Nature and Fictitious Capital: The Historical Geography of an Agrarian Question

Capitalism is produced in part through its own production of nature, but it has been argued that nature also poses certain obstacles to capitalist development. Political economists and rural sociologists have argued that in certain instances agriculture, as a form of production based in nature, has proven resistant to capitalist transformation. The Mann–Dickinson thesis still stands as one of the best such formulations. This essay argues for turning the Mann–Dickinson thesis on its head so as to ask how it is that an obstacle for one set of capital comprises an opportunity for other capitals. The essay therefore examines agriculture as a nexus of nature and circulating capital. It argues that what has been construed as a primary obstacle (the disunity of working and production time and the cumulative effects thereof) has been poorly appreciated as comprising a distinctive opportunity for capitalist investments and appropriations through the credit system. Credit, by no means an exogenous or anachronistic force, develops along with production and constitutes a social relation of production along with other such relations. These contentions are borne out in a critique of the nature-as-obstacle argument and then in a discussion of late-nineteenth- and early-twentieth-century agriculture in the United States, especially in California. In the latter discussion, I focus on the role of credit as the system that mediates the relations between nature and capital in and between different space–times. Credit, I argue, was necessarily constituted spatially and was contingently tied to the rise of agrarian formations in the American West.     

WHERE IS THE ECONOMICS IN SPATIAL ECONOMETRICS?*

ABSTRACT Spatial econometrics has been criticized by some economists because some model specifications have been driven by data‐analytic considerations rather than having a firm foundation in economic theory. In particular, this applies to the so‐called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover, it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper, we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.     

Heterogeneity of Attribute Sampling Error in Spatial Data Sets

This paper considers the standard error of the estimate of the mean of a spatially correlated variable in the case where data are obtained by a process of random sampling. Two distinct mean estimation problems are identified: estimating the area mean and estimating the population mean. Methods are described for obtaining standard error estimates in the two cases and, within the limits of publicly available information, the methods are implemented on average household income data at the census tract scale for Syracuse, New York. The purpose of the paper is to draw attention to issues of data precision in relation to sampled geographic information on averages and in particular to consider the problems of estimating standard errors using such data. The paper also examines the extent to which standard errors vary between census tracts.     

Bivariate Correlation with Spatial Data

The paper examines the use of the Pearson product moment correlation coefficient and the Spearman rank correlation coefficient on map data that are spatially correlated. Research has shown that in this case standard estimators underestimate the true sampling variance of the Pearson coefficient. The Clifford and Richardson “effective sample size” solution to this problem is described, methods for implementing the procedure are considered, and properties of the technique examined. The method is also used to adjust the Spearman test. Two applications are given and the method of Clifford and Richardson contrasted with the method of data “prewhitening.”     

A Multivariate Model for Spatio‐temporal Health Outcomes with an Application to Suicide Mortality

This article considers models for multivariate mortality outcomes (e.g., bivariate, trivariate, or higher dimensional) observed over a set of areas and through time. The model outlined here allows for spatially structured and white noise errors and for their intercorrelation. It also includes possible temporal continuity in such types of error via structured temporal effects. An extension to spatially varying regression effects is considered, as well as the option of nonparametric specification of priors for spatial residuals and regression effects. Allowing for spatially correlated intercepts or regression effects may alter inferences regarding the changing impact on mortality of socioeconomic or environmental predictors. The modeling framework is illustrated by an application to male and female suicide mortality in London, focusing on the impact on suicide of deprivation and social fragmentation (“anomie”) in the 33 London boroughs during three periods: 1979–83, 1984–88 and 1989–93. Suicide trends by age group are also considered and show considerable differences in the trends in impacts of deprivation and social fragmentation.     

Meteorological Sub‐divisional Scale Rainfall Monitoring Using Kalpana‐1 VHRR Measurements

Geostationary satellites provide measurements over a wider geographical area with high temporal sampling, while microwave measurements are more accurate but sparse. For continuous monitoring of the Indian monsoon, geostationary platform would be ideal. In this study, INSAT (Indian National Satellite) Multi‐spectral Rainfall Algorithm (IMSRA) has been used for the estimation of rainfall from Kalpana‐1 very high resolution radiometer (VHRR) measurements. IMSRA benefits from the relative advantages of infrared and microwave sensors and is operational at the India Meteorological Department (IMD). In this paper, rainfall is estimated over India at meteorological sub‐divisional scale during the south‐west monsoon season of 2009 using Kalpana‐1 satellite measurements. This is the first experimental attempt to generate meteorological sub‐divisional scale rainfall maps using Kalpana‐1 satellite measurements. The rainfall maps for the south‐west monsoon season over the Indian land region are successfully utilised as a space input for the drought monitoring of the year 2009. The results have been compared with the IMD gauge‐based accumulated rainfall maps at monthly and seasonal time scales. The qualitative comparison suggests that rainfall maps generated using the present methodology is in good agreement with the IMD rainfall maps. The quantitative comparison of the sub‐divisional monthly accumulated rainfall shows a correlation of 0.77 and standard error of 71 mm over the non‐orographic regions, whereas a correlation of 0.60 and standard error of 117 mm is observed over the orographic regions. The present study shows that Kalpana‐1 satellite‐based rainfall estimates (IMSRA technique) can act as a complementary tool for the monsoon monitoring over the Indian meteorological sub‐divisions and can be used for various meteorological and hydrological applications.     

THE ENVIRONMENTAL PERFORMANCE OF POLLUTING PLANTS: A SPATIAL ANALYSIS*

ABSTRACT This paper uses plant‐level EPA and Census data to examine spatial factors affecting environmental performance, as measured by air pollutant emissions and regulatory compliance. We find significant effects for compliance, but not for emissions. Compliance is positively spatially correlated, partly explained by spatial correlations in observed plant characteristics, suggesting influences of industry agglomeration. The use of spatial econometric methods shows only small effects of spatially lagged compliance status, and does not greatly change the estimated contributions of other spatially explicit factors. Regulatory activity has the expected effect of increasing environmental performance, both at the inspected plant and at neighboring plants, but only for plants in the same state, demonstrating the importance of jurisdictional boundaries.     

ISSUES IN SPATIAL DATA ANALYSIS

ABSTRACT.  Misspecified functional forms tend to produce biased estimates and spatially correlated errors. Imposing less structure than standard spatial lag models while being more amenable to large datasets, nonparametric and semiparametric methods offer significant advantages for spatial modeling. Fixed effect estimators have significant advantages when spatial effects are constant within well-defined zones, but their flexibility can produce variable, inefficient estimates while failing to account adequately for smooth spatial trends. Though estimators that are designed to measure treatment effects can potentially control for unobserved variables while eliminating the need to specify a functional form, they may be biased if the variables are not constant within discrete zones.     

Electoral Nationalisation,Dealignment and Realignment: Australia and the US, 1900-88

Has the behaviour of Australian voters become more 'nationalised' over time? Kemp (1978) and to a lesser extent Aitkin (1968) conclude that it has. Using more precise and comprehensive measures of nationalisation, a more standard (variance-components) statistical method and longer and directly comparable series of data, I qualify this conclusion heavily and place it on a firmer theoretical footing. I find that voting behaviour has generally been more spatially uniform in Australia than in the US, and that a sustained homogenisation-but not a 'nationalisation'-has been underway since the realignment of the late 1930s to early 1940s. Exceptionally and continually high levels of party identification, together with class-based alignments (and realignments) of electoral forces, appear to underlie the relatively high spatial uniformity of voting behaviour in Australia.     

‘Torrents of Terror’: the August 1998 Storm and the Magnitude,Frequency and Impact of Major Floods in the Illawarra Region of New South Wales

The Illawarra Region some 80 kilometres south of Sydney is characterised by a prominent coastal escarpment that rises to 700 m within 12 km of the coast and forms a locus for frequent, high intensity rainfall events. One of the most recent recorded events occurred on 17 August 1998 with rainfall intensities at several pluviometers exceeding 120 mm hr^‐1 over a duration of one hour, with up to 249 mm falling in 3.5 hours during the main storm burst. Detailed pluviometer data indicate that the storm was non‐stationary and moved down catchment producing a widespread zone of 120 mm hr^‐1 intensity rainfall over a 30 minute duration across mid‐lower catchment areas after similar intensity but longer duration rainfall in catchment headwaters. Slope‐area reconstructions of peak discharge indicate that small catchments on the escarpment within the zone of maximum intensity experienced close to 100% rainfall‐runoff relationships, with peak discharges correlated to short duration (<1 hr) peak rainfall intensities. Widespread erosion occurred particularly where urban development had encroached on natural water courses. Debris/hyperconcentrated flows originating from both anthropogenic and natural sediment sources caused damage to urban areas. This paper provides an overview of the spatial and temporal characteristics of the 17 August 1998 storm, the hydrologic and geomorphic response of the streams, and the nature of damage to urban areas. It reassesses the frequency of recent high‐magnitude rainfall/flood events in the region, discussing the relationships between rainfall intensities, estimates of flood magnitudes and stormwater channel capacities.     

Young lesbians negotiating public space: an intersectional approach through places

In this paper I analyze how different aspects of identity combine to shape the experiences of seven young lesbians in public space in Manresa, a non-metropolitan city in Catalonia. I argue that their experiences need to be understood intersectionally and spatially, as complex processes that involve the mutually constituted identities that shift in space. I focus on interviews and Relief Maps to show how these dynamics work and how they are related to larger social processes such as the heteronormalization and adultification of public space.     

Evaluating the Contemporaneity of Households at the Eden‐Farson site

Archaeologists often rely on absolute dating methods, but the standard deviations associated with these approaches often leave us wondering what to make of spatially related archaeological units, such as adjacent sites, clusters of features or regional‐scale investigations. This issue is particularly relevant to the Shoshone Protohistoric site of Eden‐Farson located in southwest Wyoming. The campsite consists of at least 11 discrete excavation units and is associated with a successful communal game drive of over 156 pronghorn. Yet the sheer number of pronghorn and the ambiguous nature of the near‐surface cultural deposits have left researchers debating whether it represents a single occupation associated with a single successful game drive. Using a multivariate refit model, I have performed a bilateral refit analysis to identify links between the excavation units. Strong evidence for contemporaneity of Eden‐Farson excavation units comes from 38 linkages between 10 of the 11 proveniences. Coeval occupation of the separate houses also allows for an analysis of food‐sharing behaviours. With the last game drive occurring in the 1870s, the Eden‐Farson site provides a rare opportunity to examine the social organisation of communal hunting. Copyright © 2013 John Wiley & Sons, Ltd.     

Beyond Moran's I: Testing for Spatial Dependence Based on the Spatial Autoregressive Model

The statistic known as Moran's I is widely used to test for the presence of spatial dependence in observations taken on a lattice. Under the null hypothesis that the data are independent and identically distributed normal random variates, the distribution of Moran's I is known, and hypothesis tests based on this statistic have been shown in the literature to have various optimality properties. Given its simplicity, Moran's I is also frequently used outside of the formal hypothesis-testing setting in exploratory analyses of spatially referenced data; however, its limitations are not very well understood. To illustrate these limitations, we show that, for data generated according to the spatial autoregressive (SAR) model, Moran's I is only a good estimator of the SAR model's spatial-dependence parameter when the parameter is close to 0. In this research, we develop an alternative closed-form measure of spatial autocorrelation, which we call APLE , because it is an approximate profile-likelihood estimator (APLE) of the SAR model's spatial-dependence parameter. We show that APLE can be used as a test statistic for, and an estimator of, the strength of spatial autocorrelation. We include both theoretical and simulation-based motivations (including comparison with the maximum-likelihood estimator), for using APLE as an estimator. In conjunction, we propose the APLE scatterplot, an exploratory graphical tool that is analogous to the Moran scatterplot, and we demonstrate that the APLE scatterplot is a better visual tool for assessing the strength of spatial autocorrelation in the data than the Moran scatterplot. In addition, Monte Carlo tests based on both APLE and Moran's I are introduced and compared. Finally, we include an analysis of the well-known Mercer and Hall wheat-yield data to illustrate the difference between APLE and Moran's I when they are used in exploratory spatial data analysis.     

A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques

Geographically weighted regression (GWR) is a technique that explores spatial nonstationarity in data‐generating processes by allowing regression coefficients to vary spatially. It is a widely applied technique across domains because it is intuitive and conforms to the well‐understood framework of regression. An alternative method to GWR that has been suggested is spatial filtering, which it has been argued provides a superior alternative to GWR by producing spatially varying regression coefficients that are not correlated with each other and which display less spatial autocorrelation. It is, therefore, worthwhile to examine these claims by comparing the output from both methods. We do this by using simulated data that represent two sets of spatially varying processes and examining how well both techniques replicate the known local parameter values. The article finds no support that spatial filtering produces local parameter estimates with superior properties. The results indicate that the original spatial filtering specification is prone to overfitting and is generally inferior to GWR, while an alternative specification that minimizes the mean square error (MSE) of coefficient estimates produces results that are similar to GWR. However, since we generally do not know the true coefficients, the MSE minimizing specification is impractical for applied research.     

An Area‐Based Approach for Estimating Extreme Precipitation Probability

Accurate estimates of heavy rainfall probabilities reduce loss of life, property, and infrastructure failure resulting from flooding. NOAA's Atlas‐14 provides point‐based precipitation exceedance probability estimates for a range of durations and recurrence intervals. While it has been used as an engineering reference, Atlas‐14 does not provide direct estimates of areal rainfall totals which provide a better predictor of flooding that leads to infrastructure failure, and more relevant input for storm water or hydrologic modeling. This study produces heavy precipitation exceedance probability estimates based on basin‐level precipitation totals. We adapted a Generalized Extreme Value distribution to estimate Intensity‐Duration‐Frequency curves from annual maximum totals. The method exploits a high‐resolution precipitation data set and uses a bootstrapping approach to borrow spatially across homogeneous regions, substituting space in lieu of long‐time series. We compared area‐based estimates of 1‐, 2‐, and 4‐day annual maximum total probabilities against point‐based estimates at rain gauges within watersheds impacted by five recent extraordinary precipitation and flooding events. We found considerable differences between point‐based and area‐based estimates. It suggests that caveats are needed when using pointed‐based estimates to represent areal estimates as model inputs for the purpose of storm water management and flood risk assessment.     

Rethinking Whiteness and Masculinity in Geography: Drinking Alcohol in the Field in Vietnam

This paper evaluates the spatial politics of fieldwork in Vietnam in order to think through the connections between whiteness, masculinity, and geography. In drawing attention to how the consumption of alcohol underwrites daily activities in Vietnam, as well as fieldwork activities, I show that research ethics are underpinned by unique spatial contexts that do not conform to conventional accounts of masculinity and whiteness in the global North. I make three interrelated arguments. First, I argue that debates in geography about whiteness and masculinity must be understood alongside fieldwork experiences in the global South. Second, I invite geographers to think through how research ethics are shaped by spatially contingent productions of whiteness and masculinity. Lastly, I challenge geographers to keep pace with how disruptive categories like whiteness and masculinity are produced outside of the global North.     

A Spatial Logit Association Model for Cluster Detection

In this paper, I propose to set out a logit spatial association model for binary spatial events and develop a scan algorithm to search for spatial associations. I extend the traditional logit model with a spatial autocorrelated component so that the model includes not only known risk factors, but also spatially autocorrelated regions as control or explanatory factors. The case study of West Virginia lung cancer shows that the model effectively captures cool and hot spots in lung cancer mortality.     

Unconditional Maximum Likelihood Estimation of Linear and Log-Linear Dynamic Models for Spatial Panels

This article hammers out the estimation of a fixed effects dynamic panel data model extended to include either spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least-squares dummy estimator, the models are first-differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first-differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function is found to exist. When exogenous variables are included, the pre-sample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period from 1963 to 1992.     

Divided we fall? The effect of manufacturing decline on the social capital of US communities

What happens to local communities when manufacturing disappears? I examine changes in associational density over nearly two decades as a proxy for social capital in US labor markets. Exploiting plausibly exogenous trade-induced shocks to local manufacturing activity, I test whether deindustrialization is associated with greater or lower organizational membership. I uncover a robust negative relationship between the two variables, particularly acute in rural and mostly-White areas. My findings, however, are sensitive to measurement: There are no clearly discernible effects of deindustrialization on social capital when I consider alternative proxies for the outcome. To reconcile these results, I present evidence suggesting that economic adversity may induce a qualitative, rather than quantitative, change in social capital.     

The Philippines insurrection and the 1902–4 cholera epidemic: Part II—Diffusion patterns in war and peace

The great epidemic of cholera which swept through the Philippine Islands in the immediate aftermath of the Philippine–American War (1899–1902) spread in two temporally distinct, but spatially concordant, waves of infection. Part I of this paper studied the first wave (wave I, March 1902 to February 1903). Here, in Part II, we explore the manner in which the second wave (wave II, May 1903 to February 1904) diffused through the same geographical areas. Using the cholera reports prepared by the Chief Quarantine Officer for the Philippine Islands and published weekly in the contemporary USPublic Health Reports, the diffusion of wave II is analysed at the geographical levels of province, island and nation. Comparison with wave I confirms that spatially contagious spread dominated in both waves at all spatial scales. However, it is shown that there were important differences between the waves in their intensity, speed of spread and resulting spatial patterns. Amelioration of the disruption brought about by the Philippine–American War may account for some of the observed differences between the two waves.