Graph and download economic data for Crude Birth Rate for the United States (SPDYNCBRTINUSA) from 1960 to 2023 about birth, crude, rate, and USA.

Dilihat dari tingkat kelahiran kasar (Crude Birth Rate - CBR) yang merupakan jumlah anak yang dilahirkan per 1.000 orang penduduk, tercatat CBR Kecamatan Karanganyar tahun 2014 adalah 14,10. Sedang menurut tingkat kematian kasar (Crude Death Rate - CDR) yang merupakan jumlah kematian per 1.000 orang penduduk, maka CDR Kecamatan Karanganyar pada tahun yang sama adalah 7,40. Angka CBR naik dibanding tahun 2013, di mana angka kelahiran kasar sebesar 13,85. Sedangkan angka CDR juga lebih tinngi dibanding tahun sebelumnya (2013), yaitu sebesar 6,47. Rasio anak terhadap wanita usia 15-49 tahun (Child Women Ratio - CWR) Kecamatan Karanganyar

Graph and download economic data for Crude Birth Rate for Ireland (SPDYNCBRTINIRL) from 1960 to 2023 about Ireland, birth, crude, and rate.

This Dataset provides comprehensive demographic information on global populations from 1950 to the present. It offers insights into various aspects of population dynamics, including population counts, gender ratios, birth and death rates, life expectancy, and migration patterns.

Column Descriptions:

SortOrder: Numeric identifier for sorting.

LocID: Location identifier.

Notes: Additional notes or comments (blank in this dataset).

ISO3_code: ISO 3-character country code.

ISO2_code: ISO 2-character country code.

SDMX_code: Statistical Data and Metadata Exchange code.

LocTypeID: Location type identifier.

LocTypeName: Location type name.

ParentID: Identifier for the parent location.

Location: Name of the location.

VarID: Identifier for the variant.

Variant: Type of population variant.

Time: Year or time period.

TPopulation1Jan: Total population on January 1st.

TPopulation1July: Total population on July 1st.

TPopulationMale1July: Total male population on July 1st.

TPopulationFemale1July: Total female population on July 1st.

PopDensity: Population density (people per square kilometer).

PopSexRatio: Population sex ratio (male/female).

MedianAgePop: Median age of the population.

NatChange: Natural change in population.

NatChangeRT: Natural change rate (per 1,000 people).

PopChange: Population change.

PopGrowthRate: Population growth rate (percentage).

DoublingTime: Time for population to double (in years).

Births: Total number of births.

Births1519: Births to mothers aged 15-19.

CBR: Crude birth rate (per 1,000 people).

TFR: Total fertility rate (average number of children per woman).

NRR: Net reproduction rate.

MAC: Mean age at childbearing.

SRB: Sex ratio at birth (male/female).

Deaths: Total number of deaths.

DeathsMale: Total male deaths.

DeathsFemale: Total female deaths.

CDR: Crude death rate (per 1,000 people).

LEx: Life expectancy at birth.

LExMale: Life expectancy for males at birth.

LExFemale: Life expectancy for females at birth.

LE15: Life expectancy at age 15.

LE15Male: Life expectancy for males at age 15.

LE15Female: Life expectancy for females at age 15.

LE65: Life expectancy at age 65.

LE65Male: Life expectancy for males at age 65.

LE65Female: Life expectancy for females at age 65.

LE80: Life expectancy at age 80.

LE80Male: Life expectancy for males at age 80.

LE80Female: Life expectancy for females at age 80.

InfantDeaths: Number of infant deaths.

IMR: Infant mortality rate (per 1,000 live births).

LBsurvivingAge1: Children surviving to age 1.

Under5Deaths: Number of deaths under age 5.

NetMigrations: Net migration rate (per 1,000 people).

CNMR: Crude net migration rate.

How to Use the Dataset:

1. Researchers can analyze demographic trends, birth and death rates, and population growth over time.
2. Policymakers can use population data to inform decisions on healthcare, education, and social services.
3. Data scientists can visualize and model population dynamics for various regions.
4. Journalists can use the dataset to report on global population trends and disparities.
5. Educators can incorporate real-world population data into lessons and research.

Please upvote and show your support if you find this dataset valuable for your research or analysis. Your feedback and contributions help make this dataset more accessible to the Kaggle community. Thank you!

In this project I will concentrate on some important factors that will affect humanity's potential to survive on planet Earth: - Global Demographic Shifts. - Inequality. - Climate change. - Resource depletion.

I chose the following countries from the data (HNP_StatsData.csv), for the bulk of the project. However, the data contains many more countries.

Australia, Belgium, Canada, China, Denmark, France, Germany, India, Italy, Japan, Mexico, Morocco, Russian Federation, South Africa, Spain, Switzerland, United Kingdom, United States.

1. Global Demographic Shifts:

First I decided to look at the Crude Birth Rate (CBR) for the above countries, for the years 1961-2021.

The crude birth rate is the number of live births occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. It is called "crude" because it does not take into account age or gender differences within the population.

Formula: CBR = Midyear population / Number of births in a year × 1000

Visualisation of CBR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F011e9ff704f24c74ab2393cdd66e8ee2%2FScreenshot%202023-09-23%2014.50.36.png.jpg?generation=1695508413958340&alt=media" alt="">

Next I looked at CDR for the above countries, for the years 1961-2021. The crude death rate is the number of deaths occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. Like the CBR, it is called "crude" because it doesn't consider the age or gender differences within the population.

Formula: CDR = Midyear population / Number of deaths in a year × 1000

Visualisation of CDR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fe79ff544bff129ec2193471f9e6b4480%2FScreenshot%202023-09-23%2014.55.38.png.jpg?generation=1695510070529372&alt=media" alt="">

Both these rates (CBR & CDR) are basic demographic indicators that give a general overview of the demographic situation in a country or region. They do offer a broad understanding of birth and death patterns.

A high CDR in a particular year or time period can be influenced by various factors, including epidemics, famines, natural disasters, wars, and social and economic changes.

Let's look at China and Morocco during the 1960s:

• The Great Chinese Famine (1959-1961): This is arguably the main reason for the high CDR in China around 1960. This devastating famine was the result of a combination of social, political, and natural factors. The "Great Leap Forward" campaign (1958–1962), initiated by Mao Zedong, aimed to transform China from an agrarian society into an industrial socialist society. One of the policy implementations was the formation of people's communes, which resulted in the collectivisation of agriculture. The government's over-reporting of grain production and subsequent excessive grain requisition, coupled with poor weather conditions and pestilence, led to widespread food shortages. The resultant famine caused the deaths of millions of Chinese people.
• Public health issues: Prior to the significant public health reforms that would later be implemented in the 1960s and 1970s, China grappled with various diseases that could have contributed to a high CDR.
• Tuberculosis (TB): Throughout the early to mid-20th century, TB was a major public health concern in China. Malnutrition, poor living conditions, and lack of access to medical treatment contributed to the spread of this bacterial disease.
• Schistosomiasis: This parasitic disease, caused by flatworms and transmitted by freshwater snails, was and still is endemic in certain regions of China. Chronic infection can lead to liver damage, kidney failure, and other complications.
• Malaria: While major strides have been made in controlling malaria in China in recent decades, in the 1960s, the disease was still prevalent in many regions of the country.
• Other diseases: Cholera, typhoid, and dysentery were also concerns in certain areas, especially given the combination of poor sanitation, water contamination, and food shortages.

Morocco in 1960: - Colonial legacy and independence: Morocco achieved independence from France and Spain in 1956. The post-independence years were marked by political instability, which can indirectly impact public health, food security, and other factors related to the death rate. - Economic conditions: Morocco, being a primarily agrarian society during that period, was vulnerable to fluctuations in agricultural output. Poor harvests due to droughts, pests, or other factors could impact food availability and lead to higher death rates. - Public health: Like many developing nations ...

CBR Dan CDR Kecamatan Mijen 2019

angka kelahiran dan kematian kasar (CBR dan CDR) diRinci menurut Desa di Dempet 2013

angka kelahiran dan kematian kassar (CBR dan CDR) 2019

ANGKA KELAHIRAN & KEMATIAN KASAR (CBR & CDR) DIRINCI MENURUT DESA DI KECAMATAN MRANGGEN TAHUN 2018

ANGKA KELAHIRAN & KEMATIAN KASAR (CBR & CDR) DIRINCI PER DESA DI KECAMATAN DEMAK 2019

Angka Kelahiran dan Kematian Kasar (CBR & CDR) dirinci per desa di Kecamatan Kebonagung tahun 2017 Nama Desa Jumlah Kelahiran 1 tahun Jumlah Kematian 1 tahun Penduduk Pertengahan Tahun CBR CDR

Dataset ini berisi tentang Banyaknya Lahir, Mati, Datang, Pindah, CBR,CDR dan Pertambahan Penduduk Alami di Kecamatan Doro

Angka Kelahiran dan Kematian Kasar (CBR & CDR ) Menurut Desa di Kecamatan Karanganyar Tahun 2014 yaitu sejumlah 985 Kelahiran /Tahun dan 517 orang Kematian / Tahun

Angka Kelahiran dan Kematian (CBR, CDR dan IMR) dirinci menurut Desa di Kecamatan Karanagtengah Kabupaten Demak Tahun 2019

Dataset ini berisi tentang Banyaknya Kelahiran dan Kematian CBR, CDR dan Pertumbuhan Alami di Kecamatan Wonopringgo

Dataset ini berisi tentang Banyaknya Kelahiran dan Kematian CBR, CDR, dan Pertumbuhan Alami Desa di Kecamatan Wiradesa

Dataset ini berisi tentang banyaknya lahir,mati,datang,pindah,CBR,CDR dan Pertambahan Alami di Kecamatan Wonokerto

Dataset ini berisi Banyaknya Lahir Mati Datang Pindah CBR CDR dan Pertambahan Penduduk Alami Kec Bojong

Graph and download economic data for Crude Birth Rate for Nigeria (SPDYNCBRTINNGA) from 1960 to 2023 about Nigeria, birth, crude, and rate.

Dataset ini berisi Banyaknya Lahir, Mati, Datang, Pindah, CBR, CDR dan Pertambahan Penduduk Alami di Kecamatan Paninggaran