scholarly journals Urban Ageing in Europe—Spatiotemporal Analysis of Determinants

2020 ◽  
Vol 9 (7) ◽  
pp. 413 ◽  
Author(s):  
Karolina Lewandowska-Gwarda ◽  
Elżbieta Antczak
Keyword(s):  
Economic Development ◽  
Data Analysis ◽  
Spatial Data ◽  
Urban Areas ◽  
The Elderly ◽  
Spatiotemporal Analysis ◽  
Population Ageing ◽  
Spatial Data Analysis ◽  
Men And Women ◽  
Level Of Economic Development

The aim of this study was to identify determinants of the population ageing process in 270 European cities. We analyzed the proportion of older people: men and women separately (aged 65 or above) in city populations in the years 1990–2018. To understand territorially-varied relationships and to increase the explained variability of phenomena, an explanatory spatial data analysis (ESDA) and geographically weighted regression (GWR) were applied. We used ArcGIS and GeoDa software in this study. In our research, we also took into account the spatial interactions as well as the structure of cities by size and level of economic development. Results of the analysis helped to explain why some urban areas are ageing faster than others. An initial data analysis indicated that the proportion of the elderly in the population was spatially diversified and dependent on gender, as well as the size and economic development of a unit. In general, elderly individuals were more willing to live in larger and highly developed cities; however, women tended to live in large areas and men in medium-sized to large urban areas. Then, we conducted the urban ageing modelling for men and women separately. The application of GWR models enabled not only the specification of the city population ageing determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual cities. Significant differences were noted in the analysis results for specific cities, which were often grouped due to similar parameter values, forming clusters that divided Europe into the eastern and western parts. Moreover, substantial differences in results were obtained for women and men.

scholarly journals ESDA (Exploratory Spatial Data Analysis) of Vegetation Cover in Urban Areas—Recognition of Vulnerabilities for the Management of Resources in Urban Green Infrastructure

2020 ◽  
Vol 12 (5) ◽  
pp. 1933
Author(s):  
Ana Clara M. Moura ◽  
Bráulio M. Fonseca
Keyword(s):  
Data Analysis ◽  
Population Density ◽  
Spatial Data ◽  
Green Infrastructure ◽  
Vegetation Cover ◽  
Urban Areas ◽  
Spatial Behavior ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Local Index

From the mapping of urban vegetation cover by high-resolution orthoimages, using IR band and NDVI classification (Normalized Difference Vegetation Index), added to three-dimensional representation obtained by LiDAR capture (Light Detection and Ranging), the volumetric values of vegetal cover are obtained as a base to construct spatial analysis in the district of Pampulha, in Belo Horizonte, investigating the role it plays in the neighborhood. The article aims to analyze the relationship between vegetation cover, income distribution and population density, as a support to urban environmental quality management. It applies Exploratory Spatial Data Analysis (ESDA) to identify the presence of clusters and patterns of spatial distribution and to examine spatial autocorrelation. The results confirm the concentration of vegetation cover in areas of high income and lower population density but the main contribution of the study is the use of a method to analyze the spatial behavior of this distribution. Calculating Moran global index and local index (LISA), these spatial combinations are mainly used to identify transformation pressures, which may result in the definition of priorities for public actions and the construction of proposals for parameterization of vegetation cover to support plans related to green infrastructure in urban areas.

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 Export diversification and economic development: A dynamic spatial data analysis

2017 ◽  
Vol 26 (3) ◽  
pp. 634-650 ◽  
Author(s):  
Roberto Basile ◽  
Aleksandra Parteka ◽  
Rosanna Pittiglio
Keyword(s):  
Economic Development ◽  
Data Analysis ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Export Diversification

PENGARUH TERAPI TERTAWA TERHADAP PENURUNAN TEKANAN DARAH LANSIA DI UPT PANTI WREDHA BUDHI DHARMA YOGYAKARTA

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Niken Setyaningrum ◽  
Andri Setyorini ◽  
Fachruddin Tri Fitrianta
Keyword(s):  
Blood Pressure ◽  
Data Analysis ◽  
The Elderly ◽  
Control Group ◽  
Group Comparison ◽  
Control Blood Pressure ◽  
Men And Women ◽  
Treatment Of Hypertension ◽  
Before And After ◽  
The Difference

ABSTRACTBackground: Hypertension is one of the most common diseases, because this disease is suffered byboth men and women, as well as adults and young people. Treatment of hypertension does not onlyrely on medications from the doctor or regulate diet alone, but it is also important to make our bodyalways relaxed. Laughter can help to control blood pressure by reducing endocrine stress andcreating a relaxed condition to deal with relaxation.Objective: The general objective of the study was to determine the effect of laughter therapy ondecreasing elderly blood pressure in UPT Panti Wredha Budhi Dharma Yogyakarta.Methods: The design used in this study is a pre-experimental design study with one group pre-posttestresearch design where there is no control group (comparison). The population in this study wereelderly aged over> 60 years at 55 UPT Panti Wredha Budhi Dharma Yogyakarta. The method oftaking in this study uses total sampling. The sample in this study were 55 elderly. Data analysis wasused to determine the difference in blood pressure before and after laughing therapy with a ratio datascale that was using Pairs T-TestResult: There is an effect of laughing therapy on blood pressure in the elderly at UPT Panti WredhaBudhi Dharma Yogyakarta marked with a significant value of 0.000 (P <0.05)

Analyzing The Violent Crime Patterns In Peninsular Malaysia : Exploratory Spatial Data Analysis (ESDA) Approach

2014 ◽  
Vol 72 (1) ◽  
Author(s):  
Syerrina Zakaria ◽  
Nuzlinda Abd. Rahman
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Violent Crime ◽  
Spatial Data ◽  
Hot Spot ◽  
Peninsular Malaysia ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Policy Makers ◽  
Violent Crime Rate

The objective of this study is to analyze the spatial cluster of crime cases in Peninsular Malaysia by using the exploratory spatial data analysis (ESDA). In order to identify and measure the spatial autocorrelation (cluster), Moran’s I index were measured. Based on the cluster analyses, the hot spot of the violent crime occurrence was mapped. Maps were constructed by overlaying hot spot of violent crime rate for the year 2001, 2005 and 2009. As a result, the hypothesis of spatial randomness was rejected indicating cluster effect existed in the study area. The findings reveal that crime was distributed nonrandomly, suggestive of positive spatial autocorrelation. The findings of this study can be used by the goverment, policy makers or responsible agencies to take any related action in term of crime prevention, human resource allocation and law enforcemant in order to overcome this important issue in the future. 

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