scholarly journals Changes in Regional Economic Resilience after the 2008 Global Economic Crisis: The Case of Korea

2021 ◽  
Vol 13 (20) ◽  
pp. 11392
Author(s):  
Seoyoung Yu ◽  
Donghyun Kim
Keyword(s):  
Economic Crisis ◽  
Spatial Data ◽  
Data Model ◽  
Census Data ◽  
Metropolitan Areas ◽  
Spatial Data Analysis ◽  
Economic Resilience ◽  
Regional Economic ◽  
Administrative Units ◽  
Economic Regions

This study investigated Korea’s regional economic resilience after the 2008 economic crisis and analyzed the spatial patterns therein from the perspective of evolution and engineering. We analyzed the employee statistics of 229 si-gun-gu (city-county-district) administrative units for the 2002–2016 period sourced from Business Census data using shift-share analysis, a panel data model, and exploratory spatial data analysis (ESDA). According to the analysis, most regions showed resilience after the crisis, revealing various patterns within the economic regions. Regarding the capital area, there were more structural improvements in Gyeonggi-do than in Seoul. For other regions, there were also more structural improvements in and around metropolitan areas. When comparing the absolute levels of post-crisis employment, the capital area showed low employment resilience in the CBD, while areas where industries such as IT and finance were clustered showed great employment resilience. In addition, non-capital areas showed a significant recovery in the manufacturing areas. This means that regional inequalities in the process of responding to economic crises are likely to include both quantitative and qualitative aspects, and that policies that accompany more structural improvements should be implemented.

Empirical Research on China's Regional Economic Distribution and Characteristics

2011 ◽  
Vol 204-210 ◽  
pp. 350-353
Author(s):  
Xiao Guang Lu ◽  
Jian Qun Zhu ◽  
Meng Ying Fan
Keyword(s):  
Division Of Labor ◽  
Empirical Research ◽  
Regional Economy ◽  
Census Data ◽  
Jiangsu Province ◽  
Regional Economic ◽  
The Past ◽  
Model Based ◽  
Economic Census ◽  
Economic Regions

According to the second economic census data of Jiangsu Province, this paper firstly uses PCA-HCA model based on provincial cities data to classify economic regions. And then, it uses BLR-HCA model to reclassify the economic regions based on counties data. Finally, it comes to the conclusion that the past regional classification ways of Jiangsu Province need to be updated. The research on regional economy is dynamic and timely, while deepening the division of labor and finance is an effective way to develop Jiangsu’s regional economy.

The Electoral Geography of Weimar Germany: Exploratory Spatial Data Analyses (ESDA) of Protestant Support for the Nazi Party

2002 ◽  
Vol 10 (3) ◽  
pp. 217-243 ◽  
Author(s):  
John O'Loughlin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Census Data ◽  
Spatial Data Analysis ◽  
Nazi Party ◽  
Ecological Inference ◽  
Inference Procedure ◽  
Individual Level ◽  
Weimar Germany ◽  
Strong Attachment

For more than half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning. Recent developments in geographic methodologies, based on Geographic Information Science (GIS) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods—global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping, and barrier definition (wombling)—are used in an exploratory spatial data analysis of the NSDAP vote. The support for the NSDAP by Protestant voters (estimated using King's ecological inference procedure) is the key correlate examined. The results from the various methods are consistent in showing a voting surface of great complexity, with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate, but is better characterized as a mosaic of support for “milieu parties,” mixed across class and other social lines, and defined by a strong attachment to local traditions, beliefs, and practices.

scholarly journals Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 380
Author(s):  
Radosław Cellmer ◽  
Aneta Cichulska ◽  
Mirosław Bełej
Keyword(s):  
Spatial Heterogeneity ◽  
Housing Market ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Housing Prices ◽  
Spatial Diversity ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Market Activity ◽  
Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

scholarly journals Macropsychological Factors Predict Regional Economic Resilience During a Major Economic Crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 95-104 ◽  
Author(s):  
Martin Obschonka ◽  
Michael Stuetzer ◽  
David B. Audretsch ◽  
Peter J. Rentfrow ◽  
Jeff Potter ◽  
...  
Keyword(s):  
Economic Crisis ◽  
Economic Resilience ◽  
Regional Economic

scholarly journals Social Capital and Economic Well-Being in Germany's Regions: An Exploratory Spatial Analysis

REGION ◽  
2016 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Katrin Botzen
Keyword(s):  
Social Capital ◽  
Spatial Data ◽  
Spatial Models ◽  
Well Being ◽  
Spatial Data Analysis ◽  
Cross Sectional ◽  
Regional Economic ◽  
Civic Associations ◽  
Units Of Analysis ◽  
Economic Well Being

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>This article explores social capital in Germany in line with Putnam’s claim that social capital benefits regional economic well-being. In particular, this macro-level study examines whether the number of civic associations, as a measure of a vibrant civil society, is related to higher GDP. Since this study uses spatial data on civic associations and official statistics concerning the German NUTS-3 regions, different spatial matrices model interdependencies among the dependent units of analysis. Exploratory spatial data analysis illustrates spatial patterns between districts as well as each variable’s radius of influence. Cross-sectional spatial models help examine social capital’s effect on regional economic well-being. Results of these analyses are two-fold: first, the geographical scope of social capital is locally concentrated, whereas the sphere of economic well-being encloses a wider area. Second, social capital correlates positively with economic well-being in Germany’s many regions.</span></p></div></div></div>

scholarly journals A COMPARATIVE SPATIAL ANALYSIS OF THE POSSIBLE EFFECTS OF ECONOMIC CRISIS IN GREECE AND RUSSIA ON PROVINCES IN TURKEY

2016 ◽  
Vol 11 (1) ◽  
Author(s):  
Ilhan Korkmaz ◽  
Fatih Celebioglu
Keyword(s):  
Foreign Trade ◽  
Economic Crisis ◽  
Spatial Data ◽  
Global Financial Crisis ◽  
Spatial Data Analysis ◽  
Debt Burden ◽  
Spatial Dimensions ◽  
Choropleth Maps ◽  
Potential Risks ◽  
A Current

After 2008 global financial crisis some Europe countries which have excessive debt burden especially Greece, Iceland, Spain etc. effected negatively more than the others. On the other hand decrease in oil prices effected negatively some exporter countries for instance Russia, Venezuela etc. in this period. Greece and Russia are neighbor countries that has significant role on Turkey’s foreign trade. In this aspect, it has been occurred some potential risks for provinces in Turkey which exporting to Greece and Russia. This study aims to examine the possible effects of Greek and Russian economic crisis for provinces of Turkey by using spatial data and techniques. To identify risky areas first, it is created different choropleth maps of Turkey by using province based export data in particular Greece and Russia. Second, spatial dimensions of potential risks are discussed. To test spatial dimensions of the variables, we perform an exploratory spatial data analysis on export values for provinces of Turkey. While our choropleth maps indicate that the some part of the country is significantly more related to foreign trade of the countries than the others, the tools of spatial statistics reveal the presence of spatial dependence across provinces. The presence of heterogeneity is reflected in the distribution of LISA statistics. Overall, this paper is original in terms of analyzing spatial dimensions of a current economic issue for provinces across Turkey.

scholarly journals The spatiality and driving forces of population ageing in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0243559
Author(s):  
Lianxia Wu ◽  
Zuyu Huang ◽  
Zehan Pan
Keyword(s):  
Economic Development ◽  
Spatial Data ◽  
Census Data ◽  
Local Economic Development ◽  
Driving Forces ◽  
Population Ageing ◽  
Spatial Data Analysis ◽  
Western China ◽  
Practical Significance ◽  
Driving Mechanism

Studying the spatial characteristics of China’s ageing and its influencing factors is of great practical significance because China has the largest elderly population in the world. Using 2000 and 2010 census data, this study explores the degree, pace, and pattern of population ageing and its driving mechanism using exploratory spatial data analysis and the geographically weighed regression model. Between 2000 and 2010, population ageing increased rapidly countrywide; yet, spatial differences between eastern and western China narrowed. The degree of provincial population ageing and its spatiality were determined by natural population growth, migration, and local economic development. Life expectancy and mortality were the primary long-term factors, and GDP per capita was the prime contributor in the early days of economic development; the migration rate was the dominant influence after 2010. China’s overall spatial differentiation of population ageing shifted from a north–south to an east–west division.

scholarly journals Social vulnerability assessment using spatial multi-criteria analysis (SEVI model) and the Social Vulnerability Index (SoVI model) – a case study for Bucharest, Romania

2013 ◽  
Vol 13 (6) ◽  
pp. 1481-1499 ◽  
Author(s):  
I. Armaș ◽  
A. Gavriș
Keyword(s):  
Spatial Data ◽  
Social Vulnerability ◽  
Critical Issue ◽  
Vulnerability Index ◽  
Capital City ◽  
Spatial Data Analysis ◽  
Limited Attention ◽  
Social Vulnerability Index ◽  
The Social ◽  
Administrative Units

Abstract. In recent decades, the development of vulnerability frameworks has enlarged the research in the natural hazards field. Despite progress in developing the vulnerability studies, there is more to investigate regarding the quantitative approach and clarification of the conceptual explanation of the social component. At the same time, some disaster-prone areas register limited attention. Among these, Romania's capital city, Bucharest, is the most earthquake-prone capital in Europe and the tenth in the world. The location is used to assess two multi-criteria methods for aggregating complex indicators: the social vulnerability index (SoVI model) and the spatial multi-criteria social vulnerability index (SEVI model). Using the data of the 2002 census we reduce the indicators through a factor analytical approach to create the indices and examine if they bear any resemblance to the known vulnerability of Bucharest city through an exploratory spatial data analysis (ESDA). This is a critical issue that may provide better understanding of the social vulnerability in the city and appropriate information for authorities and stakeholders to consider in their decision making. The study emphasizes that social vulnerability is an urban process that increased in a post-communist Bucharest, raising the concern that the population at risk lacks the capacity to cope with disasters. The assessment of the indices indicates a significant and similar clustering pattern of the census administrative units, with an overlap between the clustering areas affected by high social vulnerability. Our proposed SEVI model suggests adjustment sensitivity, useful in the expert-opinion accuracy.

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