Developing Geostatistical Space–Time Models to Predict Outpatient Treatment Burdens from Incomplete National Data

Basic health system data such as the number of patients utilizing different health facilities and the types of illness for which they are being treated are critical for managing service provision. These data requirements are generally addressed with some form of national Health Management Information System (HMIS), which coordinates the routine collection and compilation of data from national health facilities. HMIS in most developing countries are characterized by widespread underreporting. Here we present a method to adjust incomplete data to allow prediction of national outpatient treatment burdens. We demonstrate this method with the example of outpatient treatments for malaria within the Kenyan HMIS. Three alternative modeling frameworks were developed and tested in which space–time geostatistical prediction algorithms were used to predict the monthly tally of treatments for presumed malaria cases (MC) at facilities where such records were missing. Models were compared by a cross-validation exercise and the model found to most accurately predict MC incorporated available data on the total number of patients visiting each facility each month. A space–time stochastic simulation framework to accompany this model was developed and tested in order to provide estimates of both local and regional prediction uncertainty. The level of accuracy provided by the predictive model, and the accompanying estimates of uncertainty around the predictions, demonstrate how this tool can mitigate the uncertainties caused by missing data, substantially enhancing the utility of existing HMIS data to health-service decision makers.     

Integration Processes in European Research and Development: A Comparative Spatial Interaction Approach Using Project Based Research and Development Networks,Co‐Patent Networks and Co‐Publication Networks

This study focuses on integration processes in European Research and Development (R&D) by analyzing the spatiotemporal dimension of three different R&D collaboration networks across Europe. The studied networks cover different types of knowledge creation, namely project‐based R&D networks within the European Union (EU) Framework Programmes (FPs), co‐patent networks, and co‐publication networks. Integration in European R&D—one of the main pillars of the EU Science Technology and Innovation policy—refers to the harmonization of fragmented national research systems across Europe and to the free movement of knowledge and researchers. The objective is to describe and compare spatiotemporal patterns at a regional level and to estimate the evolution of separation effects over the time period 1999–2006 that influence the probability of cross‐region collaborations in the distinct networks under consideration. The study adopts a spatial interaction modeling perspective, econometrically specifying a panel generalized linear model relationship, taking into account spatial autocorrelation among flows using eigenfunction spatial filtering methods. The results show that geographical factors are a lower hurdle for R&D collaborations in the FP networks than in co‐patent networks and co‐publication networks. Furthermore, it is shown that the geographical integration is higher in the FP network.     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.     

Principles and Methods of Regional Geographical Prediction

A research methodology for purposes of geographical prediction is proposed. A region is considered the most likely object of geographical prediction, which is designed to forecast the most likely modifications of the natural environment under the impact of human activity, and the expected working and living conditions for man in the altered environment. The predictive process should focus on phenomena and processes that change through time and can be tested on the basis of known regularities and relationships. Predictive techniques may include both general scientific methods used for prediction and cause-and-effect relationships peculiar to geography. Among the most useful general techniques are extrapolation, expert evaluations, model building and comparative methods.     

HOW THE VISUAL IS SPATIAL: CONTEMPORARY SPATIAL HISTORY,NEO-MARXISM,AND THE GHOST OF BRAUDEL

Contemporary spatial history is founded on the potential for maps and other visualizations to show the historical constructedness of space, usually in broadly neo-Marxist terms, yet neo-Marxist geographical theory is famously critical of visual representation, especially mapping. At stake in this contradiction isn't just the relationship between digital enthusiasm and spatial theory (or the wider spatial turn), but the theoretical status of the visual itself in spatial scholarship. It raises a crucial question: how does visual material—everything from today's statistical maps and cutting-edge data graphics to the broader use of primary-source photographs or drawings—in fact shape our understanding of space, and what theoretical work does it do? By extension, how can humanists make critical theoretical interventions through their own visual production? This article proposes an analytic vocabulary of “visual argument” grounded in an image-focused rereading of two canonical bodies of work: the neo-Marxist theory most cited by spatial history (Henri Lefebvre, David Harvey, Doreen Massey, and Edward Soja) and the conspicuously uncited work of Fernand Braudel. By focusing on how these authors’ illustrations make claims about spatial subjectivity and the historicity of space—especially through visual relationships of background and foreground—I argue for a new way of understanding and responding to this work and to the visual project of spatial history today. A visual analysis highlights not only the limitations of neo-Marxism but also the pervasiveness of certain assumptions—shared across the neo-Marxists, Braudel, and digital visualization—about temporality, the natural/human dichotomy, and the methodological tensions between argument and visualization. I present my own mapping of Phoenix as one possibility for an argument-driven rethinking of familiar visual commitments, which also suggests a broader meditation on the relationship between visual and textual scholarship.     

Mapping the possible occurrence of archaeological sites by Bayesian inference

Regional archaeological prospections are often done by field walking, where the location of the sampled fields is often determined by factors like feared disturbance or recent plowing. The resulting data configuration can be suboptimal for spatial prediction of the archaeological potential by geostatistical methods like kriging. As an alternative, we propose a Bayesian method to map the possible occurrence of archaeological finds and compare this to indicator regression kriging. Three types of predictive models were implemented in the Bayesian context following deductive, inductive and mixed approaches to use auxiliary geographical information in the mapping. After prediction of a validation set, it was concluded that the mixed approach gave the best results in terms of map quality, and that the kriging method performed poorly. Usage of data on the presence and the absence of archaeological finds is to be preferred above usage of presence data only. Furthermore, a method is presented that filters those parts of a predictive map that are not strongly supported by evidence.     

Home as a Pocket of Local Order: Everyday Activities and The Friction of Distance

Discourse on the information society currently highlights issues of networks, flows and mobilities as prime organizers and re‐organisers of time—space relationships. Such discourse promotes notions of the flexible use of time and space, of people's decoupling from place and even of the end of geography — the belief that distance does not matter. Yet, in this article we argue that the roles of geographical stationarity and proximity in everyday life — understood as the creation and maintaining of pockets of local order — indicate the continuing and often neglected importance of the friction of distance. We demonstrate this empirically by focusing on the home as a pocket of local order, investigating the intensity and spatial extension of people's everyday activities, projects and contacts — their corporeal, virtual and medial (media‐related) mobilities — with the world outside. We support our thesis with data from the population, household and individual levels.     

INTERACTION BETWEEN PHYSICAL AND ECONOMIC GEOGRAPHY

The chairmen of the departments of physical geography and economic geography of the USSR at Moscow University call for greater interplay between the two major subfields of geography, particularly in the context of geographical research for regional planning and geographical prediction generally. The demands of a modern economy require a more integrated approach to the study of spatial systems encompassing the totality of the natural environment, settlement, production and living conditions. Such close interaction between physical and economic geography needs to be pursued at all levels of geography as a discipline, from international geographical congresses down to ordinary student field practice.     

Location and Growth in the Brazilian Northeast

Abstract Clustering of economic performance across jurisdictions has generated considerable research on the spillovers and linkages among geographical neighbors. Much of this work, however, has been aspatial, implying that the influence of location attributes on growth has been largely ignored. In this paper, we examine the contribution of location to regional economic growth using municipio‐level data for the Brazilian Northeast—a historically lagging region of the country. We test if productivity among northeastern municipios is converging to a steady state and whether spatial externalities are linked to productivity growth in individual municipios. We find that, conditional on structural characteristics, productivity among municipios is converging at about 3 percent per year. Further, productivity in individual municipios is positively associated with own‐structural characteristics but negatively associated with productivity growth in neighboring municipios. This means that there are negative spatial externalities coming from productivity improvements in neighboring regions.     

Rethinking transboundary waters: A critical hydropolitics of the Mekong basin

Efforts to understand the geographical and political complexities of transboundary river basins—both within national jurisdictions and at international levels—must embrace critical interdisciplinary perspectives. In this paper, we focus attention on underdeveloped aspects of transboundary water conflicts and cooperation—e.g., how ecological understandings of river basins are transformed within transboundary institutional arrangements; the way multiple actors in transboundary basins construct geographical scales; and how control over water is represented and exercised within governance and management institutions. We advance the notion of critical hydropolitics as a way of explicating these processes. We draw on a case study of conflict over and within the transboundary waters of the Mekong River basin to illustrate this approach. Our aim is to complement and extend ongoing research and policy debates concerning transboundary waters.     

Geographical Leadership Mobility and Policy Isomorphism: Narrowing the Regional Inequality of Social Spending in China

The link between geographical leadership mobility and policy isomorphism is rarely discussed in the extant literature. We argue that the geographical leadership mobility encourages local executives to converge their development experiences in their original working jurisdictions and their current positions. The distinct Chinese political personnel system provides an ideal environment that allows researchers to examine the isomorphic effects of geographical leadership mobility. This research builds a dataset of local social spending between 1998 and 2011 as well as a database of the leadership mobility history of provincial executives in China. Results of the spatial panel analysis (SPA) demonstrate that the geographical leadership mobility (i.e., horizontal, top-down, and bottom-up) of governors stimulates the regional isomorphism of provincial education and health care spending. The empirical findings affirm the effectiveness of the effort of the Chinese central government in narrowing the regional inequality of social welfare provision through the geographical mobility of local leadership.     

Stages and Methods of Economic-Geographic Prediction

An important technique of economic-geographic prediction is the comparative analysis of alternative combinations of productive forces that are likely to modify the existing system of the geographical division of labor and the system of economic regions. A suggested sequence of steps is designed to locate future productive capacity to a maximum extent in already developed regions with minimum inputs into new territorial development. Only industries whose growth cannot be insured in developed regions should be located in new pioneering regions. The predictive process is illustrated with particular reference to the economic development of the USSR up to the year 2000 and beyond. The principle of uniform spatial development, once a keystone of Soviet economic planning, is found to be economically unsound. Rapid returns on investment can be obtained by more intensive use of existing developed territories.     

The Spatial Association of Demographic and Population Health Characteristics with COVID-19 Prevalence Across Districts in India

In less-developed countries, the lack of granular data limits the researcher's ability to study the spatial interaction of different factors on the COVID-19 pandemic. This study designs a novel database to examine the spatial effects of demographic and population health factors on COVID-19 prevalence across 640 districts in India. The goal is to provide a robust understanding of how spatial associations and the interconnections between places influence disease spread. In addition to the linear Ordinary Least Square regression model, three spatial regression models—Spatial Lag Model, Spatial Error Model, and Geographically Weighted Regression are employed to study and compare the variables explanatory power in shaping geographic variations in the COVID-19 prevalence. We found that the local GWR model is more robust and effective at predicting spatial relationships. The findings indicate that among the demographic factors, a high share of the population living in slums is positively associated with a higher incidence of COVID-19 across districts. The spatial variations in COVID-19 deaths were explained by obesity and high blood sugar, indicating a strong association between pre-existing health conditions and COVID-19 fatalities. The study brings forth the critical factors that expose the poor and vulnerable populations to severe public health risks and highlight the application of geographical analysis vis-a-vis spatial regression models to help explain those associations.     

Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods

While the land use-street network nexus is well acknowledged, evidence for the one-way impacts of land-use patterns on street accessibility is still inadequate. The measurements of land-use patterns and street accessibility lack systematic knowledge. Their empirical correlations also lack geographical variability, constraining site-specific land-use practices. Therefore, this study overcame the aforementioned limitations by examining the two-level spatial models to formulate accessibility-oriented land plans, using a well-developed Chinese city as an example. Firstly, two landscape metrics—Euclidean Nearest-Neighbor Distance (ENN) and Similarity Index (SIMI)—were used to quantify the intra- and inter-land-use configurations, respectively. Both city-level and local accessibility were measured using spatial design network analysis. Performing both ordinary least squares (OLS) and geographically weighted regression (GWR) models, results identified the statistically significant effects of inter-land-use patterns on two-level street accessibility. An exception was that land-use configurations within residential and industrial regions were irrelevant to street accessibility. We also found GWR was a better-fitting model than OLS when estimating locally-varied accessibility, suggesting hierarchical multiscale land-use planning. Overall, locally heterogeneous evidence in this study can substantialize land use-street network interactions and support the decision-making and implementation of place-specific accessibility-oriented land use.     

信息时代的地理学研究——NCGIA对若干理论问题的探索

本文以美国国家基金委员会资助美国国家地理信息与分析中心(NCGIA)进行的瓦伦纽斯(Varenius)研究项目有关信息时代地理学研究的一系列中期研究报告与论文为基础,介绍了中心一些著名学者以地理学家独特的地理哲学思维,对信息时代的地理学研究若干理论问题所作的新探索与阐述,旨在为国内学者了解、追踪地理信息科学的新发展,开展地理信息科学的跨世纪研究提供借鉴。     

Geographically Weighted Extreme Learning Machine: A Method for Space–Time Prediction

Spatial heterogeneity has been regarded as an important issue in space–time prediction. Although some statistical methods of space–time predictions have been proposed to address spatial heterogeneity, the linear assumption makes it difficult for these methods to predict geographical processes accurately because geographical processes always involve complicated nonlinear characteristics. An extreme learning machine (ELM) has the advantage of approximating nonlinear relationships with a rapid learning speed and excellent generalization performance. However, determining how to incorporate spatial heterogeneity into an ELM to predict space–time data is an urgent problem. For this purpose, a new method called geographically weighted ELM (GWELM) is proposed to address spatial heterogeneity based on an ELM in this article. GWELM is essentially a locally varying ELM in which the parameters are regarded as functions of spatial locations, and geographically weighted least squares is applied to estimate the parameters in a local model. The proposed method is used to analyze two groups of different data sets, and the results demonstrate that the GWELM method is superior to the comparative method, which is also developed to address spatial heterogeneity.     

On the Unity of Geography

A Leningrad University physical geographer criticizes attempts to affirm the unity of geography through the creation of new disciplines like “general geography,” which would focus on study of the man-nature relationship. He contends that such a general geography, which would seek to identify general geographic laws, is advocated primarily by economic geographers who would emphasize the role of man at the expense of physical geography. Isachenko takes issue with the view that what makes any research “geographical” is its relationship to man. He contends that the criterion of whether any investigation is “geographical” is its relationship to the geosystem, defined as any natural complex, ranging from the global to the local scale. In his opinion, the unity of geography should be furthered not through the establishment of new supradisciplines, such as general geography, but through closer ties, both in methodology and in organizational terms, between the two main groups of geographical disciplines—physical geography and economic geography.     

Spatial Competition and Bounded Rationality: Retailing at the Edge of Chaos

The spatial dimension of competition among retail outlets is well researched and typically captured with spatial interaction models. A stream of theoretical research has studied the consequences of incorporating various types of dynamics into these models. We build on this research by incorporating a behavioral decision process based on bounded rationality, and by allowing for unexpected adversity in the environment in the form of exogenous shocks. Given these characteristics—spatial competition, boundedly rational decision making, and environmental adversity—we study the long-run dynamics of a model retail industry. The model reaches a stochastic steady state which is “poised,” in the sense that a shock may—or may not—trigger a wave of innovation which sweeps the entire system. Detailed investigation of this steady state shows that it has the characteristics of a general type of organization, known as self-organized criti-cality, that has been described in both theoretical biology and statistical physics.     

多尺度视角下中国城市创新网络格局及邻近性机理分析

借助2016年国家知识产权局的专利合作数据,利用复杂网络和空间分析方法对国家、区际和省内三个尺度的城市创新网络结构进行刻画,并利用负二项回归法对创新合作的邻近性机理进行了剖析。研究表明：①全国城市创新网络的整体联系较弱,网络极化现象明显,空间结构呈现出以北京为核心的放射型网络形态。区际城市创新网络的跨区域网络联系强于区内网络联系,东中西形成以区域中心城市为核心的异质性空间结构。省内城市创新网络的本地结网不足,内部联系强度低,空间形态普遍呈现出以省会城市为中心的核心-边缘结构。②回归结果证实了社会邻近、技术邻近对城市创新结网具有显著的促进作用,地理邻近则呈现出不显著的正向影响。其中,技术邻近的促进作用显著高于社会邻近,而地理邻近则需要通过社会邻近的调节效应对创新结网产生影响。