The statistic shows the population of Hangzhou in China from 1980 to 2010, with forecasts up until 2035. In 2010, the population of Hangzhou had amounted to about **** million inhabitants and was forecasted to grow up to almost *** million by 2035.

Historical dataset of population level and growth rate for the Hangzhou, China metro area from 1950 to 2025.

Population: Census: Zhejiang: Hangzhou data was reported at 11,936.010 Person th in 12-01-2020. This records an increase from the previous number of 8,700.400 Person th for 12-01-2010. Population: Census: Zhejiang: Hangzhou data is updated decadal, averaging 8,700.400 Person th from Dec 2000 (Median) to 12-01-2020, with 3 observations. The data reached an all-time high of 11,936.010 Person th in 12-01-2020 and a record low of 6,878.722 Person th in 12-01-2000. Population: Census: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: By Census.

Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data was reported at 2.300 ‰ in 2024. This records an increase from the previous number of 1.300 ‰ for 2023. Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data is updated yearly, averaging 4.120 ‰ from Dec 2010 (Median) to 2024, with 15 observations. The data reached an all-time high of 7.400 ‰ in 2017 and a record low of 1.300 ‰ in 2023. Population: Usual Residence: Natural Growth Rate: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Usual Residence: Natural Growth Rate.

Population: Zhejiang: Hangzhou: Usual Residence data was reported at 12,624.000 Person th in 2024. This records an increase from the previous number of 12,522.000 Person th for 2023. Population: Zhejiang: Hangzhou: Usual Residence data is updated yearly, averaging 9,969.500 Person th from Dec 1999 (Median) to 2024, with 22 observations. The data reached an all-time high of 12,624.000 Person th in 2024 and a record low of 6,157.500 Person th in 1999. Population: Zhejiang: Hangzhou: Usual Residence data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Population: Zhejiang: Hangzhou: Household Registration data was reported at 8,757.000 Person th in 2024. This records an increase from the previous number of 8,606.036 Person th for 2023. Population: Zhejiang: Hangzhou: Household Registration data is updated yearly, averaging 6,891.200 Person th from Dec 1996 (Median) to 2024, with 29 observations. The data reached an all-time high of 8,757.000 Person th in 2024 and a record low of 6,032.200 Person th in 1996. Population: Zhejiang: Hangzhou: Household Registration data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City.

Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data was reported at 84.800 % in 2024. This records an increase from the previous number of 84.200 % for 2023. Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data is updated yearly, averaging 79.300 % from Dec 2010 (Median) to 2024, with 15 observations. The data reached an all-time high of 84.800 % in 2024 and a record low of 73.300 % in 2010. Population: Usual Residence: Urbanization Rate: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Urbanization Rate.

1.Use 'pd.read_csv('/kaggle/input/house-price-china-hangzhou/Hangzhou House Price.csv', encoding='GBK')' to load the dataset 2.Id - 2482 rows in total 3.House name 4.House price - For most of the data, the house price means how much per square meter. However, there is some dirty data, which represents the total price of the house ( n * 10000 ) 5.House introduction - possible room numbers and house area

Population: Non-natural Change: Zhejiang: Hangzhou data was reported at 129.867 Person th in 2023. This records an increase from the previous number of 101.340 Person th for 2022. Population: Non-natural Change: Zhejiang: Hangzhou data is updated yearly, averaging 57.511 Person th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 183.686 Person th in 2021 and a record low of 29.036 Person th in 2013. Population: Non-natural Change: Zhejiang: Hangzhou data remains active status in CEIC and is reported by Hangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GE: Population: Prefecture Level City: Non-natural Change.

Population: Zhejiang: Hangzhou: Fuyang data was reported at 701.432 Person th in 2023. This records an increase from the previous number of 697.888 Person th for 2022. Population: Zhejiang: Hangzhou: Fuyang data is updated yearly, averaging 662.300 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 701.432 Person th in 2023 and a record low of 628.000 Person th in 2004. Population: Zhejiang: Hangzhou: Fuyang data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.

As of January 2024, Hangzhou in China had the highest annual metropolitan population growth rate among megacities in the Asia-Pacific region, at about five percent. In contrast, all three Japanese megacities Tokyo, Nagoya, and Osaka had the lowest annual population growth rates across APAC, with Osaka's population shrinking by 0.05 percent as of January 2024.

This statistic depicts the number of workers in Hangzhou urban area as of *************, by employment status. By the end of 2019, around *** million people were employed in private enterprises or self-employed in the urban area of Hangzhou.

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town’s industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.

As of January 2025, Hangzhou in China had the highest annual metropolitan population growth rate among megacities in the Asia-Pacific region, at about five percent. In contrast, all three Japanese megacities—Tokyo, Nagoya, and Osaka—had the lowest annual population growth rates across APAC, with Osaka's population shrinking by -0.05 percent as of January 2025.

Population: Rural: Zhejiang: Hangzhou: Jiande data was reported at 397.000 Person th in 2012. This records a decrease from the previous number of 400.000 Person th for 2011. Population: Rural: Zhejiang: Hangzhou: Jiande data is updated yearly, averaging 400.000 Person th from Dec 2004 (Median) to 2012, with 9 observations. The data reached an all-time high of 411.000 Person th in 2004 and a record low of 397.000 Person th in 2012. Population: Rural: Zhejiang: Hangzhou: Jiande data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: Rural: County Level Region.

In China, it is critical to help older adults cope with depression due to the emerging impacts of factors such as increased life expectancy and the “one-child” family planning policy. Meanwhile, differences in retirement age have different effects on health in older adults of different gender. The relationship of gender differences in social capital and depression across the elderly population was unclear. Focusing on this demographic, this study conducted a telephone survey to explore the relationship between social capital and depression. Referring to electronic medical records, we randomly selected 1,042 elderly respondents (426 men, 616 women) from four areas in Hangzhou. We used social capital measurements and the Geriatric Depression Scale (GDS-15) to assess social capital and depression, respectively, then employed a multivariate logistic regression and structural equation modeling to examine the associations between factors, along with a consideration of gender. This study was discovered that differences in both income and morbidity contributed to differences in social capital and depression. In our sample of elderly respondents, we also found gender-based differences in cognitive and structural social capital. Compared to men, women were more likely to attain higher social capital and less likely to develop depression. At the same time, social networking and social engagement had negative impacts on depression in women, which was not the case for men. We found that lower reciprocity (men and women), social work (men), and trust (women) indicated higher risks of depression. Reciprocity and social networks were significantly and negatively correlated with depression among male respondents; in the male model, factors of trust, reciprocity, and social participation had positive effects on reducing the risk of depression, while social networks had a negative effect. For elderly persons, these findings suggest that mental health is affected by differences in social capital caused by policy differences and cultural differences caused by gender differences.

The data files in this study have been created by merging individual per-county census files available as Microsoft Excel spreadsheets. Thus the study contains 111 data files, 1 file per census category. Each of these files contains merged census data for the following counties: HANGZHOU SHANGCHENG 杭州上城区 (330102), HANGZHOU XIACHENG 杭州下城区 (330103), JIANGGAN 江干区 (330104), GONGSHU 拱墅区 (330105), XIHU 西湖区 (330106), BINJIANG 滨江区 (330108), TONGLU 桐庐县 (330122), CHUNAN 淳安县 (330127), XIAOSHAN 萧山市 (330181), JIANDE 建德市 (330182), FUYANG 富阳市 (330183), YUHANG 余杭市 (330184), LINAN 临安市 (330185), HAISHU 海曙区 (330203), JIANGDONG 江东区 (330204), JIANGBEI 江北区 (330205), BEILUN 北仑区 (330206), ZHENHAI 镇海区 (330211), XIANGSHAN 象山县 (330225), NINGHAI 宁海县 (330226), YINXIAN 鄞县 (330227), YUYAO 余姚市 (330281), CIXI 慈溪市 (330282), FENGHUA 奉化市 (330283), LUCHENG 鹿城区 (330302), LONGWAN 龙湾区 (330303), OUHAI 瓯海区 (330304), DONGTOU 洞头县 (330322), YONGJIA 永嘉县 (330324), PINGYANG 平阳县 (330326), CANGNAN 苍南县 (330327), WENCHENG 文成县 (330328), TAISHUN 泰顺县 (330329), RUIAN 瑞安市 (330381), LEQING 乐清市 (330382), XIUCHENG 秀城区 (330402), XIUZHOU 秀洲区 (330411), JIASHAN 嘉善县 (330421), HAIYAN 海盐县 (330424), HAINING 海宁市 (330481), PINGHU 平湖市 (330482), TONGXIANG 桐乡市 (330483), HUZHOU 湖州市 (330501), DEQING 德清县 (330521), CHANGXING 长兴县 (330522), ANJI 安吉县 (330523), YUECHENG 越城区 (330602), SHAOXING 绍兴县 (330621), XINCHANG 新昌县 (330624), ZHUJI 诸暨市 (330681), SHANGYU 上虞市 (330682), SHENGZHOU 嵊州市 (330683), WUCHENG 婺城区 (330702), JINHUA 金华县 (330721), WUYI 武义县 (330723), PUJIANG 浦江县 (330726), PANAN 磐安县 (330727), LANXI 兰溪市 (330781), YIWU 义乌市 (330782), DONGYANG 东阳市 (330783), YONGKANG 永康市 (330784), KECHENG 柯城区 (330802), QUXIAN 衢县 (330821), CHANGSHAN 常山县 (330822), KAIHUA 开化县 (330824), LONGYOU 龙游县 (330825), JIANGSHAN 江山市 (330881), DINGHAI 定海区 (330902), PUTUO 普陀区 (330903), DAISHAN 岱山县 (330921), SHENGSI 嵊泗县 (330922), JIAOJIANG 椒江区 (331002), HUANGYAN 黄岩区 (331003), LUQIAO 路桥区 (331004), YUHUAN 玉环县 (331021), SANMEN 三门县 (331022), TIANTAI 天台县 (331023), XIANJU 仙居县 (331024), WENLING 温岭市 (331081), LINHAI 临海市 (331082), LIANDU 莲都区 (331102), QINGTIAN 青田县 (331121), JINYUN 缙云县 (331122), SUICHANG 遂昌县 (331123), SONGYANG 松阳县 (331124), YUNHE 云和县 (331125), QINGYUAN 庆元县 (331126), JINGNINGSHEZUZIZHIXIAN 景宁畲族自治县 (331127), LONGQUAN 龙泉市 (331181).

Population: Zhejiang: Hangzhou: Tonglu data was reported at 418.326 Person th in 2023. This records a decrease from the previous number of 419.276 Person th for 2022. Population: Zhejiang: Hangzhou: Tonglu data is updated yearly, averaging 406.950 Person th from Dec 2004 (Median) to 2023, with 20 observations. The data reached an all-time high of 419.276 Person th in 2022 and a record low of 394.000 Person th in 2004. Population: Zhejiang: Hangzhou: Tonglu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: Population: County Level Region.

List of factors influencing the land economic density.

BackgroundIn December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features.MethodsOur study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome.ResultsThe receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p