Purpose of the Review This paper reviews progress in the field of landscape ecology related to the development of landscape metrics (i.e., spatial pattern indices). We first review the major formative historical developments that contributed to the coalescence of landscape metrics as a sub-field of landscape ecology and then examine recent literature highlighting several shortcomings related to their utility for understanding ecological processes and discuss several alternative approaches. Recent Findings Recent research recognizes some limitations of the patch-mosaic model (PMM), including the landscape metrics based on it, for capturing landscape heterogeneity and measuring functionality. Collapsing land cover information into nominal classes complicates identification of ecologically meaningful relationships and effective management. We explore several alternative methods for capturing landscape functionality and spatial heterogeneity including graph-based networks and gradient surface models with associated surface metrics. Summary With complementary patch-based, gradient, and graph network models available, the goal for landscape ecologists is to select the correct approach, or combination of approaches, for investigating the issue at hand. Biases associated with the modifiable areal unit problem (MAUP) and its connection to heterogeneity and scale-both grain and extent-complicate these decisions, but empirical tools from spatial allometry may improve the ability for landscape ecologists to assess where metrics are capturing ecological processes versus the scale-dependency of the metrics themselves.
The scientific method is predicated on the assumption that research designs and results can be reproduced and replicated. However, recent findings in some disciplines suggest that many studies fail to reach this standard, moving issues surrounding reproducibility and replicability forward into the research agenda of those fields. While the topic has yet to become a point of controversy in geography, the intricacies of geographic phenomena and spatial data analysis make the field vulnerable to criticism. This commentary discusses how uncertainties related to the conception, measurement, analysis, and communication of geographic analyses contribute to difficulties in the reproduction and replication of geographic research. Investigating how these uncertainties collectively impact the reproducibility and replicability of spatial data analyses should be a critical focus of future Geographical Analysis research. A call to action for geographers to improve the reproducibility and replicability of their work and specific recommendations on how Geographical Analysis might facilitate this process conclude the commentary.
Local spatial statistics measure and test for spatial association for a variable or variables of interest in a geographic neighborhood surrounding a predefined location. Most applications adopt a single scale of analysis but give little attention to the scale of the process generating the data. Alternatively, when the researcher is uncertain about the process scale, local statistics may examine a number of scales. In these cases, it is important to include a correction for multiple testing when evaluating the statistical significance of each local statistic, something that is rarely done. Consequently, local statistics are more likely to identify significant relationships, even when no meaningful spatial association exists. In this article, we develop a methodology for the local Moran statistic that provides both an empirical estimate of the spatial scale of association and an assessment of the significance of the statistic for that scale. The key idea is to test a number of possible choices for the statistic's weight matrix and then account for the multiple testing associated with the number of weight matrices examined. Unlike previous research, our statistic avoids the use of simulation to determine statistical significance in the presence of multiple testing. To test the validity of our approach, we constructed a numerical example to assess the statistic's performance and conducted an empirical study using leukemia data from central New York state. The developed statistic addresses the need for the empirical determination of weights and spatial scale. The test therefore addresses the common weakness of many applications, where weights are defined exogenously, with little or no thought given to either the definition or its implications. Los indicadores locales (local spatial statistics) evalúan la asociación espacial de una o varias variables de interés dada un área predefinida y sus áreas vecinas. La mayoría de dichas medidas utilizan una escala única de análisis y prestan poca atención a la escala del proceso de generación de los datos. En los casos en los que el investigador no está seguro de la escala del proceso, las los indicadores locales pueden ser evaluados a varias escalas. En dichos casos, cuando se hace la evaluación de la significancia estadística de cada indicador local, es importante incorporar una corrección para pruebas múltiples (multiple tests), un ajuste que raramente se realiza en la gran mayoría de estudios. Debido al problema de pruebas múltiples, los indicadores locales son más propensos a identificar relaciones significativas, incluso cuando no existe asociación espacial significativa alguna. En este artículo los autores desarrollan una metodología que produce un índice local de Moran que proporciona tanto una estimación empírica de la escala espacial de la asociación así como una evaluación de la importancia del indicador para dicha escala. La idea clave es poner a prueba una serie de opciones posibles para la definición de la matriz de pesos espaciales (spatial weight matrix) del índi...
Introduction Bioscience is a major growth industry within the United States. The bioscience industry directly employed over 1.3 million Americans in 2006 (Battelle Technology Partnership Practice, 2008). To capture the regional economic growth potential of biotechnology, each US state has in place programs and incentives to attract and develop local biotechnology industries. Insights from the study of the evolution of this sector often contribute to the design of these policy incentives (eg wet labs and other facilities). However, much of the existing research on the biotechnology industry focuses on strategic considerations motivating geographic patterns in one area of the biotechnology industry, human therapeutics and diagnostics. Not surprisingly, then, research treats the industry homogenously. Firms pursue similar goals using similar strategies with emphasis placed on how a continual need for innovation drives firm collaboration strategy and location choice. However, the industry may be segmented into a number of subsectors, in order to provide insights into variations in collaboration and location strategies (Audretsch and Feldman 2003; Clark, 2006). In this paper we adopt such an approach, segmenting the industry into health (1) and agricultural (2) biotechnology sectors. The purpose of this paper is to characterize firm-specific differences that exist between health and agricultural biotechnology firms. Specifically, we use a survey instrument to understand how firms in each sector perceive and use different forms of collaboration and location factors in their operations. Collaboration and location decisions are commonly stressed in theoretical frameworks explaining and analyzing
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.