Toward an Ecological Explanation of Agricultural Unemployment in Bangladesh

Cultural ecology theoretical framework was found suitable to explain unemployment in agriculture in three villages in Bangladesh. Path analysis and multiple regression statistics were used to examine the direct, indirect, and total effects of eight independent human, social, environmental, and technology variables as well as their aggregate contribution on agricultural unemployment. The multiple regression model explains 86.2% of the total variation in unemployment in agriculture; they were followed by environmental constraints and labor saving technology variables.     

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

贵州省猫跳河流域耕地空间分布格局模拟

选取贵州省猫跳河流域1995年度海拔高度、坡度、人口密度、人均GDP等栅格数据,通过随机空间采样,建立1995年耕地空间分布概率多元Logistic回归方程。运用GIS技术,结合2000年相关数据,模拟2000年该流域的耕地空间分布格局,并与实际的2000年耕地空间分布格局进行验证,结果较为理想,为土地利用变化的空间分布格局模拟提供一种新方法。     

THE IMPORTANCE OF SCALE IN MEASURING THE SECONDARY EFFECTS IN REGRESSION ANALYSIS

This study proposes a method of using ecological¹ variables in regression analysis to identify the weak or secondary effects of independent variables without introducing spurious correlation due to aggregation.     

Green Costs,Red Ink: Determinants of Municipal Defensive Expenditures inOntario

Governments at all levels expend resources to defend the environment from the adverse effects of human activity. Currently little is known about the processes and variables associated with these environmentally “defensive” expenditures. This paper attempts to fill this void by addressing the following research question: What factors induce municipal governments to expend scarce resources to defend against the adverse effectsof environmental change? The paper tackles this question with a combination of traditional statistical analysis and new environmental accounting techniques.Specifically, budgetary content analyses and secondary data are combined to develop a multiple regression model. The model uses the 1991 per capita defensive expenditures of 832 municipal governments as the response variable. These data are aggregated into 49 regions to ensure administrative commensurability. Model selection began with a wide array of predictor variablesthought to exert an influence on the defensive expenditures. The final model includes four predictor variables: provincial transfer payments for defensive expenditures per capita, property taxes per capita, median household income, and total pollution output per region. These four variables are significantly and positively associated with 73.5% of the variation in defensive expenditures (adjusted R² = .735, p < .0001). Aninterpretation of the results suggests a need for a more place-sensitive approach to environmental accounting to ensure differences in environmental quality do not confound the analysis.     

Interpreting What is Rural and Urban for Western U.S. Counties

The paper examines how rural and urban are interpreted and applied to counties in eleven states in the western United States. After reviewing various conceptual approaches, we turn to a three-part analysis of county commissioners' perceptions and census data to identify characteristics associated with urban-ness and rurality. The analysis involves comparing qualitative and quantitative survey and interview data, using a multiple regression analysis to correlate census variables with commissioners' perceptions of their home counties, and using cluster analysis techniques on census variables to identify patterns and unevenness in rurality and urbanness. Three characteristics mentioned by the widest range of commissioners and found to be statistically significant in the regression analysis were population concentration, total population, and the agricultural land base. Population concentration, in particular, was identified most frequently by interviewees as the single most important variable in characterizing an urban county and had the most meaningful contribution to predicting commissioners' perceptions of their home counties in the regression analysis. The cluster analysis identified five county types: largest urban centers, growing regional hubs, high growth rural, dispersed rural, and stable rural agriculture. These county types were widely distributed, reflecting the spatial unevenness of macroscale processes operating across eleven western states.     

Interpreting What is Rural and Urban for Western U.S. Counties

The paper examines how rural and urban are interpreted and applied to counties in eleven states in the western United States. After reviewing various conceptual approaches, we turn to a three‐part analysis of county commissioners' perceptions and census data to identify characteristics associated with urban‐ness and rurality. The analysis involves comparing qualitative and quantitative survey and interview data, using a multiple regression analysis to correlate census variables with commissioners' perceptions of their home counties, and using cluster analysis techniques on census variables to identify patterns and unevenness in rurality and urbanness. Three characteristics mentioned by the widest range of commissioners and found to be statistically significant in the regression analysis were population concentration, total population, and the agricultural land base. Population concentration, in particular, was identified most frequently by interviewees as the single most important variable in characterizing an urban county and had the most meaningful contribution to predicting commissioners' perceptions of their home counties in the regression analysis. The cluster analysis identified five county types: largest urban centers, growing regional hubs, high growth rural, dispersed rural, and stable rural agriculture. These county types were widely distributed, reflecting the spatial unevenness of macroscale processes operating across eleven western states.     

中国表层土壤全氮的空间模拟分析

基于第二次全国土壤普查5336个典型土壤剖面数据,分析表土全氮(A层)与环境因素的相关关系,利用多元回归模型和HASM模型结合的方法模拟中国国家尺度上表层土壤全氮的空间分布格局。结果表明:对350个检验点模拟结果的平均绝对误差和平均相对误差为0.67g·kg^-1和61.06%,与普通克里格法相比分别降低了0.05g·kg^-1和17.53%;对样点分布较少的西北地区的模拟结果也更符合实际情况。多元回归模型和HASM模型结合的方法考虑了环境因素的影响,可作为目前模拟大尺度土壤性质空间分布相对有效的方法。     

Responding to Negative Public Attitudes towards Immigration through Analysis and Policy: regional and unemployment dimensions

ABSTRACT

This paper examines two key dimensions of the impact of immigration for Australia and related policy aspects. One is sub-national and the other is national. They are, first, the regional location aspects of immigration and, second, the aggregate unemployment implications of immigration. These are chosen so as to focus on two important issues that condition public attitudes towards immigration. In relation to the first, there is a common positive view that channelling migration towards regional areas assists regional development and reduces pressure on metropolitan areas. The paper reviews regional concepts embodied in Australian immigration policy and the ways in which visa arrangements have implemented policies geared towards the regional dispersal of immigrants. Using official data, it discusses the demographic impacts of these policies and, in particular, considers the extent to which immigrants to regional Australia remain there over the longer term. In relation to unemployment, a common concern is that immigrants take jobs from local workers. The paper examines—using statistical regression methodology—the relationship between immigration and national aggregate unemployment in Australia. It evaluates the net consequences of immigration for both existing residents and new arrivals together. The paper concludes that, with good policy design in each case, regional location encouragement can be effective for immigrants and that immigrants need not take more jobs than they create. The analysis demonstrates that mixed-methods approaches to important social science issues can be productive, and helpful also for policy. Evidence, such as that presented in this paper, offers a powerful basis from which to counter negative public and political discourses surrounding immigration in contemporary Australia.     

Using Dummy Variables to Estimate Economic Base Multipliers

Economic base analysis is a cost-effective and accurate means of predicting employment impacts in relatively small and uncomplicated regional (community) economies. Extending previous research using the Arizona Community Data Set, this paper estimates economic base multipliers in different types of communities by introducing dummy variables into regression equations. Total employment, transfer payments, and population potential are used as explanatory variables. Manufacturing centers are shown to have larger multipliers than diversified places, mining settlements, service and trade communities, and utility towns.     

Assessing Collaborative Conservation: A Case Survey of Output,Outcome, and Impact Measures Used in the Empirical Literature

Abstract

Much existing research on collaborative conservation has focused on process, even as researchers have called for greater attention to explaining what results these processes yield. It is time to take stock of collaborative conservation research by mapping what kinds of variables researchers are including in analyses. Here we conduct a case survey from the SCAPE database of environmental decision-making cases. We include cases involving collaboration across government, environmental protection, and resource exploitation interests in western democratic countries. Results reveal patterns in what researchers include in their outputs, outcomes, and impacts measures of collaborative conservation. While there is little difference by publication type (peer-reviewed journals, scholarly book chapters, or gray literature) or over time, we find significant differences in explicit measures across variable types. In particular, variables more proximate to process in a logic chain are more often measured, as are social rather than ecological variables.     

Benthos of a perennially-astatic, saline, soda lake in Mexico

The effects of multiple stressors on the benthic macroinvertebrate community were monitored in Tecuitlapa Norte, a shallow, perennially-astatic, warm, mesosaline, sodaalkaline lake in Mexico. Physico-chemical and biological variables were determined monthly for one year. Tecuitlapa Norte displayed a clear seasonal environmental pattern (dry and rainy seasons). The benthic macroinvertebrate community consisted of five species:Culicoides occidentalis sonorensis Jorgensen,Ephydra hians Say,Stratiomys sp.,Eristalis sp., andLimnophora sp. of which the first two were dominant and the rest scarce.C. occidentalis was the most important species numerically (76 percent of the total), whileE. hians dominated the biomass (73 percent of the total). Primarily salinity and secondarily pH appear to be the most important environmental factors controlling dominance of benthic organisms in Tecuitlapa Norte. Seasonal abundance dynamics of the dominant organisms was associated with phases in their reproductive cycles: environmentally-triggered (i.e., temperature rise, water-level descent) pupation and emergence periods. We concluded that whereas physical and chemical factors (i.e., salinity, pH) exerted the primary control on benthic macroinvertebrate community composition in Tecuitlapa Norte, another assembly of variables (e.g., water-level, temperature) influenced species distribution and abundance.     

Modern influences on chironomid distribution in western Ireland: potential for palaeoenvironmental reconstruction

Ireland provides a unique setting for the study of past climates, as its climate is dominated by westerly airflow from the North Atlantic and readily responsive to changes in North Atlantic circulation patterns. Although there has been substantial research on Ireland’s past environments, quantitative palaeolimnological research, especially chironomid-based research, has been lacking. In order to further develop chironomid-based palaeolimnological investigations, a calibration set was constructed to determine the dominant environmental controls on modern chironomids in western Ireland. Chironomid subfossils were collected from surface sediments of 50 lakes. The lakes were characterised with 36 environmental variables, including physical attributes, lake water characteristics, lake sediment characteristics and land cover within each catchment. In this exploratory study, no specific environmental variable was targeted and lakes were chosen to span gradients of latitude, elevation, depth and trophic status. Redundancy analysis showed that six environmental variables—mean July air temperature, lake depth, dissolved organic carbon, and percentage catchment land cover of agriculture, peat bog and scrubland—captured a large and statistically significant portion of the variance in the chironomid data. July temperature and agricultural land cover were the most dominant environmental variables, with July temperature proving the most suitable for inference model development. A classical weighted-averaging model was developed to estimate July air temperature, with a coefficient of determination (r _jack ² ) of 0.60 and root mean square error of prediction (RMSEP) of 0.57 °C. Results suggest that summer temperature is the dominant influence on chironomid distribution across a wide variety of lake types, and the relatively small RMSEP should allow for more accurate reconstructions of Ireland’s relatively subdued Holocene temperature fluctuations.     

A new approach to quantifying soil temperature responses to changing air temperature and snow cover

Seasonal snow cover provides an effective insulating barrier, separating shallow soil (0.25 m) from direct localized meteorological conditions. The effectiveness of this barrier is evident in a lag in the soil temperature response to changing air temperature. The causal relationship between air and soil temperatures is largely because of the presence or absence of snow cover, and is frequently characterized using linear regression analysis. However, the magnitude of the dampening effect of snow cover on the temperature response in shallow soils is obscured in linear regressions. In this study the author used multiple linear regression (MLR) with dummy predictor variables to quantify the degree of dampening between air and shallow soil temperatures in the presence and absence of snow cover at four Greenland sites. The dummy variables defining snow cover conditions were z = 0 for the absence of snow and z = 1 for the presence of snow cover. The MLR was reduced to two simple linear equations that were analyzed relative to z = 0 and z = 1 to enable validation of the selected equations. Compared with ordinary linear regression of the datasets, the MLR analysis yielded stronger coefficients of multiple determination and less variation in the estimated regression variables.     

Incorporation of multi-scale spatial autocorrelation in soil moisture–landscape modeling

Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R², Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.     

Spatio-temporal analysis of land-use conversion in the eastern coastal China during 1996–2005

Based on the acquaintance of the regional background of urban-rural transformational development and investigations on the spot,this paper discusses the holistic situation, dominant factors and mechanism of arable land loss and land for construction occupation in the coastal area of China over the last decade,with the aid of GIS technology.Conclusions of the research are summarized as follows：（1）the arable land had been continuously decreasing from 1996 to 2005,with a loss of 1,708,700 hm^2 and an average decrement of 170,900 hm^2 per year;（2）land for construction increased 1,373,700 hm^2 ,with an average increment of 153,200 hm^2 per year;（3）total area of encroachment on arable land for construction between 1996 and 2005 was 1,053,100 hm^2 ,accounting for 34.03%of the arable land loss in the same period,the percentages of which used for industrial land（INL）,transportation land（TRL）,rural construction land（RUL）and town construction land（TOL）are 45.03%,15.8%,15.47%and 11.5%,respectively;and（4）the fluctuation of the increase of construction land and encroachment on arable land in the area were deeply influenced by the nation＇s macroscopic land-use policies and development level of regional economy.The growth of population and advancement of technology promoted the rapid industrialization, construction of transportation infrastructures,rural urbanization and expansion of rural settlements in the eastern coastal area,and therefore were the primary driving forces of land-use conversion.     

A Review of “Politics and Practice in Economic Geography”

Abstract

This study examines the spatial distribution and enforcement of air polluting facilities in the state of New Jersey, as listed in the U.S. Environmental Protection Agency's Aerometric Information Retrieval System. Results show that air-polluting facilities tend to concentrate near minority neighborhoods, although this relationship is partially explained by factors of population density, manufacturing employment, and land use. Other results suggest that facilities in areas with a relatively high percentage of minority population tend to have a weaker record of environmental enforcement as compared to other facilities. Of the socioeconomic variables considered, employment in manufacturing appears to be the most strongly related to environmental enforcement.     

Integration of linear programming and GIS for land-use modelling

Abstract

Geographical Information Systems (GIS) are becoming basic tools for a wide variety of earth science and land-use applications. This article presents linear programming (LP) as a promising tool for spatial modelling within a GIS. Although LP is not properly a spatial technique, it may be used to optimize spatial distributions or to guide the integration of variables. An example of the use of LP in land-use planning is described, with minimizing rural unemployment as the main goal. Technical, financial and ecological constraints are established to show the influence of several limitations on achieving the optimal solution. LP makes it possible to achieve optimal land-use, where the objective is maximized and the constraints respected. LP can also be used to simulate different planning scenarios, by modifying both the objective function coefficients and the constraints. The integration of LP and GIS is presented in two phases: (i) acquisition of attribute data for the LP model, and (ii) modelling and mapping the results.     

Estimating Climate-Change Impacts on Colorado Plateau Snowpack Using Downscaling Methods

This study develops observed climate-based downscaling transfer functions that are used with general circulation model (GCM) output to assess potential global-change impacts on Upper Colorado Plateau, USA, water resources. Daily automated snow water equivalent stations are used with 700 mb atmospheric circulation to determine empirical transfer functions. Downscaling methodologies using multiple regression and neural networks are evaluated, with the neural network results explaining approximately 70%of the daily snowfall variance. The neural network-based transfer functions are used with the GENESIS GCM to simulate snowfall characteristics in both a 1xCO₂ and a 2xCO₂ climate. While the total precipitation simulated by the 2xCO₂ analysis remains nearly the same as today, less will fall as snow; specifically, snow water equivalence shows significant reduction (except in the Green River Basin) as the snow season is reduced by 58 days on average.     

PLANT ADAPTATION,FIRM ENVIRONMENTS AND LOCATION ANALYSIS

Principal components analysis of fracture trace and sinkhole characteristics near 33 wells drawing water from the Ocala aquifer identified three hydrogeologically significant components. Multiple regression analysis using these components as independent variables showed that the flow of water to a well bore is influenced in order of importance by (1) proximity to a zone of high secondary permeability; (2) average aquifer transmissivity near the well; and (3) degree of cavity development in the closest zone of high secondary permeability.