scholarly journals The CHilean Internal Migration (CHIM) database: Temporally consistent spatial data for the analysis of human mobility

REGION ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 1 ◽  
Author(s):  
Francisco Rowe
Keyword(s):  
Internal Migration ◽  
Spatial Data ◽  
Census Data ◽  
Human Mobility ◽  
Occupational Groups ◽  
Industry Sector ◽  
Coding Schemes ◽  
Industrial Sectors ◽  
Migration Data ◽  
Municipal Boundaries

Changes in zonal boundaries and coding schemes severely compromise temporal comparison of data. In Chile, the Population and Housing census is the only comprehensive source of internal migration data, but municipal boundaries and occupation and industry sector coding schemes have undergone various changes which hamper the temporal comparability of census data. This paper presents the CHilean Internal Migration database developed by Rowe and Bell (2013) which provides a temporally consistent framework for the analysis of internal migration over a period covering twenty-five years from 1977 to 2002. Specifically, it offers a hierarchical system of 304 municipalities, 51 provinces and 13 regions, and 10 occupational groups and 11 industrial sectors which are temporally consistent over the 1977-82, 1987-92 and 1997-2002 census intervals. The database can be downloaded from: https://s3.amazonaws.com/geoda/data/CHIM.zip.

scholarly journals The CHilean Internal Migration (CHIM) database: Temporally consistent spatial data for the analysis of human mobility

REGION ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 1
Author(s):  
Francisco Rowe
Keyword(s):  
Internal Migration ◽  
Spatial Data ◽  
Census Data ◽  
Human Mobility ◽  
Occupational Groups ◽  
Industry Sector ◽  
Coding Schemes ◽  
Industrial Sectors ◽  
Migration Data ◽  
Municipal Boundaries

Changes in zonal boundaries and coding schemes severely compromise temporal comparison of data. In Chile, the Population and Housing census is the only comprehensive source of internal migration data, but municipal boundaries and occupation and industry sector coding schemes have undergone various changes which hamper the temporal comparability of census data. This paper presents the CHilean Internal Migration database developed by Rowe and Bell (2013) which provides a temporally consistent framework for the analysis of internal migration over a period covering twenty-five years from 1977 to 2002. Specifically, it offers a hierarchical system of 304 municipalities, 51 provinces and 13 regions, and 10 occupational groups and 11 industrial sectors which are temporally consistent over the 1977-82, 1987-92 and 1997-2002 census intervals. The database can be downloaded from: https://s3.amazonaws.com/geoda/data/CHIM.zip.

scholarly journals How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001–2011

2021 ◽  
Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth
Keyword(s):  
Administrative Data ◽  
Internal Migration ◽  
Census Data ◽  
Local Population ◽  
Population Data ◽  
Population Estimates ◽  
Demographic Groups ◽  
Small Areas ◽  
Intercensal Period ◽  
Statistical Agencies

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

scholarly journals Changing boundaries: Overcoming modifiable areal unit problems related to unemployment data in South Africa

2016 ◽  
Vol Volume 112 (Number 3/4) ◽  
Author(s):  
Gina Weir-Smith ◽  
◽  
Keyword(s):  
South Africa ◽  
Rural Areas ◽  
Census Data ◽  
Metropolitan Areas ◽  
Geographic Area ◽  
Areal Unit ◽  
Areal Interpolation ◽  
Unemployment Data ◽  
Municipal Boundaries ◽  
Longitudinal Comparison

Abstract The longitudinal comparison of census data in spatial format is often problematic because of changes in administrative boundaries. Such shifting boundaries are referred to as the modifiable areal unit problem (MAUP). This article utilises unemployment data between 1991 and 2007 in South Africa to illustrate the challenge and proposes ways to overcome it. Various censuses in South Africa use different reporting geographies. Unemployment data for magisterial districts of census 1991 and 1996 were re-modelled to the 2005 municipal boundaries. This article showed that areal interpolation to a common administrative boundary could overcome these reporting obstacles. The results confirmed more accurate interpolations in rural areas with standard errors below 3300. Conversely, the largest errors were recorded in the metropolitan areas. Huge increases in unemployment between 1996 and 2001 statistics were also evident, especially in the metropolitan areas. Although such areas are more complex in nature, making it more difficult to accurately calculate census data, the increase in unemployment could also be the result of census taking methods. The article concludes that socio-economic data should be available at the smallest possible geographic area to ensure more accurate results in interpolation. It also recommends that new output areas be conceptualised to create a seamless database of census data from 1991 to 2011 in South Africa.

scholarly journals Spatial Dynamics of Internal Migration Flows of the Skilled and Unskilled in Mexico

2020 ◽  
Author(s):  
Miguel Flores Segovia ◽  
Eliud Silva
Keyword(s):  
Internal Migration ◽  
Census Data ◽  
Relative Concentration ◽  
Spatial Dynamics ◽  
Unskilled Labor ◽  
Geographical Patterns ◽  
Economic Agglomeration ◽  
Skilled And Unskilled Labor ◽  
Migratory Patterns ◽  
Relationship Of

ABSTRACT: The dynamics of the internal migration is a crucial element in the composition of the workforce of a certain region, so its analysis contributes to the better understanding of labor markets and sociodemographic changes in a region. In order to characterize the most recent patterns of migratory flows of skilled and unskilled labor, census data are considered for the periods 1995-2000, 2005-2010 and 2010-2015. The analysis considers different indicators that describe the intensity and relative concentration of interstate migration. Changes in migratory patterns are evident; a lower concentration of internal migration whose effect is more marked for unskilled labor. That is, it is observed that the number of states that play a preponderant role in the redistribution of labor in Mexico has increased. The relationship of domestic labor mobility is evident to the regional transformation as a result of new geographical patterns of location of investment, production and economic agglomeration.

scholarly journals A Fine Spatial Resolution Modeling of Urban Carbon Emissions: A Case Study of Shanghai, China

2021 ◽  
Author(s):  
Cheng Huang ◽  
Qianlai Zhuang ◽  
Xing Meng ◽  
Peng Zhu ◽  
Ji Han ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Spatial Resolution ◽  
Spatial Data ◽  
Carbon Dioxide Emissions ◽  
Web Crawler ◽  
Traditional Methods ◽  
Climate Change Research ◽  
Industrial Sectors ◽  
Rate Of Increase ◽  
The City

Abstract Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. However, fine resolution spatial data of CEs deficient. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. In addition, the method of this study is better than traditional methods with details and heterogeneity of CEs, the CEs distribution close to reality. The results indicated that there was a consistent increase in CEs during 2000–2015, with a low rate of increase during 2009–2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Decoupling between the economic development and CEs in the city was observed during in 2000–2015 owing to technological progress and reduction in CEs intensity.

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.

Bigdata-based investigation of heterogeneous migration and population distribution

2021 ◽  
Author(s):  
Zhiwei Fan ◽  
L. Xiong ◽  
Bo Zheng
Keyword(s):  
Census Data ◽  
Population Distribution ◽  
Model Simulation ◽  
Human Mobility ◽  
Economic Systems ◽  
Migration Patterns ◽  
Demographic Trend ◽  
Education Levels ◽  
The Uk ◽  
Migration Probability

Abstract Human mobility is very important in understanding complex social and economic systems. With massive empirical datasets from the China Household Finance Survey and the National Statistics in the UK, we construct a migration probability matrix, and analyze the heterogeneous migration patterns. We then develop a random walk model to dynamically simulate the population distribution. In the stationary state, the resulting population distribution is in good agreement with the real statistical data. For comparison, simulations with an optimized gravity model and other datasets such as the census data in China are also performed. Further, the model simulation is applied to predict the demographic trend with different education levels. Our method could be generally extended to other real communities and internet worlds.

Exploring Blue- and White-Collar Employees' Well-Being at Work System

2018 ◽  
Vol 10 (2) ◽  
pp. 14-34 ◽  
Author(s):  
Seppo Tapio Vayrynen ◽  
Heli Katariina Kiema-Junes
Keyword(s):  
Work Environment ◽  
White Collar ◽  
Well Being ◽  
Occupational Group ◽  
Occupational Groups ◽  
Work System ◽  
National Statistics ◽  
Industrial Sectors ◽  
Information And Communication ◽  
Traditional Work

The aim of this study was to examine occupational group-related differences in well-being at work (WBW) indicators ranging from real accidents, absences and retirement to experienced pleasure at work. Occupational group included two categories: blue- and white-collar employees. The study is based on analysing national statistics or ones of various industrial sectors (Study 1), or bases on findings of questionnaires in Finnish case companies (N=7) (Studies 2 and 3). WBW questionnaires answered by 3500 employees. Analysis utilised data that employees of two occupational groups, or company and national statistics revealed about WBW. Analysis was based on factors related to employee, task, tool, organisation and work environment (traditional work system (WS)), psychosocial factors, and information and communication within WS. The biggest and statistically significant differences were emphasised in results and discussion. Although two groups' roles and tasks provide reasons for many differences, the ones should be measured, thoroughly discussed and consciously managed.

scholarly journals Delimiting tropical mountain ecoregions for conservation

2011 ◽  
Vol 38 (3) ◽  
pp. 312-324 ◽  
Author(s):  
PHILIP J. PLATTS ◽  
NEIL D. BURGESS ◽  
ROY E. GEREAU ◽  
JON C. LOVETT ◽  
ANDREW R. MARSHALL ◽  
...  
Keyword(s):  
Spatial Data ◽  
Forest Cover ◽  
Census Data ◽  
Conservation Strategies ◽  
Mountain Regions ◽  
Landform Classification ◽  
Tropical Mountain ◽  
Eastern Arc ◽  
Straightforward Solution ◽  
New Research

SUMMARYEcological regions aggregate habitats with similar biophysical characteristics within well-defined boundaries, providing spatially consistent platforms for monitoring, managing and forecasting the health of interrelated ecosystems. A major obstacle to the implementation of this approach is imprecise and inconsistent boundary placement. For globally important mountain regions such as the Eastern Arc (Tanzania and Kenya), where qualitative definitions of biophysical affinity are well established, rule-based methods for landform classification provide a straightforward solution to ambiguities in region extent. The method presented in this paper encompasses the majority of both contemporary and estimated preclearance forest cover within strict topographical limits. Many of the species here tentatively considered ‘near-endemic’ could be reclassified as strictly endemic according to the derived boundaries. LandScan and census data show population density inside the ecoregion to be higher than in rural lowlands, and lowland settlement to be most probable within 30 km. This definition should help to align landscape scale conservation strategies in the Eastern Arc and promote new research in areas of predicted, but as yet undocumented, biological importance. Similar methods could work well in other regions where mountain extent is poorly resolved. Spatial data accompany the online version of this article.

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