scholarly journals Identification of Shrinking Cities on the Main Island of Taiwan Based on Census Data and Population Registers: A Spatial Analysis

2021 ◽  
Vol 10 (10) ◽  
pp. 694
Author(s):  
Di Hu
Keyword(s):  
Cluster Analysis ◽  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Census Data ◽  
Population Decline ◽  
Low Fertility ◽  
Spatial Data Analysis ◽  
Spatial Autocorrelation Analysis ◽  
Shrinking Cities ◽  
Autocorrelation Analysis

At the end of the 20th century, the phenomenon of urban shrinkage received widespread attention, with population decline as its core characteristic. In 2020, the Taiwanese population had negative growth and faced a low fertility rate and an aging population. This study used exploratory spatial data analysis to identify shrinking cities in Taiwan based on census data and population registers. The results indicated that Taiwan has 11 shrinking counties and 202 shrinking towns. Urban shrinkage occurred in the 1980s and continued from the suburbanization stage to the re-urbanization stage. Five types of spatial patterns in the 11 shrinking counties were observed. In the majority of the shrinking counties, towns with high population densities were unable to avoid shrinkage. A global spatial autocorrelation analysis indicated that shrinkage and non-shrinkage have become increasingly apparent at the town level since 2005. A local spatial autocorrelation analysis indicates that the spatial clustering of towns with population growth or decline from 2000 to 2020 has changed. Based on each town's development, a two-step cluster analysis was conducted in which all towns were divided into four categories. Shrinking towns exist in each category, but with a different proportion. Based on the results of two-step cluster analysis combined with spatial analysis, this study discovered that both urbanization and suburbanization cause shrinkage in Taiwan, but the affected localities are distinct. For most shrinking counties, their spatial model indicates a relationship between shrinking and the urbanization of their towns. Keelung City and Chiayi City have the most potential to reverse the shrinkage. This study helps authorities better manage growth and implement regional revitalization.

scholarly journals Comparação e mapeamento de grupos municipais do estado do Rio Grande do Sul a partir de indicadores socioeconômicos

2020 ◽  
Vol 42 ◽  
pp. e36
Author(s):  
Mariana Motta Dias da Silva ◽  
Augusto Maciel da Silva ◽  
Enio Junior Seidel ◽  
Ana Lucia Souza Silva Mateus ◽  
Angela Pellegrin Ansuj
Keyword(s):  
Public Policy ◽  
Cluster Analysis ◽  
Regional Development ◽  
Census Data ◽  
Rio Grande ◽  
Spatial Development ◽  
The State ◽  
Spatial Behavior ◽  
Rio Grande Do Sul ◽  
Thematic Maps

Understanding how particular regions behave and / or resemble socioeconomic aspects can contribute to regional development. Thus, this paper aims to identify similarities of socio-spatial behavior existing among the municipalities of the state of Rio Grande do Sul, based on census data, and to evaluate the changes that have occurred over the years. For this, a cluster analysis of the municipalities was performed before some indicators. With the clusters  was verified the existence of difference in the behavior between the groups through non-parametric tests and thematic maps were created that bring the behavior of the regions in a spatial way. Five clusters of municipalities were detected in 1991 and four clusters in the years 2000 and 2010. It is also noticeable the socio-spatial development over the years. However in 2010 is when the greatest evolution occurs for most municipalities. From the generated clusters it is possible to outline public policy strategies and further prioritize the measures that should be taken by the competent bodies in each of the regions studied.

scholarly journals Creating a typology of parishes in England and Wales: Mining 1881 census data

2015 ◽  
Vol 2 ◽  
pp. 38-57
Author(s):  
Kevin Schürer ◽  
Tatiana Penkova
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Census Data ◽  
Principal Component ◽  
Component Analysis ◽  
Victorian England ◽  
England And Wales ◽  
Individual Level ◽  
Stick Model ◽  
And Cluster Analysis

The paper presents the application of principal component analysis and cluster analysis to historical individual level census data in order to explore social and economic variations and patterns in household structure across mid-Victorian England and Wales. Principal component analysis is used in order to identify and eliminate unimportant attributes within the data and the aggregation of the remaining attributes. By combining Kaiser’s rule and the Broken-stick model, four principal components are selected for subsequent data modelling. Cluster analysis is used in order to identify associations and structure within the data. A hierarchy of cluster structures is constructed with two, three, four and five clusters in 21-dimensional data space. The main differences between clusters are described in this paper.

scholarly journals INFLUENCE OF THE LAURACEAE FAMILY ON THE DYNAMICS OF A MIXED OMBROPHILOUS FOREST REMNANT

FLORESTA ◽  
2020 ◽  
Vol 51 (1) ◽  
pp. 164
Author(s):  
Wesllen Schuhli Kieras ◽  
Sebastião Do Amaral Machado ◽  
Allan Libanio Pelissari ◽  
Vinicius Costa Cysneiros ◽  
Samuel Alves Da Silva
Keyword(s):  
Cluster Analysis ◽  
Canonical Correlation ◽  
Transition Matrix ◽  
Census Data ◽  
Forest Type ◽  
Diameter Distribution ◽  
Forest Exploitation ◽  
Forest Remnant ◽  
Wilks Lambda ◽  
Ombrophilous Forest

Lauraceae family has one of the highest values of importance in the Mixed Ombrophilous Forest (MOF). The commercial value of some of its species was a reason for intense forest exploitation in the southern region of Brazil. Considering the hypothesis that it provides an essential subsidy for the constitution of this forest type, the aim of this study was to identify and quantify the influence of Lauraceae family in the dynamics of a 15.2 ha MOF remnant. Census data were collected every three years, since 2007, in which all trees with a circumference at 1.3 m height equal to or greater than 30 cm were identified and measured. Dynamics were analyzed by periodic increment in diameter, recruitment, and mortality through the measurement periods, while cluster multivariate analysis and canonical correlation were applied for grouping species and assessing their importance on the forest remnant dynamics. Diameter distribution prognosis of Lauraceae and its species was obtained through a transition matrix. Eleven tree species of Lauraceae family were identified, which showed decreasing diameter distribution and value of importance equals to 9.51%. Using cluster analysis, five groups were obtained, while the canonical correlation of 0.551 was considered moderate and statistically significant by Wilks’ Lambda test. By the projection of diameter distribution, it was verified that the study community is stable and self-regenerative. Although it is considered moderate, the influence of family on the forest remnant tends to increase with the advance of ecological succession.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

2020 ◽  
pp. 1-25
Author(s):  
Matthew L. Hall ◽  
Stephanie De Anda
Keyword(s):  
Cluster Analysis ◽  
Exposure Assessment ◽  
Hard Of Hearing ◽  
Assessment Tool ◽  
Communication Mode ◽  
Developmental Potential ◽  
Language Access ◽  
Retest Reliability ◽  
Language Exposure ◽  
Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

Sign in / Sign up

Export Citation Format

Share Document