Data Dictionary

ID
id
Type
numeric
Label
index

ID
year
Type
date
Label
Year

ID
state_name
Type
text
Label
State Name

ID
state_code
Type
text
Label
State Code

ID
climate_vul_in
Type
numeric
Label
Climate vulnerability index

ID
geo_land_area
Type
numeric
Label
Geographical land area as per 2019

ID
forest_land_area
Type
numeric
Label
Forest land area as per 2019 in square kilometres

ID
agri_land_area
Type
numeric
Label
Agriculture land area as per 2014-2015 in hectare

ID
waste_land_area
Type
numeric
Label
Wasteland area as per 2015-2016 in square kilometres

ID
irrigated_area
Type
numeric
Label
Irrigated area as per 2014-2015 (%)

ID
gdp_const_price
Type
numeric
Label
Gross domestic product at constant price (2011-12)

ID
households_below_poverty
Type
numeric
Label
Percentage of households below poverty line as per 2011

ID
infant_mortality
Type
numeric
Label
Infant mortality rate as per 2011

ID
poverty_rate
Type
numeric
Label
Poverty rate as per 2011

ID
income_from_natural_resources
Type
numeric
Label
Proportion of income from natural resources like agriculture, forestry, livestock and fishery to gross domestic product

ID
out_peren_trees_to_total_agri_out
Type
numeric
Label
Proportion of output from perennial trees to total value of agricultural and allied output

ID
mar_small_op_land_holding
Type
numeric
Label
Marginal and small operational land holding (%)

ID
yield_variability
Type
numeric
Label
Coefficient of variation or yield variability of food grains

ID
area_under_pmfby_wbcis
Type
numeric
Label
Proportion of area under pradhan mantri fasal bima yojana (pmfby) and restructured weather based crop insurance scheme (wbcis)

ID
rainfed_agriculture
Type
numeric
Label
Proportion of rainfed agriculture

ID
forest_land_area_per_population
Type
numeric
Label
Forest land area in square kilometres per 1,000 rural population

ID
women_workforce
Type
numeric
Label
Women in the overall workforce (%)

ID
employed_under_mgnrega
Type
numeric
Label
Average person day per household employed under mahatma gandhi national rural employment guarantee act (mgnrega)

ID
road_rail_density
Type
numeric
Label
Road and rail density

ID
health_workers_per_population
Type
numeric
Label
Density of health care workers per lakh population

ID
vector_diseases_per_population
Type
numeric
Label
Vector borne diseases like dengue, chikungunya, acute encephalitis syndrome, japanese encephalitis, malaria per 1,000 population

ID
water_diseases_per_population
Type
numeric
Label
Water borne diseases like cholera, typhoid, acute diarrhoea per 1,000 population

Additional Information

Field Value
Data last updated November 15, 2023
Metadata last updated August 22, 2024
Created October 6, 2023
Format CSV
License Open Data Commons Attribution License
Additional infonan
Data extraction pagehttps://ndap.niti.gov.in/dataset/7160
Data insightsThe vulnerability assessment can assist in ranking and identification of the most vulnerable states and help states prioritise adaptation planning and investments.This data-driven approach provides a clear roadmap for states to allocate resources efficiently and take proactive measures to safeguard their communities and ecosystems in the face of climate change. It may also aid to plan disaster management.It is aimed at policymakers and decisionmakers as a first step to prioritise locations for addressing climate risk at a holistic level within a vulnerability-hazard-exposure framework
Data last updated2022-04-04 00:00:00
Data retreival date2022-06-15 00:00:00
Datastore activeTrue
FrequencyOnce
GranularityState
Has viewsTrue
Id9c02edb9-feac-4ae0-8685-bdf5db5cbfe4
Idp readyTrue
Lgd mappingyes
MethodologyThe methodology for vulnerability assessment is a systematic process involving the definition of scope, selection of assessment type, tier methods, sector, spatial scale, and assessment period. Indicators crucial for vulnerability evaluation are identified and quantified, with normalization techniques ensuring consistency. Indicators are then weighted and aggregated, providing a comprehensive representation of vulnerability. Through this structured approach, vulnerabilities are ranked, allowing for focused adaptation planning
No indicators25
Package ide4ccd3aa-beb8-436c-9ceb-db3998a10314
Position0
Size6.4 KiB
Skumost-climate_vulnerability_assessment-st-yr-aaa
Stateactive
States uts no36
Url typeupload
Years covered2,019
Methodology The methodology for vulnerability assessment is a systematic process involving the definition of scope, selection of assessment type, tier methods, sector, spatial scale, and assessment period. Indicators crucial for vulnerability evaluation are identified and quantified, with normalization techniques ensuring consistency. Indicators are then weighted and aggregated, providing a comprehensive representation of vulnerability. Through this structured approach, vulnerabilities are ranked, allowing for focused adaptation planning
Indicators
Similar Resources
Granularity Level State
Data Extraction Page https://ndap.niti.gov.in/dataset/7160
Data Retreival Date 2022-06-15 00:00:00
Data Last Updated 2022-04-04 00:00:00
Sku most-climate_vulnerability_assessment-st-yr-aaa
Dataset Frequency Once
Years Covered 2019
No of States/UT(s) 36
No of Districts
No of Tehsils/blocks
No of Gram Panchayats
Additional Information nan
Number of Indicators 25
Insights from the dataset The vulnerability assessment can assist in ranking and identification of the most vulnerable states and help states prioritise adaptation planning and investments.This data-driven approach provides a clear roadmap for states to allocate resources efficiently and take proactive measures to safeguard their communities and ecosystems in the face of climate change. It may also aid to plan disaster management.It is aimed at policymakers and decisionmakers as a first step to prioritise locations for addressing climate risk at a holistic level within a vulnerability-hazard-exposure framework
IDP Ready Yes
LGD Mapping Yes