Abstract
Spatial data are used in many scientific domains including analyses of Ecosystem Services (ES) and Natural Capital (NC), with results used to inform planning and policy. However, the data spatial scale (or support) has a fundamental impact on analysis outputs and, thus, process understanding and inference. The Modifiable Areal Unit Problem (MAUP) describes the effects of scale on analyses of spatial data and outputs, but it has been ignored in much environmental research, including evaluations of land use with respect to ES and NC. This paper illustrates the MAUP through an ES optimisation problem. The results show that MAUP effects are unpredictable and nonlinear, with discontinuities specific to the spatial properties of the case study. Four key recommendations are as follows: (1) The MAUP should always be tested for in ES evaluations. This is commonly performed in socio-economic analyses. (2) Spatial aggregation scales should be matched to process granularity by identifying the aggregation scale at which processes are considered to be stable (stationary) with respect to variances, covariances, and other moments. (3) Aggregation scales should be evaluated along with the scale of decision making (e.g., agricultural field, farm holding, and catchment). (4) Researchers in ES and related disciplines should up-skill themselves in spatial analysis and core paradigms related to scale to overcome the scale blindness commonly found in much research.
Highlights
Spatial scale—the spatial scale of measurement or in geostatistics, spatial support—has huge impacts on spatial analyses, model outputs and, process understanding
There are a number of recommendations arising from this work for research that incorporates analyses of land use, Ecosystem Service (ES) and Natural Capital (NC) evaluations: 1. Modifiable Areal Unit Problem (MAUP) should always be tested for
Any analysis of spatial data should routinely test for MAUP in order to understand the specific impacts of aggregations scales relative to the spatial support of the process being investigated
Summary
Spatial scale—the spatial scale of measurement or in geostatistics, spatial support—has huge impacts on spatial analyses, model outputs and, process understanding. Spake et al [2] applied forest models captured over stands (a specific spatial unit in forestry) to 10 km gridded data and Finch et al [3] used a nutrient delivery model constructed over a 50 m grid to make inferences on 1 km squares Such scale mismatches affect the robustness of the results and have implications for the reliability of any policy or planning recommendations arising from them. This paper seeks to highlight the importance of considering and evaluating the impact of scale using a hypothetical ES optimisation problem In so doing, it addresses this key methodological gap in current approaches to landscape, land use, NC and ES evaluations related to scale and the impacts of the Modifiable Areal Unit Problem or MAUP [4–6]. MAUP’s implications for any landscape or land use evaluation framework are profound: it results in divergent evaluations [7], mis-specification of ES trade-offs and synergies [17] and the mislocation of hot spots [19] compounding an already difficult task of identifying locally appropriate land use scenarios [20]
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