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 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 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.

scholarly journals World War I, Nationalism, and the Rise of the Nazi Party

2021 ◽  
Author(s):  
Alexander De Juan ◽  
Felix Haass ◽  
Carlo Koos ◽  
Sascha Riaz ◽  
Thomas Tichelbaecker
Keyword(s):  
World War I ◽  
Community Level ◽  
Nazi Party ◽  
World War ◽  
Individual Level ◽  
Weimar Germany ◽  
Electoral Support ◽  
Local Impact ◽  
Effects Of War ◽  
Nationalist Parties

Did World War I facilitate the rise of the Nazi party in Weimar Germany? We revisit this question by shifting the focus on the localized effects of war casualties on nationalist attitudes. We argue that mass warfare can promote nationalist attitudes by amplifying in-group preferences. Our empirical analysis leverages a unique individual-level dataset of all 8.6 million German soldiers who died or were wounded during World War I. To causally identify the effect of community level exposure to WW1, we leverage plausibly exogenous variation in the death rate of soldiers across small geographic localities. We find that throughout the interwar period, electoral support for nationalist parties including the NSDAP was about 2 percentage points higher in regions highly affected by the war. Additional analyses drawing on data on NSDAP party entries, WW1 memorials, and Nazi autobiographies indicate that our results reflect a broad, community-level impact of the war driven by war losses and public memory rather than returning veterans. Our findings advance our understanding of the local impact of WW1 on the rise of the Nazi party in Germany and the longer-term legacies of indirect exposure to violence on preferences for nationalist ideologies.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

Sign in / Sign up

Export Citation Format

Share Document