Decentralization of the Zagreb urban region

Background: Mortality is important to study population change in the country; infant mortality is considered as principal component balancing the child sex ratio. In this study authors aimed to analyze how mortality rates and child sex ratios are different in urban and rural areas and how its growth statistics are changing over years. Objectives of the study were to quantify infant mortality rates change over time and check the means among mortality indicators.Methods: The study was conducted using secondary data obtained from various issues and reports published by Registrar General and Census Commissioner, India for a period of 10 years from 2006 to 2016. The obtained data on mortality indicators were subjected to basic statistical analysis using percent change and paired t-test.Results: The Infant mortality rate which was reduced by 23 points indicating reduction of 67.65 percent control over a period from 2006 to 2016. Further, results show that, in case of urban mortality, there was significant difference between mortality indicators during study period, the p-value (0.011) was less than level of significance (0.05) so we reject the null hypothesis and it is concluded that there is significant difference between the means of urban mortality indicators over a period of from 2006 to 2016.Conclusions: The infant mortality rate frequently provided as a key indicator of overall the development. There is need for stable child sex ratio; health of children and women are essential for better growth and reaching stable child sex ratio for the ever increasing population.

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A GIS Approach to Analyzing the Spatial Pattern of Baseline Resilience Indicators for Community (BRIC)

Water ◽

10.3390/w12051401 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1401 ◽

Cited By ~ 4

Author(s):

Chien-Hao Sung ◽

Shyue-Cherng Liaw

Keyword(s):

Natural Hazards ◽

Principal Component ◽

Community Resilience ◽

The Other ◽

Urban Region ◽

Specific Situation ◽

Social Resilience ◽

Mountain Areas ◽

Other Hand ◽

Resilience Indicators

We explore the baseline resilience to natural hazards through the Baseline Resilience Indicators for Community (BRIC) in northeastern Taiwan. Based on the specific situation of our study site, we slightly modified the BRIC. Due to the correlation between some of the subcomponents, we apply principal component analysis (PCA) to solve this issue. Therefore, we slightly changed the classification of subcomponents. We aggregated economic resilience, social resilience, and community capital resilience into socioeconomic community resilience. The result of geographically weighted regression (GWR) shows that even though we modified the indicator, the BRIC we built is still valid. Through spatial autocorrelation analysis, it reveals that the urban region in plain areas is the cluster of high resilience areas. On the other hand, almost all the entire mountain areas are the cluster of low resilience areas. The topography is the most important factor to cause this distribution. Plain areas have favorable characteristics to trigger development and create high socioeconomic community resilience. Mountain areas, on the other hand, do not have these advantages. The distribution of institutional and infrastructure subcomponents shows no particular pattern. That is to say, institutional and infrastructure subcomponents do not influence the distribution of BRIC. The difference in socioeconomic community resilience causes the uneven distribution of baseline resilience to natural hazards. Nevertheless, the distribution of institutional and infrastructure resources is also a crucial issue. In our case, although the distribution of institutional and infrastructure follows the “distributive justice” approach and distribution randomly, whether it is an appropriate approach is still under debate.

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Research on IKONOS shadow extraction in urban region based on the principal component fusion information distort

10.1117/12.865156 ◽

2010 ◽

Author(s):

Cunjun Li ◽

Jihua Wang ◽

Qian Wang ◽

Wenjiang Huang ◽

Xingang Xu

Keyword(s):

Principal Component ◽

Urban Region

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Population and spatial development of settlements in Ljubljana Urban Region after 2002

Dela ◽

10.4312/dela.42.1.75-93 ◽

2014 ◽

pp. 75-93

Author(s):

Dejan Rebernik

Keyword(s):

Population Growth ◽

Urban Sprawl ◽

Population Change ◽

Spatial Development ◽

Settlement Pattern ◽

Urban Region ◽

Population Development

The paper is analysing spatial and population development of settlements in Ljubljana Urban Region after 2002. On the basis of population change we determined the main urbanisation processes in the region. To the end of 1970s fast population growth was due to immigration from rural parts of Slovenia and the rest of Yugoslavia. In the 1980s and 1990s deconcentration of population within the region with intense suburbanisation were the main processes. After 2002 the fastest population growth was in in the rural hinterland. Dispersed settlement pattern with all negative implications of urban sprawl is thus characteristic.

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PUSH AND PULL FACTORS OF MIGRATION: A STUDY OF INTER-STATE MIGRANTS OF HUGLI INDUSTRIAL BELT

10.36106/gjra/7906953 ◽

2020 ◽

pp. 1-4

Author(s):

Rajesh Das ◽

Snehamanju Basu

Keyword(s):

Urban Areas ◽

Industrial Development ◽

Population Change ◽

Source Region ◽

Social Factor ◽

Principal Component ◽

Living Environment ◽

Long Distance ◽

Periodic Movement ◽

Inter State

Migration is the periodic movement of population by breaking social and cultural ties from the original place of living. After fertility and mortality, it is the third important component of population growth or population change. The dominant theory in explaining causes of migration is the neoclassical theory which states that “migration is stimulated primarily by rational economic considerations of relative benets and costs, mostly nancial but also Psychological” (Todaro and Smith, 2006). Industrial development in India attracted the marginal population, deserted and divorcee females to be engaged as unskilled laborer. Opportunity of employment and higher wages generated the current of migration from rural to urban areas both short and long distance. Determinants like marriage, job opportunity, wages, kinship, family association, living environment, industrial growth, security, bad economic condition of the source region, loss of job, farming conditions are taken as factors of migration. To know the factors those who are reasonably explain migration under study area Factor Analysis by the method of Principal Component Analysis has been done. This is helpful to understand the correlation of variables. However, data explain that economic factors are dominating over social factor in case of unskilled inter-state labour migration within the region in early stages while social factors are main reasons of migration in later stage.

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Novel approach of association rule mining for tree canopy assessment

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v10.i3.pp771-779 ◽

2021 ◽

Vol 10 (3) ◽

pp. 771

Author(s):

Nilkamal More ◽

V. B. Nikam ◽

Biplab Banerjee

Keyword(s):

Urban Areas ◽

Satellite Images ◽

Satellite Image ◽

Vegetation Indices ◽

Principal Component ◽

Optimal Number ◽

Tree Canopy ◽

Urban Region ◽

Apriori Algorithm ◽

Novel Approach

The evolution of technology and availability of voluminous satellite images are bringing a new scenario in satellite image classification where a performance efficient method for predictive analysis of satellite images for land cover classification needs to be devised. As urban areas are growing at faster rate, special attention needs to be given to solve tree canopy assessment problem. Vegetation indices are calculated from spectral information of satellite images. Hundreds of such vegetation indices are available to detect vegetation from a satellite image. The contribution of this paper is designing an improved Apriori algorithm to select optimal number of vegetation indices for tree canopy assessment. In this research, we propose a novel computational approach that allows the improvement of results. It selects optimal combination of vegetation indices and applies principal component analysis on it. It uses a greedy approach based on Apriori algorithm. This study emphasizes on assessment of tree canopy using GPU-enabled environment for performance-efficient assessment. The results achieved, are comparable to state-of-the-art techniques, with an accuracy of 96%. The research has considered 4 years data for Mumbai city of India. This research is useful for Green India Mission of India to assess tree canopy of urban region.

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Population and spatial development of settlements in Ljubljana Urban Region after 2002

Dela ◽

10.4312/dela.42.75-93 ◽

2014 ◽

pp. 75-93

Author(s):

Dejan Rebernik

Keyword(s):

Population Growth ◽

Urban Sprawl ◽

Population Change ◽

Spatial Development ◽

Settlement Pattern ◽

Urban Region ◽

Population Development

The paper is analysing spatial and population development of settlements in Ljubljana Urban Region after 2002. On the basis of population change we determined the main urbanisation processes in the region. To the end of 1970s fast population growth was due to immigration from rural parts of Slovenia and the rest of Yugoslavia. In the 1980s and 1990s deconcentration of population within the region with intense suburbanisation were the main processes. After 2002 the fastest population growth was in in the rural hinterland. Dispersed settlement pattern with all negative implications of urban sprawl is thus characteristic.

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Digital Processing of STEM Images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097752 ◽

1981 ◽

Vol 39 ◽

pp. 236-237

Author(s):

A. V. Crewe ◽

M. Ohtsuki

Keyword(s):

High Resolution ◽

Low Dose ◽

Assembly Language ◽

Processing System ◽

Principal Component ◽

Digital Processing ◽

Image Analysis System ◽

Black And White ◽

Analysis System ◽

Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

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Scanning x-ray fluorescence microscopy and principal component analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100133394 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1752-1753

Author(s):

Brian Cross

Keyword(s):

Principal Component Analysis ◽

Fluorescence Microscopy ◽

Spatial Resolution ◽

High Speed ◽

Image Acquisition ◽

Principal Component ◽

Fluorescence Microscope ◽

Acquisition Rate ◽

X Ray ◽

Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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