The document compares the age and sex structure of Uttar Pradesh and Kerala based on 2011 Census data. It finds that Kerala has higher sex ratios, lower youth dependency, and an older population compared to Uttar Pradesh. Over time, both regions showed a transition to lower fertility and mortality, though Kerala transitioned earlier and more rapidly as indicated by its older population structure in 2011.
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Our concern is with the sex ratio in infancy and childhood, and we use this in order to examine the magnitude and implications
of gender imbalance. More precisely, our focus in this paper is on the sex ratio (defined as the number of males per 100 females) from birth to 6 years of age—we shall refer to it simply as the child sex ratio (CSR). The narrowness of our focus has two advantages. First, whereas the overall population sex ratio is a complex aggregate that depends on many factors, the natural determinants of the child sex ratio are more limited, allowing us a cleaner analysis. Second, it is this ratio that is liable to be affected by selective abortions, whereas the population sex ratio moves only a little with these new developments.
Nelson's dominant and distinctiveness FunctionNazrul Islam
This is an explanation of urban functional classification based on arithmetic mean and standard deviation as proposed by Howard Nelson in citing proper practical examples.
Mining, cleaning and sorting through open data is difficult. That's why we've done the work for you. Here are our insights and analyses on one of the world's largest data collection efforts – the 2011 Indian Census.
I’m professional presentation maker . These presentations are for sale for 20$ each, if required you can contact me on my gmail id bestpptmaker@gmail.com and you can also suggest me topics for your required presentations
Our concern is with the sex ratio in infancy and childhood, and we use this in order to examine the magnitude and implications
of gender imbalance. More precisely, our focus in this paper is on the sex ratio (defined as the number of males per 100 females) from birth to 6 years of age—we shall refer to it simply as the child sex ratio (CSR). The narrowness of our focus has two advantages. First, whereas the overall population sex ratio is a complex aggregate that depends on many factors, the natural determinants of the child sex ratio are more limited, allowing us a cleaner analysis. Second, it is this ratio that is liable to be affected by selective abortions, whereas the population sex ratio moves only a little with these new developments.
Nelson's dominant and distinctiveness FunctionNazrul Islam
This is an explanation of urban functional classification based on arithmetic mean and standard deviation as proposed by Howard Nelson in citing proper practical examples.
Mining, cleaning and sorting through open data is difficult. That's why we've done the work for you. Here are our insights and analyses on one of the world's largest data collection efforts – the 2011 Indian Census.
Regional disparity in India - Animated
Regional disparity in India ,regional disparity and planning ,geography ,rich and poor ,development in india ,india ,developing country ,equity ,equilibrium ,disparity ,environmental geography ,human resources
THE POPULATION CENSUS IN INDIA is a main topic in indian demography..this ppt contains basic information regarding indian census...
it was presented & uploaded by:
MANOJKUMAR A
1st m.tech urban & regional planning..
IDS MANASAGANGOTHRI , MYSORE, KARNATAKA
DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHAKamlesh Kumar
Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness.
Tribal sub plan and Special Component PlanApurv Vivek
The Sub-Plan as presented in the summary is unique in many respects. It is mentioned in the preface that 'through the planning process was initiated about 25 years ago in the country, the rate of economic development of all the weaker sections of the community including the Adivasis has been extremely low in spite of special programmes for them'. The Sub-Plan proposed an allocation of Rs 130 crores for tribal areas in different districts.
By going through this presentation, students will be able to understand the meaning and derivation of the word 'Geography', definition of 'Geography' as a discipline and the plurality character of Geography
Population distribution, density, growth and compositionharsh raj
THIS IS MY FIRST POWER POINT. I THINK IT IS VERY HELPFUL FOR YOU. IT LOOKS LOOK GREAT AND ATTRACTIVE. IT ATTRACTS YOU.
THANK YOU AND FOLLOW AND LIKE PLEASE ..
The Presentation contains all the details related to Child Labour in India. The causes of Child Labour to the Forward steps that need to be taken to prevent child labour.
The presentation also details about a very well known NGO - Bachpan Bachao Andolan which is working on preventing Child Labour since ages.
- Ashmita Nahar
Regional disparity in India - Animated
Regional disparity in India ,regional disparity and planning ,geography ,rich and poor ,development in india ,india ,developing country ,equity ,equilibrium ,disparity ,environmental geography ,human resources
THE POPULATION CENSUS IN INDIA is a main topic in indian demography..this ppt contains basic information regarding indian census...
it was presented & uploaded by:
MANOJKUMAR A
1st m.tech urban & regional planning..
IDS MANASAGANGOTHRI , MYSORE, KARNATAKA
DEMOGRAPHIC PROFILE OF CONTINENTAL ODISHAKamlesh Kumar
Although the state is endowed with vast natural resources it has remained on the bottom of the developmental chart of the nation. With such a reserve of natural resources and human resource potential, it is like a hibernating beast which must awake for good. Stealing the limelight of the most favourable smart city, the capital is growing like never before along with a few more cities. Yet the state remains mostly rural and lagging in most aspects except for the coastal regions. My analysis is that the state has not been given its due attention in planning which is the reason for its present backwardness.
Tribal sub plan and Special Component PlanApurv Vivek
The Sub-Plan as presented in the summary is unique in many respects. It is mentioned in the preface that 'through the planning process was initiated about 25 years ago in the country, the rate of economic development of all the weaker sections of the community including the Adivasis has been extremely low in spite of special programmes for them'. The Sub-Plan proposed an allocation of Rs 130 crores for tribal areas in different districts.
By going through this presentation, students will be able to understand the meaning and derivation of the word 'Geography', definition of 'Geography' as a discipline and the plurality character of Geography
Population distribution, density, growth and compositionharsh raj
THIS IS MY FIRST POWER POINT. I THINK IT IS VERY HELPFUL FOR YOU. IT LOOKS LOOK GREAT AND ATTRACTIVE. IT ATTRACTS YOU.
THANK YOU AND FOLLOW AND LIKE PLEASE ..
The Presentation contains all the details related to Child Labour in India. The causes of Child Labour to the Forward steps that need to be taken to prevent child labour.
The presentation also details about a very well known NGO - Bachpan Bachao Andolan which is working on preventing Child Labour since ages.
- Ashmita Nahar
A Discourse on Gender Disparity: A Study on Taluks of Belagavi Districtijtsrd
The modern period witnessed the increased gender disparity reflected in sex-ratio, literacy and education, employment and wage-rates and several other socio-cultural and behavioral indicators of empowerment.(Nangia, 2005) Gender equality is more than a goal in itself. It is a precondition for meeting the challenges of reducing poverty, promoting sustainable development and building good governance -Kofi Annan (Personal, Archive, Mahanta, & Nayak, 2013). The present paper examines the extent of gender disparity in Belagavi District based on literacy and sex ratio using secondary data. We found that literacy rate in study area was 76.93 % in 2001 which is increased to 82.90 % in 2011 and sex ratio in the study area was 960 in 2001 which is increased to 973 in 2011. There are wide disparities from Rural to Urban sex ratio as well as Rural to Urban literacy rate. The urban sex ratio is higher than rural sex ratio in study area. The Rural sex ratio is 970 and urban sex ratio is 979 females per thousand males in the 2011. We found that in Belagavi district, there is reduction in gender disparities from 2001 to 2011 but the reduction rate is very slow. Manjunatha N K | Dr. S M Hurakadli"A Discourse on Gender Disparity: A Study on Taluks of Belagavi District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2437.pdf http://www.ijtsrd.com/humanities-and-the-arts/geography/2437/a-discourse-on-gender-disparity-a-study-on-taluks-of-belagavi-district/manjunatha-n-k
Patterns of Gender Disparities in Belagavi Districtijtsrd
Gender Disparity hold back the growth of individuals the development of nation s and the evolution of societies to the disadvantages of both women and men. Inequality between men and women is one of the most crucial disparities in many societies, and this is particularly so in India. On one level, gender disparity can be narrowly defined as the purely descriptive observation of different outcomes between males, and females. It contained in sex ratio, literacy, workforce, and education and wage rates and several other socio cultural and behavioral indicators of empowerment Nangia 2005 . .The present paper examines the extent of gender disparity in Belagavi District based on literacy and sex ratio using secondary data. We found that literacy rate in study area was 82.90 in 2011 which is increased to 89.82 in 2021 as per census of 2011 male 93.78 and female 85.84 and sex ratio in the study area was 973 in 2011 which is increased to 988 in 2021. There are wide disparities from Rural to Urban sex ratio as well as Rural to Urban literacy rate. The urban sex ratio is higher than rural sex ratio in study area. The Rural sex ratio is 970 and urban sex ratio is 973 females per thousand males in the 2021. We found that in Belagavi district, there is reduction in gender disparities from 2011 to 2021 but the reduction rate is very slow. Shashikala G. Hande | Dr. S. M. Hurakadli "Patterns of Gender Disparities in Belagavi District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4 , June 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50321.pdf Paper URL: https://www.ijtsrd.com/humanities-and-the-arts/geography/50321/patterns-of-gender-disparities-in-belagavi-district/shashikala-g-hande
Dr. Jean Dreze and Reetika Khera in 2012 computed Human Development Index for 20 Major Indian states using the then available latest data. HDI that they developed was combined with its variant that addressed the status of Children in the 0-5 years age group specifically. Their findings were published in Economic and Political Weekly.
Employment Scenario of Lakshadweep: An Overview
Dr. Abdul Azeez N.P.
Assistant Professor of Economics,
Aligarh Muslim University Malappuram Centre abdulazeeznp@gmail.com
Demography addresses human populations as population per se, that is, their sizes and structures.
It is the scientific study of human population.
Demographic processes :
1. fertility 4. migration &
2. mortality 5. social mobility
3. marriage
As part of our India Marketing course by Mr. Harish Bijoor, here are various insights on Census 2011 based on certain parameters as specified in the course.
Dr Ellina Samantroy's presentation at UNICEF Innocenti's Inception Scoping Workshop for Evidence on Educational Strategies to Address Child Labour in India & Bangladesh, held in New Delhi in November 2019.
This report highlights the data and demographics of scheduled tribes (ST) in 30 states and union territories of India, as documented in Census 2011. The report makes comparisons from 1961 by showing population trends as well as decadal growth rate. It also focuses on the livelihoods of people from scheduled castes and tribes (SC/ST) in rural as well as urban India.
Presently, 705 ethnic groups are notified as STs in the country. In the decade before Census 2011, there were some changes in the lists of STs in states and union territories, and with the addition or deletion of certain tribes, some areas show either an increase or a decline in the numbers.
Similar to Age and Sex Structure of Uttar Pradesh & Kerala: A comparative Study (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
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The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
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The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
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Learn SQL from basic queries to Advance queriesmanishkhaire30
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Age and Sex Structure of Uttar Pradesh & Kerala: A comparative Study
1. AGE & SEX STRUCTURE OF
UTTAR PRADESH & KERALA:
A COMPARATIVE STUDY
Aman, Nandlal, Pankaj & Rahul
International Institute for Population Science (IIPS)
Govandi Station Road, Deonar, Mumbai-400088
2. Compare the age and sex structure of Uttar Pradesh and Kerala.
How they differ from each other? Compute the various measures
of age structure and compare them.
Outline:
Introduction
Part 1: Current Age & Sex Specific Demographic Profile
of Uttar Pradesh and Kerala
Part 2: Comparative analysis of current Age-Sex Specific
Demography of Uttar Pradesh, Kerala and India
Part 3: Transition over time: A comparative analysis
Conclusion
3. CURRENT AGE & SEX SPECIFIC
DEMOGRAPHIC PROFILE OF UP & KERALA
Source: Census 2011
Part-1
4. Total Population: 199,812,341 ≈ 20 Crore
(16.5% of India’s Total Population)
Male Popln
: 104,480,510 | Female Popln
: 95,331,831
Sex Ratio*: 912
(Rural: 918 | Urban: 894)
Child Sex Ratio*: 902
(Rural: 906 | Urban: 885)
Sex Ratio at Birth*: 897
(Rural: 902 | Urban: 877)
_____________________________________
*Females per 1000 males
Uttar Pradesh: Sex Ratios
52.3%
47.7%
Male Female
7. Median Age: 21.5 years ≈ 22 years
Dependency Ratio (DR): 69.18
Old DR: 8.30
Young DR: 60.87
Ageing Index: 14
Potential Support Ratio: 12
Proportion of Youth: 21%
Uttar Pradesh: Age Measures
36%
59%
5%
Children Working Age Elderly
____________________________
*Children: 0 to 14; Youth: 15 to 24; Working Age: 15 to 64; Elderly: 65+
8. Total Population: 33,387,677 ≈ 3.3 Crore
(2.8% of India’s Total Population)
Male Popln: 16,021,290 | Female Popln: 17,366,387
Sex Ratio*: 1084
(Rural: 1078 | Urban: 1091)
Child Sex Ratio*: 964
(Rural: 965 | Urban: 963)
Sex Ratio at Birth*: 978
(Rural: 981 | Urban: 974)
_____________________________________
*Females per 1000 males
Kerala: Sex Ratios
48.0%
52.0%
Male Female
11. Median Age: 32 years
Dependency Ratio (DR): 46.6
Old DR: 12.2
Young DR: 34.4
Ageing Index: 35.5
Potential Support Ratio: 8.2
Proportion of Youth: 16%
Kerala: Age Measures
24%
68%
8%
Children Working Age Elderly
____________________________
*Children: 0 to 14; Youth: 15 to 24; Working Age: 15 to 64; Ealderly: 65+
13. Population Distribution by Sex
52.3%
47.7%
Uttar Pradesh
(2011)
Male Female
48.0%
52.0%
Kerala (2011)
Male Female
51.5%
48.5%
India (2011)
Male Female
14. Sex Ratio*
912 918
894
943 949
929
1084 1078 1091
800
850
900
950
1000
1050
1100
1150
Sex Ratio
(Total)
Sex Ratio
(Rural)
Sex Ratio
(Urban)
Uttar Pradesh India Kerala
____________________________
*Females per 1000 males
15. Child Sex Ratios* (CSR)
902 906
885
919 923
905
964 965 963
800
850
900
950
1000
1050
1100
CSR (Total) CSR (Rural) CSR (Urban)
Uttar Pradesh India Kerala
____________________________
*Females per 1000 males
16. Sex Ratios at Birth* (SRB)
897 902
877
910 912 905
978 981 974
800
850
900
950
1000
1050
1100
SRB (Total) SRB (Rural) SRB (Urban)
Uttar Pradesh India Kerala
____________________________
*Females per 1000 males
17. Population by Broad Age-groups
36%
59%
5%
21%
31%
64%
5%
19%
24%
68%
8%
16%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Children Working Age Elderly Youth
Uttar Pradesh India Kerala
____________________________
*Children: 0 to 14; Youth: 15 to 24; Working Age: 15 to 64; Elderly: 65+
22. Other Age Measures
Indicators Kerala India U.P.
Median Age 32 25 22
Potential Support Ratio 8 11 12
Ageing Index 35 18 14
Life Expectancy 75 68 65
____________________________
*Potential Support Ratio= P(15-64)/P(65+)
*Aging Index= =100* P(65+)/P(0-14)
23. TRANSITION OVER TIME: A COMPARATIVE
ANALYSIS
Sources: Census of India & Demographics of Population Ageing
in India
Part-3
24. Child Sex Ratios* (CSR)
927
916
902
945
927
919
958 963 964
800
850
900
950
1000
1991 2001 2011
Uttar Pradesh India Kerala
____________________________
*Females per 1000 males
26. ‘0 to 14’ Age-group Population
10
15
20
25
30
35
40
45
50
1961 1971 1981 1991 2001 2011 2021
InPercentage(%)
Uttar Pradesh India Kerala
Children Population
27. ‘15 to 59’ Age-group Population
45
50
55
60
65
70
1961 1971 1981 1991 2001 2011 2021
InPercentage(%)
Uttar Pradesh India Kerala
Working Age Population
38. Age-Sex structure of Kerala highly resembles to that of the
Developed nations.
Age-Sex structure of Uttar Pradesh highly resembles to that of
the Developing nations.
Among all Indian states Uttar Pradesh and Kerala represents
two opposite extremes for almost all age-sex measures.
Conclusion
Thank You