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
Abstract: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 integratin… Show more
“…Since NTL is positively correlated with population distribution, it is often used as an indicator to reflect population distribution. Meanwhile, combined with EVI, which is negatively correlated with impervious water surface, HSI is used as the component affecting population distribution in this paper [ 15 , 31 , 32 ]. In order to improve the simulation accuracy, we also combined POI data, which reflects social perception, and use the kernel density values of various POIs as the distance between residential buildings and the intensive points of various industries, so as to explore the influence of various industries on the distribution of resident population.…”
Section: Methodsmentioning
confidence: 99%
“… 10.1371/journal.pone.0269100.g003 Fig 3 (A) The Luojia 1–01 NTL imagery of Qingdao in 2018; (b) The EVI imagery of Qingdao in 2018. We used the bilinear interpolation algorithm to unify the spatial resolution of Luojia 1–01 NTL data (Fig 3A) and EVI data (Fig 3B) to 100m, and calculated HSI values for Qingdao City in 2018 by using ( 3 ) [ 32 ]. where L nor denotes the normalized RB values of the Luojia 1–01 NTL imagery, which was calculated as follows: where L max and L min , respectively, denote the maximum and minimum RB values of the Luojia 1–01 NTL imagery in Qingdao.…”
Section: Methodsmentioning
confidence: 99%
“… (A) The Luojia 1–01 NTL imagery of Qingdao in 2018; (b) The EVI imagery of Qingdao in 2018. We used the bilinear interpolation algorithm to unify the spatial resolution of Luojia 1–01 NTL data (Fig 3A) and EVI data (Fig 3B) to 100m, and calculated HSI values for Qingdao City in 2018 by using ( 3 ) [ 32 ]. …”
The study of population spatialization has provided important basic data for urban planning, development, environment and other issues. With the development of urbanization, urban residential buildings are getting higher and higher, and the difference between urban and rural population density is getting larger and larger. At present, most population spatial studies adopt the grid scale, and the population in buildings is evenly divided into various grids, which will lead to the neglect of the population distribution in vertical space, and the authenticity is not strong. In order to improve the accuracy of the population distribution, this paper studied the spatial distribution of population at the building scale, combined the digital surface model (DSM) and the digital elevation model (DEM) to calculate the floor of buildings, and proposed a new index based on the total floor area of residential buildings, called residential population index (RPI). RPI is directly related to the number of people a building can accommodate, so it can effectively estimate the population of both urban and rural areas even if the structure of urban and rural buildings is very different. In addition, this paper combined remote sensing monitoring data with geographic big data and adopted principal component regression (PCR) method to construct RPI prediction model to obtain building-scale population distribution data of Qingdao in 2018, providing ideas for population spatialization research. Through field sampling survey and overall assessment, the results were basically consistent with the actual residential situation. The average error with field survey samples is 14.5%. The R2 is 0.643 and the urbanization rate is 69.7%, which are all higher than WorldPop data set. Therefore, this method can reflect the specific distribution of urban resident population, enhance the heterogeneity and complexity of population distribution, and the estimated results have important reference significance for urban management, urban resource allocation, environmental protection and other fields.
“…Since NTL is positively correlated with population distribution, it is often used as an indicator to reflect population distribution. Meanwhile, combined with EVI, which is negatively correlated with impervious water surface, HSI is used as the component affecting population distribution in this paper [ 15 , 31 , 32 ]. In order to improve the simulation accuracy, we also combined POI data, which reflects social perception, and use the kernel density values of various POIs as the distance between residential buildings and the intensive points of various industries, so as to explore the influence of various industries on the distribution of resident population.…”
Section: Methodsmentioning
confidence: 99%
“… 10.1371/journal.pone.0269100.g003 Fig 3 (A) The Luojia 1–01 NTL imagery of Qingdao in 2018; (b) The EVI imagery of Qingdao in 2018. We used the bilinear interpolation algorithm to unify the spatial resolution of Luojia 1–01 NTL data (Fig 3A) and EVI data (Fig 3B) to 100m, and calculated HSI values for Qingdao City in 2018 by using ( 3 ) [ 32 ]. where L nor denotes the normalized RB values of the Luojia 1–01 NTL imagery, which was calculated as follows: where L max and L min , respectively, denote the maximum and minimum RB values of the Luojia 1–01 NTL imagery in Qingdao.…”
Section: Methodsmentioning
confidence: 99%
“… (A) The Luojia 1–01 NTL imagery of Qingdao in 2018; (b) The EVI imagery of Qingdao in 2018. We used the bilinear interpolation algorithm to unify the spatial resolution of Luojia 1–01 NTL data (Fig 3A) and EVI data (Fig 3B) to 100m, and calculated HSI values for Qingdao City in 2018 by using ( 3 ) [ 32 ]. …”
The study of population spatialization has provided important basic data for urban planning, development, environment and other issues. With the development of urbanization, urban residential buildings are getting higher and higher, and the difference between urban and rural population density is getting larger and larger. At present, most population spatial studies adopt the grid scale, and the population in buildings is evenly divided into various grids, which will lead to the neglect of the population distribution in vertical space, and the authenticity is not strong. In order to improve the accuracy of the population distribution, this paper studied the spatial distribution of population at the building scale, combined the digital surface model (DSM) and the digital elevation model (DEM) to calculate the floor of buildings, and proposed a new index based on the total floor area of residential buildings, called residential population index (RPI). RPI is directly related to the number of people a building can accommodate, so it can effectively estimate the population of both urban and rural areas even if the structure of urban and rural buildings is very different. In addition, this paper combined remote sensing monitoring data with geographic big data and adopted principal component regression (PCR) method to construct RPI prediction model to obtain building-scale population distribution data of Qingdao in 2018, providing ideas for population spatialization research. Through field sampling survey and overall assessment, the results were basically consistent with the actual residential situation. The average error with field survey samples is 14.5%. The R2 is 0.643 and the urbanization rate is 69.7%, which are all higher than WorldPop data set. Therefore, this method can reflect the specific distribution of urban resident population, enhance the heterogeneity and complexity of population distribution, and the estimated results have important reference significance for urban management, urban resource allocation, environmental protection and other fields.
“…Nighttime light (NTL) data as useful remote sensing data with spatial morphology information and intensity information have been proved to be significantly correlated with large areas of population distribution and have been widely used in generating gridded population distribution [18], [19]. However, the Defense Meteorological Satellite Program and Operational Linescan System (DMSP-OLS) NTL images have some inherent drawbacks and quality problems, such as blooming, saturation, and coarse resolution (∼1 km) [20], [21], [22], [23]. The Suomi National Polar-orbiting Partnership (Suomi-NPP) was launched in October 2011, which acquired Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) NTL data is considered to be the successor product to DMSP/OLS.…”
Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (R² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.
“…However, the application of DMSP/OLS data is relatively rare, because the sensor stopped working in 2013 and the spatial resolution is too low(1KM). The emergence of Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data effectively overcomes the shortcomings of DMSP-OLS data in spatio-temporal resolution [27], and such data are more suitable for the study of human social and economic activities [28,29]. Sutton K. et al used DMSP/OLS night light data to estimate the global population based on a global scale, and found that the global population is about 6.3 billion [30].…”
Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R2) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research.
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