Measuring biological heterogeneity in the northern mixed prairie: a remote sensing approach

Biological heterogeneity, defined as the degree of dissimilarity between biological variables (e.g., biomass, green vegetation and Leaf Area Index [LAI]), is one of the most important and widely applicable concepts in ecology due to its close link with biodiversity. To investigate grassland biological heterogeneity, we selected three transects extending from upland to valley grasslands at Grasslands National Park (GNP), Canada, representing the northern mixed grassland. For the purposes of our analysis, three types of data were collected: remote sensing ground level hyperspectral data, biological data (LAI, biomass, and vegetation cover) and environmental data (soil moisture, organic content, and bulk density). Methodologically, field-level remote sensing data were used to calculate spectral vegetation indices. These indices, plus the biological variables, were then used in regression analyses with the goal of assessing the feasibility of using remote sensing data to study biological heterogeneity. The results indicate that it is feasible to use ground-level remote sensing data to represent biological variables. These indices can explain about 40–60 percent of the biological variation. Semivariogram analyses were further applied on these data to investigate their range of spatial variation. Spatial variations in the     

Using Linear Integer Programming for Multi‐Site Land‐Use Allocation

Research in the area of spatial decision support (SDS) and resource allocation has recently generated increased attention for integrating optimization techniques with GIS. In this paper we address the use of spatial optimization techniques for solving multi‐site land‐use allocation (MLUA) problems, where MLUA refers to the optimal allocation of multiple sites of different land uses to an area. We solve an MLUA problem using four different integer programs (IP), of which three are linear integer programs. The IPs are formulated for a raster‐based GIS environment and are designed to minimize development costs and to maximize compactness of the allocated land use. The preference for either minimizing costs or maximizing compactness has been made operational by including a weighting factor. The IPs are evaluated on their speed and their efficacy for handling large databases. All four IPs yielded the optimal solution within a reasonable amount of time, for an area of 8 × 8 cells. The fastest model was successfully applied to a case study involving an area of 30 × 30 cells. The case study demonstrates the practical use of linear IPs for spatial decision support issues.     

Optimizing the Spatial Structure of the Agricultural Production Function

Important characteristics of spatial agricultural production functions are derived by introducing a non‐negative curvilinear spatial demand function for production input intensities. Given the usual neoclassical rationale assumptions of spatial demand for capital and labor inputs under competitive environment of farming in developing agricultural economies, the optimal production levels are determined by optimizing spatial demand for production inputs. Decreasing price‐to‐transport costs ratio (that is, decrease in the prices of capital goods or increase in freight rates) and increasing wage‐to‐travel costs ratio (that is, increase in labor wages or decrease in the travel rate) expand the limits of the (spatial) optimal boundary of the demand for agricultural capital goods and labor input respectively. These effects occur on account of the operation of (positive) spatial price gradient and (negative) wage‐gradient in the market region. It may be noted that elasticities of demand for production factors are spatially variant and have significant effects on the alterations in the structure of agricultural production. However, the spatial optimal solution of production has a complicated relationship with them. The price elasticity has negative and wage elasticity has positive spatial gradients in the market region. Farmers located in the periphery of the market region are not much affected by the proportionate changes occurring in the prices of agricultural capital goods but are more sensitive to the proportional changes in labor wages. Because of a decreasing trend in capital input demand and increase in labor input with distance from the market, capital‐product diminishes with a decreasing rate and labor‐product increases with an increasing rate in the spatial structure of agricultural production. As a result, capital‐labor ratio falls toward zero, which raises profit rate per unit of capital investment especially in the outer part of the market region. The equilibria of optimal production with price elasticity as well as of capital intensity with labor employment (that is, capital‐labor ratio as unity) determine spatial limits of the optimal production zone which is shifted outward subject to the provision of cheap transportation, stabilizing market prices and/or increasing wage rate at the market center. It will help in extending outwardly the optimal spatial limits of capital investment and will mobilize capital resources of farmers in the periphery for efficient and competitive capital‐dominated farming.     

The Response of Isolated Bridges Accounting for Spatial Variability of Ground Motion

The effects of the spatial variability of ground motion (loss of coherence, wave passage, and local site conditions) on the response of isolated bridges are investigated.

Therefore, a statistical approach is adopted to represent uncertainties in both the bridge configuration and the ground motion variability. The response of isolated bridges, designed for a standard input motion, under a spatially varying ground motion, is evaluated by nonlinear time-history analyses; the system performance is measured by the displacement demand on isolators.

Results show that the phenomenon affects the structural response considerably; the demand increases for the majority of isolators, irrespective of bridge configuration.     

北京都市区就业空间分异与职住空间错位行业识别

城市就业空间分异与职住空间错位是反映城市空间结构特征且存在密切联系的两项重要议题。分析了北京都市区就业空间分异特征，识别了对职住空间错位影响显著的行业。结果表明：①从城市中心向外延伸，分布产业高度具有显著的梯度性和圈层结构特征；②就业行业空间分布可分为四种类型：中心城区集中就业的高端商业和生产性服务业，中心城区为主近郊区集聚就业的文化科技型服务业，近郊就业为主的传统服务业和生活服务业，远郊就业为主的保障性服务业和生产制造业；③批发和零售业，信息传输、软件和信息技术服务业，金融业，居民服务、修理和其他服务业及教育业等五大行业对北京都市区职住空间错位影响显著。     

Assessing Regional Crop Irrigation Requirements and Streamflow Availability for Irrigation Development in Saginaw Bay,Michigan

Spatial and temporal variability of soil, crop, and climate significantly affects the estimates of regional irrigation requirements. This paper incorporates soil association data and multiple-station, multiple-year climate data into four simulation models, CERES-MAIZE, SOYGRO, BEANGRO, and YIELD to account for the spatial and temporal variability of the input parameters in estimating the evapotranspiration rates and irrigation requirements of corn, drybean, soybean, and sugarbeet production in the Saginaw Bay basin, Michigan. The Thiessen method was used to delineate the spatial coverage of the weather stations in the study watershed. The results of the simulated crop irrigation requirements at the soil association level were multiplied by the weights of each soil association area to derive regional irrigation requirements. The availability of streamflow at different exceedence probabilities was evaluated to determine the maximum irrigation area the stream is able to sustain without causing water quality degradation. Through a case study of the Saginaw Bay irrigation development, this paper demonstrates that the spatial and temporal variability of soil, crop, and climate can be well represented in the simulation models to provide more realistic simulation results in support of irrigation decision making.     

Data Integration Issues in Research Supporting Sustainable Natural Resource Management

Current decision‐making in natural resource use and management aims at delivering ecologically‐sustainable development to achieve conservation and economic benefits. The process of guiding natural resource use requires the integration of social, economic and biophysical information on which to base management decisions. This paper discusses the integration of socio‐economic information for natural resource management (NRM) planning and decision‐making in the Australian context. A comprehensive resource of socio‐economic data is the Census, which is undertaken every five years by the Australian Bureau of Statistics (ABS) for the whole of Australia. Unfortunately there are qualitative and quantitative issues stemming from the use of ABS census data maps for NRM decision‐making, as they are at a different scale to and the boundaries do not coincide with biophysical information. These issues include the variable shape of collection districts, the use of enumerated data for population‐based statistics, the large size of collection districts in low populated areas, and the averaging of socio‐economic information over the collection districts. Examples highlight these issues and show a way forwards in improving data integration, which includes simple spatial overlay methods and regression modelling.     

Comment réorganiser le réseau postal suisse? Une application de l'aide à la décision territoriale

How to reorganize the Swiss postal network? A case study of spatial decision making‐support. This article presents a case study of spatial decision making‐support in relation to the Swiss Post and its efforts to reorganize the postal network in eight Swiss cities. The first part of the article describes the methodology and highlights selected original elements used in the processing of geographic information (spatial analysis and multi‐criteria analysis). The second part, more analytical, examines the process used to apply the method defined in the previous section. Based on a constructivist approach of decision‐support, it explores the modes of interaction between available information and decision makers. The analysis shows that the decision maker is relatively difficult to identify since the decision making process includes a set of partial and dependent decisions that are taken by a diversity of people. In conclusion, geographic information played an important role, not only on the decision, but mainly in allowing decision makers to better understand spatial complexity     

Managing Environmental Impacts of Recreation and Tourism in Rainforests of the Wet Tropics of Queensland World Heritage Area

This paper describes environmental impacts of tourism and recreation activities in the world heritage listed rainforests of northeast Australia and presents management strategies for sustainable visitor use of the protected area. Tropical rainforests are characterised by their low resistance and moderate to high resilience to impacts associated with human visitation. Visitor use in the World Heritage Area is mostly associated with walking tracks, camping areas, day use areas and off‐road vehicle use of old forestry roads and tracks. Adverse environmental impacts range from vegetation trampling, soil compaction, water contamination and soil erosion at the local scale through to spread of weeds, feral animals and soil pathogens along extensive networks of old forestry roads and tracks at the regional scale. Concentration of visitor use is the most desirable management strategy for controlling adverse impacts at most World Heritage Area visitor nodes and sites, and includes methods such as site hardening and shielding to contain impacts. For dispersed visitor activities, such as off‐road vehicle driving and long‐distance walking, application of best practice methods by the tourist industry and recreational users such as removal of mud and soils from vehicle tyres and hiking boots before entering pathogen‐free catchments, together with seasonal closure of roads and tracks, are the preferred management strategies. Retention of canopy cover at camping areas and day use areas, as well as along walking tracks and forestry roads is a simple, yet effective, management strategy for reduction of a range of adverse impacts, including dispersal of weeds and feral animals, edge effects, soil erosion and nutrient loss, road kill and linear barrier effects on rainforest fauna.     

NEW DIRECTIONS FOR URBAN ECONOMIC MODELS OF LAND USE CHANGE: INCORPORATING SPATIAL DYNAMICS AND HETEROGENEITY*

ABSTRACT We review the usefulness of urban spatial economic models of land use change for the study and policy analysis of spatial land use–environment interactions. We find that meaningful progress has been made in econometric and monocentric models extended to account for multiple sources of spatial heterogeneity and in the development of general equilibrium models with spatial dynamics. Despite these advances, more work is needed in developing models with greater realism. Most agent‐based computational models of urban land use change currently lack economic fundamentals, but provide a flexible means of linking microlevel behavior and interactions with macrolevel land use dynamics. In combination with empirical methods to identify parameters, this framework provides a promising approach to modeling spatial land use dynamics and policy effects.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

Land suitability assessment for locating industrial parks: a hybrid multi criteria decision‐making approach using Geographical Information System

Industrial areas play a critical role in urban and regional planning, especially for developing countries where reliable strategies for these areas can promote economic and environmental efficiency. The present study provides an integration of hybrid multi‐criteria decision‐making (MCDM) theories and Geographical Information System (GIS) processes in order to assess the suitability of an industrial location. Unlike traditional models, an efficient decision analysis demands handling uncertainties and considering dependencies between criteria. The proposed MCDM framework uses fuzzy theory because of the vagueness of experts' judgements. Moreover, Decision‐Making Trial and Evaluation Laboratory (DEMATEL) is employed to investigate interrelationships among the criteria. In the proposed empirical solution, analytic network process (ANP) principles are used to deal with systematic interactions. We considered several factors such as accessibility, topography, proximity, and socioeconomic characteristics in the decision‐making procedure for a sustainable industrial park. The system is applied to Hamadan province, Iran to determine appropriate locations that are the results of the aggregation of criteria maps in a GIS environment. The results demonstrate that accessibility and economic indicators are essential for choosing an industrial park's location. Additionally, the proposed method can be applied for an efficiency evaluation of available industrial parks.     

Forecasting in a Small and Unstable Regional Economy Using Regime Shifting Models: The Case of Extremadura

We consider forecasting in a small and unstable regional economy subject to structural breaks. In this context, we work with two types of regime‐shifting databased models using cointegration theory. The objective of the present work is to analyze the out‐of‐sample forecasting performance of the two approaches used to construct a short‐term regional econometric model: stochastic and deterministic time varying parameters models. The forecasting experiments will be illustrated by specifying, and estimating an econometric model for Extremadura, a small and unstable region in southwestern Spain.     

The Meul Index: A non‐destructive method for estimating the cortical index in skeletal samples

The study of osteoporosis in past populations provides insight into the history and evolution of this condition. The Cortical Index is a relative measure of cortical bone often used for identifying individuals with age‐related bone loss associated with osteoporosis. Current methods of measuring the Cortical Index involve radiography or cutting actual sections of long bones at the mid‐shaft. We propose a new index, the Meul Index as means for estimating the Cortical Index without the use of radiography. Based on a preliminary study of 13 individuals consisting of 9 males, 3 females and one unknown sex, ranging in age from young to older adult, the Meul Index is highly correlated with the Cortical Index (r = 0.903). The proposed Meul Index provides a non‐destructive and inexpensive means for studying age‐related bone loss in prehistoric and historic populations represented by skeletal samples. Our research builds on the recent publication of Silva, A.M., Crubezy, E. and Cunha, E. 2008. Bone weight: new reference values based on a modern Portuguese identified skeletal collection. Int. J. Osteoarchaeol. DOI: 10.1002/oa.998. Copyright © 2009 John Wiley & Sons, Ltd.     

A framework for statistical inferential decisions in spatial pattern analysis

The desire of many geographical information science (GIS) practitioners to undertake sophisticated spatial pattern analysis has been facilitated by the increasing availability of specialised software and the appearance of pedagogic papers illustrating the application of various techniques. However, the appropriate use of these techniques also requires an understanding of the nature of hypothesis testing and statistical inference for spatial data. Since there is little information currently available to aid the GIS practitioner in this regard, we offer such guidance here. We do so by revisiting the steps involved in spatial pattern analysis. Our perspective is based on the notion of spatial stochastic models and is presented as a decision tree. The four levels of the tree (i.e., sequential decisions) are associated with the assumptions, the type of data representation and the types of questions asked by the analyst. We emphasise the scientific and educational challenges involved.     

Assessing Regional Crop Irrigation Requirements and Streamflow Availability for Irrigation Development in Saginaw Bay,Michigan

Spatial and temporal variability of soil, crop, and climate significantly affects the estimates of regional irrigation requirements. This paper incorporates soil association data and multiple-station, multiple-year climate data into four simulation models, CERES-MAIZE, SOYGRO, BEANGRO, and YIELD to account for the spatial and temporal variability of the input parameters in estimating the evapotranspiration rates and irrigation requirements of corn, drybean, soybean, and sugarbeet production in the Saginaw Bay basin, Michigan. The Thiessen method was used to delineate the spatial coverage of the weather stations in the study watershed. The results of the simulated crop irrigation requirements at the soil association level were multiplied by the weights of each soil association area to derive regional irrigation requirements. The availability of streamflow at different exceedence probabilities was evaluated to determine the maximum irrigation area the stream is able to sustain without causing water quality degradation. Through a case study of the Saginaw Bay irrigation development, this paper demonstrates that the spatial and temporal variability of soil, crop, and climate can be well represented in the simulation models to provide more realistic simulation results in support of irrigation decision making.     

Model Uncertainty in Matrix Exponential Spatial Growth Regression Models

This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, k‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.     

Using an Evolutionary Algorithm in Multiobjective Geographic Analysis for Land Use Allocation and Decision Supporting

Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision‐making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA‐II 1 (we call it HNSGA‐II), along with Geographical Information System (GIS) 2 to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA‐II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision‐makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.     

Individual Accessibility Revisited: Implications for Geographical Analysis in the Twenty‐first Century

Analytical methods for evaluating accessibility have been based on a spatial logic through which the impedance of distance shapes mobility and urban form through processes of locational and travel decision making. These methods are not suitable for understanding individual experiences because of recent changes in the processes underlying contemporary urbanism and the increasing importance of information and communications technologies (ICTs) in people's daily lives. In this paper we argue that analysis of individual accessibility can no longer ignore the complexities and opportunities brought forth by these changes. Further, we argue that the effect of distance on the spatial structure of contemporary cities and human spatial behavior has become much more complicated than what has been conceived in conventional urban models and concepts of accessibility. We suggest that the methods and measures formulated around the mid‐twentieth century are becoming increasingly inadequate for grappling with the complex relationships among urban form, mobility, and individual accessibility. We consider some new possibilities for modeling individual accessibility and their implications for geographical analysis in the twenty‐first century.     

ESTIMATION OF THE AGE OF A PRE‐INDUSTRIAL IRON SITE IN KOREA*

A pre‐industrial iron‐smelting site in Korea, whose period of active use is placed on typological grounds between the eighth and 18th centuries ad , has been dated using accelerator mass spectrometry and thermoluminescence. The ages of two charcoal samples were 77 ± 25 and 81 ± 25 years bp , with the calibrated date for both ranging from ad 1700 to ad 1955, while a clay fragment dated to ad 1650 ± 50. The results from typology and radiocarbon seem insignificant due to the considerable uncertainties involved, but in combination with the thermoluminescence result they place the most probable date of the site at around ad 1700.