scholarly journals Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

2020 ◽  
Vol 12 (5) ◽  
pp. 839 ◽  
Author(s):  
Jiping Cao ◽  
Yumin Chen ◽  
John P. Wilson ◽  
Huangyuan Tan ◽  
Jiaxin Yang ◽  
...  
Keyword(s):  
Spatial Autocorrelation ◽  
Infrared Imaging ◽  
Population Distribution ◽  
Population Data ◽  
Ordinary Least Squares ◽  
Local Economy ◽  
Spatial Error ◽  
Reference Models ◽  
Spatial Methods ◽  
Permanent Resident

Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

scholarly journals Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China

2021 ◽  
Vol 13 (23) ◽  
pp. 13477
Author(s):  
Yang Wang ◽  
Xiaoli Yue ◽  
Hong’ou Zhang ◽  
Yongxian Su ◽  
Jing Qin
Keyword(s):  
Sustainable Development ◽  
Social Environment ◽  
Physical Environment ◽  
Population Distribution ◽  
Ordinary Least Squares ◽  
Floating Population ◽  
Urban District ◽  
Spatial Error ◽  
Population Proportion ◽  
The Impact

The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development.

scholarly journals Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Babak Khavari ◽  
Alexandros Korkovelos ◽  
Andreas Sahlberg ◽  
Mark Howells ◽  
Francesco Fuso Nerini
Keyword(s):  
World Bank ◽  
Population Distribution ◽  
Population Data ◽  
Sub Saharan Africa ◽  
Demographic And Health Surveys ◽  
Middle Income ◽  
Disease Response ◽  
The World Bank ◽  
The World ◽  
Urban Rural

AbstractHuman settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.

scholarly journals Testing Seven Hypotheses to Determine What Explains the Current Planthopper (Fulgoridae) Geographical and Species Richness Patterns in China

Insects ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 892
Author(s):  
Zheng-Xue Zhao ◽  
Lin Yang ◽  
Jian-Kun Long ◽  
Zhi-Min Chang ◽  
Zheng-Xiang Zhou ◽  
...  
Keyword(s):  
Species Richness ◽  
Large Scale ◽  
Net Primary Productivity ◽  
Model Averaging ◽  
Ordinary Least Squares ◽  
Environmental Variable ◽  
Vital Role ◽  
Mean Annual Temperature ◽  
Spatial Error ◽  
Richness Patterns

Although many hypotheses have been proposed to understand the mechanisms underlying large-scale richness patterns, the environmental determinants are still poorly understood, particularly in insects. Here, we tested the relative contributions of seven hypotheses previously proposed to explain planthopper richness patterns in China. The richness patterns were visualized at a 1° × 1° grid size, using 14,722 distribution records for 1335 planthoppers. We used ordinary least squares and spatial error simultaneous autoregressive models to examine the relationships between richness and single environmental variables and employed model averaging to assess the environmental variable relative roles. Species richness was unevenly distributed, with high species numbers occurring in the central and southern mountainous areas. The mean annual temperature change since the Last Glacial Maximum was the most important factor for richness patterns, followed by mean annual temperature and net primary productivity. Therefore, historical climate stability, ambient energy, and productivity hypotheses were supported strongly, but orogenic processes and geological isolation may also play a vital role.

scholarly journals A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter

2019 ◽  
Vol 06 (01) ◽  
pp. 17-28 ◽  
Author(s):  
Hoang Pham ◽  
David H. Pham
Keyword(s):  
Machine Learning ◽  
Population Distribution ◽  
Real Life ◽  
Population Data ◽  
Correction Method ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Large Sample Size ◽  
Bernoulli Distribution ◽  
Machine Learning Approach

In real-life applications, we often do not have population data but we can collect several samples from a large sample size of data. In this paper, we propose a median-based machine-learning approach and algorithm to predict the parameter of the Bernoulli distribution. We illustrate the proposed median approach by generating various sample datasets from Bernoulli population distribution to validate the accuracy of the proposed approach. We also analyze the effectiveness of the median methods using machine-learning techniques including correction method and logistic regression. Our results show that the median-based measure outperforms the mean measure in the applications of machine learning using sampling distribution approaches.

scholarly journals Multiscale effects of habitat and surrounding matrices on waterbird diversity in the Yangtze River Floodplain

2020 ◽  
Author(s):  
Boyu Gao ◽  
Peng Gong ◽  
Wenyuan Zhang ◽  
Jun Yang ◽  
Yali Si
Keyword(s):  
Yangtze River ◽  
Structural Features ◽  
Ordinary Least Squares ◽  
Habitat Connectivity ◽  
The Yangtze River ◽  
Least Squares Regression ◽  
Spatial Error ◽  
River Floodplain ◽  
Surrounding Landscape ◽  
Shape Complexity

Abstract Context With the expansion in urbanization, understanding how biodiversity responds to the altered landscape becomes a major concern. Most studies focus on habitat effects on biodiversity, yet much less attention has been paid to surrounding landscape matrices and their joint effects. Objective We investigated how habitat and landscape matrices affect waterbird diversity across scales in the Yangtze River Floodplain, a typical area with high biodiversity and severe human-wildlife conflict. Methods The compositional and structural features of the landscape were calculated at fine and coarse scales. The ordinary least squares regression model was adopted, following a test showing no significant spatial autocorrelation in the spatial lag and spatial error models, to estimate the relationship between landscape metrics and waterbird diversity. Results Well-connected grassland and shrub surrounded by isolated and regular-shaped developed area maintained higher waterbird diversity at fine scales. Regular-shaped developed area and cropland, irregular-shaped forest, and aggregated distribution of wetland and shrub positively affected waterbird diversity at coarse scales. Conclusions Habitat and landscape matrices jointly affected waterbird diversity. Regular-shaped developed area facilitated higher waterbird diversity and showed the most pronounced effect at coarse scales. The conservation efforts should not only focus on habitat quality and capacity, but also habitat connectivity and complexity when formulating development plans. We suggest planners minimize the expansion of the developed area into critical habitats and leave buffers to maintain habitat connectivity and shape complexity to reduce the disturbance to birds. Our findings provide important insights and practical measures to protect biodiversity in human-dominated landscapes.

scholarly journals Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany

2020 ◽  
Vol 12 (10) ◽  
pp. 3976 ◽  
Author(s):  
Sebastian Eichhorn
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Population Distribution ◽  
Population Data ◽  
Added Value ◽  
Moran’S I ◽  
Dispersion Measure ◽  
Test Statistics ◽  
Moran's I ◽  
Urban Density

High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation.

GIS Based Spatialization of Population Data in Meijiang River Basin

2013 ◽  
Vol 295-298 ◽  
pp. 2378-2383 ◽  
Author(s):  
Xiang Gui Zeng ◽  
Ge Ying Lai ◽  
Fa Zhao Yi ◽  
Ling Ling Zhang
Keyword(s):  
Land Use ◽  
Population Density ◽  
River Basin ◽  
Population Distribution ◽  
Population Data ◽  
Fusion Technology ◽  
Processing Technologies ◽  
Source Data ◽  
Density Map ◽  
Main Factors

This paper used GIS spatial analysis and data processing technologies and multi-source data fusion technology to spatialize the population data of Meijiang river basin. Land use was selected as the index factor and the settlements as the indicative factor. Selected terrain, roads and rivers were the main influencing factors and were further classified into several sub-factors. During the simulation, we first calculated the weight indexes of sub-factors on the settlements distribution and then fused the indexes to calculate the weight indexes of the main factors. Second we calculated the weight indexes of settlements on the population distribution. Last we fused the weight indexes of the main factors and the weight indexes of settlements to obtain the population density indexes of whole region and then generated the 100m×100m resolution raster population density map.

Geographical Distribution and Risk Factors of Low Birth Weight in Nigeria

2020 ◽  
Vol 10 (4) ◽  
pp. 234-248
Author(s):  
Richard Adeleke ◽  
Tolulope Osayomi ◽  
Ayodeji E. Iyanda
Keyword(s):  
Risk Factors ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Prevalence Rate ◽  
Spatial Epidemiology ◽  
Ordinary Least Squares ◽  
Health Concern ◽  
Geographical Differences ◽  
Spatial Error ◽  
Geographical Variations

BACKGROUNDLow birth weight (LBW) directly or indirectly accounts for 60% to 80% of all neonatal deaths globally, and it has become an issue of serious health concern with Nigeria with one of the highest infant mortality rates (74/1,000) in the world. Despite the severe health impact, little is understood on the geographical differences in maternal socioeconomic and environmental factors that affect LBW across the states in Nigeria.METHODUsing the spatial epidemiological approach, this study examined the geographical variations in LBW and associated risk factors in Nigeria with the aid of spatial statistics.RESULTSThere was a regional LBW corridor in the extreme north with Yobe state with the highest prevalence rate. Maternal educational attainment and acute maternal malnutrition explained 65.4% (ordinary least squares model) and 70.5% (spatial error model, SEM) of the variation in the geographical pattern of LBW.CONCLUSIONLBW remains an issue of serious health concern in Nigeria. The finding of this study would shed more light on the spatial epidemiology of LBW in Nigeria and also guide public health programs in curtailing the high prevalence rate of LBW.RECOMMENDATIONSThe study recommends health education on nutrition in pregnancy and the need to improve health literacy among women so as to check the high LBW prevalence.

The Correlations Dimension of Lilium regale Spatial Patterns

2014 ◽  
Vol 675-677 ◽  
pp. 1120-1124
Author(s):  
Zi Song Yang
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Correlation ◽  
Spatial Patterns ◽  
Correlation Dimension ◽  
Population Distribution ◽  
Fractal Theory ◽  
Scaling Properties ◽  
Lilium Regale ◽  
Correlation Degree

In this paper, the correlation dimension of the population distribution of L. regale were analyzed by fractal theory. The results show: (1) The fractal characters in different areas are obvious; (2)In most cases, the correlation dimension of L. regale population are so high ranged from 1.4664 to 1.7384, indicating higher individual spatial correlation degree and little difference of the scaling properties of spatial autocorrelation of individuals in different plots; (3)Irregular distributions, and great difference of scaling; (4) Ten correlation dimensions of L. regale are changing as the latitude regularly decreases or increases.

scholarly journals The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1209
Author(s):  
Shichuan Yu ◽  
Fei Wang ◽  
Mei Qu ◽  
Binhou Yu ◽  
Zhong Zhao
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
The Loess Plateau ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Geographically Weighted Regression Model ◽  
Lag Model

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

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