Ashton Shortridge

Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for Multidisciplinary and Multiscale Integration

Abstract Traditional approaches to studying human–environment interactions often ignore individual-level information, do not account for complexities, or fail to integrate cross-scale or cross-discipline data and methods, thus, in many situations, resulting in a great loss in predictive or explanatory power. This article reports on the development, implementation, validation, and results of an agent-based spatial model that addresses such issues. Using data from Wolong Nature Reserve for giant pandas (China), the model simulates the impact of the growing rural population on the forests and panda habitat. The households in Wolong follow a traditional rural lifestyle, in which fuelwood consumption has been shown to cause panda habitat degradation. By tracking the life history of individual persons and the dynamics of households, this model equips household agents with “knowledge” about themselves, other agents, and the environment and allows individual agents to interact with each other and the environment through their activities in accordance with a set of artificial-intelligence rules. The households and environment coevolve over time and space, resulting in macroscopic human and habitat dynamics. The results from the model may have value for understanding the roles of socioeconomic and demographic factors, for identifying particular areas of special concern, and for conservation policy making. In addition to the specific results of the study, the general approach described here may provide researchers with a useful general framework to capture complex human–environment interactions, to incorporate individual-level information, and to help integrate multidisciplinary research efforts, theories, data, and methods across varying spatial and temporal scales.

Measuring geographic access to health care: raster and network-based methods

BackgroundInequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted.MethodsWe examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment.ResultsIn both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan.ConclusionsDifferences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Pastoral Herd Management, Drought Coping Strategies, and Cattle Mobility in Southern Kenya

Livestock mobility facilitates opportunistic grazing management strategies that pastoralists employ to counter environmental variability in rangelands. One such strategy is moving livestock to temporary camps that are closer to areas of underutilized forage during times of drought. In areas where pastoralists graze near large protected areas, movement into protected areas, where both forage quantity and quality are higher, is also a common strategy. The aim of this study is to test hypotheses of herd relocation and effects of seasonality and herd size on spatially explicit parameters of cattle mobility for Maasai pastoralists along the northern border of a protected area in Kenya. Modified Global Positioning System (GPS) collars were placed on cattle from ten Maasai households that recorded three parameters of mobility for hundreds of grazing orbits from August 2005 to August 2006. Data were grouped by two constraints—seasonality and herd size—and tested against the two types of enclosure locations (temporary camps and permanent settlements). Hypotheses were formed on the basis of the current knowledge within the literature and analyzed using a series of analyses of variance. Results suggest that household relocation reduces the stress faced by pastoralists and their cattle during the drought by (1) lowering the average total daily distance and time traveled by cattle, (2) directing cattle toward the protected area, and (3) concentrating cattle grazing in distinct areas within the protected area. Herd size was found to have no effect on duration of travel for pastoralists that choose not to relocate during the drought. The research demonstrates how the use of modified low-cost GPS collars can be an effective tool for capturing parameters of mobility and for inferring pastoralist–livestock–rangeland relationships.

Complex systems models and the management of error and uncertainty

For the complex systems modeller, uncertainty is ever-present. While uncertainty cannot be eliminated, we suggest that formally incorporating an assessment of uncertainty into our models can provide great benefits. Sources of uncertainty arise from the model itself, theoretical flaws, design flaws, and logical errors. Management of uncertainty and error in complex systems models calls for a structure for uncertainty identification and a clarification of terminology. In this paper, we define complex systems and place complex systems models into a common typology leading to the introduction of complex systems specific issues of error and uncertainty. We provide examples of complex system models of land use change with foci on errors and uncertainty and finally discuss the role of data in building complex systems models.

Assessing alternatives for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales

We introduce and evaluate three methods for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales: (a) sequential categorical swapping, (b) simultaneous categorical swapping, and (c) simulated annealing. Method 1, a modification of a binary pixel-swapping algorithm, allocates each class in turn to maximize internal spatial autocorrelation. Method 2 simultaneously examines all pairs of cell-class combinations within a pixel to determine the most appropriate pairs of sub-pixels to swap. Method 3 employs simulated annealing to swap cells. While convergence is relatively slow, Method 3 offers increased flexibility. Each method is applied to a classified Landsat-7 ETM+ dataset that has been resampled to a spatial resolution of 210 m, and evaluated for accuracy performance and computational efficiency.

Weighting function alternatives for a subpixel allocation model

This study investigates the “pixel-swapping” optimization algorithm proposed by Atkinson for predicting subpixel landcover distribution. Two limitations of this method are assessed: the arbitrary spatial range value and the arbitrary exponential model for characterizing spatial autocorrelation. Various alternative weighting functions are evaluated. For this assessment, two different simulation models are employed to develop spatially autocorrelated binary class raster maps. These rasters are then resampled to generate sets of representative medium-resolution class maps. Prior to conducting the subpixel allocation, the relationship between cell resolution and spatial autocorrelation, as measured by Moran’s I, is evaluated. It is discovered that the form of this relationship depends upon the simulation model. For all tested weighting functions (Nearest Neighbor, Gaussian, Exponential, and IDW), the pixel swapping method increased classification accuracy compared with the initial random allocation of subpixels. Nearest Neighbor allocation performs as well as the more complex models of spatial structure.

Comparison and validation of SRTM and ASTER GDEM for a subtropical landscape in Southeastern China

This paper evaluates the quality characteristics of existing versions of 3-arc-second SRTM and 1-arc-second GDEM over Anji County, Zhejiang, China using reference elevations from a high-quality 1:10 k topographic map. Results show that SRTM has higher accuracy (RMSE =12.44 m) than GDEM (RMSE=14.20 m for Version 1, 12.76 m for Version 2); however, unsatisfactory void filling and an overall 1/2 pixel shift exits in SRTM version 4.1. Although spurious elevations over omitted water bodies still persist, GDEM Version 2 demonstrated significant improvement over Version 1. Accuracies of both SRTM and GDEM decrease on steeper slopes. Aspect also influences both the magnitude and the sign of errors. DEM accuracy in non-forested areas is considerably higher than that in forested areas. SRTM version 4.1 and GDEM version 2 possessed actual spatial resolutions of 90 m, despite both of them failed to match their nominal accuracies. SRTM Version 4.1 could be the first choice, while ASTER GDEM Version 2 would be a good alternative for areas where extensive voids exist in SRTM.

Practical limits of Moran’s autocorrelation index for raster class maps

Abstract This paper is concerned with the calculation of the Moran index of spatial contiguity, also known as Moran’s I, on binary raster maps. While the theoretical range of I extends from roughly −1 to 1 for a raster map, in practice possible I values are much more restricted. The nature of the restriction is due to the rigid cell framework, which defines contiguity, as well as the relative proportions of ones to zeros in the binary raster. This paper presents experimental findings demonstrating the practical limits of Moran’s I under a variety of different proportions and contiguity measures. Results for negatively autocorrelated surfaces are particularly noteworthy; unequal proportions of ones and zeros can result in minimal I values considerably larger than −1, and in many cases considerably larger than 0. Implications for the use of I on raster maps are considered, as is the potential relevance of negative spatial autocorrelation and its measurement.

Spatial assessment of attitudes toward tigers in Nepal.

In many regions around the world, wildlife impacts on people (e.g., crop raiding, attacks on people) engender negative attitudes toward wildlife. Negative attitudes predict behaviors that undermine wildlife management and conservation efforts (e.g., by exacerbating retaliatory killing of wildlife). Our study (1) evaluated attitudes of local people toward the globally endangered tiger (Panthera tigris) in Nepal’s Chitwan National Park; and (2) modeled and mapped spatial clusters of attitudes toward tigers. Factors characterizing a person’s position in society (i.e., socioeconomic and cultural factors) influenced attitudes toward tigers more than past experiences with tigers (e.g., livestock attacks). A spatial cluster of negative attitudes toward tigers was associated with concentrations of people with less formal education, people from marginalized ethnic groups, and tiger attacks on people. Our study provides insights and descriptions of techniques to improve attitudes toward wildlife in Chitwan and many regions around the world with similar conservation challenges.

The influence of land cover on Shuttle Radar Topography Mission (SRTM) elevations in low-relief areas.

The Shuttle Radar Topography Mission (SRTM), the first relatively high spatial resolution near-global digital elevation dataset, possesses great utility for a wide array of environmental applications worldwide. This article concerns the accuracy of SRTM in low-relief areas with heterogeneous vegetation cover. Three questions were addressed about low-relief SRTM topographic representation: to what extent are errors spatially autocorrelated, and how should this influence sample design? Is spatial resolution or production method more important for explaining elevation differences? How dominant is the association of vegetation cover with SRTM elevation error? Two low-relief sites in Louisiana, USA, were analyzed to determine the nature and impact of SRTM error in such areas. Light detection and ranging (LiDAR) data were employed as reference, and SRTM elevations were contrasted with the US National Elevation Dataset (NED). Spatial autocorrelation of errors persisted hundreds of meters spatially in low-relief topography; production method was more critical than spatial resolution, and elevation error due to vegetation canopy effects could actually dominate the SRTM representation of the landscape. Indeed, low-lying, forested, riparian areas may be represented as substantially higher than surrounding agricultural areas, leading to an inverted terrain model.