Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

Download Full-text

Population distribution characteristics and its relationship with natural factors in karst mountainous areas of Northwest Guangxi

Journal of Geography and Cartography ◽

10.24294/jgc.v3i1.1303 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Shana Shi ◽

Bingkang Xie ◽

Baoqing Hu ◽

Chuanyong Tang ◽

Yan Yan ◽

...

Keyword(s):

Spatial Distribution ◽

Population Density ◽

Population Distribution ◽

Basic Unit ◽

Distribution Characteristics ◽

Mountainous Areas ◽

Mountain Area ◽

Natural Factors ◽

The Impact ◽

Population Spatial Distribution

The smallest administrative unit of the sixth national census-township (town) is selected as the basic unit, the population spatial distribution characteristics at the township (town) level in karst mountainous areas of northwest Guangxi are analyzed by using Lorenz curve and spatial correlation analysis method, and the influence intensity of natural factors on regional population spatial distribution is detected by using geographic detector method. The results show that: 1. the spatial distribution of population at the township (town) level has the characteristics of imbalance, showing generally significant positive correlation and certain aggregation; 2. there are significant differences in the impact of the spatial distribution of various natural factors on the population distribution. For the towns without karst distribution in the northwest and central south of the study area, the population density increases with the increase of factors conducive to human residence, but the average population density is only 79 people / km2. In the towns with karst distribution in the East and south, the spatial distribution of population density and natural factors is not a simple increase or decrease relationship, but fluctuates with the change of karst distribution area. 3. The factor detection results of the geographic detector show that the altitude has the greatest impact on the spatial distribution of population. The interactive detection results show that the impact intensity of any two natural factors after superposition and interaction presents nonlinear enhancement and two factor enhancement. It can be seen that the karst mountain area in northwest Guangxi is similar to other areas. Altitude is one of the main factors affecting the spatial distribution of population, but the river network density and unique geological landform of karst mountain area have a strong catalytic effect on the spatial distribution of population. The superposition and interaction with other factors can further strengthen the impact on population distribution.

Download Full-text

Can More Accurate Night-Time Remote Sensing Data Simulate a More Detailed Population Distribution?

Sustainability ◽

10.3390/su11164488 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4488 ◽

Cited By ~ 5

Author(s):

Nannan Gao ◽

Fen Li ◽

Hui Zeng ◽

Daniël van Bilsen ◽

Martin De Jong

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Large Scale ◽

Population Distribution ◽

Remote Sensing Data ◽

Urban Land Use ◽

Residential Areas ◽

Night Time ◽

Sensing Data ◽

Population Distributions

Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.

Download Full-text

Study on Population Distribution Pattern at the County Level of China

Sustainability ◽

10.3390/su10103598 ◽

2018 ◽

Vol 10 (10) ◽

pp. 3598 ◽

Cited By ~ 10

Author(s):

Minmin Li ◽

Biao He ◽

Renzhong Guo ◽

You Li ◽

Yu Chen ◽

...

Keyword(s):

Spatial Distribution ◽

Distribution Pattern ◽

Urban Areas ◽

System Analysis ◽

Population Distribution ◽

National Level ◽

County Level ◽

Rapid Urbanization ◽

Structure Of Population ◽

Distribution Structure

With the accelerating urbanization process, the population increasingly concentrates in urban areas. In view of the huge population in China and a series of problems in the process of rapid urbanization, there are no unified measures for characterizing the population pattern. This study explores the distribution pattern of the Chinese population and proposes a spatial distribution structure of population using GIS (Geographic Information System) analysis. The main findings are as follows: (1) In 2015, the distribution of population density in China presents a pattern of high in the southeast and low in the northwest based on the county-level administrative regions. The population main lives in the southeast of China based on the “Hu Huanyong Line”. (2) There is a great difference of the spatial correlation between land area, population and GDP (Gross Domestic Product) in China. The economic concentration in China is higher than the population concentration. In the areas where population and GDP are aggregated, per capita GDP is higher. (3) Based on the areas with highly aggregated population and GDP, the spatial distribution structure of population of “1 + 4 + 11” for China’s urbanization is put forward, namely, one national-level aggregated area of population and GDP, 4 regional-level aggregated areas of population and GDP, and 11 local regionally aggregated areas of population and GDP. This spatial structure represents an attempt to explore the direction of China’s urbanization, and it can be used to optimize the spatial development pattern and provide scientific guidance for the future urbanization plan.

Download Full-text

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 ◽

10.3390/s20185032 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5032 ◽

Cited By ~ 1

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.

Download Full-text

ESTIMATING HOUSING VACANCY RATE IN QINGDAO CITY WITH NPP-VIIRS NIGHTTIME LIGHT AND GEOGRAPHICAL NATIONAL CONDITIONS MONITORING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1319-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1319-1326 ◽

Cited By ~ 2

Author(s):

X. Niu

Keyword(s):

Population Distribution ◽

Monitoring Data ◽

Basic Unit ◽

Distribution Data ◽

Vacancy Rate ◽

Rapid Urbanization ◽

Nighttime Light ◽

Qingdao City ◽

The City ◽

Housing Vacancy

Accompanying China's rapid urbanization in recent decades, especially in the new millennium, the housing problem has become one of the most important issues. The estimation and analysis of housing vacancy rate (HVR) can assist decision-making in solving this puzzle. It is particularly significant to government departments. This paper proposed a practical model for estimating the HVR in Qingdao city using NPP-VIIRS nighttime light composed data, Geographic National Conditions Monitoring data (GNCMD) and resident population distribution data. The main steps are: Firstly, pre-process the data, and finally forming a series of data sets with 500*500 grid as the basic unit; Secondly, select 400 grids of different types within the city as sample grids for SVM training, and establish a reasonable HVR model; Thirdly, using the model to estimate HVR in Qingdao and employing spatial statistical analysis methods to reveal the spatial differentiation pattern of HVR in this city; Finally test the accuracy of the model with two different methods. The results conclude that HVR in the southeastern coastal area of Qingdao city is relatively low and the low-low clusters distributed in patches. Simultaneously, in other regions it shows the tendency of the low value accumulation in the downtown area and the increasing trend towards the outer suburbs. Meanwhile the suburban and scenery regions by the side of the sea and mountains are likely to be the most vacant part of the city.

Download Full-text

Estimating Early-Winter Antarctic Sea Ice Thickness From Deformed Ice Morphology

10.5194/tc-2019-140 ◽

2019 ◽

Cited By ~ 1

Author(s):

M. Jeffrey Mei ◽

Ted Maksym ◽

Hanumant Singh

Keyword(s):

Neural Network ◽

Sea Ice ◽

Relative Error ◽

Snow Depth ◽

Large Scale ◽

Small Scale ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Mean Relative Error ◽

Antarctic Sea Ice

Abstract. Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness can be estimated from surface elevation measurements, such as those from airborne/satellite LiDAR, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic sea ice thickness have errors as high as ~ 50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate sea ice thickness at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate sea ice thickness at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ~ 15 %). Moreover, the neural network does not require specifying snow depth/density, which increases its potential applications to other LiDAR datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season, which is much lower than the mean relative error for a linear fit (errors up to 47 %). This method may be extended to lower-resolution, larger-footprint data such as such as IceBridge, and suggests a possible avenue to reduce errors in satellite estimates of Antarctic sea ice thickness from ICESat-2 over current methods, especially at smaller scale.

Download Full-text

A Spatial Distribution Equilibrium Evaluation of Health Service Resources at Community Grid Scale in Yichang, China

Sustainability ◽

10.3390/su12010052 ◽

2019 ◽

Vol 12 (1) ◽

pp. 52

Author(s):

Jingyuan Chen ◽

Yuqi Bai ◽

Pei Zhang ◽

Jingyuan Qiu ◽

Yichun Hu ◽

...

Keyword(s):

Spatial Distribution ◽

Health Service ◽

Health Services ◽

City Planning ◽

Population Distribution ◽

Smart Cities ◽

Distribution Data ◽

Healthy Cities ◽

Healthy City ◽

Planning And Management

Whether the supplies of health services and related facilities meet the demand is a critical issue when developing healthy cities. The importance of health services and related facilities in public health promotion has been adequately proved. However, since the community population and resource data are usually available at the scale of an administrative region; it is very difficult to perform further fine-scaled spatial distribution equilibrium evaluation studies. Such kinds of activities are highly expected for precise urban planning and management. Yichang is located in Hubei province, the central part of China, along the Yangzi River. It is leading both of China’s smart cities demonstration project and China’s healthy cities pilot project. Yichang has defined 1271 community grids for urban management and service, where each grid consists of 200 households generally with its population distribution data routinely updated. The research set the 15-min walking distances of the residents as impedance factors, and the numbers and the types of health service resources as attractiveness factors for accessibility evaluation. The resource ratio, richness and per capita number of various health service resources that can be reached within 15 min from the community grid building is used as spatial distribution equilibrium evaluation indicators. The entropy weight method is used to assign the indicator weight value. The obtained fine-scale evaluation results were analyzed. In this way, a community grid-scale spatial distribution equilibrium evaluation of health service resources in Yichang was performed. The proposed research could be of value for rapid and precise evaluation of spatial distribution equilibrium evaluation of a variety of healthy city resources, to support healthy city planning and management.

Download Full-text

A Comprehensive Analysis of the Changes in Precipitation Patterns over Beijing during 1960–2012

Advances in Meteorology ◽

10.1155/2019/6364040 ◽

2019 ◽

Vol 2019 ◽

pp. 1-22 ◽

Cited By ~ 2

Author(s):

Xiaomeng Song ◽

Jianyun Zhang ◽

Chunhua Zhang ◽

Xianju Zou

Keyword(s):

Spatial Distribution ◽

Urban Areas ◽

Climatic Conditions ◽

Spatiotemporal Variability ◽

Precipitation Pattern ◽

Mountainous Areas ◽

Mountain Areas ◽

Annual Series ◽

Rain Gauges ◽

The Mean

Precipitation pattern has changed over many regions in recent decades, which may cause the risk of flood or drought. In this study, the main objective is to evaluate the spatiotemporal variability of precipitation in Beijing from 1960 to 2012. First, the mean monthly, seasonal, and annual precipitation series were used to analyze the temporal variation using regression, Mann–Kendall (M-K) test, Sen’s slope, and Pettitt tests. The results showed that the annual mean precipitation had a clear decreasing trend, with the statistically significant decrease in summer (especially in July and August) and significant increase in spring (especially in May). Although the decreasing trend is shown in the precipitation concentration indicators, the temporal uneven distribution of precipitation has unchanged. Subsequently, the precipitation time series at 30 stations over Beijing were used to evaluate the changes in precipitation pattern. The results showed that the annual series for the most rain gauges had decreasing trends with gradual changes. The spatial distribution of precipitation and other indices is geographically consistent, reflecting the principal physiographic and climatic conditions. At the same time, the effects of the terrain and urban development on the precipitation spatial distribution were detected. Generally, the large and heavy precipitations frequently occur in the plain areas, while the precipitation in the mountain areas is dominated by the small and medium precipitation. As a whole, the total precipitation in the plain areas (558.8 mm) was slightly higher than that in the mountainous areas (533.0 mm), while the precipitation in the urban areas (575.9 mm) was much higher than in the surrounding suburb areas (538.9 mm) during 1960–2012. The differences between the plain and mountainous areas during the period of 1960–1979, 1980–1999, and 2000–2012 were 24.2 mm, 32.6 mm, and 17.7 mm, respectively. The differences in precipitation between the urban and suburb areas for the three periods were 32.9 mm, 45.2 mm, and 31.0 mm, respectively, with the amount accounting for 5.51%, 7.66%, and 5.94% of the mean precipitation in the urban areas for the corresponding periods.

Download Full-text

An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia

Remote Sensing ◽

10.3390/rs13061171 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1171

Author(s):

Mohammed Alahmadi ◽

Shawky Mansour ◽

David Martin ◽

Peter Atkinson

Keyword(s):

Spatial Information ◽

Population Distribution ◽

Population Data ◽

Mean Relative Error ◽

Nighttime Lights ◽

Bare Land ◽

Distribution Mapping ◽

Nighttime Light ◽

Saturation Effects ◽

Population Mapping

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.

Download Full-text

Research on Improvement of Rural area Population Spatial Distribution Model Based on Random Forests

10.5194/egusphere-egu2020-13129 ◽

2020 ◽

Author(s):

Chun Dong

Keyword(s):

Spatial Distribution ◽

Rural Area ◽

Rural Areas ◽

Rural Population ◽

Large Scale ◽

Population Distribution ◽

Development Planning ◽

Geographical Information ◽

Distribution Model ◽

Fine Scale

<p>Since the founding of New China, especially since the reform and opening up, China has experienced the fastest economic development and the most profound population migration in history. The large-scale migration of China's rural population and labor force is particularly evident. China's rural population accounts for 40.42% in 2019. China's rural population is large, and urban-rural and regional differences are also large. Due to the current data and information limitations and the characteristics of China's national conditions, there are very few related studies on China's overall rural population.</p><p>Fine-scale population distribution data at the fine scale play an essential role in numerous fields, for example urban planning and management, and disaster assessment and developing population differentiation policies. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited many fine resolution population spatialization studies. However, most of the existing population spatialization methods have been studied at the regional or urban scale, and few studies have been conducted on the unit population in rural areas. In view of the fact that existing demographic data cannot meet the actual needs of analysis, management and scientific research in terms of spatial precision, a new population distribution estimation method combining nighttime lighting and residential building attributes is proposed in our study. In view of this, studying the spatial distribution of the population in rural areas is used as the purpose of this article. Based on the night light data, natural city boundaries are determined. A rural area delineation method based on Head-to-Tail segmentation classification combined with administrative village verification is proposed, which provides a feasible method for large-scale automatic extraction of rural area boundaries. Coupled with POI (Points of Interest) data, based on elevation, slope, night light images, and land cover, the population spatialization model of the random forest is developed and improved based on the weight of the house properties and light intensity. Finally, a high-precision population distribution dataset is obtained, which is closer to the actual population distribution. The research results show that based on the proposed population spatialization model, street demographic values can be fitted better, and the basis for more accurate population estimation is laid. It provides a reference for data fusion and is of great significance for rural area development planning.</p>

Download Full-text