Empirical Zoning Distributions for Small Area Data

It is well established that using data summaries for a set of geographic areas or zones to estimate the parameters of a statistical model, commonly called ecological inference, frequently leads to the modifiable area unit problem (MAUP). In this article, the zoning effect of the MAUP is investigated for a range of scales. A zoning distribution is defined, and then used to characterize the zoning effect for parameter estimates from ecological analyses. Zone‐independent parameter estimates are obtained using the mean of the zoning distribution, and assessed using the variance of the zoning distribution. Zoning distributions are illustrated for parameter estimates from two ecological regression models at multiple scales using Australian National Health Survey data. For both a continuous response variable and a binary response variable, the empirical zoning distributions are unimodal, relatively symmetrical with appreciable variation, even when based on a large number of zones. The “ecological mean,” or expected value of the empirical zoning distribution at each scale, displays systematic variation with scale and the zoning distribution variance also depends on scale. The results demonstrate that the zoning effect should not be ignored, and the sensitivity of ecological analysis results to the analysis zones should be assessed.     

Ecological Inference and Spatial Variation of Individual Behavior: National Divide and Elections in Catalonia

When in geography one reconstructs individual behavior starting from aggregated data through ecological inference, a crucial aspect is the spatial variation of individual behavior. Basic ecological inference methods treat areas as if they were all exchangeable, which in geographical applications is questionable due to the existence of contextual effects that relate to area location and induce spatial dependence. Here that assumption is avoided by basing ecological inference on a model that simultaneously does a cluster analysis, grouping together areas with similar individual behavior, and an ecological inference analysis in each cluster, estimating the individual behavior in the areas of each group. That allows one to capture most of the spatial dependence and summarize the individual behavior at a local level through the behavior estimated for each cluster. This approach is used to investigate vote switching in Catalonia, where voters split across a national allegiance divide on top of the ideological divide. That leads to Catalans having a lot of options to choose from, and to them voting differently depending on whether the election is for the Catalan parliament or for the Spanish parliament. To investigate that, the results in the two most recent pairs of such elections are analyzed by simultaneously clustering areas based on the similarity of their vote and vote switch patterns, and estimating one vote switch pattern for each cluster. As a result, Catalonia is partitioned into four clusters that have a strong spatial structure, with all the areas in the same cluster having similar demographic composition. The estimated vote switch patterns are quite different across clusters but very similar across pairs of elections, and they help assess how the differential voter turnout and the strategic dual vote effects vary in space.     

Determinants of Variation in Population–Employment Interaction Findings: A Quasi‐Experimental Meta‐Analysis. 人口‐就业相互作用的决定因素研究：一种准实验元分析法

This article reports about a metaregression analysis of empirical results generated using data for the northern Netherlands (1988–2002) in order to investigate the ambiguity in results in the population–employment interaction literature. Specifically, the analysis deals with the issue whether “jobs follow people” or “people follow jobs.” The article starts with introducing the basics of quasi‐experimental meta‐analysis and with identifying some advantages of using quasi‐experimental meta‐analysis as compared with the standard meta‐analysis approach. Two subsequent sections document the selection of the population–employment interaction model and salient characteristics of the data set as well as the setup of the primary analyses. A total of 4,050 quasi‐experimental empirical results for the jobs–people direction of causality are generated using different specifications and estimators for a spatial econometric interaction model. The subsequent metaregression analysis reveals that the empirical results are largely shaped by the spatial, temporal, and employment characteristics of the data sampling. The results also appear much more sensitive to different measurements of the model's key variables when compared with alternative specifications of the spatial weights matrix. The main determinant driving empirical results about jobs–people causality are differences in model specification and estimation, as revealed by an inherent bias in parameter estimates and misguided inferences for some of the commonly used specifications. Finally, suggestions for future research are identified.     

Graphical Inference in Geographical Research

Graphical inference, a process refined by Buja et al., can be a useful tool for geographers as it provides a visual and spatial method to test null hypotheses. The core idea is to generate sample datasets from a null hypothesis to visually compare with the actual dataset. The comparison is performed from a line‐up of graphs where a single graph of the actual data is hidden among multiple graphs of sample data. If the real data is discernible, the null hypothesis can be rejected. Here, we illustrate the utility of graphical inference using examples from climatology, biogeography, and health geography. The examples include inferences about location of the mean, change across space and time, and clustering. We show that graphical inference is a useful technique to answer a broad range of common questions in geographical datasets. This approach is needed to avoid the common pitfalls of “straw man” hypotheses and “p‐hacking” as datasets become increasingly larger and more complex.     

Maria's Burden: Contract Cleaning and the Crisis of Social Reproduction in Post‐apartheid South Africa

The life of Maria Dlamini, a contract cleaner at the University of the Witwatersrand, is used to explore continuities and discontinuities between the apartheid labour regime and the neoliberal, post‐apartheid order in South Africa. As South African institutions have adopted neoliberal market strategies, the growth in the contracting‐out of cleaning has intensified work and reduced wages and benefits for many workers. Significantly, as was the case with the migrant labor system under apartheid, it has also increasingly displaced the burden of social reproduction onto the households and communities of the working poor. Whereas the racial spatial order under apartheid was dictated by national‐level political decisions, through use of the concept of “boundary drawing”, we show how the language of the market justifies new exclusions based upon the micro‐politics of the “rational” restructuring of institutions such as universities.     

THE “INS” AND “OUTS” OF HISTORY: REVISION AS NON‐PLACE

Revision in history is conventionally characterized as a linear sequence of changes over time. Drawing together the contributions of those engaged in historiographical debates that are often associated with the term “revision,” however, we find our attention directed to the spaces rather than the sequences of history. Contributions to historical debates are characterized by the marked use of spatial imagery and spatialized language. These used to suggest both the demarcation of the “space of history” and the erasure of existing historiographies from that space. Bearing these features in mind, the essay argues that traditional, temporally oriented explanations for revision in history, such as Thomas S. Kuhn's Structure of Scientific Revolutions, miss the mark, and that a more promising line of explanation arises from the combined use of Michel Foucault's idea of “heterotopias” and Marc Augé's idea of “non‐places.” Revision in history is to be found where writers use imagery to move readers away from rival historiographies and to control their movement in the space of history toward their desired vision. Revision is thus associated more with control than with liberation.     

The prey as patch model: optimal handling of resources with diminishing returns

Foraging theory provides archaeology with a valuable set of tools for investigating the constraints that influenced procurement decisions of the past. The prey-choice model has been used extensively by archaeologists, but has significant limitations given the nature of archaeological data. This paper suggests that the seldom-used Marginal Value Theorem (MVT) is a valuable tool for examining the ecological constraints on foraging decisions and merits further archaeological application. Ethnoarchaeological and experimental cases are presented demonstrating how patch–gains curves can be generated from quantitative data on butchering return rates and handling times. Results indicate that such curves are diminishing return functions. This provides a basis for examining the linkage between processing intensity and resource fluctuation. This model allows archaeologists to address the relationship between attribute-states of faunal remains and predicted optimal post-acquisition decisions. The MVT is valuable to ethnoarchaeology because it identifies how mean foraging return rate influences the handling of acquired prey and makes quantified predictions of return rate based on processing intensity. The MVT can also be applied to archaeological studies of foraging behavior and processing intensity as it can be used to estimate the set of environmental constraints in which a given kill was made (e.g., “good” vs. “bad” times). This approach may also identify the degree to which certain currencies, such as fat, are optimized at the expense of others, such as total caloric intake.     

Local Indicators of Spatial Association—LISA

The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the “spatial” aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the G_i and G*_i statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify “outliers,” as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.     

An Application of Taylor's Power Law to Measure Overdispersion of the Unemployed in English Labor Markets

Taylor's power law (TPL) is the power relation between mean densities and variance of natural populations, and described as one of ecology's few ubiquitous laws. Although the power model has been increasingly applied in social systems modeling, including economics, this article, using English and Welsh economic data as an applied example, suggests that TPL ought to be imported more carefully. The article seeks to convince readers that ecological population methodologies can have an important role in analysis of human spatial behavior, and that this function should not be diminished in pursuit of quick interdisciplinary results. Through the production of “scale‐adjusted dispersion indicators,” the article proposes an application of TPL that is quite different from its use in ecological modeling.     

Appreciating the heterogeneity in the unity of Africa: A socio‐ecological perspective on Africa's geographies

The African continent is known by various metaphors and geographies, but for many there are also unknowns about the continent. Geopolitically, Africa is a continent that is considered remote—an economically emerging continent seen as entangled in persistent challenges of wars, political dictatorship, poverty, disease, and more recently migration. Given these predispositions it is typical to stereotype events, practice, and behaviour as “African.” There is, however, now recognition of the continent as emerging economic power house. But unpacking the diversity of Africa reveals a huge potential with respect to resource endowments, diversity of ecology, socio‐cultural economic advancement, politics, language, and demographics. Colonial history coupled with traditional Africa shaped the geopolitical boundaries that have added to the confusion about this massive and diverse continent. Intellectual discourses either amplify the differences due to specificities of geographical focus or generalizations such as the contested notion of “African.” However, using socio‐ecological lenses, Africa is unified by these very differences in addition to being a massive landmass with several big and small island states. Appreciating these differences is useful to understanding the observed patterns of social, economic, and political systems that unify the continent. This paper illustrates the notion of “African” to describe the heterogeneous nature of a “unified” continent. Some illustrative examples between Africa and other continents are used.     

Estimating the likelihood of fallacious ecological inference: linear ecological regression in the presence of context effects

In situations where the only reliable data source is electoral data at the aggregate level for a geographic unit such as voting precincts, social scientists have sought to use ecological regression techniques to recreate the voting behavior of particular groups without committing the ecological fallacy of the sort warned of by Robinson (1950). Until quite recently, the most common use of ecological regression techniques was for the analysis of historical data, e.g., determining the social groups that supported the Nazis. In the US, however, in a development little known to political geographers, within the past decade, social science expert witnesses have testified in hundreds of cases involving minority voting rights—cases where a central issue has been to ascertain the extent of white/Anglo support for black or Hispanic (or more recently, Asian-American) candidates in elections involving candidates of more than one race/ ethnic background. One issue in several recent voting rights cases has been whether linear regression methods can validly be used to model racial voting patterns, with experts for defendant jurisdictions claiming that the presence of contextual effects may invalidate the assumptions underlying linear models and give rise to an ecological fallacy. For data on voting patterns in small geographic units such as voting precincts, we show that the presence of a plausible type of race-related contextual effect will, in general, lead to a quadratic relationship between minority share of the electorate and the share of the vote received by the minority candidate. However, when either a substantial proportion of the electorate is located in racially homogeneous or near homogeneous precincts (as often occurs given the patterns of residential concentration in US cities), or when the contextual effects are small, we show that the estimates of the key parameters of interest in voting rights litigation, namely the proportions of minority and non-minority voters who voted for the minority candidate(s), will be very well approximated by a bivariate linear model. In such circumstances, fallacies of ecological inference can be avoided with near certainty, despite the fact that the linear model that has been used to fit the data omits potentially important variables and regression diagnostics for it would show heteroskedasticity, while a quadratic regression would provide coefficients that are essentially uninterpretable in terms of the parameter of interest. This result has potentially broader implications for the reliability of ecological inference because it demonstrates that, under plausible assumptions, a simple ecological model can yield accurate results despite theoretical errors in model specification—because those errors prove to be of little or no practical significance.     

From Big Noise to Big Data: Toward the Verification of Large Data sets for Understanding Regional Retail Flows

There has been much excitement among quantitative geographers about newly available data sets, characterized by high volume, velocity, and variety. This phenomenon is often labeled as “Big Data” and has contributed to methodological and empirical advances, particularly in the areas of visualization and analysis of social networks. However, a fourth v—veracity (or lack thereof)—has been conspicuously lacking from the literature. This article sets out to test the potential for verifying large data sets. It does this by cross‐comparing three unrelated estimates of retail flows—human movements from home locations to shopping centers—derived from the following geo‐coded sources: (1) a major mobile telephone service provider; (2) a commercial consumer survey; and (3) geotagged Twitter messages. Three spatial interaction models also provided estimates of flow: constrained and unconstrained versions of the “gravity model” and the recently developed “radiation model.” We found positive relationships between all data‐based and theoretical sources of estimated retail flows. Based on the analysis, the mobile telephone data fitted the modeled flows and consumer survey data closely, while flows obtained directly from the Twitter data diverged from other sources. The research highlights the importance of verification in flow data derived from new sources and demonstrates methods for achieving this.     

Historical geography and the society/nature “problematic”: the perspective of J. F. Schouw,G. P. Marsh and E. Reclus

The “retreat” of the recent past within geography to a conception of the discipline as an ahistoric science which is either spatial or ecological is seen to be an atavism—a throwback to a disciplinary framework or “problematic” which dichotomizes human society and nature into fixed exclusive categories. This essay explores an alternative “problematic” which integrates society's spatial and ecological dimensions in a study of the historical process of “dialectical” interaction between society and its geographic environment, and the political and economic consequences of this interaction. The significance of this alternative approach is elucidated through an examination of its emergence, at the time of the origins of modern geography in the early nineteenth century. Its developing importance for the present-day position of the discipline is exemplified in the work of three prominent, socially engaged, nineteenth-century geographers. Although these geographers have tended to be either ignored or misunderstood in the recent literature, their approach has much to offer the field at a time when its division into ahistoric spatial and ecological disciplines is being questioned.     

South–South Cooperation and the Geographies of Latin America–Caribbean Integration and Development: A Socio‐Spatial Approach

Structured around the case of South–South cooperation in the construction of “complementary economic zones” among the member states of the ALBA‐TCP, Petrocaribe, CARICOM and MERCOSUR, this article argues for a socio‐spatial approach to the study of the Latin America–Caribbean integration and development. Two interrelated arguments are developed: first, in contrast to methodologically nationalist approaches, which typically view the regionalisms that are to form the complementary economic zones as ideologically separate, incompatible or conflicting projects, a socio‐spatial approach in conjunction with a South–South cooperation analytical lens explains their commonality and, subsequently, their interrelatedness and convergence. Second, while this South–South cooperation space is not per se non‐capitalist, a socio‐spatial analysis also facilitates “seeing” the production of a socialist “counter‐space” within this South–South cooperation structure.     

2. CONSTRUCTIONS OF “HOME,”“FRONT,” AND WOMEN'S MILITARY EMPLOYMENT IN FIRST‐WORLD‐WAR BRITAIN: A SPATIAL INTERPRETATION

In First‐World‐War Britain, women's ambition to perform noncombatant duties for the military faced considerable public opposition. Nevertheless, by late 1916 up to 10,000 members of the female volunteer corps were working for the army, laying the foundation for some 90,000 auxiliaries of the official Women's Services, who filled support positions in the armed forces in the second half of the war. This essay focuses on the public debate in which the volunteers overcame their critics to understand how they obtained sufficient popular consent for their martial work. I explain the process in terms of shifting hegemonic understandings of space. As critics' arguments in the debate indicate, the gender attribution of war participation was organized and represented spatially, assigning men to the warlike “front” as warriors and women to the peaceful “home” as civilians. To redefine the meaning of these gendered wartime spaces, women volunteers deployed rival spatial discourses and practices in their campaign for martial employment. The essay explores the progress of these competing definitions through feminist and spatial theories, including gender performativity, discursively constructed and constructive spaces, and heterotopias. I argue that the upheaval caused by the war in gender and spatial norms undermined absolute conceptualizations of space with dichotomous binary areas on which critics drew for their arguments and reinforced more recent, relative spatialities, including the cultural construction of militarized heterotopic sites in between and paralleling both “home” and “front” for soldiers in training or recovery. The volunteers' efforts to gain access to military employment both contributed to and were supported by this shift. Heterotopic sites offered ideal discursive locations for constructing the new gender role of auxiliary soldiering through the performance of martial training and work, and competing spatial definitions provided arguments through which they could justify their activities to both critics and supporters.     

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.     

Rethinking Soft Power in the Post‐Blockade Times: The Case of Qatar

This study seeks to advance the understanding of the utility of “soft power” by exploring the case of Qatar. The country's approach is conceptualized as “nested power” through the examination of its political strategies before and after the regional blockade in 2017. The role of soft and nested power in Qatar has already been examined through various vantage points, such as small state diplomacy, mediation, and sports. Since the blockade has been for Qatar a great strategic dilemma, examination of how it affected power dynamics reveals the salience as well as the resilience of Qatar's soft and nested power. The article will discuss the concepts of “soft” and “nested” powers and their relevance to the state of Qatar in general and it will focus in the final section on the post‐blockade period. In doing so, we also seek useful approaches, which can be compatible with, and even advance “global international relations” (IR). The movement to make IR more global and inclusive is a welcome feature of the current century, reflective of the burgeoning role of the “Global South.”     

Estimating the Size of Social Networks

It is notoriously difficult to define and estimate the size of individuals' social networks. Methods are suggested for estimating the size of such networks, using known rates of mobility together with survey data on the number of acquaintances of individuals who have recently moved. Special attention is given to the likely correlation between mobility rates and network size. Estimates are found to be consistent with previous estimates in the literature. The relation of this approach to multiplicity sampling and to “the small-world problem” is also discussed.     

The People's Climate March: Environmental populism as political genre

Contemporary political ecological research on populism has demonstrated how authoritarian and strongarm tactics come to be hitched to reductive symbolic representations of “the people,” often with disastrous environmental impacts. Advocates of “left-populism” argue that such research can give the erroneous impression that “populism” and “authoritarianism” are essentially synonymous. Skeptical of formalist arguments, Gramscians argue that populism, as a quite variegated and fundamentally spatial phenomenon, must be viewed historically, in situ. But all three arguments share a quick assessment of populism, without always attending to its embedded multiplicity. Bringing together insights from Stuart Hall and Lauren Berlant, this article seeks to expand geographical understandings of the dynamic forms and styles of environmental politics by proposing thinking of populism as a political genre. This theoretical schema helps to cut through formalist versus historicist debates while directing attention to the affective scenes through which populism is performed. In order to demonstrate the utility of examining populism's genre and scenes, I examine political essays written surrounding the 2014 People’s Climate March. Essays debated activist expectations concerning political subjectivity, tactics, scales of action, signifiers, and aesthetics for best confronting global inequality and the climate crisis alike. Through contesting the meaning of “the people” and “populism,” divergent leftist political interpretations both repeated and tweaked generic populist forms. By examining the performative construction and contestation of “the people” through languages and spaces of climate action, I advocate a humble yet still critical political ecological approach to understanding contemporary populism.