GPS-based measurement of geographic spillovers in tourism – example of Polish districts

The agglomeration phenomenon in tourism often spreads beyond the borders of territorial units what is referred to as geographic spillovers. However, the measurement of spatial concentration of tourism demand and economic activity is usually based on statistics collected within regional administrative boundaries and omits the spatial interdependency between neighboring regions. Recognition of such spatial interdependency in the standard procedure to define neighborhood relies on the distance between geometric means (centroids) of territorial units which, however, rarely reflects real ‘centers’ of tourism agglomerations and leads to errors and biased results. Hence, we propose to modify the measures of the neighborhood with the use of GPS coordinates of tourism firms and attractions in order to designate their regional central tendencies and thus to correct (shift in space) localization of centroids of territorial units. We test the usefulness of the new approach to obtain a more precise measurement of spatial concentration when tourism spills over beyond the boundaries of territorial units using the example of Polish districts. We employ the exploratory spatial data analysis (spatial statistics) and spatial regression models – to assess the difference between using traditional centroids and GPS coordinates in defining neighborhood and determining spillover effects in regional analysis. Furthermore we apply the new method into the model of tourism potential in order to identify spillover effects in Polish regions. We use the data collected by Central Statistical Office (tourists staying overnight in 379 districts in 2014) and by Polish Tourist Organization (14,390 GPS coordinates of individual entities: tourism firms and attractions). The neighborhood determined with the use of GPS coordinates to measure the distance between centers of tourism agglomerations eliminates the dependence of the results on the administrative boundaries – but only to some degree. The challenge is to identify tourism agglomeration phenomenon as such, based on the mobility of tourists in space.     

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.     

Exploring Nonlinear Spatial Dependence in the Tails

In many instances it is of interest to measure the degree of similarity between neighboring regions. Spatial autocorrelation measures are the most popular means of doing it. However, such measures only capture a global linear relationship between regions, whereas in many circumstances a more general instrument is required. For instance, in economic poverty analysis or environmental applications (and in other cases where we are interested in extreme events and threshold exceedances) we should be more interested in the spatial pattern in the tails of the joint distributions. In this article we introduce some exploratory tool that focuses on the bivariate joint tails behavior to detect a pattern of spatial regularities. The method will be illustrated with reference to simulated environmental data.     

SPATIAL DEPENDENCY OF SEGREGATION INDICES

A few researchers have mentioned the scale sensitivity of segregation index, D. In this paper, I discuss analytically and empirically why using large enumeration areal units usually results in low segregation measures, and using small areal units produces relatively high segregation measures. The discussion is also applicable to the multi-group variant of D. A major finding is that if people of the same ethnic groups are positively spatially auto-correlated, increasing the size of areal units of analysis may not lower D initially, because only people of the same group are added. But enlarging the areal units subsequently may include population of other ethnic groups, and therefore could lower D. However, if the boundaries of the larger enumeration units are drawn to include only population of the same group, then D will not change significantly. Both the spatial autocorrelation of ethnic group population and zonal pattern are critical factors in determining the scale sensitivity of D.     

On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

Monitoring population characteristics and their patterns of spatial evolution are fundamental components for urban management and policy decision‐making. Societal issues such as health, transport, or crime are often explored using a range of models describing the urban dynamics of population attributes at specific scales that can be seen as complementary. Using and simulating data at different scales of aggregation asks for the need to analyze and compare spatiotemporal variations in order to better understand the model behaviors and emerging properties of the geosimulation. This article analyzes the uses of the entropy measure in the literature and constraining factors needed for its potential extension to explore the variations in geographic and time scales. In particular, the article discusses the need for a truly spatial entropy that takes into account the spatial contiguities of the observations usually aggregated within a zoning system of areal units. Two generic solutions are exposed for the various geometries and attribute structures used for census‐related analyses; they are based on existing measures for point data using (i) co‐occurrences of observations and (ii) discriminant ratios of distances between groups of observations. Their extensions to areal compositional data are articulated around their conceptual changes and geocomputational challenges. A revisited and new version of the entropy decomposition theorem, encompassing a spatiality concept semantically related to correlation, is also presented as efficiently reusing the constrained hierarchical zoning system of administrative units to enable discovery of emerging spatial pattern features from the geosimulation. A comparison of the results between the classical use of entropy and the spatial entropy framework devised shows the flexibility and added capabilities of the approach for new types of analyses, thus allowing new insight into studies of population dynamics.     

Application of Quantitative Methods of Settlement Analysis to a Lower-Level (Intra—Oblast) Economic Regionalization

Two quantitative techniques—the mean center of population of the old centrography school, and the potential of population—are applied to settlement analysis in Eastern Siberia for purposes of lower economic regionalization. The migrations of the mean center of population of Yakut ASSR and Irkutsk Oblast are interpreted in terms of regional development trends from the 1920s to the 1960s. Analyses of the potential of population for different dates suggest the formation of areal production nodes that can be used in delimiting lower economic regions. The boundaries between such lower-order regions tend to pass through undeveloped and sparsely settled areas that show up as low values on maps of the potential of population.     

INTEGRATION OF IMMIGRANTS,BRIDGING SOCIAL CAPITAL,ETHNICITY, AND LOCALITY

This paper analyses a uniquely detailed data set of social integration characteristics of immigrants belonging to four non‐native ethnic groups (i.e., Turks, Moroccans, Surinamese, and Antilleans) living in Dutch neighborhoods. It is well known that an individual's level of social integration is related to the ethnic composition and economic development of an immigrant's residential locality, as well as the generation of the immigrant. Yet, what is not known is whether the social and economic characteristics of adjacent or neighboring localities also influence an individual's level of social integration. Using a multilevel hierarchical analysis with spatial interaction effects, we examine the extent to which four social integration aspects of the bridging social capital of these immigrant groups are related to their ethnicity, their generation, their immediate locality, and the effects of the neighboring localities. Our findings regarding the effects of the ethnic concentration and economic development of the immediate locality along with the immigrant's generation broadly concur with existing studies. At the same time, however, we also find that the features of neighboring localities exert an additional influence on an individual's social integration over and above those related to the immigrant's generation and immediate locality. These additional spatial spillover effects are broadly in line with those associated with the immediate locality, but they are also sensitive to particular proxies for social integration which are employed. These spatial spillover effects on social capital and social integration have not been observed before.     

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.     

淮海经济区城市空间影响范围与城市经济区划分

运用因子分析法求取淮海经济区20个地级市的综合实力指数及等级划分。根据城市综合实力指数和等级划分,选取4个一级城市作为中心城市,然后运用断裂点公式计算中心城市与邻近城市的空间影响距离。再根据空间影响距离划分各中心城市经济影响区范围。最后以城市经济影响区为基础,综合考虑各城市的区域特征、发展潜力、现有经济规划、历史习惯和人文特征,确定城市经济区界限。研究淮海经济区城市经济区形成和划分的目的是指明各城市在整个区域内劳动地域分工中的地位,了解城市经济发展现状和主要特点。     

Bayesian Areal Wombling for Geographical Boundary Analysis

In the analysis of spatially referenced data, interest often focuses not on prediction of the spatially indexed variable itself, but on boundary analysis , that is, the determination of boundaries on the map that separate areas of higher and lower values. Existing boundary analysis methods are sometimes generically referred to as wombling , after a foundational article by Womble (1951). When data are available at point level (e.g., exact latitude and longitude of disease cases), such boundaries are most naturally obtained by locating the points of steepest ascent or descent on the fitted spatial surface (Banerjee, Gelfand, and Sirmans 2003). In this article, we propose related methods for areal data (i.e., data which consist only of sums or averages over geopolitical regions). Such methods are valuable in determining boundaries for data sets that, perhaps due to confidentiality concerns, are available only in ecological (aggregated) format, or are only collected this way (e.g., delivery of health-care or cost information). After a brief review of existing algorithmic techniques (including that implemented in the commercial software BoundarySeer), we propose a fully model-based framework for areal wombling, using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo methods. We explore the suitability of various existing hierarchical and spatial software packages (notably S-plus and WinBUGS) to the task, and show the approach's superiority over existing nonstochastic alternatives, both in terms of utility and average mean square error behavior. We also illustrate our methods (as well as the solution of advanced modeling issues such as simultaneous inference) using colorectal cancer late detection data collected at the county level in the state of Minnesota.     

Economic Policies and Local Development: Some Reflections

Every national system is also a sum of all its regional economies. Then, there are economically differentiated regions within nations. If the foregoing is true, a crucial problem for economic policy is determining which policies should be pursued at the national level (national policies), and on what grounds, and which should instead be developed at the local, regional level (local policies). What policies influence the development and competitiveness of regional economies? This article tries to discuss these issues.The first causal relation to examine is that between national economic policies and the competitiveness of a country's regions. The picture grows even more complex if we consider that there are national policies which have explicitly geographical objectives. Then, why not count on national policies, even locally differentiated ones? Having answered this question this paper addresses the opposite issue: why should we not view the entirety, or at least the largest part, of national economic policy--and development policies in particular--as simply the sum of local economic policies? How can one distinguish between the local effects of national economic policies and those of local development policies? A further problem is providing a satisfactory definition of regions (i.e. the areas in which policies are to be implemented) and of their boundaries.     

Complexity and Redundancy in Binary Maps

The complexity of binary maps that is provided by the areal arrangement of colors is considered, and measured using information theory. In addition, information theory provides other measures that have an interpretation in a map context. One of these, redundancy, is examined and found to bear a striking empirical relationship to a spatial autocorrelation statistic. It is argued that spatial autocorrelation is, conceptually as well as empirically, the two-dimensional equivalent of redundancy. It too measures the extent to which the occurrence of an event (color) in an areal unit constrains, or makes more probable, the occurrence of an event in a neighboring areal unit.     

SPATIAL DISCONTINUITY FOR THE IMPACT ASSESSMENT OF THE EU REGIONAL POLICY: THE CASE OF ITALIAN OBJECTIVE 1 REGIONS*

The economic impact of the Regional Policy of the European Union is still controversial. This paper exploits administrative boundaries as spatial discontinuity to estimate the causal effect of this policy on the Italian Objective 1 regions’ employment. The analysis, developed both in a border strategy framework (municipalities contiguous to the policy‐change boundary) and with more traditional RDD models balanced by spatial forcing variables (centroids’ distance and coordinates), shows that the EU Regional Policy produced a positive impact on employment levels, without any displacement of economic activities away from nontreated regions and a concentration of the impact in key economic sectors.     

EUROPEAN UNION REGIONAL PRODUCTIVITY DYNAMICS: A “DISTRIBUTIONAL” APPROACH*

ABSTRACT Working within the “distributional approach,” this research offers evidence of past and future polarization in aggregate regional labor productivity in EU‐15. This finding is robust to alternative definitions of the spatial units used and does not appear to be the result of regional structural change. Spatial effects are found but are of rather localized nature. Turning to separate sectoral analyses suggests that past and future polarization in labor productivity across European regions may be associated with regional productivity differences in the services sectors.     

A HIERARCHICAL REGIONALIZATION OF RSFSR ADMINISTRATIVE UNITS USING 1966–69 MIGRATION DATA

The coefficients of migration intensity calculated by Rybakovskiy (see preceding article) are used to derive a system of migration regions, which are defined as groups of administrative units with maximum internal interaction and a minimum flow across migration-defining boundaries. The technique is based on the assumption that gross migration flows may provide a reasonably objective basis for determining practical boundaries within a national territory. The technique yields a hierarchy of regionalizations that is then compared with the official Soviet system of major economic regions. None of the 10 major economic regions in their entirety turn out to conform to strong migration components, although one migration cluster, focused on the Northwest, corresponds fairly closely to the official Northwest region. For the rest, administrative units of the Far East and Siberia are grouped together in one migration cluster, as are the southern Volga region and the North Caucasus. Units of the Central [Industrial] region also tend to cluster with those of the Central Chernozem region. However, the most tightly connected migration dyads and triads are found to be wholly contained within the official economic regions, so that the most highly intensive migration streams are found not to cross the boundaries between the official regions. (The study was supported by a grant of the National Science Foundation for work on applications of graph—theoretic clustering and transaction flow analysis in geography and regional science.)     

边境区位、边境区经济合作的理论与实践--以辽宁省-朝鲜边境地区经济合作为例

欧洲和北美的实践表明,边境地区经济合作具有推动经济全球化和区域经济一体化的作用,同时,经济全球化和区域经济一体化又促进了边境地区经济合作的发展。本文从边境区位的理论分析入手,探讨了边境区位再创造问题,并分析边境区位再创造与边境区经济合作的内在联系。最后,对辽宁省和朝鲜之间边境区经济合作做了实证研究。     

A Geostatistical Framework for Area‐to‐Point Spatial Interpolation

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data and the sought‐after point predictions. In particular, it is proved that appropriate modeling of all area‐to‐area and area‐to‐point covariances required by the geostatistical frame‐work yields coherent (mass‐preserving or pycnophylactic) predictions. In other words, the areal average (or areal total) of point predictions within any arbitrary area informed by an areal‐average (or areal‐total) datum is equal to that particular datum. In addition, the proposed geostatistical framework offers the unique advantage of providing a measure of the reliability (standard error) of each point prediction. It is also demonstrated that several existing approaches for area‐to‐point interpolation can be viewed within this geostatistical framework. More precisely, it is shown that (i) the choropleth map case corresponds to the geostatistical solution under the assumption of spatial independence at the point support level; (ii) several forms of kernel smoothing can be regarded as alternative (albeit sometimes incoherent) implementations of the geostatistical approach; and (iii) Tobler's smooth pycnophylactic interpolation, on a quasi‐infinite domain without non‐negativity constraints, corresponds to the geostatistical solution when the semivariogram model adopted at the point support level is identified to the free‐space Green's functions (linear in 1‐D or logarithmic in 2‐D) of Poisson's partial differential equation. In lieu of a formal case study, several 1‐D examples are given to illustrate pertinent concepts.     

How to improve the quality of life in peripheral and lagging regions by policy measures? Examining the effects of two different policies in Germany

Peripheral regions commonly appear to be less attractive to live in and policymakers all over the world are applying various measures to make them more attractive. This paper analyzes the effects of two very different measures: The German municipal fiscal equalization scheme and the German structural funds for economically weak areas (GRW). It focusses on the impact on perceived quality of life, measured through interregional migration between German labor market regions. Using a spatial vector autoregressive panel model, we find evidence that equalization transfers have a significant positive impact on regional net migration and contribute to the aim of regional equity. These effects are especially found for regions with low endogenous fiscal capacities. GRW funding reveals no significant effects on net migration rates in total, but short-term effects in rural regions.     

ON THE RELATIONSHIP BETWEEN SECTORAL AND SPATIAL MANAGEMENT IN THE ELECTRIC POWER INDUSTRY OF THE USSR

The management system of the electric power industry of the USSR operates at two technological levels: a lower level of 92 regional power systems, each encompassing one or more oblast-type civil divisions or a small union republic; an upper level in which regional systems are combined into 11 unified power systems, where peak loads can be more easily moved between time zones. In addition there are electric power administrations within the RSFSR and ministries within the union republics, concerned mainly with administrative functions. Power administrations often do not coincide with unified systems, creating problems of efficient electric power management. Furthermore, the boundaries of unified power systems and power administrations often do not conform to the boundaries of major economic regions, which are used for current and long-term planning of the Soviet economy. This does not necessarily mean that the power systems are out of step with the economic regions. Soviet economic regionalization theory has long stressed the important region-shaping role of electric power systems. It may well be that in some cases, the boundaries of economic regions, not the power systems, require adjustment.