MAPPING GENDER INEQUALITY IN INDIA - Home | PennIUR · DISTRICTS OF INDIA STUDY AREA: The expected...
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MAPPING GENDER INEQUALITY IN INDIA TOTAL URBAN RURAL Mapping gender inequality by female disadvantage(fd) index fd index = %(pop female - pop male )/ pop female Working Population Population of children (0-6 years) Total Population Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage (0 -6 years) Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage (w o rk ing) Most Disadvantage Least Disadvantage Literate Population Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage Index Most Disadvantage Least Disadvantage Ì Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female disadvantage (0 -6 years) Most Advantage Least Advantage Ì Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage (W o rk ing) Most Disadvantage Least Disadvantage Ì Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend C H ILD_ IN D Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Legend Female Disadvantage (W o rk ing) Most Disadvantage Least Disadvantage $ll 0aps are classified in quantile classification system. Darker colors indicate more disadYantaged districts. &lusters of higher Yalue significance are identified in 5ed and clus- ters of loZ Yalue significance are shoZn in blue. Copyright: ©2013 Esri, DeLorme, NAVTEQ Female Disadvantage (Literacy) Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Female Disadvantage (Literacy) Most Disadvantage Least Disadvantage Copyright: ©2013 Esri, DeLorme, NAVTEQ Female Disadvantage Index (Literacy) Most Disadvantage Least Disadvantage */2%$/ 025$16 Ƃi¬ 5(68/T6 MUSA Capstone project by: Shrobona Karkun Data Sources: A) Primary Census Abstracts, 2011 Census of India, B) Spatial Data:: DIVA-GIS LARP 745 Advanced Applications in GIS, Spring 2014 dynasties from 0iddle (ast $sia and (urope, Zhich has shaped its tradi- tions and socio cultural backdrop. 8nfortunately, changes in religious and regional dogmas also led to a culture Zhere Zomen are treated diƲerently from men and futher leading to diƲerent treatment giYen to Zomen in Yarious parts of the country. :hile Zomen are natural heirs and leaders of matriarchial society in .erala in 6outhern ,ndia, a neZ born girl child in +aryana and 5aMasthan 1orthern ,ndia has to strug- gle for her oZn life and from being Yictim of infanticide. Due to the regional cultural diƲerences, it can be said the gender in- equalities Zould haYe geographical Yariations as Zell. )urther, Yarying leYels of access to education, employment opportunities, infant mortal- ity has shoZn Yarying leYels of gender bias in urban and rural context as Zell. 5esearch has also shoZn correlation of gender bias Zith &hil- dren and :omens health, &rimes against Zomen and 4uality of /ife. ,ndia is a country of a va- riety of cultural traditions. Throughout its rich his- tory Zhich dates back to %&, ,ndia has been invaded time and again by STUDY AREA: The expected patterns of inequalities are hypothised to be influenced by local cultures and is thus, ex - pected to transcend the political lines of states. Thus, to capture inequalities in the finest geographical unit possible, the chosen unit for study of gender inequal- ity are districts. Districts make for a good sample size (n=535) for this study- for it is more detailed than states and yet not as numerous as sub-districts. 0($685,1* *(1D(5 ,1(48$/,T,(6 :hile the most common unit of measuring inequalities is the sex ratio num- ber of males per females, it is diƳcult to Mudge hoZ much disadYantage do Zomen face by gender bias. The *ender ,nequalities ,ndex *,, , suggested by 8nited 1ations DeYelopment 3rogram considers income and health factors as Zell. +oZeYer, for sake of simplicity, inequality here has been measured by the )emale DisadYantage )D ,ndex. This index considers the percentage of population gap betZeen the sexes Zith respect to the female population and is mapped for Yarious demographic sections. The clusters and outliers along Zith their cores Zere identified by /ocal ,ndices of 6patial $utocorrelation /,6$. /,6$ also helps identify the cores of each cluster. PROJECT GOALS: 3rimarily to map gender inequality of Yarious demographic indicators at dis- trict leYel and then specifically at urban and rural context. 6econdary, to ex - plore the possibility of spatial autocorrelation in inequality to tie in to regional and cultural diƲerences. 5(68/T6 /oZer Yalues of the )emale DisadYantage ,ndex shoZs greater gaps betZeen male and female population. &lusters of high and loZ Yalues are apparent from the mapping exercise. ,n totality, the pattern of gaps betZeen the male and female population is similar in total, urban and rural scenarios. :hile similarities in patterns are Yisually discernable betZeen the patterns of total , the urban and the rural sections of the literate population the pattern similarities of Zorking population cannot be distinguished betZeen each section. The population gaps amongst children are similar betZeen total and rural populations. ,n general, maMority of high Yalues represented in red are more Yisible in the southern region and the maMority of loZ Yalues represented in blue are apparent in the northern regions. %ut, demogrpahic data cannot be conclusiYe by itself. )urther analysis Zith health , income data and correlation studies can help bring a more clearer picturer and aid in deYelopment strategies for Zomen.
Transcript of MAPPING GENDER INEQUALITY IN INDIA - Home | PennIUR · DISTRICTS OF INDIA STUDY AREA: The expected...
MAPPING GENDER INEQUALITY IN INDIA
TOTA
LU
RBAN
RURA
L
Mapping gender inequality by female disadvantage(fd) indexfd index = %(pop
female- pop
male)/ pop
female
Working PopulationPopulation of children (0-6 years)Total Population
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage (0 - 6 years)
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage (w o rk ing)
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
Literate Population
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage Index
Most D
isadv
antag
e
Leas
t Disa
dvan
tage
Ì
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale disadvantage (0 - 6 years)
Most A
dvan
tage
Leas
t Adv
antag
e
Ì
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage (W o rk ing)
Most D
isadv
antag
e
Leas
t Disa
dvan
tage
Ì
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendC H ILD_ IN D
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
Copyright: ©2013 Esri, DeLorme, NAVTEQ
LegendFemale Disadvantage (W o rk ing)
Most Disa
dvanta
ge
Leas
t Disa
dvanta
ge
ll aps are classified in quantile classification system. Darker colors indicate more disad antaged districts. lusters of higher alue significance are identified in ed and clus-
ters of lo alue significance are sho n in blue.
Copyright: ©2013 Esri, DeLorme, NAVTEQ
Female Disadvantage (Literacy)
Most D
isadv
antage
Least
Disadv
antage
Copyright: ©2013 Esri, DeLorme, NAVTEQ
Female Disadvantage (Literacy)
Mos
t Disa
dvant
age
Leas
t Disa
dvant
age
Copyright: ©2013 Esri, DeLorme, NAVTEQ
Female Disadvantage Index (Literacy)
Most D
isadv
antag
e
Leas
t Disa
dvan
tage
i T
v
MUSA Capstone project by: Shrobona KarkunData Sources: A) Primary Census Abstracts, 2011 Census of India,
B) Spatial Data:: DIVA-GIS LARP 745 Advanced Applications in GIS, Spring 2014
dynasties from iddle ast sia and urope, hich has shaped its tradi-tions and socio cultural backdrop. nfortunately, changes in religious and regional dogmas also led to a culture here omen are treated di erently from men and futher leading to di erent treatment gi en to
omen in arious parts of the country. hile omen are natural heirs and leaders of matriarchial society in erala in outhern ndia , a neborn girl child in aryana and a asthan orthern ndia has to strug-gle for her o n life and from being ictim of infanticide.Due to the regional cultural di erences, it can be said the gender in-equalities ould ha e geographical ariations as ell. urther, arying le els of access to education, employment opportunities, infant mortal-ity has sho n arying le els of gender bias in urban and rural context as ell. esearch has also sho n correlation of gender bias ith hil-dren and omens health, rimes against omen and uality of ife.
ndia is a country of a va-riety of cultural traditions.
Throughout its rich his-tory hich dates back to
, ndia has been invaded time and again by
DISTRICTS OF INDIA
STUDY AREA:The expected patterns of inequalities are hypothised to be influenced by local cultures and is thus, ex-pected to transcend the political lines of states. Thus, to capture inequalities in the finest geographical unit possible, the chosen unit for study of gender inequal-ity are districts. Districts make for a good sample size (n=535) for this study- for it is more detailed than states and yet not as numerous as sub-districts.
D Thile the most common unit of measuring inequalities is the sex ratio num-
ber of males per females , it is di cult to udge ho much disad antage do omen face by gender bias. The ender nequalities ndex , suggested by nited ations De elopment rogram considers income and health factors as ell. o e er, for sake of simplicity, inequality here has been measured by the emale Disad antage D ndex. This index considers the percentage of population gap bet een the sexes ith respect to the female population and is mapped for arious demographic sections. The clusters and outliers along ith their cores ere identified by ocal ndices of patial utocorrelation .
also helps identify the cores of each cluster.
PROJECT GOALS:rimarily to map gender inequality of arious demographic indicators at dis-
trict le el and then specifically at urban and rural context. econdary, to ex-plore the possibility of spatial autocorrelation in inequality to tie in to regional
and cultural di erences.
To er alues of the emale Disad antage ndex sho s greater gaps bet een male and female population. lusters of high and lo alues are apparent from the mapping exercise. n totality, the pattern of gaps bet een the male and female population is
similar in total, urban and rural scenarios. hile similarities in patterns are isually discernable bet een the patterns of total , the urban and the rural sections of the literate population the pattern similarities of orking population cannot be distinguished bet een each section. The population gaps amongst children are similar bet een total and rural populations. n general, ma ority of high alues represented in red are more isible in the southern region and the ma ority of lo alues represented in blue are apparent in the northern regions. ut, demogrpahic data cannot be conclusi e by itself. urther analysis ith health , income data and correlation studies can help bring a more clearer picturer and aid in de elopment strategies for omen.
