Search Results (2)

A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are many population products derived from remote sensing nighttime light (NTL) and other auxiliary data, they are limited by the coarse spatial resolution of NTL data. As a result, the outcomes’ spatial resolution is restricted, and it cannot meet the requirements of some applications. To address this limitation, this study employs the nighttime light data provided by the SDGSAT-1 satellite, which has a spatial resolution of 10 m, and land use data as auxiliary data to disaggregate the population distribution data from WorldPop data (100 m resolution) to a high resolution of 10 m. The case study conducted in Guilin, China, using the multi-class weighted dasymetric mapping method shows that the total error during the disaggregation is 0.63%, and the accuracy of 146 towns in the study area is represented by an R² of 0.99. In comparison to the WorldPop data, the result’s information entropy and spatial frequency increases by 345% and 1142%, respectively, which demonstrates the effectiveness of this approach in studying population distributions with high spatial resolution. Full article

Accurate and precise spatial population data are critical to the allocation of resources for socioeconomic development and to the decision-making process for environmental management in any country. However, this type of data is not always directly available but can be estimated through spatial statistical analysis. The geo-spatialized population estimates data can provide indispensable evidence for analyzing the potential ecological threats of anthropogenic activities in ecologically protected watersheds. In this study, we applied a multiclass dasymetric mapping to estimate the geospatial distribution of the residential population of JBNERR (a natural research reserve that is located across two municipalities in southeastern Puerto Rico). We then analyzed the spatial variation of the population residing within the reserve watershed over a thirty-year period from 1990 to 2010. The result showed that the population increased by 19.5% with a growth rate of 0.97%, adding 5583 new inhabitants from 1990 to 2010 for the entire area. Where the highest population density corresponds to an urban developed area, with 254.8 ± 12.3 inhab/900 m² in 1990, 71.2 ± 7.1 inhab/900 m² in 2000, and 94.0 ± 4.8 inhab/900 m² in 2010. It was followed by pastures or open areas that increased their maximum population density from 1990 to 2000 but decreased from 2000 to 2010, unlike urban areas. Our methods and results help assess the impact of urban growth on ecologically fragile areas, such as urban development in JBNERR, that may indirectly threaten the recreational activities and ecological envrionments within protected areas. Full article