scholarly journals The Expansion of Urban Area and the Spatial Distribution of Urban Population Density in Seoul City

1978 ◽  
Vol 30 (2) ◽  
pp. 166-178
Author(s):  
Jun-yong SUNG
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Urban Area ◽  
Urban Population

scholarly journals An Improved Method of Determining Human Population Distribution Based on Luojia 1-01 Nighttime Light Imagery and Road Network Data—A Case Study of the City of Shenzhen

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5032 ◽  
Author(s):  
Qiang Zhou ◽  
Yuanmao Zheng ◽  
Jinyuan Shao ◽  
Yinglun Lin ◽  
Haowei Wang
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Spatial Resolution ◽  
Urban Population ◽  
Road Network ◽  
Population Distribution ◽  
Small Scale ◽  
Human Settlement ◽  
Nighttime Light ◽  
The City

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.

scholarly journals Spatial Distribution Estimates of the Urban Population Using DSM and DEM Data in China

2018 ◽  
Vol 7 (11) ◽  
pp. 435 ◽  
Author(s):  
Junlin Zhang ◽  
Wei Xu ◽  
Lianjie Qin ◽  
Yugang Tian
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Urban Population ◽  
Large Scale ◽  
Census Data ◽  
Significance Test ◽  
Surface Model ◽  
Building Height ◽  
Elevation Model ◽  
Height Data

Spatial distribution and population density are important parameters in studies on urban development, resource allocation, emergency management, and risk analysis. High-resolution height data can be used to estimate the total or spatial pattern of the urban population for small study areas, e.g., the downtown area of a city or a community. However, there has been no case of population estimation for large areas. This paper tries to estimate the urban population of prefectural cities in China using building height data. Building height in urban population settlement (Mdiffs) was first extracted using the digital surface model (DSM), digital elevation model (DEM), and land use data. Then, the relationships between the census-based urban population density (CPD) and the Mdiffs density (MDD) for different regions were regressed. Using these results, the urban population for prefectural cities of China was finally estimated. The results showed that a good linear correlation was found between Mdiffs and the census data in each type of region, as all the adjusted R2 values were above 0.9 and all the models passed the significance test (95% confidence level). The ratio of the estimated population to the census population (PER) was between 0.7 and 1.3 for 76% of the cities in China. This is the first attempt to estimate the urban population using building height data for prefectural cities in China. This method produced reasonable results and can be effectively used for spatial distribution estimates of the urban population in large scale areas.

scholarly journals The Spatial Distribution and Changing Pattern of Urban Population Density

1976 ◽  
Vol 85 (5) ◽  
pp. 237-254 ◽  
Author(s):  
Hiroshi MORIKAWA
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Urban Population

scholarly journals Effectiveness of Government Policies in Controlling COVID-19 in India

2020 ◽  
pp. 002073142098374
Author(s):  
Ashutosh Pandey ◽  
Nitin Kishore Saxena
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Rural Population ◽  
Urban Areas ◽  
Urban Population ◽  
Demographic Factors ◽  
Poverty Rate ◽  
Government Policies ◽  
Cumulative Number ◽  
Population Number

The purpose of this study is to find the demographic factors associated with the spread of COVID-19 and to suggest a measure for identifying the effectiveness of government policies in controlling COVID-19. The study hypothesizes that the cumulative number of confirmed COVID-19 patients depends on the urban population, rural population, number of persons older than 50, population density, and poverty rate. A log-linear model is used to test the stated hypothesis, with the cumulative number of confirmed COVID-19 patients up to period [Formula: see text] as a dependent variable and demographic factors as an independent variable. The policy effectiveness indicator is calculated by taking the difference of the COVID rank of the [Formula: see text]th state based on the predicted model and the actual COVID rank of the [Formula: see text]th state[Formula: see text]Our study finds that the urban population significantly impacts the spread of COVID-19. On the other hand, demographic factors such as rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus, people residing in urban areas face a significant threat of COVID-19 as compared to people in rural areas.

Urban population density function of two polar variables

1975 ◽  
Vol 9 (1) ◽  
pp. 63-68
Author(s):  
Emile Sanders
Keyword(s):  
Population Density ◽  
Density Function ◽  
Urban Population

Estimating Urban Population Density Using Remotely Sensed Imagery Products

2021 ◽  
pp. 132-149
Author(s):  
Dimitris Triantakonstantis ◽  
Demetris Stathakis ◽  
Zoi Papadopoulou
Keyword(s):  
Population Density ◽  
Urban Population ◽  
Remotely Sensed ◽  
Remotely Sensed Imagery

scholarly journals Occurrence of tuberculosis cases in Crato, Ceará, from 2002 to 2011: a spatial analisys of specific standards

2015 ◽  
Vol 18 (2) ◽  
pp. 313-325 ◽  
Author(s):  
Mayrla Lima Pinto ◽  
Talina Carla da Silva ◽  
Lidiane Cristina Félix Gomes ◽  
Maria Rita Bertolozzi ◽  
Lourdes Milagros Mendoza Villavicencio ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Urban Area ◽  
Temporal Trend ◽  
Kernel Method ◽  
Quantitative Approach ◽  
Point Pattern ◽  
Hybrid Design ◽  
Population Densities ◽  
Population Groups

OBJECTIVE: to analyze the spatial distribution of tuberculosis in Crato, Ceará, Brazil, from 2002 to 2011, aiming to check for a point pattern. METHODS: This is an ecological, temporal trend and hybrid design study, with a quantitative approach. A total of 261 cases of tuberculosis were geo-referenced and 20 (7.1%) were considered as losses due to the lack of address. The profile of patients in 10 years of study was in accordance with the following pattern: men aged between 20 and 59 years, with low schooling, affected by the pulmonary form of tuberculosis and who were cured from the disease. RESULTS: The analysis of the spatial distribution of tuberculosis points out that in the period of study, new cases of the disease were not distributed on a regular basis, indicating a clustered spatial pattern, confirmed by the L-function. The map with the density of new cases estimated by the Kernel method showed that the "hot" areas are more concentrated in the vicinity of the central urban area. CONCLUSION: The study allowed pointing out areas of higher and lower concentration of tuberculosis, identifying the spatial pattern, but it also recognized that the disease has not reached all of the population groups with the same intensity. Those who were most vulnerable were the ones who lived in regions with higher population densities, precarious living conditions, and with intense flow of people.

scholarly journals A Quantitative Evaluation of the Multiple Narratives of the Recent Sahelian Regreening*

2016 ◽  
Vol 8 (1) ◽  
pp. 67-83 ◽  
Author(s):  
Mimi Stith ◽  
Alessandra Giannini ◽  
John del Corral ◽  
Susana Adamo ◽  
Alex de Sherbinin
Keyword(s):  
Spatial Distribution ◽  
Population Density ◽  
Environmental Change ◽  
Burkina Faso ◽  
Social Dimensions ◽  
Adaptation To Climate Change ◽  
Southern Central ◽  
Second Category ◽  
The Impact

Abstract A spatial analysis is presented that aims to synthesize the evidence for climate and social dimensions of the “regreening” of the Sahel. Using an independently constructed archival database of donor-funded interventions in Burkina Faso, Mali, Niger, and Senegal in response to the persistence of drought in the 1970s and 1980s, the spatial distribution of these interventions is examined in relation to population density and to trends in precipitation and in greenness. Three categories of environmental change are classified: 1) regions at the northern grassland/shrubland edge of the Sahel where NDVI varies interannually with precipitation, 2) densely populated cropland regions of the Sahel where significant trends in precipitation and NDVI decouple at interannual time scales, and 3) regions at the southern savanna edge of the Sahel where NDVI variation is independent of precipitation. Examination of the spatial distribution of environmental change, number of development projects, and population density brings to the fore the second category, covering the cropland areas where population density and regreening are higher than average. While few, regions in this category coincide with emerging hotspots of regreening in northern Burkina Faso and southern central Niger known from case study literature. In examining the impact of efforts to rejuvenate the Sahelian environment and livelihoods in the aftermath of the droughts of the 1970s and 1980s against the backdrop of a varying and uncertain climate, the transition from desertification to regreening discourses is framed in the context of adaptation to climate change.

scholarly journals PENGARUH FAKTOR EKONOMI, SOSIAL EKONOMI DAN IKLIM TERHADAP BENCANA ALAM DI INDONESIA

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 539
Author(s):  
Surya Irmayani ◽  
Zul Azhar ◽  
Melti Roza Adry
Keyword(s):  
Economic Growth ◽  
Population Density ◽  
Natural Disasters ◽  
Urban Population ◽  
Participation Rate ◽  
Least Square ◽  
Ordinary Least Square ◽  
Descriptive Research ◽  
Negative Effect ◽  
Education Participation

This purpose of the research  are to the analyse the Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall in terms of Damage Natural Disasters in Indonesia. This type of research is associative descriptive research. This study is based on data 2015 obtained from institutions and related institution. Methods that being used are Ordinary Least Square (OLS). The estimation results show that Economic Growth has a significant negative effect the Damage Natural Disasters in Indonesia, Education Participation Rate has a not significant effect the Damage Natural Disasters in Indonesia, Urban Population has a significant positive effect the Damage Natural Disasters in Indonesia, Population Density has a not significant effect the Damage Natural Disasters in Indonesia, Number of rainfall has a not significant effect the Damage Natural Disasters in Indonesia. Keywords: Economic Growth, Education Participation Rate, Urban Population, Population Density, Number of Rainfall

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