![]() ![]() Another frequently used NTL data are from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor carried by the weather satellite Suomi NPP. However, its limitations of low spatial and radiometric resolutions, blooming effect, and over-saturation problem would result in underestimation in high-density areas (e.g., urban centers), making it only applicable to large-scale estimations (e.g., city or larger scales) (Elvidge et al., 1999). NTL from the US Air Force Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS), released in the early 1990s, is widely used for population mapping (Yu et al., 2018). NTL data capture the footprint of light on the earth’s surface generated by human activities, and its brightness level directly indicates the intensity of human activities, providing support for inferring population distribution (Song et al., 2020). The most popular remote sensing data include nighttime light (NTL) and optical satellite data (Wang et al., 2018). Therefore, remote sensing data are extensively used due to their ability to record continuous observations across time and space (Chen et al., 2020). To address this limitation, scientists seek observation data reflecting human activity to provide richer spatial and temporal information for population mapping. Although census data have a scientific collection process and high accuracy, the insufficient information in the temporal dimension caused by low update frequency (5–10 years) makes census data hard to meet the needs of many real-world applications, leading to the increase of analysis bias, imbalance of resource allocation, and inefficiency of government management (Song, 2019 Xu et al., 2021). In most scenarios, the population data used in these applications are derived from officially published census data. Timely and accurate population distribution data play an essential role in a wide range of critical applications, including but not limited to epidemiological studies (Li et al., 2020), natural disaster management (Chen, Wu, Jin et al., 2022), climate change analysis (O’Neill et al., 2020), ecological vulnerability assessment (He et al., 2018), emergency response (Tian et al., 2020), environmental monitoring (He et al., 2021), and sustainable urban planning (Long et al., 2022). To our best knowledge, this study is the first work to apply AutoGluon in population mapping, and the framework’s efficient and automated modeling capabilities will contribute to larger-scale and finer spatial-temporal population spatialization studies. ![]() Furthermore, the derived monthly population maps and the revealed spatial pattern of population dynamics in China are consistent with earlier studies, suggesting the robustness of the proposed framework in cross-time mapping. The proposed framework was applied to estimate county-level population density in China and received a good result with an r 2 of 0.974 and an RMSD of 427.61, which is better than the performances of current mainstream population mapping frameworks in terms of estimation accuracy. Therefore, in this study, using limited publicly available datasets and an automatic ensemble learning model (AutoGluon), we presented an efficient framework to simplify the model training and prediction process. However, the over-complex models and the strict data requirement resulting from the constant quest for increased accuracy pose challenges to the repeatability of many population spatialization frameworks. Benefiting from the emergence of multi-source geospatial datasets and the development of spatial statistics and machine learning, multi-scale population mapping with high temporal resolutions has been made possible. Known as the Triads, they made the town famous for their drug production and trade, gambling and prostitution between the 50s and 70s.Timely and accurate population mapping plays an essential role in a wide range of critical applications. Instead, the Chinese mafia had full reign of the city. There were no laws, regulations or building codes enforced, except for one - that buildings could not be any higher than 13/14 blocks due to the airport being so close by. Despite being situated in Hong Kong, the Chinese and British-run Hong Kong governments ignored the activities and existence of this location. This meant that there was a lack of housing in Hong Kong, and therefore, the first few high rise buildings were erected on the space in Kowloon. However, after World War II, Hong Kong experienced a massive influx of Chinese immigrants. It began as a low-rise squatter village in the early 20th century. Initially, a Chinese military fort eventually evolved into a squatters’ village that grew into the ‘city of darkness’. The History □ Kowloon Walled City was a densely populated and ungoverned settlement in Kowloon. ![]()
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