of soil particle-size distribution heterogeneity. The results of this study enrich the knowledge of the spatial variability of soil properties and provide a reference and additional information for the quantitative characterization of soil particle-size distribution heterogeneity and soil management in this research area. Keywords: Geostatistics, Multifractal analysis, Relationship, Soil property.


2019 ◽  
Vol 35 (2) ◽  
pp. 221-230
Author(s):  
Gengxing Zhao ◽  
Chao Dong ◽  
Xiaona Chen ◽  
Baowei Su

Abstract.The spatial variability of farmland soil nutrients on different scales is important for farming as it forms the basis for the efficient utilization of soil nutrients and precision fertilization. Survey points were distributed throughout the study area on three different scales (county, field, and block). Research on the scale effect of the spatial variability of available nitrogen (AN), available phosphorus (AP), and available potassium (AK) involved a combination of classical statistics, geostatistics, and Geographic Information System (GIS) techniques. Results indicated that the three kinds of nutrients presented moderate variation intensity on the three scales. All of the nutrients tested exhibited strong spatial autocorrelation, indicating that spatial variability was primarily affected by structural factors, including climate, soil type and topography. As the sampling scale decreased, the nutrients showing weak variation at the large scale exhibited great variation at the small scale; the spatial autocorrelation of these three nutrients first became greater and then weakened; the distance of the spatial autocorrelation shortened gradually. Furthermore, the patch density value of the soil nutrient map increased, which indicated that the distribution of nutrients tended to be more fragile. When combined, sampling methods on the multi-scale allowed us to obtain real and systematic soil information. This study explored scale characteristics and the effects of spatial variability with regards to the primary nutrients available on farmland and provided a theoretical basis to effectively understand the nutrient status of regional farmland and improve the efficacy of soil sampling. Keywords: Multi-scale, Geostatistics, Patch density, Fractal dimension, Kriging interpolation.


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