Dataset Information

Fine-scale population spatialization data of China in 2018 based on real location-based big data.

ABSTRACT: Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. We take the average of Tencent user location big data as a measure of ambient population. The county-level statistical population data in 2018 was used as the assigned input data. The log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latter to a 0.01° grid, and the ambient population data of mainland China was obtained. Extracting street-level (lower than county-level) statistics for accuracy testing, we found that POP2018 has the best fit with the actual permanent population (R² = 0.91), and the error is the smallest (MSE_POP2018 = 22.48 WorldPop = 37.24 LandScan = 100.91). This research supplemented in the refined spatial distribution data of people between census years, as well as presenting the application technique of big data in ambient population estimation and zoning mapping.

SUBMITTER: Chen M

PROVIDER: S-EPMC9568591 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Fine-scale population spatialization data of China in 2018 based on real location-based big data.

Chen Mingxing M Xian Yue Y Huang Yaohuan Y Zhang Xiaoping X Hu Maogui M Guo Shasha S Chen Liangkan L Liang Longwu L

Scientific data 20221014 1

Accurate location-based big data has a high resolution and a direct interaction with human activities, allowing for fine-scale population spatial data to be realized. We take the average of Tencent user location big data as a measure of ambient population. The county-level statistical population data in 2018 was used as the assigned input data. The log linear spatially weighted regression model was used to establish the relationship between location data and statistical data to allocate the latt ...[more]

PMID: 36241886

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Project description:ObjectiveThis is a comprehensive overview of long-term cancer survival in Zhejiang Province, China. Hybrid analysis, a combination of cohort and period analysis, has been proposed to derive up-to-date cancer survival estimates. Using this approach, we aimed to timely and accurately analyze the 5-year relative survival (RS) and net survival (NS) in cancer registries of Zhejiang Province, China.MethodsA total of 255,725 new cancer cases diagnosed during 2013-2017 were included in 14 cancer registries in Zhejiang Province, China, with a follow-up on vital status until the end of 2019. The hybrid analysis was used to calculate the 5-year RS and 5-year NS during 2018-2019 for overall and stratifications by sex, cancer type, region, and age at diagnosis.ResultsDuring 2018-2019, the age-standardized 5-year RS and NS for overall cancer in Zhejiang was 47.5% and 48.6%, respectively. The age-standardized 5-year RS for cancers of women (55.4%) was higher than that of men (40.0%), and the rate of urban areas (49.7%) was higher than that of rural areas (43.1%). The 5-year RS declined along with age, from 84.4% for ages <45 years to 23.7% for ages >74 years. Our results of the RS and NS showed the similar trend and no significant difference. The top five cancers with top age-standardized 5-year RS were thyroid cancer (96.0%), breast cancer (84.3%), testicular cancer (79.9%), prostate cancer (77.2%), and bladder cancer (70.6%), and the five cancers with the lowest age-standardized 5-year RS were pancreatic cancer (6.0%), liver cancer (15.6%), gallbladder cancer (17.1%), esophageal cancer (22.7%), and leukemia (31.0%).ConclusionsWe reported the most up-to-date 5-year cancer RS and NS in Zhejiang Province, China for the first time, and found that the 5-year survival for cancer patients in Zhejiang during 2018-2019 was relatively high. The population-based cancer registries are recognized as key policy tools that can be used to evaluate both the impact of cancer prevention strategies and the effectiveness of health systems.

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Dataset Information

Fine-scale population spatialization data of China in 2018 based on real location-based big data.

Publications

Fine-scale population spatialization data of China in 2018 based on real location-based big data.

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