Does Spatial Clustering of Foreign Direct Investment Foster Global Production Networks? The Case of Qingdao,China

This article examines how spatial clustering of foreign direct investment (FDI) can foster a global production network (GPN). The cluster concept, in general, stressed inter-firm relations between local and regional firms while neglecting global–local linkages in the spatial context. In contrast, GPN focuses on the advantages of international production organization and governance structure but underestimates regional or localized channels of production arrangement. By integrating the conceptual framework of spatial clustering of FDI and GPN, this paper provides empirical evidence of various channels to knowledge and innovation transfer between domestic firms and foreign-invested enterprises. This paper demonstrates that the different types of linkages formation are key determinant factors for establishing cluster through contributing to local economic development.     

The Spatial Patterns of Networks,Hierarchies and Subsidiaries

This article reviews evidence and arguments linking the networking behaviour of firms with geographic distance, before examining the spatial relationships of electronics firms in the three major electronics centres in Spain. The focus is on the spatial pattern and extent of different types of inter-firm relations. Based on the analysis of 184 surveyed establishments, displaying different ownership and organization characteristics, the results show that while regional linkages are important, significant extra-regional linkages are also maintained by firms in regional clusters. The spatial extent of linkages depends on the mode of relations; arm's length, network and hierarchy relations show different spatial patterns, as do different types of cooperation. The importance of extra-regional linkages also varies with firm- and plant-specific characteristics. Extra-regional linkages are more common among larger and more R&D-intensive firms, firms with greater presence in the rest of the country and firms with more experience of cooperation and more stable relationships.     

ECONOMIC GEOGRAPHY WITHIN AND BETWEEN EUROPEAN NATIONS: THE ROLE OF MARKET POTENTIAL AND DENSITY ACROSS SPACE AND TIME*

ABSTRACT In explaining the uneven spatial distribution of economic activity, urban economics, and new economic geography (NEG) dominate recent research in economics. A main difference between these two approaches is that NEG stresses the role of spatial linkages whereas urban economics does not do so. We estimate simple versions of these two views on economic geography and also establish if the relevance of spatial linkages varies across aggregation levels or time. For our sample of 14 European countries and 213 corresponding regions, we find that spatial linkages are more important at the country level and that its relevance varies across time.     

基于空间均衡视角的区域高质量发展内涵界定与状态评价——以陕西省为例

本文构建了区域高质量发展的空间均衡度与空间均衡发展度两个指标，衡量了2017年陕西10个地级市的区域高质量发展水平。结论如下：①陕西经济发展质量呈西安与榆林“两极驱动”的空间格局；社会发展质量呈“中心化”发展空间格局；资源环境发展质量呈南高北低的空间格局。②空间均衡视角下，陕西高质量发展呈现以西安为增长极，西安以东以南地区空间均衡发展度低，西安以西以北地区空间均衡发展度高的空间格局。在发展意义上能达到空间均衡状态的只有西安，其他地级市均处于不同程度失衡状态。     

"The Problem of Spatial Autocorrelation" and Local Spatial Statistics

This article examines the relationship between spatial dependency and spatial heterogeneity, two properties unique to spatial data. The property of spatial dependence has led to a large body of research into spatial autocorrelation and also, largely independently, into geostatistics. The property of spatial heterogeneity has led to a growing awareness of the limitation of global statistics and the value of local statistics and local statistical models. The article concludes with a discussion of how the two properties can be accommodated within the same modelling framework.     

Surveying Subjective Phenomena: A Review Essay

Abstract

The geographic perspective on human behavior emphasizes the notion of context. Context suggests that human behavior is partly determined by the place in which individuals and groups act, and in turn these actions re-create places. It follows that an element of history is the contextual setting of activity. Spatial statistics are introduced as a tool for modeling context in historical studies. Spatial dependence serves to operationalize the specificity of place and the diffusion of processes between places. Spatial heterogeneity serves to operationalize the idea that behavior is regionally specific rather than uniform across space. Both spatial dependence and spatial heterogeneity are included in spatial regression models. A spatial statistical analysis of the Nazi Party vote in Bavaria between 1928 and 1932 is adopted to illustrate the argument.     

What Problem? Spatial Autocorrelation and Geographic Information Science

In retrospect it is the word "problem" in the title that seems most remarkable about the Cliff and Ord article. Spatial autocorrelation is indeed a problem in standard inferential statistics, which was developed to handle controlled experiments, when these methods are used to generalize from natural experiments. From the perspective of geographic information science, however, spatial dependence is a defining characteristic of geographic data that makes many of the functions of geographic information systems possible. The almost universal presence of spatial heterogeneity in such data also argues against generalization and is made explicit in the recent development of place-based analytic techniques. The final section argues for a new approach to the teaching of quantitative methods in the environmental and social sciences that treats natural experiments, spatial dependence, and spatial heterogeneity as the norm.     

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.     

A Local Model for Multivariate Analysis: Extending Wartenberg’s Multivariate Spatial Correlation

Local analysis can provide specific information about individual observations that is often useful in understanding nonstationary interactions among variables. This paper extends the application of Wartenberg’s Multivariate Spatial Correlation (MSC) method to a local setting. The original MSC can be considered as an adaptation of Principal Component Analysis for spatial effects with respect to spatial autocorrelation. The extended MSC method described in this paper, however, further incorporates another spatial effect, spatial heterogeneity, by the addition of geographic weights in standardizing the data and in calculating the spatial association weight matrix. The extension allows more local analysis and facilitates additional visualization of the results. The geographically weighted MSC is illustrated and justified using the classic dataset collected by André-Michel Guerry on moral statistics in 1830s France.     

Services in Russia: past,present, and future

In spite of ambitious modernization plans of the government, structural change in Russia is not proceeding in the direction of highly developed economies. Part of the lag in the tertiarization process can be explained by the fact that, as during the Soviet period, the role of services in the economy is still ignored, particularly regarding innovation. This paper aims to contribute to the analysis of the evolution of services in Russia since the mid-1990s and its future prospects by compiling data from the World Bank, the International Labor Organization, Rosstat, and the World Input–Output Database. Two complementary and novel perspectives are adopted: a production system perspective and a spatial perspective. Regarding services in the Russian production system, comparison of interindustry linkages reveals that, in addition to traditional relevant activities like wholesale/retail trade and transport and communications, business services are acquiring great importance as production process inputs. Concerning the spatial perspective, we observe a greater presence of non-market services in less densely populated federal districts, while strategic service activities are highly concentrated in the most dynamic ones. Analysis of major Russian cities confirmed the trend of business services concentrating in densely populated and highly innovative cities. In brief, services, and in particular business services, can be a means to boost innovation and growth in Russia, but more attention from political leaders is necessary for these activities. The modernization process should abandon its current “high-technology industry myopia” and take into consideration the innovative, and especially, the knowledge-diffuser role, that services can exert.     

公共服务视野下的城市空间正义问题——以山东省Q市为例

以山东省Q市为例，从公共服务满意度视角探讨城市空间正义问题及其作用路径。研究发现，近年来我国城市存在公共服务视野下明显的空间不正义问题，临近省市政府能享受更多、更好的公共服务，而区政府并不显著，且远离政府权力中心的边缘地带更加严重。不过，这种空间不正义问题更多源自省市政府权力的空间资源配置失衡，区政府没有足够话语权；主要在市辖区层面，街道尺度并不显著。受制于公共产品属性不同，不同公共服务的空间不正义问题有明显差异，弱排他性或弱竞争性的问题较小。另外，权力空间化通过公共产品配置数量和质量影响空间不正义问题，且质量层面远大于数量层面。新时代城市要从提高公共产品质量入手实现空间正义。     

In Search of Causality: A Regional Approach to Urban Growth in Eighteenth-century England

The simple equation of industrialisation and urbanisation contains much truth: recent national surveys confirm that rapid coal-based industrial growth spawned massive urban development in Britain. And yet at the local level a wide diversity of growth experiences reflect a complex and overlapping nexus of growth stimuli which defy attempts at generalisation. In taking a regional perspective, this paper seeks to occupy a productive middle ground in this ongoing debate. Placing urban growth in north-west England into its real economic and geographical context, it recognises the complexities of urban growth but transcends the narrowness of the particular. In so doing, it challenges some of the certainties of industrial urbanisation, emphasising instead the importance of rural industry, urban commercial functions and inter-place linkages. Most fundamentally, it argues for a more nuanced understanding of the geography of causality.     

Single and Multiscale Models of Process Spatial Heterogeneity

Recent work in local spatial modeling has affirmed and broadened interest in multivariate local spatial analysis. Two broad approaches have emerged: Geographically Weighted Regression (GWR) which follows a frequentist perspective and Bayesian Spatially Varying Coefficients models. Although several comparisons between the two approaches exist, recent developments, particularly in GWR, mean that these are incomplete and missing some important axes of comparison. Consequently, there is a need for a more thorough comparison of the two families of local estimators, including recent developments in multiscale variants and their relative performance under controlled conditions. We find that while both types of local models generally perform similarly on a series of criteria, some interesting and important differences exist.     

长春市住宅价格空间分异与影响因素研究

基于持续年份住宅数据、利用空间热点分析和地理加权回归模型，对2011年以来长春市住宅价格分布的空间分异现象和住宅价格影响因素分布的空间异质性进行研究。结果显示：①近年来长春市住宅小区存在向内填充与向外扩散并进，圈层扩展和扇形放射融合的演化趋势，其中西南-东南扇面是居住空间扩展的主要方向；②2011年以来长春市住宅价格呈现出以南部为指向的扇面增长的空间特征，高价住宅街区由点状分布到片状扩散。通过半变异函数和冷热点聚类分析方法指出，长春市住宅空间的南北分异现象显著，住宅价格的低值与高值聚类以铁路为界线存在显著的空间隔离；③就POI数量而言，住宅小区周边银行保险、医疗、住宿、休闲娱乐数量对住宅价格产生较强影响，同时以上因素作用效应的空间差异性同样显著。     

Applying Local Indicators of Spatial Association to Analyze Longitudinal Data: The Absolute Perspective

Local Indicators of Spatial Association (LISA) are a class of spatial statistical methods that have been widely applied in various scientific fields. When applying LISA to make longitudinal comparisons of spatial data, a common way is to run LISA analysis at each time point, then compare the results to infer the distributional dynamics of spatial processes. Given that LISA hinges on the global mean value that often varies across time, the LISA result generated at time T_i reflects the spatial patterns strictly with respect to T_i. Therefore, the typical comparative cross-sectional analysis with LISA can only characterize the relative distributional dynamics. However, the relative perspective alone is inadequate to comprehend the full picture, as the patterns are not directly associated with the changes of the spatial process’s intensity. We argue that it is important to obtain the absolute distribution dynamics to complement the relative perspective, especially for tracking how spatial processes evolve across time at the local level. We develop a solution that modifies the significance test when implementing LISA analysis of longitudinal data to reveal and visualize the absolute distribution dynamics. Experiments were conducted with Mongolian livestock data and Rwanda population data.     

Urban Textural Analysis from Remote Sensor Data: Lacunarity Measurements Based on the Differential Box Counting Method

Lacunarity is related to the spatial distribution of gap or hole sizes. For low lacunarity, all gap sizes are the same and geometric objects are deemed homogeneous; conversely, for high lacunarity, gap sizes are variable and objects are therefore heterogeneous. Textures that are homogeneous at small scales can be quite heterogeneous at large scales and vice versa, and hence, lacunarity can be considered a scale-dependent measure of heterogeneity or texture. In this article, we use a lacunarity method based on a differential box counting approach to identify urban land-use and land-cover classes from satellite sensor data. Our methodology focuses on two different gliding box methods to compute lacunarity values and demonstrate a mirror extension approach for a local moving window. The extension approach overcomes, or at least minimizes, the boundary problem. The results from our study suggest that the overlapping box approach is more effective than the skipping box approach, but that there is no significant difference between window sizes. Our work represents a contribution to not only advances in textural and spatial metrics as used in remote-sensing pattern interpretation but also for broadening understanding of the computational geometry of nonlinear shape models of which lacunarity is the reciprocal of fractal theory.     

Assessing the Cluster Correspondence between Paired Point Locations

Some complex geographic events are associated with multiple point locations. Such events include, but are not limited to, those describing linkages between and among places. The term multi‐location event is used in the paper to refer to these geographical phenomena. Through formalization of the multi‐location event problem, this paper situates the analysis of multi‐location events within the broad context of point pattern analysis techniques. Two alternative approaches (Vector autocorrelation analysis and cluster correspondence analysis) to the spatial dependence of paired‐location events (i.e., two‐location events) are explored, with a discussion of their appropriateness to general multi‐location event problems. The research proposes a framework of cluster correspondence analysis for the detection of local non‐stationarities in the spatial process generating multi‐location events. A new algorithm for local analysis of cluster correspondence is proposed. It is implemented on a large‐scale dataset of vehicle theft and recovery location pairs in Buffalo, New York.     

Scale economies and the geographic concentration of industry

In recent empirical literature on spatial agglomeration, manypapers find evidence consistent with location-specific externalitiesof some sort. Our willingness to accept evidence of agglomerationeconomies depends on how well key estimation problems have beenaddressed. Three issues are particularly troublesome for identifyingagglomeration effects: unobserved regional characteristics,simultaneity in regional data, and multiple sources of externalities.Two empirical results appear to be robust to problems createdby the first two issues: (a) individual wages are increasingin the presence of more-educated workers in the local laborforce, which is consistent with localized human-capital externalities,and (b) long-run industry growth is higher in locations witha wider range of industrial activities, which suggests thatfirms benefit from being in more diverse urban environments.Other evidence is supportive of agglomeration effects relatedto regional demand linkages and short-run, industry-specificexternalities.     

Spatial equilibrium in deviations: An application to skill‐premium and skill‐mix heterogeneity

In this paper, we propose a novel—and general‐purpose—modeling approach. We give a linear representation of the spatial general equilibrium, expressed in terms of local percentage deviations from the benchmark case of symmetry, where all the areas in the economy are taken to be initially identical. To illustrate the flexibility of our approach, we revisit the literature on the spatial heterogeneity of local skill premia and local skill mix. We show that our approach is able to encompass a variety of alternative explanations in a simple “unifying framework.” Finally, we exploit a graphical version of the model to show how to implement empirical tests.