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 Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

scholarly journals Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

2020 ◽  
Vol 12 (3) ◽  
pp. 1231 ◽  
Author(s):  
Fahao Wang ◽  
Weidong Lu ◽  
Jingyun Zheng ◽  
Shicheng Li ◽  
Xuezhen Zhang
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
Population Density ◽  
Census Data ◽  
Gansu Province ◽  
Spatially Explicit ◽  
Random Forest Regression ◽  
Independent Variables ◽  
Population Weighting

This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures (Fu, in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient (R2) of 0.82, a positive reduction of error (RE, 0.72) and a coefficient of efficiency (CE) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was ~9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.

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 Revealing the Correlation between Population Density and the Spatial Distribution of Urban Public Service Facilities with Mobile Phone Data

2020 ◽  
Vol 9 (1) ◽  
pp. 38 ◽  
Author(s):  
Yi Shi ◽  
Junyan Yang ◽  
Peiyu Shen
Keyword(s):  
Land Use ◽  
Population Density ◽  
Mobile Phone ◽  
Spatial Correlation ◽  
Public Service ◽  
Urban Population ◽  
Census Data ◽  
Remote Sensing Data ◽  
Mobile Phone Data ◽  
Night Time

Some studies have confirmed the association between urban public services and population density; however, other studies using census data, for example, have arrived at the opposite conclusion. Mobile signaling data provide new technological tools to investigate the subject. Based on the data of 20 million 2G mobile phone users in downtown Shanghai and the land use data of urban public service facilities, this study explores the spatiotemporal correlation between population density and public service facilities’ locations in downtown Shanghai and its variation laws. The correlation between individual population density at day vs. night and urban public service facilities distribution was also examined from a dynamic perspective. The results show a correlation between service facilities’ locations and urban population density at different times of the day. As a result, the average population density observed over a long period of time (day-time periodicity or longer) with census data or remote sensing data does not directly correlation with the distribution of public service facilities despite its correlation with public service facilities distribution. Among them, there is a significant spatial correlation between public service facilities and daytime population density and a significant spatial correlation between non-public service facilities and night-time population density. The spatial and temporal changes in the relationship between urban population density and service facilities is due to changing crowd behavior; however, the density of specific types of behavior is the real factor that affects the layout of urban public service facilities. The results show that mobile signaling data and land use data of service facilities are of great value for studying the spatiotemporal correlations between urban population density and service facilities.

scholarly journals Suitability Evaluation and Layout Optimization of the Spatial Distribution of Rural Residential Areas

2020 ◽  
Vol 12 (6) ◽  
pp. 2409 ◽  
Author(s):  
Pengfei Guo ◽  
Fangfang Zhang ◽  
Haiying Wang ◽  
Fen Qin
Keyword(s):  
Spatial Distribution ◽  
Large Scale ◽  
Layout Optimization ◽  
Optimization Strategy ◽  
Rural Settlements ◽  
Residential Areas ◽  
Study Results ◽  
Administrative Area ◽  
Elevation Model ◽  
Resistance Value

A reasonable layout optimization strategy of rural residential areas can improve the quality of life of rural residents and promote rural revitalization. Evaluating the suitability of rural residential areas is the basis of layout optimization. Based on 1:100,000 land cover data and a digital elevation model (30 m) for the Henan Province, China, we used the minimum cumulative resistance model to evaluate the spatial distribution suitability of rural settlements in the Zhengzhou administrative area (abbreviated: Zhengzhou). Then, we used a weighted Voronoi diagram to determine the scope of influence of central villages and determined the direction of relocation for the “combined migration” rural residential areas. The study results support the following conclusions: (1) the comprehensive resistance value of rural residential areas in the Northeastern part of Zhengzhou is low and the suitability is high. However, the comprehensive resistance value of the Southwestern part is high and the suitability is low. (2) The study area can be divided into highly suitable areas, suitable areas, generally suitable areas, unsuitable areas, and extremely unsuitable areas. Unsuitable areas and extremely unsuitable areas accounted for 33.66% of the total area and included 662 rural residential areas. (3) The rural residential areas were divided into four types of optimization: urbanization, key development, controlled development, and combined migration. Based on an analysis of the characteristics of each type of rural residential area, we proposed corresponding optimization strategies. The results remedy the lack of layout optimization strategies for large-scale rural residential areas and can provide support for the optimization of the layout of rural residential areas in Zhengzhou. Furthermore, the research techniques may apply to other regions.

Large-scale mapping of the catenas vegetation in Subarctic tundra

1995 ◽  
pp. 3-21
Author(s):  
S. S. Kholod
Keyword(s):  
Spatial Distribution ◽  
Vegetation Cover ◽  
Large Scale ◽  
Block Diagram ◽  
Abiotic Factors ◽  
Ecological Factors ◽  
Spatial Arrangement ◽  
Geological Time ◽  
Ecological Barriers ◽  
Functional Zones

One of the most difficult tasks in large-scale vegetation mapping is the clarification of mechanisms of the internal integration of vegetation cover territorial units. Traditional way of searching such mechanisms is the study of ecological factors controlling the space heterogeneity of vegetation cover. In essence, this is autecological analysis of vegetation. We propose another way of searching the mechanisms of territorial integration of vegetation. It is connected with intracoenotic interrelation, in particular, with the changing role of edificator synusium in a community along the altitudinal gradient. This way of searching is illustrated in the model-plot in subarctic tundra of Central Chukotka. Our further suggestion concerns the way of depicting these mechanisms on large-scale vegetation map. As a model object we chose the catena, that is the landscape formation including all geomorphjc positions of a slope, joint by the process of moving the material down the slope. The process of peneplanation of a mountain system for a long geological time favours to the levelling the lower (accumulative) parts of slopes. The colonization of these parts of the slope by the vegetation variants, corresponding to the lowest part of catena is the result of peneplanation. Vegetation of this part of catena makes a certain biogeocoenotic work which is the levelling of the small infralandscape limits and of the boundaries in vegetation cover. This process we name as the continualization on catena. In this process the variants of vegetation in the lower part of catena are being broken into separate synusiums. This is the process of decumbation of layers described by V. B. Sochava. Up to the slope the edificator power of the shrub synusiums sharply decreases. Moss and herb synusium have "to seek" the habitats similar to those under the shrub canopy. The competition between the synusium arises resulting in arrangement of a certain spatial assemblage of vegetation cover elements. In such assemblage the position of each element is determined by both biotic (interrelation with other coenotic elements) and abiotic (presence of appropriate habitats) factors. Taking into account the biogeocoenotic character of the process of continualization on catena we name such spatial assemblage an exolutionary-biogeocoenotic series. The space within each evolutionary-biogeocoenotic series is divided by ecological barriers into some functional zones. In each of the such zones the struggle between synusiums has its individual expression and direction. In the start zone of catena (extensive pediment) the interrelations of synusiums and layers control the mutual spatial arrangement of these elements at the largest extent. Here, as a rule, there predominate edificator synusiums of low and dwarfshrubs. In the first order limit zone (the bend of pediment to the above part of the slope) one-species herb and moss synusiums, oftenly substituting each other in similar habitats, get prevalence. In the zone of active colonization of slope (denudation slope) the coenotic factor has the least role in the spatial distribution of the vegetation cover elements. In particular, phytocoenotic interactions take place only within separate microcoenoses of herbs, mosses and lichens. In the zone of the attenuation of continualization process (the upper most parts of slope, crests) phytocoenotic interactions are almost absent and the spatial distribution of vegetation cover elements depends exclusively on the abiotic factors. The principal scheme of the distribution of vegetation cover elements and the disposition of functional zones on catena are shown on block-diagram (fig. 1).

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