In 2024, the total population of the Guangdong - Hong Kong - Macao Greater Bay Area in Greater China ranged at ***** million. The Guangdong - Hong Kong - Macao Greater Bay Area is the largest and most populated urban area in the world.

In 2023, the total population of the Guangdong - Hong Kong - Macao Greater Bay Area reached around **** million. In terms of population, China's Greater Bay Area was larger than other major Bay Areas in the world. However, per capita GDP was only about half of that in the Tokyo Bay Area and only one seventh of that in the San Francisco Bay Area.

This statistic illustrates the population of the Guangdong - Hong Kong - Macao Greater Bay Area cities in 2024. That year, the population of Guangzhou amounted to approximately ***** million people, making it the largest city by population in the region.

The fast urbanization has eroded the city boundaries and made the mega-city region. It also brings great challenges to the sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) has been widely used to simulate the change of spatial features, i.e., land-use, population, economy, etc., which foster the spatial planning and policy-making. But they focus on one feature thus ignore their inter-wined influences. This study proposes the spatial cooperative simulation (SCS) approach to simulate the land use-population-economy changes in the megacity region. CA is used to forecast the spatial process of one feature. The interactions among multiple features are represented by taking one feature as the dynamic driving factor. The CA model is iteratively trained to capture the cooperative influence of multiple features. The train process will be repeated until the total errors is converged.

In 2024, the total land area of the Guangdong - Hong Kong - Macao Greater Bay Area cities amounted to around ****** square kilometers. The land area of Zhaoqing alone was nearly ****** square kilometers, making it the largest city by area in the region. In terms of population size, however, Zhaoqing is one of the smaller cities in the Greater Bay Area.

Abstract

The Greater Bay Area Cancer Registry (GBACR), in compliance with California state law, gathers information about all cancers diagnosed or treated in a nine-county area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa...

Documentation

PHS does NOT host these data. This listing is information only.

The Greater Bay Area Cancer Registry (GBACR), in compliance with California state law, gathers information about all cancers diagnosed or treated in a nine-county area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa Clara and Santa Cruz). This information is obtained from medical records provided by hospitals, doctors\342\200\231 offices, and other related facilities.

The information, stored under secure conditions with strict regulations that protect confidentiality, helps the GBACR understand cancer occurrence and survival in the Greater Bay Area. For each patient, the information includes basic demographic facts like age, gender, and race/ethnicity, as well as cancer type, extent of disease, treatment and survival. Combined over the diverse Bay Area population, this information gives the GBACR and all users an opportunity to learn how such characteristics may be related to cancer causes, mortality, care and prevention.

In addition to its local use, information collected by the GBACR becomes part of state and federal population-based registries whose mission is to monitor cancer occurrence at the state and national levels, respectively. Data from the GBACR have contributed to the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) program since 1973. The nine counties are also part of the statewide California Cancer Registry (CCR), which conducts essential monitoring of cancer occurrence and survival in California.

GBACR data are of the highest quality, as recognized by national and international registry standard-setting organizations, including SEER, the National Program for Cancer Registries, and the North American Association for Central Cancer Registries (NAACCR).

The CPIC has also started collecting data on environmenal factors. These data are available in the The California Neighborhoods Data System. This a new resource for examining the impact of neighborhood characteristics on cancer incidence and outcomes in populations includes a compilation of existing geospatial and other secondary data for characterizing contextual factors

A summary and description of social and built environment data and measures in the California Neighborhoods Data System (2010) can be found here: Social and Built Environment Data and Measures

More information about this new data source can be found here: The California Neighborhoods Data System

Patient characteristics All reported cancer cases in the state of California.

Data overview Data categories Socioeconomic status Racial/ethnic composition Immigration/acculturation characteristics Racial/ethnic residential segregation Population density Urbanicity (Rural/Urban) Housing Businesses Commuting Street connectivity Parks Farmers Markets Traffic density Crime Tapestry Segmentation

Notes To apply for these data, you can see instructions here: https://www.ccrcal.org/retrieve-data/data-for-researchers/how-to-request-ccr-data/

This study used both temperature-humidity and wind efficiency indices at three time-scale resolutions (year, season, and month) for the first time, to analyze the spatio–temporal evolution of urban climate comfort in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). The main factors affecting human-settlement climate comfort were elucidated and the annual changes in both indices used in the study area exhibited fluctuating growth from 2005 to 2020. Moreover, the annual growth of the temperature-humidity and wind efficiency indices in the southern cities of the GBA was relatively fast. In contrast, the annual growth of these indices in the northern cities of the GBA was relatively slow. Overall, the climate of the human-settlement environments in the GBA was the most comfortable in spring and autumn, and summer and winter were characterized by hot and cold climate conditions, respectively. We did not identify any prominent change in the climate comfort of spring and autumn from 2005 to 2020; however, the climate comfort degree deteriorated in summer and ameliorated in winter. On a monthly scale, the human-settlement environments in the GBA were the coldest in December and the hottest in July. The urban human settlements were cold in January and February, hot in May, June, August, and September, and the most comfortable in March, April, October, and November in 2020. We analyzed the factors affecting the climate comfort of human-settlement environments in the study area and found that elevation, gross industrial production, population scale, and construction land area were the most influential parameters. Notably, the impact of natural factors on the climate comfort of human-settlement environments was more significant than that of anthropogenic factors. Moreover, the related factors affected the temperature-humidity index more strongly than the wind efficiency index. Overall, our results provide data-driven guidelines for improving the climate comfort of urban human settlements in the GBA.

We took nine cities belonging to Guangdong Province in Guangdong-Hong Kong-Macao Greater Bay Area as an example. Through the collection of statistical data, Weibo user behavior data and DMSP/OLS data, the identification system of shrinking city composed of 45 indexes from the four dimensions of economy, population, spatial geography and administration, is preliminarily established.

By 2035, nearly ** million people are predicted to call Guangzhou home. As one of the key cities in the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou’s vibrancy is very attractive to people searching for their opportunities there.

Megacity – Guangzhou

As China’s cities become increasingly urbanized, the demographic of this megacity has also changed considerably over the years, with more and more Chinese locals and foreigners opting to dwell in Guangzhou for work and cultural opportunities. Together with Beijing, Shanghai and Shenzhen, Guangzhou is listed as one of China’s first-tier cities, indicating its great economic power and developing potential. Guangzhou has been a large port of China for over *** thousand years and has contributed significantly to the economic and cultural exchange between China and the world. Today, the Guangzhou Port is one of the largest in the world.

Multicultural hub

The traces of immigrants from different times to this city can be easily found in Guangzhou’s architecture. In the former colonial area, there are still plenty of old western style buildings. Today’s Guangzhou is one of the Chinese cities with the highest density of skyscrapers in some business areas. The Canton Tower, landmark of Guangzhou, is *** meters tall and the second tallest tower in the world after Tokyo Skytree. In this capital city of the Guangdong province, Cantonese culture is highly respected and well developed. Guangzhou is also one of the Chinese cities with the largest foreign population. Cantonese, Mandarin and English are the widely used languages of the residents in Guangzhou.

Low-carbon economy is not only an important topic for the globe but also a serious challenge for China with its economy entering a new level. Based on the DEA-undesirable model and Malmquist index model, urban agglomeration of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area from 2010 to 2021 were selected as research samples. Based on that, a panel generalized method of moments model was constructed to analyze the effects of the education level, technological development, and their interaction on urban carbon emission efficiency. It found that 1) the carbon emission efficiency of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area urban agglomerations shows a steady growth trend, but the overall level is low and there are regional differences, among which pure technical efficiency mainly limits the improvement of comprehensive efficiency; 2) the education level and technological development have a high positive correlation on urban carbon emission, and their interaction is conducive to the improvement of carbon emission efficiency. The carbon emission efficiency has a significant advantage under the influence of control variables, such as the economic development level, industrial structure upgrading, opening-up degree, and Internet penetration rate. 3) According to the economic dimension and population dimension, the samples of the Yangtze River Delta and the Guangdong–Hong Kong–Macao Greater Bay Area were divided into large cities and small cities, and regression results showed no substantial changes. It shows that the research conclusion is scientific. According to the aforementioned conclusion, this paper puts forward corresponding countermeasures and suggestions.

Seasonal concentration index and Herfindahl index of public attention index in China.

With the rapidly growing population and socioeconomic development of the Guangdong–Hong Kong–Marco Greater Bay Area of China, inputs of diverse contaminants have rapidly increased. This poses threats to the water quality of the Pearl River Estuary (PRE) and adjacent seas. To provide valuable information to assist the governors, stakeholders, and decision-makers in tracking changes in environmental conditions, daily nowcasts and two-day forecasts from the ecological prediction system, namely the Coupled Great Bay Ecological Environmental Prediction System (CGEEPS), has been setup. These forecast systems have been built on the Coupled Ocean–Atmosphere–Wave–Sediment Transport modelling system. This comprises an atmospheric Weather Research Forecasting module and an oceanic Regional Ocean Modelling System module. Daily real-time nowcasts and 2-day forecasts of temperature, salinity, NO2 + NO3, chlorophyll, and pH are continuously available. Visualizations of the forecasts are available on a local website (http://www.gbaycarbontest.xyz:8008/). This paper describes the setup of the environmental forecasting system, evaluates model hindcast simulations from 2014 to 2018, and investigates downscaling and two-way coupling with the regional atmospheric model. The results shown that though CGEEPS lacks accuracy in predicting the absolute value for biological and biogeochemical environmental variables. It is quite informative to predict the spatio-temporal variability of ecological environmental changes associated with extreme weather events. Our study will benefit of developing real-time marine biogeochemical and ecosystem forecast tool for oceanic regions heavily impact by extreme weathers.

Annual population estimates as of July 1st, by census metropolitan area and census agglomeration, single year of age, five-year age group and gender, based on the Standard Geographical Classification (SGC) 2021.

In 2023, the population of the San Francisco-Oakland-Berkeley metropolitan area in the United States was about 4.57 million people. This is a slight decrease from the previous year, when the population was about 4.58 million people.

Shenzhen is one of the fastest growing cities in China. Based on estimates, the population of Shenzhen is expected to reach over ** million by 2035. This rapidly growing city is attracting an increasing number of young Chinese, who want to start and grow their careers.

Development history of Shenzhen 

Shenzhen is located next to Hong Kong, one of the key financial and business centers of the world.  The city has a short history - Shenzhen wasn’t technically a city until 1979. Now, it is home to the largest economy in China’s Greater Bay Area, surpassing its neighbor Hong Kong. Shenzhen is also called China’s Silicon Valley, since many China’s tech-giants are headquartered there. As a rising financial center, Shenzhen also hosts one of the two Stock Exchanges in Mainland China. The headquarter of China’s leading insurance company Ping An Insurance is in Shenzhen as well.

Immigration to Shenzhen 

Enticed by its fast-developing economy, people from across the whole country have relocated to Shenzhen to take their chances at new job and life opportunities. In its 40-year development, countless migrant workers have contributed to this city’s construction projects and labor-intensive manufacturing production. Many young graduates have found it easier to find a job in Shenzhen compared to other first-tier cities. Promotion opportunities have attracted top talent in many sectors to come to this city. Accordingly, with the rise of population, the cost of housing in Shenzhen has also seen a drastic increase.

Quantifying the temporal variation of wastewater treatment plant (WWTP) discharges is essential for water pollution control and environment protection in metropolitan areas. This study develops an ensemble machine learning (ML) model to predict discharges from WWTPs and to quantify the contribution of extraneous water (mixed precipitation and infiltrated groundwater) by leveraging the power of ML and population migration big data. The approach is applied to predict the discharges at 265 WWTPs in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. The major conclusions are as follows. First, the ensemble ML model provides an efficient and reliable way to predict WWTP discharges using data easily accessible to the public. The predicted treated sewage amount increased from 20.4 × 106 m3/day in 2015 to 24.5 × 106 m3/day in 2020. Second, the predictors, including daily precipitation, average precipitation of past proceeding days, daily temperature, and population migration, play different roles in predicting different city’s discharges. Finally, mixed precipitation and infiltrated groundwater account for, on average, 1.6 and 10.3% of total discharges from WWTPs in the GBA. This study represents the first attempt to bring population migration big data into data-driven environmental engineering modeling and can be easily extended to predict other environmental variables of concern.