Mapping the Geography of R&D: What Can We Learn for Regional Innovation Policy in the Czech Republic and Beyond?

Administrative regions do not necessarily correspond to areas that are homogenous in terms of innovation intensity. Although this has been recognized in the literature, quantitative evidence that explicitly considers this problem is rare. Using spatial exploratory analysis on detailed regional data derived from a census of R&D performers in the Czech Republic, we identify local spatial clusters of R&D activities and assess the extent of their (mis)match with administrative borders. Overall, the results support the arguments for regionalization of innovation policy. However, the existing policy units do not appear well suited for this purpose. On one hand, there is a need for policy coordination between multiple administrative regions. On the other hand, however, there are diverse patterns within them. Similar problems are likely to haunt the regionalization process in many other countries, which is alarming, as the regional accent of innovation policies becomes ever more vehement over time.     

On the Performance of the Subtour Elimination Constraints Approach for the p‐Regions Problem: A Computational Study

The p‐regions is a mixed integer programming (MIP) model for the exhaustive clustering of a set of n geographic areas into p spatially contiguous regions while minimizing measures of intraregional heterogeneity. This is an NP‐hard problem that requires a constant research of strategies to increase the size of instances that can be solved using exact optimization techniques. In this article, we explore the benefits of an iterative process that begins by solving the relaxed version of the p‐regions that removes the constraints that guarantee the spatial contiguity of the regions. Then, additional constraints are incorporated iteratively to solve spatial discontinuities in the regions. In particular we explore the relationship between the level of spatial autocorrelation of the aggregation variable and the benefits obtained from this iterative process. The results show that high levels of spatial autocorrelation reduce computational times because the spatial patterns tend to create spatially contiguous regions. However, we found that the greatest benefits are obtained in two situations: (1) when ; and (2) when the parameter p is close to the number of clusters in the spatial pattern of the aggregation variable.     

济南都市圈城市化空间分异特征及其引导策略

随着快速城市化的发展,都市圈成为我国大城市功能地域组织的重要形式。本文对济南都市圈城市化发展的空间分异特征和问题进行了分析,发现济南都市圈中虽然济南具有较高的集聚规模,但却缺乏相应的辐射带动能力,圈内多数县域尤其是黄河以北地区城市化滞后,加之其它中心城市实力不够强大,严重阻碍了济南及其都市圈竞争优势的发挥。由此提出了"强化核心、多元中心、区域联动、县域支撑"的城市化空间布局策略,并以城乡土地资源合理利用为核心,分别就土地分类管制和分区引导等两方面城市化协调引导机制进行了探讨。     

Methods of Mapping and Identifying Small Clusters of Rare Diseases with Applications to Geographical Epidemiology

Geographical variation operates at a variety of scales. Methods of mapping variation in disease incidence between different countries or even counties are relatively well developed. When, however, the question relates to much smaller aggregations (that is, “clustering”), attention has mainly been restricted to areas near putative point hazards; the majority of cases are excluded from such an investigation. In this study we show how clustering may be investigated and displayed in such a way that it becomes a powerful tool in epidemiological research. As examples we use incidence data from the Yorkshire health region for selected childhood cancers and adult haematopoietic malignancies. The methods would readily extend to any small clusters of rare events.     

Adaptive Cell Tower Location Using Geostatistics. 基于地统计学的适应性基站发射塔选址研究

In this article, we address the problem of allocating an additional cell tower (or a set of towers) to an existing cellular network, maximizing the call completion probability. Our approach is derived from the adaptive spatial sampling problem using kriging, capitalizing on spatial correlation between cell phone signal strength data points and accounting for terrain morphology. Cell phone demand is reflected by population counts in the form of weights. The objective function, which is the weighted call completion probability, is highly nonlinear and complex (nondifferentiable and discontinuous). Sequential and simultaneous discrete optimization techniques are presented, and heuristics such as simulated annealing and Nelder–Mead are suggested to solve our problem. The adaptive spatial sampling problem is defined and related to the additional facility location problem. The approach is illustrated using data on cell phone call completion probability in a rural region of Erie County in western New York, and accounts for terrain variation using a line‐of‐sight approach. Finally, the computational results of sequential and simultaneous approaches are compared. Our model is also applicable to other facility location problems that aim to minimize the uncertainty associated with a customer visiting a new facility that has been added to an existing set of facilities.     

Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations

Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph-based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph-based cuts and expert-defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph-based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph-based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert-defined Bailey's ecoregions.     

Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters

The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm , is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure.     

flowAMOEBA: Identifying Regions of Anomalous Spatial Interactions

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set.     

Back to the Future? Regionalism in South‐East Asia Under Unilateral Pressure

This article assesses the prospects for regionalization in South-East Asia. It takes as its point of departure the contradiction between a regionalized and a unilateral world order as typically pursued by the EU and US respectively. It acknowledges the commonly accepted thesis that since September 11, 2001, the US has increasingly exercised a unilateral world order and that this poses a challenge to global regionalization. South-East Asia, a conflict-ridden, previously 'peripheral', region with a'successful' regionalization has been depicted as a 'second front' in the war against terrorism and is thus eligible for considerable US pressure. In this context, the 'ASEAN way', commonly benignly viewed, has been criticized for being shallow, 'allowing' terrorism to operate regionally. However, since 2001, and especially after the Bali bombings in 2003, ASEAN, as well as its member states, have devoted themselves to the war against terrorism. To some extent this has allowed the US a great influence in individual countries and altered regionalization. However, at the same time, the US 'needs' South-East Asian regional organization for combating international terrorism. Moreover, the US offensive in South-East Asia has caused both Japan and China to respond and strike deals on regional cooperation with ASEAN/South-East Asia, achieving long-awaited progress. Thus, the unilateral approach to global order does not, de facto, counteract regionalization, but rather operates through it, and to some extent triggers it. The counterintuitive conclusion is thus that an increasing unilateral pressure may not preclude a continued global regionalization, and that these two orders are not necessarily incompatible.     

Designing Reliable Center Systems: A Vector Assignment Center Location Problem

The p‐center problem is one of the most important models in location theory. Its objective is to place a fixed number of facilities so that the maximum service distance for all customers is as small as possible. This article develops a reliable p‐center problem that can account for system vulnerability and facility failure. A basic assumption is that located centers can fail with a given probability and a customer will fall back to the closest nonfailing center for service. The proposed model seeks to minimize the expected value of the maximum service distance for a service system. In addition, the proposed model is general and can be used to solve other fault‐tolerant center location problems such as the (p, q)‐center problem using appropriate assignment vectors. I present an integer programming formulation of the model and computational experiments, and then conclude with a summary of findings and point out possible future work.     

POPULATION DISPERSION VS. CONCENTRATION IN A TWO‐REGION MIGRATION MODEL WITH ENDOGENOUS NATURAL AMENITIES*

ABSTRACT Contrary to the predictions of the basic spatial equilibrium model, the long‐run distribution of population across rural U.S. counties with high‐valued natural amenities has become relatively more concentrated versus dispersed. We provide an explanation by developing a two‐region model with mobile labor, production externalities and endogenous natural amenities. We find that strong preferences for natural amenities generally foster population dispersion. However, such preferences can also lead to population concentration when ecological degradation is low and man‐made capital is a relatively scarce input into natural amenity production. Investments that enhance natural amenities are found to reduce the divergence between the steady state and optimal outcomes.     

ACCOUNTING FOR NEIGHBORING EFFECTS IN MEASURES OF SPATIAL CONCENTRATION*

ABSTRACT A common problem with spatial economic concentration measures based on areal data (e.g., Gini, Herfindhal, entropy, and Ellison‐Glaeser indices) is accounting for the position of regions in space. While they purport to measure spatial clustering, these statistics are confined to calculations within individual areal units. They are insensitive to the proximity of regions or to neighboring effects. Clearly, since spillovers do not recognize areal units, economic clusters may cross regional boundaries. Yet with current measures, any industrial agglomeration that traverses boundaries will be chopped into two or more pieces. Activity in adjacent spatial units is treated in exactly the same way as activity in far‐flung, nonadjacent areas. This paper shows how popular measures of spatial concentration relying on areal data can be modified to account for neighboring effects. With a U.S. application, we also demonstrate that the new instruments we propose are easy to implement and can be valuable in regional analysis.     

Resolution of the Fifth Congress of the Geographical Society USSR

A 1978 collection of articles devoted to geocybernetics, a subfield of geography concerned with spatial aspects of management, focuses on the problem of administration of the Soviet economy through a system of socio-economic regions at three levels–macro, meso and micro levels. The reviewer examines critically some of the basic ideas of regionalization underlying the proposed new discipline. He questions, in particular, what he considers an exaggerated research emphasis on problems of middle–level and lower–level regionalization without adequate attention to the issue of the Soviet Union's macroregions.     

MEASURING THE IMPACT OF SEA‐LEVEL RISE ON COASTAL REAL ESTATE: A HEDONIC PROPERTY MODEL APPROACH*

ABSTRACT This study estimates the impact of sea‐level rise on coastal real estate in North Carolina using a unique integration of geospatial and hedonic property data. With rates of sea‐level rise approximately double the global average, North Carolina has one of the most vulnerable coastlines in the United States. A range of modest sea‐level rise scenarios based on the IPCC Fourth Assessment Report projections (2007) are considered for four counties of North Carolina—New Hanover, Dare, Carteret, and Bertie—which represent a cross‐section of the state's coastline in geographical distribution and economic development. High‐resolution topographic LIDAR (light detection and ranging) data are used to provide accurate inundation maps for the properties that will be at risk under six different sea‐level rise scenarios. A simulation approach based on spatial hedonic models is used to provide consistent estimates of the property value losses. Considering just four coastal counties in North Carolina, the value of residential property loss without discounting in 2030 (2080) is estimated to be about $179 ($526) million for the mid‐range sea‐level rise scenarios. Low‐lying and heavily developed areas in the northern coastline are comparatively more vulnerable to the effect of sea‐level rise than the other areas.     

地貌与村落扩展:1753-1982年河北南部村落研究

本文采用历史文献、地名考证、GIS空间定位等方法,复原了河北南部四十余县清前期村落分布状况,并结合现代资料,探讨了200多年来该地区村落扩展的空间特征。研究发现河北南部地区的村落,在过去二百多年间,不论从数量上还是分布上都有很大的变化。而且这种变化存在明显的区域差异。作者认为地貌形态是造成这种区域差异的重要原因,是控制长时间大尺度村落发展非常重要的影响因素。     

Relations among Clusters in Six Chinese City Regions

Despite the considerable number of papers that have discussed industrial clusters, it is surprising that there is little research evidence on relations among clusters. This article collects longitudinal data on three low-tech and two high-tech industrial clusters in six cities in the dynamic Pearl River Delta of the People's Republic of China. The findings provide empirical support to both the Marshall–Arrow–Romer model, which argues for the importance of homogeneous (specialized) clusters and the Jacobs model, which argues for the importance of heterogeneous (diversified) clusters. Both homogeneous and heterogeneous clusters in the same region influence one another's cluster size and economic output.     

Spatial Cluster Detection in Spatial Flow Data

As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.     

Does Econometric Methodology Matter? An Analysis of Public Policy Using Spatial Econometric Techniques

A popular approach to examining the effects of public policy has been to rely on a spatial data sample of border counties as in Holmes (1998)—border counties from a sample of states that are used in conjunction with least‐squares estimation techniques in an attempt to isolate the policy impact while controlling for spatial dependence that often arises from latent or unobserved variables. This technique is in the spirit of control‐group methodologies from the laboratory sciences. This paper contrasts border‐county estimation results from Holmes' (1998) approach and those from a related methodology set forth in Holcombe and Lacombe (2003), with estimates from a spatial autoregressive model explicitly accounting for within‐state and between‐state public policy effects. As an illustration, the paper examines the effects of Aid to Families with Dependent Children (AFDC) and Food Stamp payments on female‐headed households and female labor force participation using the three different methods.     

The p‐Compact‐regions Problem

The p‐compact‐regions problem involves the search for an aggregation of n atomic spatial units into p‐compact, contiguous regions. This article reports our efforts in designing a heuristic framework—MERGE (memory‐based randomized greedy and edge reassignment)—to solve this problem through phases of dealing, randomized greedy, and edge reassignment. This MERGE heuristic is able to memorize (ME of MERGE) the potential best moves toward an optimal solution at each phase of the procedure such that the search efficiency can be greatly improved. A dealing phase grows seeded regions into a viable size. A randomized greedy (RG of MERGE) approach completes the regions' growth and generates a feasible set of p‐regions. The edge‐reassigning local search (E of MERGE) fine‐tunes the results toward better objectives. In addition, a normalized moment of inertia (NMI) is introduced as the method of choice in computing the compactness of each region. We discuss in detail how MERGE works and how this new compactness measure can be seamlessly integrated into different phases of the proposed regionalization procedure. The performance of MERGE is evaluated through the use of both a small and a large p‐compact‐regions problem motivated by modeling the regional economy of Southern California. We expect this work to contribute to the regionalization theory and practice literature. Theoretically, we formulate a new model for the family of p‐compact‐regions problems. The novel NMI introduced in the model provides an accurate, robust, and efficient measure of compactness, which is a key objective for p‐compact‐regions problems. Practically, we developed the MERGE heuristic, proven to be effective and efficient in solving this nonlinear optimization problem to near optimality. El problema de regiones compactas tipo p (p‐compact regions) consiste en la búsqueda de la agregación de n unidades espaciales atómicas que produzca regiones contiguas de tipo p‐compacto. Este artículo reporta los esfuerzos de los autores en el diseño de un marco heurístico MERGE (memory‐based randomized greedy and edge‐reassignment) el cual resuelve este problema a través de las fases de la negociación (dealing), codicia aleatorizada (randomized greedy), y la reasignación de bordes (edge reassignment). El heurístico MERGE es capaz de memorizar ( ME de MERGE) los mejo)res desplazamientos posibles hacia una solución óptima en cada fase del procedimiento de tal manera que la eficiencia de la búsqueda puede ser mejorada en gran medida. La fase de negociación crea regiones “sembradas” aleatoriamente y las hace crecer en diferentes tamaños . El componente de codicia aleatorio (RG de MERGE) completa la fase de crecimiento de las regiones y genera un conjunto factible de regiones tipo p. La reasignación de bordes se realiza vía una búsqueda local (E de MERGE) que afina los resultados con el fin the alcanzar los objetivos. Además, el enfoque propuesto aquí utiliza el momento de inercia normalizado (normalized momento of inertia‐NMI) como método para el cálculo de la compacidad de cada región. El artículo discute en detalle el funcionamiento de MERGE y cómo esta nueva medida compacidad puede integrarse perfectamente en las diferentes fases del procedimiento de regionalización propuesto. Para ilustrar y evaluar el desempeño de MERGE, el metodo es aplicado a dos problemas de p‐compact regions, uno grande y uno pequeño, basados en el modelado de la economía regional del sur de California. Los autores esperan que este trabajo contribuya a la literatura teórica y práctica de la regionalización. En términos teóricos, se formula un nuevo modelo de la familia de problemas de p‐compact regions. El NMI equipa al modelo con una novedosa forma de obtener una medida exacta, robusta y eficiente de compacidad, que es un objetivo clave para los problemas región compacta tipo p. En términos prácticos, se desarrolla MERGE, un procedimiento heurístico que ha demostrado ser eficaz y eficiente en la solución de este problema de optimización no lineal de manera casi óptima. p紧凑区域问题包含寻找一种聚集方法,将n个不可分割的空间单元集合成p紧凑的邻近区域。本文阐述了一种启发式架构MERGE(基于记忆的随机贪婪和边界再赋值算法),通过处理、随机贪婪和边界再分配这几个阶段解决p紧凑问题。MERGE启发式框架能存储 处理过程每个阶段中,向一个最优解的潜在最佳移动方式,从而可极大地提升搜索效率。一个处理阶段可将种子区域生长到可行大小。而一个随机贪婪阶段能完成区域增长并生成p区域的可行集。在边界再分配的局部搜索阶段对结果进行微调以达到更好的目标。此外,引入标准化的惯性矩作为每个区域紧凑度计算的选择方法。本文详细讨论了MERGE的工作原理,以及这种新的紧凑度测算方法如何能无缝地整合到所提出的区域化流程的不同阶段。通过在南部加州区域经济建模中一小一大两个p紧凑区域问题的应用,对MERGE的性能进行评估,期望该工作能够对区域化理论和实践作出贡献。理论上,提出了可解决p紧凑区域这类问题的新模型。在模型中引入新颖的标准化惯性矩这一精确的、鲁棒的和有效的紧凑度度量方法,是解决p紧凑区域问题的关键目标;实践上,本文发展了MERGE启发式框架,并证明了它在解决这种非线性优化问题近优性的有效性和高效性。     

Regional development processes and policies in Australia: a review of research 1990–2002

This article offers a selective review of Australian research on regional development. The themes reviewed include divergence and convergence, resource dependent regional growth, the spatial centralization of the economy, spatial divisions, the social construction of regional identity and regional problems, differentiation between the capital cities and between rural areas, indigenous issues, the suburbanization versus centralization debate, the regional effects of economic reform, regional policy debates, and industry clusters. Australia illustrates regional development processes in a low population density, resource dependent, medium sized economy, managed by neo‐liberal economic policies and with limited government intervention in regional policy.