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 Autokorelasi Spasial untuk Identifikasi Pola Hubungan Kemiskinan di Jawa Timur

2012 ◽  
Vol 3 (1) ◽  
pp. 217
Author(s):  
Rokhana Dwi Bekti
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Moran’S I ◽  
Poor People ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Moran's I ◽  
Relationship Of ◽  
The Relationship

Spatial autocorrelation is a spatial analysis to determine the relationship pattern or correlation among some locations (observation). On the poverty case of East Java, this method will provide important information for analyze the relationship of poverty characteristics in each district or cities. Therefore, in this research performed spatial autocorrelation analysis on the data of East Java’s poverty. The method used is moran's I test and Local Indicator of Spatial Autocorrelation (LISA). The analysis showed that by the moran's I test, there is spatial autocorrelation found in the percentage of poor people amount in East Java, both in 2006 and 2007. While by LISA, obtained the conclusion that there is a significant grouping of district or cities.

scholarly journals DETECTION OF THE SPATIAL VARIATIONS OF LOCAL POPULATIONS FROM THE VIEWPOINT OF URBAN STRUCTURE ANALYSIS

2021 ◽  
Vol V-4-2021 ◽  
pp. 177-185
Author(s):  
K. Kumagai ◽  
Y. Kameda
Keyword(s):  
Spatial Autocorrelation ◽  
Local Population ◽  
Population Decline ◽  
Spatial Dynamics ◽  
Population Data ◽  
Urban Structure ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Local Populations ◽  
The Difference

Abstract. In Japan, population decline is one of the important issues that need to be tackled in socioeconomic fields. We apply an analysis method composed of a spatial autocorrelation analysis to the local population data generated through the 1995 and 2015 national census, and try to detect their spatial dynamics in this study. Through making experimental verification of the distance parameter of the spatial autocorrelation analysis, we newly define 2 indices with respect to the size and area where lower local populations are distributed in urbanized areas. It is shown that the local population dynamics are described by the difference of the 2 indices among 1995 and 2015. The expansion of areas where local population decline occurred seems to be detected by our proposed approach.

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals Spatial and Temporal Analysis of Lung Cancer in Shenzhen, 2008–2018

2020 ◽  
Vol 18 (1) ◽  
pp. 26
Author(s):  
Lin Lei ◽  
Anyan Huang ◽  
Weicong Cai ◽  
Ling Liang ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Autocorrelation ◽  
Incidence Rate ◽  
Cancer Incidence ◽  
Southern China ◽  
Percentage Change ◽  
Spatial Autocorrelation Analysis ◽  
Lung Cancer Incidence ◽  
Annual Percentage Change ◽  
Autocorrelation Analysis

Lung cancer is the most commonly diagnosed cancer in China. The incidence trend and geographical distribution of lung cancer in southern China have not been reported. The present study explored the temporal trend and spatial distribution of lung cancer incidence in Shenzhen from 2008 to 2018. The lung cancer incidence data were obtained from the registered population in the Shenzhen Cancer Registry System between 2008 and 2018. The standardized incidence rates of lung cancer were analyzed by using the joinpoint regression model. The Moran’s I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Shenzhen. From 2008 to 2018, the average crude incidence rate of lung cancer was 27.1 (1/100,000), with an annual percentage change of 2.7% (p < 0.05). The largest average proportion of histological type of lung cancer was determined as adenocarcinoma (69.1%), and an increasing trend was observed in females, with an average annual percentage change of 14.7%. The spatial autocorrelation analysis indicated some sites in Shenzhen as a high incidence rate spatial clustering area. Understanding the incidence patterns of lung cancer is useful for monitoring and prevention.

Spatial genetic substructure within natural populations of jack pine (Pinus banksiana)

1991 ◽  
Vol 69 (3) ◽  
pp. 547-551 ◽  
Author(s):  
Chang Yi Xie ◽  
Peggy Knowles
Keyword(s):  
Spatial Autocorrelation ◽  
Pinus Banksiana ◽  
Pollen Dispersal ◽  
Natural Populations ◽  
Jack Pine ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Long Distance ◽  
Genetic Substructure ◽  
Good Agreement

Spatial autocorrelation analysis was used to investigate the geographic distribution of allozyme genotypes within three natural populations of jack pine (Pinus banksiana Lamb.). Results indicate that genetic substructuring within these populations is very weak and the extent differs among populations. These results are in good agreement with those inferred from mating-system studies. Factors such as the species' predominantly outbreeding system, high mortality of selfs and inbreds prior to reproduction, long-distance pollen dispersal, and the absence of strong microhabitat selection may be responsible for the observed weak genetic substructuring. Key words: jack pine, Pinus banksiana, genetic substructure, allozyme, spatial autocorrelation analysis.

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