A geospatial analysis of multi-scalar regional inequality in China and in metropolitan regions
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Urban agglomeration
The returns to scale (RTS) parameter of urban production functions often has been used to test for the existence of agglomeration economies in urban areas. The production function has been viewed as a convenient device for bridging the gap between the theory of agglomeration and its measurement. Though the studies in this area recently have become much more refined, additional research has been needed in some of the more basic methodological and procedural issues connected with the empirical implementation of the production function approach. The principal objective of this study is to obtain a more accurate estimate of RTS for the manufacturing sector of urban agglomerations in the U.S. The conclusion is that the economies of agglomeration may be more complex than originally thought and it may be fruitful to examine more closely the underlying factors involved.
Urban agglomeration
Returns to scale
Production function
Production theory
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This paper presents a review of the spatial pattern analysis of individual trees in natural forest stands by the quadrat and distance methods. These methods have been used for three purposes (1) to identify spatial pattern, (i.e. uniform, random, or aggregated), (2) to estimate density and (3) to analyze spatial pattern structure. The separate use for each of the above purposes causes bias in density estimation. The concept of "intensity and grain" developed by PIELOU (1977) was applied to overview spatial pattern analysis. The intensity expressed by an index of aggregation has three roles; (1) for identifying spatial pattern, (2) for correcting bias in density estimation and (3) for analyzing grain of spatial pattern, i.e. mosaics, or sizes of clumps etc. Spatial pattern analysis methods from the point of the three roles are reviewed and spatial patterns of individual trees in natural forests and the problems in natural forest inventories discussed.
Quadrat
Point pattern analysis
Natural forest
Pattern analysis
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Seed dispersal should leave a signature on the spatial distribution of recruits that can be quantified using sophisticated techniques of spatial pattern analysis. Here we study spatial patterns of five frugivore-dispersed tropical tree species at the Barro Colorado Island forest, Panama, to describe detailed properties of the spatial patterns of recruits and to investigate whether these patterns were produced by temporally consistent mechanisms. Our spatial point pattern analyses detected the existence of surprising spatial structures, such as double-cluster and superposition patterns, and they allowed for a detailed quantification of their properties. The spatial recruitment patterns were composed of two independent components comprising a random component and a component showing a complex spatial pattern with two critical scales of clustering. The analysis allowed an estimation of the relative contribution of scatter dispersal versus clump dispersal in effective seed dispersal for our study species. Additionally, the cluster characteristics were temporally consistent over 25 years and correlated with several species traits. We are just beginning to discover the richness of spatial patterns found at tropical forests, and we are confident that a combination of advanced point pattern analysis with field data will allow for significant advances in establishing the link between spatial patterns and processes.
Point pattern analysis
Panama
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Analysing changes in spatial point patterns: A proposal using data from a forest thinning experiment
Spatial patterns reveal critical features at the individual and community levels. However, how to evaluate changes in spatial characteristics remains largely unexplored. We assess spatial changes in spatial point patterns by augmenting current statistical functions and indices. We fitted functions to describe unmarked and marked (tree size) spatial patterns using data from a large-scale silvicultural experiment in southern Chile. Furthermore, we compute the mingling index to represent spatial tree diversity. We proffer the pair correlation function difference before and after treatment to detect changes in the unmarked-point pattern of trees and the semivariogram-ratio to evaluate changes in the marked-point pattern. Our research provides a quantitative assessment of a critical aspect of forest heterogeneity: changes in spatial unmarked and marked-point patterns. The proposed approach can be a powerful tool for quantifying the impacts of disturbances and silvicultural treatments on spatial patterns in forest ecosystems.
Point pattern analysis
Variogram
Thinning
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Organisms usually benefit from heterogeneous conditions, but, by doing so, may reduce the degree of heterogeneity. The question therefore arises how heterogeneity is maintained. We investigated within-year spatiotemporal patterns in a monospecific stand of a submerged plant (fennel pondweed, Potamogeton pectinatus), with the novelty that we distinguished between different forms of heterogeneity: spatial variance (the frequency distribution of densities) and spatial pattern (the spatial distribution of densities). We repeatedly measured plant biomass that was affected by swan predation, winter mortality, and summer regrowth. Spatial variance was enhanced mostly by swan foraging, despite the fact that swans appear to exploit patches to the same threshold level. Spatial pattern, which had vanished after swan foraging, reestablished due to spatial pattern in winter mortality and was further enhanced by plant regrowth. We found that variance and pattern each have their own temporal dynamics and are maintained by different biological processes. We therefore advocate that it is pivotal to distinguish between variance and pattern in the study of spatial heterogeneity.
Persistence (discontinuity)
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Present study discussed spatial pattern techniques with respect to plant pathogens. Study was framed under four segments related with (a) basics of spatial pattern analytical tools and ideal strategies for their detection, (b) overview of certain techniques largely being utilized for different host-pathogens, (c) multivariate analysis to identify patterns for use of various spatial indices and finally (d) identification of gaps relate with spatial pattern analysis of pathogens. Spatial pattern indices used in fifty nine host-pathogen studies were evaluated and there utilization frequencies were accessed through skweness, kurtosis, Kolmogorov-Smirnov and Chi-square. Agglomerative Hierarchical Cluster classified the studies into six different groups. Following gaps were identified (a) some indices still not worked out for pathogens spatial patterns detection (b) a generalized framework still requires for phylogenetic connection between hosts or with its family and with distribution patterns of pathogen, (c) inter and intra-steric (pathogen and host) factors controlling spatial patterns of pathogen rarely approached, (d) spatiotemporal aspects of pattern detection are also not fully explored and (e) very few studies have approached management practices based on spatial patterns of pathogens. Some open ended tasks are: what is the line of difference between specialized and non-specialized pathogen for their spatial pattern? Is the availability of high inoculums ensuring uniform pattern? (if all other conditions are conducive), factor that controls random and clumped patterns and information’s on year to year variations of pathogen spatial patterns with similar crop/host.
Spatial organization
Hierarchical clustering
Pattern analysis
Identification
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Abstract Complex spatial patterns are common in coastal marine systems, but mechanisms underlying their formation are disputed. Most empirical work has focused on exogeneous spatially structured disturbances as the leading cause of pattern formation in species assemblages. However, theoretical and observational studies suggest that complex spatial patterns, such as power laws in gap-size distribution, may result from endogenous self-organized processes involving local-scale interactions. The lack of studies simultaneously assessing the influence of spatially variable disturbances and local-scale interactions has fuelled the idea that exogenous and endogenous processes are mutually exclusive explanations of spatial patterns in marine ecosystems. To assess the relative contribution of endogenous and exogenous processes in the emergence of spatial patterns, an intertidal assemblage of algae and invertebrates was exposed for 2 years to various combinations of intensity and spatial patterns of disturbance. Localized disturbances impinging at the margins of previously disturbed clearings and homogenous disturbances without any spatial pattern generated heterogeneous distributions of disturbed gaps and macroalgal patches, characterized by a truncated or a pure power-law scaling. Spatially varying disturbances produced a spatial gradient in the distribution of algal patches and, to a lesser extent, also a power-scaling in both patch- and gap-size distributions. These results suggest that exogenous and endogenous processes are not mutually exclusive forces that can lead to the formation of similar spatial patterns in species assemblages.
Spatial heterogeneity
Spatial organization
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Complex spatial patterns are common in coastal marine systems, but mechanisms underlying their formation are disputed. Most empirical work has focused on exogenous spatially structured disturbances as the leading cause of pattern formation in species assemblages. However, theoretical and observational studies suggest that complex spatial patterns, such as power laws in gap‐size distribution, may result from endogenous self‐organized processes involving local‐scale interactions. The lack of studies simultaneously assessing the influence of spatially variable disturbances and local‐scale interactions has fuelled the idea that exogenous and endogenous processes are mutually exclusive explanations of spatial patterns in marine ecosystems. To assess the relative contribution of endogenous and exogenous processes in the emergence of spatial patterns, an intertidal assemblage of algae was exposed for two years to various combinations of intensity and spatial patterns of disturbance. Localized disturbances impinging at the margins of previously disturbed clearings and homogenous disturbances without any spatial pattern generated heterogeneous distributions of disturbed gaps and macroalgal patches, characterized by a power‐law scaling. Spatially varying disturbances produced a spatial gradient in the distribution of algal patches and, to a lesser extent, also a power‐law scaling in both patch‐ and gap‐size distributions. These results suggest that exogenous and endogenous processes are not mutually exclusive forces that can lead to the formation of similar spatial patterns in species assemblages.
Spatial heterogeneity
Spatial organization
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Spatiotemporal pattern
Spatial heterogeneity
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