Positioning for the future: Trends in technology, big data, 2020 round of censuses and post 2015 development agenda



Similar documents
GOVERNMENT OF INDIA MINISTRY OF STATISTICS AND PROGRAMME IMPLEMENTATION CENTRAL STATISTICS OFFICE 8 Shravana, 1936 Dated the 30 th July 2014

LGD Impact on PRIASoft

Qatar National Geospatial Infrastructure

Functions and Activities of National Statistical Systems Training academy

Lessons Learned from MDG Monitoring From A Statistical Perspective

Sharthi Laldaparsad. Statistics South Africa Executive Manager: Policy Research & Analysis. 1 August 2014, Washington DC, USA. Contents...

A Sub-Scheme under AMRUT

Results of the survey:-

TANZANIA - Agricultural Sample Census Explanatory notes

INDIVIDUAL COURSE DETAILS

Transparent Chennai. Best Practice Documentation June Researched and Documented By. OneWorld Foundation India

Teaching and Training of Agricultural Statistics

Prevalence and Factors Affecting the Utilisation of Health Insurance among Families of Rural Karnataka, India

COUNTRY REPORT GEOSPATIAL INFORMATION MANAGEMENT IN NEPAL

Ronald Jansen, Karoly Kovacs, Luis González Trade Statistics Branch United Nations Statistics Division

Gram Panchayat & Ward Boundary Delineation and Assets mapping

DIGITIZATION NATIONAL POPULATION REGISTER TRAINING MANUAL (ADMINISTRATOR) 2011

Citizen participation has become an increasingly

Online Integrated Information System For Demography In Nigeria Based On Browser- Server Structure

Government of Nagaland Department of Personnel & Administrative Reforms ( Administrative Reforms Branch )

The Terms of reference (ToR) for conducting Rapid EIA study for the proposed project is described below:

Review of Best Practice in Road Crash Database and Analysis System Design Blair Turner ARRB Group Ltd

Big Data for Official Statistics The 2030 Agenda for Sustainable Development

7. ASSESSING EXISTING INFORMATION SYSTEMS AND INFORMATION NEEDS: INFORMATION GAP ANALYSIS

Socio-Economic and Caste Census

CHAPTER 6 CODING PROCEDURE

Towards an E-Governance Grid for India (E-GGI): An Architectural Framework for Citizen Services Delivery

Geographical Information Systems with Remote Sensing

Making Geospatial Data Available and Accessible in Jamaica

POLICY FOR WEBSITE DEVELOPMENT, HOSTING AND MAINTENANCE Department of Information Technology GOVERNMENT OF PUNJAB

No. J-11011/2/2008-NREGA Government of India Ministry of Rural development NREGA Division

Agenzia del Territorio - Largo Leopardi, Roma - THE ITALIAN CADASTRAL SYSTEM MANAGING BODY AGENZIA DEL TERRITORIO

Strategic Plan of Ministry of Statistics and Programme Implementation

* Note: a cadastral system is the complete coverage of a country for purposes like taxation, whereas a land registration system is exclusively

NREGAsoft : Strengthening National Rural Employment Guarantee Scheme (NREGS) ( implementation

Concepts and Definitions Used in NSS

DISTRIBUTIVE TRADE STATISTICS IN INDIA

3.0 Results and Discussions

MONITORING INFORMATION SYSTEM

Terms of Reference Concurrent Monitoring of Mid Day Meal (MDM) in Odisha

FORM 6 [See rules 13(1) and 26] Application for inclusion of name in electoral roll

Internet Connectivity Among Aboriginal Communities in Canada

communication tower means a tower or structure built to support equipment used to transmit communication signals;

Important Issues on Ageing in India Recommendations To Planning Commission- Will social improvements for elderly grow by 8 %?

THE LAW ON NATIONAL COUNCILS OF NATIONAL MINORITIES I. GENERAL PROVISIONS. Article 1

AADHAAR E-KYC SERVICE

Guidance for Flood Risk Analysis and Mapping. Changes Since Last FIRM

Coventry Development Plan 2016 Appendix 89. Glossary of Key Terms

2016 State Data Center Annual Training Conference April 4-8, 2016 U.S. Census Bureau Headquarters

CHAPTER 7 LOCAL GOVERNMENT

Walk in Interview for the empanelment of State Master Trainers

A GIS helps you answer questions and solve problems by looking at your data in a way that is quickly understood and easily shared.

Internal Migration and Regional Disparities in India

CRVS and Identity Management

Enterprise Geographic Information System for Bangalore City

Press Note on Poverty Estimates,

Registrar On-Boarding Process

CHAPTER VI ON PRIORITY SECTOR LENDING

Background Paper for formulation of National Data Sharing and Accessibility Policy (NDSAP)

SYLLABUS FOR RECRUITMENT TEST FOR THE POST OF ASSISTANT TOWN PLANNER (GROUP-A) IN TOWN & COUNTRY PLANNING DEPARTMENT, HARYANA

Five-Year Strategic Plan ( ) HEALTH INFORMATION SYSTEM MYANMAR

Vital Events Registration as an Effective Tool to Monitor Social Security Cash Transfer in Nepalese Context

Policy and Regulations Faridabad (India)

EFFECTIVE SCHOOL MANAGEMENT COMMITTEES

Knowledge Management policy for Health - Service, Education and Research

ANUKRET ON ORGANIZATION AND FUNCTIONING OF THE MINISTRY OF LAND MANAGEMENT, URBANIZATION AND CONSTRUCTION

CROATIAN BUREAU OF STATISTICS REPUBLIC OF CROATIA MAIN (STATISTICAL) BUSINESS PROCESSES INSTRUCTIONS FOR FILLING OUT THE TEMPLATE

Globalization and Global Food Crises: The Role of Official Statistics in African Context

Innovative Mobile Technologies improving health in developing countries. Professor Kristin Braa Department of Informatics University of Oslo

Consumer Price Index Numbers - Separately for Rural and Urban Areas and also Combined (Rural plus Urban)

Geographic Information Systems

User Manual For MIS Helpdesk

Paper Title: Ubiquitous and Integrated Portfolio management of Federal & State projects

Demography. Focus on the three contributors to population change: Fertility, mortality, and migration

From Cadastre to Land Governance

Introduction to GIS (Basics, Data, Analysis) & Case Studies. 13 th May Content. What is GIS?

The Northwest Arkansas Travel Demand Model

Performance Management Through E-Government

TERMS OF REFERENCE TO DEVELOP THE MANAGEMENT INFORMATION SYSTEM AND PROVIDE TECHNICAL SUPPORT FOR THE CONDITIONAL CASH TRANSFER PROGRAM IN BANGLADESH

REDEFINING POVERTY LINES AND SURVEY OF BPL FAMILIES. ( Rural Areas)

PRIVATE MEDICAL PRACTITIONERS ASSOCIATION

Country Paper: Automation of data capture, data processing and dissemination of the 2009 National Population and Housing Census in Vanuatu.

Asset Management. Framework and Guidelines. Part 2 - Asset Management Guidelines for Western Australian Local Governments p1.

REVISED SCHEME OF FINANCIAL ASSISTANCE FOR APPOINTMENT OF LANGUAGE TEACHERS AFTER INCORPORATING THE PROPOSED MODIFICATIONS

Transcription:

Positioning for the future: Trends in technology, big data, 2020 round of censuses and post 2015 development agenda Tarak Chandra Patra, ISS Data Processing Division, National Sample Survey Office Ministry of Statistics & Programme Implementation Government of India

Statistical System in India India has a federal structure of Government As per the Constitution of India Statistics is a subject in the Concurrent list and it is dealt by both Central Govt. and State Govt. The Ministry of Statistics and Programme Implementation (MoSPI) is the nodal agency for planning and facilitating the integrated development of the statistical system in the country NSSO under MoSPI is responsible for conducting large scale socioeconomic sample surveys of national interest NSSO generates estimates which are important for planning & policy making and to measure effects of implementation of different Govt. projects DPD under NSSO works as the processing agency as well as developing software for that including tabulation and sharing its software with State Governments

Organisation chart of NSSO MoSPI CSO NSSO Survey Design & Research Division (SDRD) Field Operations Division (DPD) Data Processing Division (DPD) Co ordination & Publication Division (CPD) 6 Zonal Offices 49 Regional Offices 118 SROs 8 Data Processing Centres

Administrative Units State (36) District (640) Rural Village Panchayats Village (6,40,867) Hamlets Urban Towns (statutory/ census) Electoral wards

NSSO experiences Data are collected by Field Operations Division (FOD) by canvassing hard copy of schedules by visiting selected sample households Sample households are the ultimate stage units and aerial units - Census villages (rural)/ Urban Frame Survey (UFS) blocks (urban) are the First Stage Units These villages/ UFS blocks are selected randomly by using in-house developed software by DPD from the list of census villages/ list of UFS blocks Maintenance and updation of frame is one of the most important process and it is continuous For rural India Census list of Villages forms frame for selection In this context Urban Frame Survey plays a crucial role for urban part of the country

Urban Frame Survey UFS covers the whole Indian union comprising of 7933 cities/towns (4041 statutory and 3892 census towns) as per the Census 2011 Under the UFS, all the towns are physically surveyed and separate UFS blocks (with 80-200 households) and IV units (a group of blocks: 20-50) are carved out for covering the whole geographical area of the town UFS Blocks are updated over a five year period, each process of updation is called as phase Auxiliary information on type of area such as slum area, residential area, industrial area, education, hospital, bazar(market) area, prohibited area etc. collected during the survey/ updation These help in selection of blocks with certain characteristics for the socio economic surveys Landmarks such as school, post office, bus stand, clinic etc. are recorded for each block for the purpose of proper identification The boundaries of the UFS blocks have been frozen

Urban Frame Survey Two notional maps are prepared, one is IV Unit map showing the location of all the blocks included in the Unit Other one is the town map depicting the location and relative positioning of IV units in the town NSSO has initiated the conversion of IV unit maps to GIS framework in a uniform scale with the help of National Informatics Centre (NIC) UFS helps to update both rural and urban frame of survey by incorporating newly declared towns and deleting them from rural part Deurbanised towns are deleted from urban list and included in rural part Metadata of UFS are also available

Urban Frame Survey notional map of a town

Data Processing Division (DPD) Data Processing Division (DPD) is responsible for processing of data collected by FOD in paper schedules Around 1.71 crores of records are processed by DPD in a Quinquennial round for consumption expenditure survey DPD handles data of different subjects every year. So, no standard set of software. However, the data processing system remains more or less the same Data are entered & verified by using in-house developed software of DPD in RDBMS platform using ORACLE as back end database This enables on line validation and checking of data, process has become much easier to control and manage Time frame for finalisation of survey results of unit level data of a round is one year after the end of field work of data collection Various complicated (multivariate tables ranges from 600 or more) are generated by DPD using its own tabulation package DOSTAB

NSSO Initiative In order to reduce the time frame further to release the final unit level data to the users and policy makers and researchers NSSO has been pursuing different experiments so that the basic objective of reducing time can be achieved without compromising its standard quality One of them is the experiment of decentralisation of data entry at FOD It is also being thought of data collection by electronic media using data collection software instead of canvassing paper schedule Main constraint in this regard is length of the NSS schedules and their complexities Experiment of shortening and simplification of schedules of various subjects has been taken up by NSSO

NSSO Initiative As Statistics is in the Concurrent list of Constitution of India, State Governments also participate in NSS surveys One important objective of a sample survey is to generate estimates at lower level of administrative unit such as district or sub-district level for planning and policy making Sample size of NSSO is not enough to generate robust estimate below the state level In order to generate district level estimate pooling of central sample data (done by NSSO) and state sample data (done by various state Governments) is required To meet this requirement DPD has been sharing all its software and technical inputs for processing of their data whereas other branches of NSSO also share their survey instruments with state governments DPD has developed software to test poolability of some important parameters so that estimate can be built for these parameters at district level

Population Census experiences In the 2010-2011 round Census of India implemented three different approaches a traditional paper-schedule-based approach for the Housing and Population Census, a biometric database for the National Population Register, and direct data collection on an electronic platform for the Socio-Economic and Caste Census For use in Census 2011, information on changes in the jurisdiction of the administrative boundaries of 35 States/Union Territories, 640 districts, 5,924 sub-districts, 7,935 towns and 6,40,867 villages were meticulously collected along with official notifications and maps Digitised maps were prepared using latest GIS software. Detailed digital maps of 33 capital cities of the country based on satellite imagery were also prepared These maps show detailed layout of buildings, houses, other structures, road network and important landmarks and were used in Census 2011 Household level information not available

Population Census experiences The Census Schedules were scanned using high speed duplex scanners and information read using ICR technology Use of the ICR technology not only saved time for data capture and data tabulation thus ultimately making it available to the users early, but also was very cost effective saving public money In the National Population Register the digital database that contain certain basic demographic characteristics of every usual resident of India along with 3 biometric attributes - 10 fingerprints, 2 iris prints and photograph It is envisaged that the register would be a dynamic one with linkages to the Civil Registration System (CRS) to ensure that every birth & death would be properly reflected in the electronic database In the Socio Economic and Caste Census (SECC) tablet PCs were used for collection of data where database created during NPR was preloaded The methodology of direct data capture has exciting implications for the future of census in India

MDG framework in India India s MDGs framework is based on the 2003 United Nations Development Goals (UNDGs) guidelines on concepts, definition and methodology of MDGs indicators 53 indicators (48 basic and 5 alternatives) recognized by this framework has been contextualized for India All the 8 Millennium Development Goals, 12 of the 18 targets (target 1 to target 11 and target 18 ) are relevant for India These targets and indicators under the 8 Goals constitute the instrument for statistical tracking of the MDGs in India MoSPI is engaged in the task of statistically tracking the MDGs on the basis of data sets generated by different Ministries/ Departments and information gathered from periodic national surveys and censuses Some of the indicators achieved ahead of given dead line and some are expected to reach closer

Note for the future MDGs have helped in bringing a much needed focus and pressure on basic development issues and in turn led the governments at national and sub national levels to do better planning and implement more intensive policies and programmes In India the various development programmes / schems are formulated and implemented under the Five year Plans (FYP) The 12 th FYP (2012-2017) goal is to achieve Faster, More Inclusive and Sustainable Growth which is in conformity with the MDGs The 12 th Plan has identified 25 core indicators which reflect the vision of rapid, sustainable and more inclusive growth The 12 th Plan also emphasizes to encourage research and innovation for more use of geospatial information An in-house geospatial software solution for crop production forecasting (FASAL) is already operational using information from different sources like remote sensing, meteorology and land observations

THANKS for ATTENTION