Myanmar’s Census: Lessons from Sri Lanka
Jacqueline Homann
LBJ School of Public Affairs
11 December 2014
Urban GIS & Introduction to Geographic Information Systems
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Jacqueline Homann / Myanmar’s Census: Lessons from Sri Lanka / PA 388K-62615 / Fall 2014
I. EXECUTIVE SUMMARY
The 2014 census in Myanmar was a truly historic event. It was the first time in over 30
years that the government was attempting to measure the population. This major
undertaking was a joint effort by the Ministry of Immigration and Population and the
United Nations Population Fund. Preparations began in 2012 with the mapping of over
80,000 enumeration areas. Efforts to mobilize public awareness of the census were
largely successful. For the over 140 official ethnic groups, the census was seen as an
opportunity to be represented and participate in a nation-wide effort under the new
democratic government.
At the end of the enumeration process the team of international and national observers
characterized the data collection as a relative success, with the exception of a handful of
states. These states included Kachin and Kayin, where non-state rebel groups cut off
multiple villages from enumerators, as well as Rakhine State, where the government
willfully ignored the Rohingya minority population. It is estimated that over 1,100,000
people were not included in the data as a result.[i]
The issue of partial census enumeration is problematic considering that those living in
areas not reached by government are likely vulnerable populations in most need of
government services and poverty interventions. To better understand the risks associated
with partial census enumeration in Myanmar, this report looks at census data collection
through the lens of the Sri Lanka experience. In 2001, due to the ongoing civil conflict in
the north and east of the country, Sri Lanka undertook a census that only reached 18 out
of 25 districts. Ten years later, in 2011, the government again undertook a population and
housing census, but this time reached all districts of the country.
This report seeks to understand possible issues arising from incomplete census data. It
looks at the vulnerability of Sri Lanka’s population, focusing particularly on districts that
were not enumerated in 2001. For the purposes of this report, vulnerability is measured
by three census indicators, including employment, housing material and migration.
Analysis of these indicators show that there are differences between the north and east
and the rest of Sri Lanka. These findings, however, describe a very general trend. It is
recommended that additional research using other indicators, such as a Household
Income and Expenditure Survey, be conducted in order to understand the level of
vulnerability faced by populations. Regardless, the findings provide a valuable lesson for
Myanmar. Excluding populations from the census leads to inaccurate data and the risk of
uninformed policies that may not reach the most vulnerable.
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II. INTRODUCTION
As countries move further along the development path governments need better data to
make informed social and economic policies. Census data is a critical source of this
information. Collecting sub-national, comprehensive data can help governments elaborate
a broad spectrum of policies related to poverty, gender, health, etc. In particular, for
groups that often face barriers to a full and dignified life, such as the unemployed, the
undereducated, women and girls, and members of excluded minorities, the census is an
opportunity to affirm their existence in society. Collecting data on the distribution of
competing castes, ethnic or tribal groups, for example, can help ensure more equitable
resource allocation in addition to protecting the identity of these groups.
i. The 2014 Myanmar census
A problem arises, however, when data is
not collected consistently or excludes
certain members of the population.
Myanmar’s recent census faces this
problem. The country’s 2014 census was
the first conducted since 1983.1 As the
country transitions from a military junta to
a democratically elected government,
census data can play an important role in
helping determine political representation
as well as socially and economically just
policies. A joint effort between Myanmar’s
Ministry of Immigration and Population
and the United Nations Population Fund,
the census was conducted between 29
March and 10 April 2014.
Myanmar is a country of diverse ethnic,
religious and linguistic groups. The Bamar
ethnic majority, which is majority
Buddhist, dominates current political life.
In the most recent census, eight major
ethnic groups and 135 subgroups were officially recognized. Critics of the census,
however, charge the government with underreporting or failing to report a handful of
ethnic and religious minorities, including the Rohingya, the Kachin and the Karen.
The Rohingya are a Muslim minority group located in Rakhine State who are not
officially recognized as citizens of Myanmar and are instead considered illegal Benagli
1
Data from the 1983 census is very limited. Information provided on Myanmar’s Department of
Population website includes state level data on population and population by gender as well as country
level data on population by age group. Given these limitations data from the 1983 census will not be
considered for this report.
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immigrants. Ongoing human rights abuses against the Rohingya are widely documented,
including physical abuse, restrictions on their freedom of movement, marriage and the
registration of children.[ii] According to independent census observations, Rohingya
households were either partially enumerated, meaning that their ethnicity was not
recorded, or left out completely. In official reports, census observers qualified the
enumeration of the Rohingya as a “complete failure.”[iii]
Security concerns limited access to multiple villages in Kachin and Kayin states. Despite
negotiations with the government prior to census enumeration, the Kachin Independent
Organization (KIO), an ethnic insurgent group, cut off an estimated 25 villages from
officials. Villages in Kayin State were also affected by the intransigence of the Kayin
National Union (KNU), another insurgent group. These actors have been struggling for
independence from the government for decades and disrupting the census may have been
one way to further divide themselves from the center.[iv] The result is an incomplete
picture of Myanmar’s population.
ii. Parallels with the Sri Lankan census
In 2001, Sri Lanka faced a similar problem
of partial census enumeration. At this
time, a civil war between the majority
Sinhalese ethnic group and minority Sri
Lankan Tamil insurgent group, known as
the Liberation Tigers of Tamil Eelam
(LTTE), was being fought. Rebelcontrolled areas in the north and east were
inaccessible due to safety concerns.
Further, the LTTE did not want the
government to have accurate data on the
local population under their control. As a
result, only 18 out of 25 districts were
enumerated.[v]
The end of the civil war was declared in
2009 after 26 years of conflict. In 2011 the
Department of Census and Statistics
(DCS) undertook a decennial census of the
entire population of the island. The 2011
census was the first time in 30 years that the entire country was enumerated, as previous
censuses had all been affected by political violence.[vi]
iii. Administrative divisions
Myanmar’s first-level administrative division includes 14 regions and states. These two
division categories are equal according to the 2008 Constitution. Regions are traditionally
inhabited by the Bamar majority while states are inhabited by major ethnic minorities.
Naypitaw, which includes the capital, is a union territory.[vii] Myanmar’s second-level
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administrative division is comprised of 67 districts, followed by 330 townships. The 2014
census included 81,750 enumeration areas covering most of the country.[viii]
Sri Lanka’s first-level administrative division includes nine provinces. These are
followed by 25 districts, then 331 divisions.[ix] The 2001 and 2011 censuses, however,
aggregate at the district and division levels, and do not include overall data for provinces.
This report will therefore use districts, the second administrative level, as the largest
administrative division. In 2011 there were 65,012 total enumeration areas.[x]
iv. Literature review
A literature review on poverty mapping in Sri Lanka uncovered several studies
combining the 2001 census dataset with other surveys. A 2005 study from the World
Bank and Sri Lanka’s Department of Census and Statistics mapped the sub-district
distribution of poverty estimates based on the Household Income and Expenditure
Survey.[xi] A follow up World Bank article from 2007 provided additional analysis of
the policy implications of poverty mapping.[xii] The International Water Management
Institute undertook a mapping exercise in 2005 using census data and the Population and
Agriculture Census as well as the Consumption and Expenditure Survey.[xiii] An earlier
study from the University of Sri Jayewardenepura, Sri Lanka combined census
population data with disaggregated land use data for Colombo metropolitan area to
understand the forces and trends behind urbanization.[xiv] Rather than combining census
data with other information, the aim of this report is to restrict mapping and analysis to
census data in order to better understand the opportunities of the census tool itself.
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III. PROBLEM STATEMENT
Partial census enumeration is problematic because it prevents policymakers from gaining
a comprehensive view of a country’s population. When areas are left out, government
cannot develop targeted policies to address the issues most relevant to inhabitants.
Further, in areas that are inaccessible or willfully ignored by government, inhabitants
probably have little chance of taking advantage of services. These areas, therefore, most
likely experience higher rates of vulnerability and underdevelopment and have fewer
opportunities to break out of a cycle of poverty.
This paper will attempt to outline potential issues that can arise due to incomplete census
data by studying the information gap between the 2001 and 2011 Sri Lankan censuses.
Analysis of available census data from 2011, in particular from districts in Sri Lanka that
were previously not enumerated, is expected to reveal critical demographic, economic
and social information. These districts are expected to show higher signs of poverty, as
measured by employment, housing infrastructure and migration.
Should higher poverty rates exist, a clear case for targeted policy interventions in
vulnerable areas can be made. For Myanmar, this underscores the importance of
collecting comprehensive census data in the future. This is especially true for areas that
were not previously enumerated, as inhabitants probably have the highest need for
government services.
With these issues in mind, this paper will attempt to answer the following questions:
 What kind of data disparities occurred between the 2001 and 2011 the Sri Lankan
census?
 How might this affect government policies?
 What are the lessons learned that can be applied to Myanmar going forward?
IV. METHODOLOGY
i. Overview of data needs
My report addresses what kind of information and conclusions can be drawn from census
data and the inherent risks of collecting incomplete census data. I became interested in
this topic because I had read critiques about the 2014 Myanmar census and its failure to
enumerate the entire population. I therefore wanted to look more closely at the available
data and understand what conclusions could be drawn about the population and how
missing data might affect these conclusions.
In order to understand the risks associated with incomplete census data I wanted to focus
on a country that had experienced a sequence of incomplete census enumeration followed
by complete census enumeration. Sri Lanka fit this pattern. Further, data was available
online and in English, making a comparison study between the two enumeration years
possible.
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As part of my hypothesis I wanted to understand how the census can reveal areas of
vulnerable populations and the needs of these populations. In order to understand this I
needed census data that could serve as proxies for vulnerability. I chose three proxies,
including employment, housing material and migration. Employment is a good indicator
of how capable people are of supporting themselves and their families. Housing material
can give us a general picture of people’s overall material wealth. Permanent structures
made of brick are generally more well off than semi-permanent structures made of mud
or palm mats. Finally, migration can be used as a proxy for vulnerability in that migrants
probably face more challenges finding jobs or integrating into a community when they
have lack social capital to fall back on.
Since incomplete census enumeration was associated in Myanmar with ethnic tension, I
also needed data on ethnic location. For Sri Lanka this was available through the census.
For Myanmar, however, this data was not included in the provisional results. I therefore
needed to find non-census sources for ethnic data.
ii. How data was obtained
In order to investigate census data I went directly to the relevant census partner or
authority in each country. For Myanmar this is the United Nations Population Fund
(http://countryoffice.unfpa.org/myanmar/census/). For Sri Lanka this is the Department
of Census and Statistics (http://www.statistics.gov.lk/).
For both countries, reports were available in PDF format only. Transferring data to excel,
using open source conversion software was a time consuming process. Unfortunately the
format of the data dictated at what level I focused my report. Both Myanmar and Sri
Lanka had data down to the third-level administrative district. There are over 300 entities
at this level for each country, so given time constraints I was unable to use this level of
data. This challenge supports the need for machine-readable open census data.
For information on ethnicity in Myanmar I used Geo-referencing of Ethnic Groups
(GREG)
from
the
Swiss
Federal
Institute
of
Technology
Zurich
(http://www.icr.ethz.ch/data/other/greg). This dataset was updated as of January 2010. It
is limited in that it does not map the Rohingya population. To understand where this
ethnic group might be located I used information on camps for internally displaced
persons in Rakhine State provided by the World Health Organization (WHO)
(http://www.searo.who.int/entity/emergencies/crises/SEARO_ESR_2_2012.pdf).
iii. What data was obtained
Myanmar census data is still being processed, however, limited provisionary data is
available. This data includes information on urban/rural/female/male population,
household size, and population residing in institutions.
In the case of Sri Lanka, the available census data is quite robust and includes a number
of population and housing indicators. Since I wanted to focus on vulnerable populations I
looked at employment, housing and migration, which I thought would help illustrate
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areas where people experienced the most instability and poverty. Further, to explore the
ethnic dynamic in Sri Lanka I used data for six out of the eight ethnic groups. I excluded
two minor ethnic groups because they were only added to the most recent census as
official ethnicities, and would not have been included as a group in 2001.
iv. Steps taken during analysis
The following steps were taken to conduct my analysis for Myanmar:
 I knew that the central problems that occurred during the enumeration process in
Myanmar were based on ethnic issues. The Rohingya, Karen and Kachin people
had either been ignored or partially enumerate. I therefore started by looking at
where these people were located. Since this data was not available through the
official census website I relied on 2010 GREG data.
 I then looked at what was possible to map with the data available from the 2014
Myanmar census and if there were any distinctions among the excluded areas of
the country. I started by mapping the population of each State/Region and
reviewing estimates of how many people were not enumerated. I also compared
urban vs. rural, male vs. female populations and average household sizes.
 Taking a closer look at the Rohingya population in Rakhine State I mapped the
location of IDP camps using data from the WHO. On top of this I also mapped
main waterways, towns and roads, to see what kind of environment the Rohingya
were living in and their access to the rest of the state.
The following steps were taken to conduct my analysis for Sri Lanka:
 The civil war in Sri Lanka, which disrupted the 2001 census enumeration, was
driven by ethnic tensions. I therefore wanted to understand the ethnic dispersion
in the country and how this might then relate to my vulnerability indicators. I used
a dot symbology, whereby each dot represented 500 people. Each ethnic group
was symbolized by a different colored dot.
 Before looking at vulnerability I first mapped population data from 2011 as well
as the change in population from 2001 to 2011. The data for districts that were not
enumerated in 2001 was based on estimates by the Department of Census and
Statistics.
 I then looked at three different proxy indicators for vulnerability, including
employment, housing material and migration data from the 2011 census. For
employment I mapped both employment and unemployment rates for total
population over the age of 15 for each district.
 For housing material I used a pie chart symbology to indicate the ratio of different
materials used in walls for housing by district. I then did a hotspot analysis for
brick, considered as a high-end material, and kadjan, a lower-end material.
 Finally, I looked at migration data, including total number of self-reporting
migrants by district and their reasons for migration. Using the same percentage
scale, I mapped the eight different reasons for migration that were available in the
census questionnaire. The identical scale made comparison across the migration
reasons possible.
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V. FINDINGS
i. Ethnic composition of Myanmar
This first map presents data using geo-referenced data from the Swiss Federal Institute of
Technology on the location of ethnic groups in Myanmar.
9
ii. Mapping of census data from the 2014 Myanmar census.
The following maps were created using available census data from the 2014 Myanmar census. The first map shows recorded
populations for all states/regions. The second map contrasts different data from the census, including rural vs. urban, female vs. male
populations and average household sizes per district.
10
iii. Detail of Rakhine State, Myanmar
The final map in this section depicts the population of Rakhine State in greater detail and
includes major towns, roads and waterways. Townships that contain camps for internally
displaced persons are also indicated.
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iv. Ethnic composition of Sri Lanka
The following maps use data from the 2001 and 2011 Sri Lankan census. The first map
presents the population of major ethnic groups per district in 2011, both with the
dominant Sinhalese ethnic group and without.
12
v. Population data
These maps show the recorded population in 2011 as well as the population change between 2001 and 2011. The population for 2001
in districts that were not enumerated are based on estimates by the Sri Lanka Department of Census and Statistics.
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vi. Employment
Below are aggregate employment and unemployment rates for males and females in the workforce over the age of 15 by district.
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vii. Type of housing material
These maps show the different kinds of material used for housing walls. A hotspot analysis for has been done for brick and for kadjan,
which is a woven mat made out of coconut leaves.
15
viii. Migration
Total migration numbers per district and reasons for migration are presented below.
16
VI. ANALYSIS
i. Myanmar census analysis
Given the limited amount of data currently available from the Myanmar census it is
difficult to draw any conclusions about vulnerable populations. The map on urban/rural,
female/male and average household size does not seem to reveal conclusive results that
distinguish states that were partially enumerated from the rest of the country. In order to
understand issues of vulnerability further census data is needed, such as information on
employment or disability.
Official population estimates have been made for Kachin, Kayin and Rakhine states that
attempt to account for missing populations. These estimates, however, are no substitute
for data. The problem of estimates is demonstrated by the map of Sri Lanka’s population
change from 2001 to 2011. While the rest of Sri Lanka experienced population growth,
the northern districts of Kilinochchi, Mullaitive and Mannar actually experienced
population declines. In Mannar, for example, the population decreased by 34 percent. The
reason for this decline may be conflict deaths or migration away from conflict zones. It
may also be due to poor estimations by the population by the Department of Census and
Statistics. If the latter is true, this map shows that estimations may be prone to a high
degree of miscalculation and are no substitute for actual data. This error is an important
lesson for Myanmar.
ii. Sri Lanka census analysis
The mapping of census data from Sri Lanka provided a number of interesting results that
support the need for accurate and complete census data. In terms of ethnic dispersion, it is
clear from the census that Sri Lanka is still divided geographically between the dominant
Sinhalese and minority Sri Lankan Tamils. The map on ethnic distribution shows that
Tamils remain concentrated in the north and east while Sinhalese continue to dominate
the rest of the country. This finding is important for the rest of the analysis, which will
look at vulnerability indicators, focusing particularly in districts that were left out of the
census in 2001.
The first vulnerability indicator used is employment. For the 2011 census, employment
was measured for men and women over the age of 15 who are economically active.
Based on the relevant maps, it appears that in general, the northern and eastern states
experience lower rates of employment than the rest of the country, with the district of
Jaffna having only 39% employment. In terms of unemployment, the difference between
the eastern and northern districts and the rest of the country is also stark. Unemployment
appears to be relatively higher in the north, east and south. The distinction between
former conflict and non-conflict areas is less clear however. For example, Vavuniya, a
former conflict area, has the highest rate of unemployment at 7.7 percent. The district of
Ampara also has a high unemployment rate at 7.2 percent, but was not a conflict area in
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2001. This suggests other factors are at play in the southern districts that affect
unemployment rates.
The second vulnerability indicator used is housing material. From the map depicting the
different types of housing material, it is clear that there are differences separating the
regions. It appears that in the northern and southern districts the primary material for the
construction of walls is cement, while in the central, western and eastern districts it is
brick. Kadjan, which is a traditional woven mat made out of palm leaves is also used in
the north with some frequency.
A hotspot analysis was done for brick, which was interpreted as a proxy for wealth and
for kadjan, which was interpreted as a proxy for less wealth. For brick, the hotspot
analysis did reveal clustering of high use in the western districts and low use in the
northern districts, however, neither were statistically significant. For kadjan, there was
clustering of high use in the north and also one district in the west, however, no statistical
significance was found. These findings show that a hotspot analysis for housing material
may not be the most appropriate methodology to understand the question of material
wellbeing.
The final indicator for vulnerability is migration. It appears that migration to the north
and east is relatively low, compared to the rest of the country. In districts such as Mannar
and Batticaloa, total migration was less than 40,000, while in Colombo, total migration
was over 230,000. Higher rates of migration to and around Colombo appear to be driven
by marriage, employment and accompanying a family member. Migration to the north,
on the other hand, is driven by resettlement after being displaced. This appears to be a
legacy from the civil war, in which families and individuals driven from conflict areas are
returning home. It is unclear from this data what kind of life such migrants face once they
return home. Further study is needed on their re-integration into the post-conflict
community.
The findings from this mapping exercise of Sri Lanka’s census data show that it is
possible identify the location of vulnerable populations. Given the findings on
employment, for example, it is clear that the northern and eastern districts are less well
off than the other districts. As a response, the Sri Lankan government should devote more
resources to helping improve the economic situation in areas that have the highest
unemployment rates. For the housing material study it is difficult to make conclusive
statements in light of the fact that the hotspot analysis was not statistically significant. It
is clear from the map, however, that there are distinctions among the different regions of
the country. Finally, on the issue of migration, the Sri Lankan government should closely
follow how migrants to the northern region are faring and if reintegration into postconflict society is succeeding.
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iii. Conclusion
The mapping of census information from Sri Lanka shows the importance of having data
on the entire country in order to understand where vulnerable populations are located.
Without this information it is impossible to make informed policy decisions for poverty
interventions. This study, however, also shows the limitations of census data and the need
to combine it with other datasets, such as the Household Income and Expenditure Survey
or the Demographic and Health Survey. Further, to obtain a true understanding of
vulnerable locations it is necessary to disaggregate the data even further. Studying data
by census tract or at least the third administrative level would lead to more robust
conclusions.
In Myanmar the risks of incomplete information are clear. Considering the diversity of
ethnic groups in the country, Myanmar needs to account for all of its minority
populations. In such a heterogeneous country, giving voice to these minorities is an
important step towards developing a more robust democracy. Unfortunately, the 2014
census signals that Myanmar still has room for reform until all voices are counted.
VII. REFERENCES
Census data
i.
ii.
iii.
iv.
“Census of Population and Housing,” Department of Census and Statistics,
Ministry of Finance and Planning, Sri Lanka, 2012,
http://www.statistics.gov.lk/PopHouSat/CPH2011/Pages/Activities/Reports/CPH_
2012_5Per_Rpt.pdf.
“Census of Population and Housing,” Department of Census and Statistics,
Ministry of Finance and Planning, Sri Lanka, 2001,
http://www.statistics.gov.lk/PopHouSat/Pop_Chra.asp.
“Census Report Volume (I),” Department of Population, Ministry of Immigration
and Population, Myanmar, August 2014,
http://countryoffice.unfpa.org/myanmar/drive/Census_Provisional_Results_2014_
ENG.pdf.
Other
i.
ii.
iii.
“Census Observation Mission Report: 2014 Population and Housing Census,”
Republic of the Union of Myanmar, May 2014,
http://countryoffice.unfpa.org/myanmar/drive/Censusobservationmissionreport_E
NG.pdf.
“Two-child policy violates human rights of Myanmar’s Rohingya Muslims,” May
31, 2013, UN News Centre,
http://www.un.org/apps/news/story.asp?NewsID=45055#.VIX3SDHF98E.
Paul Mooney, “Ethnic Strife Blurs Burma’s First Census in 30 Years,” The
Irrawaddy, September 1, 2014, accessed November 2, 2014,
19
Jacqueline Homann / Myanmar’s Census: Lessons from Sri Lanka / PA 388K-62615 / Fall 2014
iv.
v.
vi.
vii.
viii.
ix.
x.
xi.
xii.
xiii.
xiv.
http://www.irrawaddy.org/burma/ethnic-strife-blurs-burmas-first-census-30years.html.
“Burma Insurgency,” last modified April 12, 2014,
http://www.globalsecurity.org/military/world/war/burma.htm.
Kalinga Tudor Silva, “Politics of Ethnicity and Population Censuses in Sri
Lanka,” Draft paper, University of Peradeniya, Sri Lanka,
http://www.ciqss.umontreal.ca/Docs/SSDE/pdf/Silva.pdf.
“Census of Population and Housing,” Department of Census and Statistics,
Ministry of Finance and Planning, Sri Lanka, 2012.
Hamish Nixon et al., State and Region Governments in Myanmar, The Asia
Foundation, September 2013,
http://asiafoundation.org/resources/pdfs/StateandRegionGovernmentsinMyanmar
CESDTAF.PDF.
“Census Report Volume (I),” Department of Population, Ministry of Immigration
and Population, Myanmar.
“Census of Population and Housing,” Department of Census and Statistics,
Ministry of Finance and Planning, Sri Lanka, 2012.
Ibid.
“A Poverty Map for Sri Lanka – Findings and Lessons,” The World Bank,
Department of Census and Statistics, Sri Lanka, October 2005, accessed
December 11, 2014,
http://sedac.ciesin.columbia.edu/povmap/downloads/methods/Sri_Lanka_povmap
.pdf.
Tara Vishwanath and Nobuo Yoshida, “Poverty Maps in Sri Lanka,” in More
Than a Pretty Picture: Using Poverty Maps to Design Better Policies and
Interventions, ed. Tara Bedi et al., The World Bank, 2007, accessed December 9,
2014, Chttp://siteresources.worldbank.org/INTPGI/Resources/3426741092157888460/493860-1192739384563/10412-12_p225-240.pdf.
Upali A. Amarasinghe et al., Locating the Poor: Spatially Disaggregated Poverty
Maps for Sri Lanka, International Water Management Institute, 2005), accessed
December 9, 2014,
https://books.google.com/books?id=iOp3C6EH7TkC&pg=PA22&lpg=PA22&dq
=sri+lanka+census+mapping+results&source=bl&ots=ERlFUDP8Ki&sig=Yh2D
YclC7ZnN0fQOJXVAnxHxTEA&hl=en&sa=X&ei=t9SHVLu_FcerggSh_IGYC
Q&ved=0CB8Q6AEwATgU#v=onepage&q=sri%20lanka%20census%20mappin
g%20results&f=false.
Padma Weerakoon, “Geovisualization of urban densities using GIS: Case Study
in Colombo Metropolitan Area, Sri Lanka,” University of Sri Jayewardenepura,
Sri Lanka, accessed December 9, 2014,
http://www.geos.ed.ac.uk/~gisteac/proceedingsonline/GISRUK2013/gisruk2013_
submission_79.pdf.
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APPENDIX
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Map 1: East Asia & Pacific
1. Download world shapefile from DIVA-GIS (http://www.diva-gis.org/Data).
2. Project shapefile to Indian 1954 UTM Zone 47N.
3. Select by attribute for “NAME” = [World Bank countries of East Asia and
Pacific].
4. Export data and save as “EastAsia”.
5. Change symbology color for East Asia.
6. Select by attribute for “NAME” = “Myanmar”.
7. Export data and save as “EastAsiaMyanmar”.
8. Change symbology color for Myanmar.
9. Label major bodies of water and countries of East Asia and the Pacific.
10. Change data frame background color to blue.
Map 2: South Asia
1. Download world shapefile from DIVA-GIS.
2. Project shapefile to Asia South Albers Equal Area Conic.
3. Select by attribute for “NAME” = [World Bank countries for South Asia].
4. Export data and save as “SouthAsia”.
5. Change symbology color for South Asia.
6. Select by attribute for “NAME” = “Sri Lanka”.
7. Export data and save as “SouthAsiaSriLanka”.
8. Change symbology color for Sri Lanka.
9. Label major bodies of water and countries of South Asia.
10. Change data frame background color to blue.
Map 3: Myanmar Regions & States
1. Download country shapefiles for Myanmar, Vietnam, Nepal, Thailand, Laos,
Cambodia, India, China, Bhutan, Bangladesh from Global Administrative Areas
(GADM; http://www.gadm.org/country).
2. Download Myanmar state shapefile “States Region Boundaries_Apr2014”
shapefile from Myanmar Information Management Unit (MIMU;
http://www.themimu.info/gis-resources).
3. Project shapefiles to Indian 1954 UTM Zone 47N.
4. Change symbology to define different states/regions.
5. Download cities “Town Location_Feb2012” from MIMU.
6. Select by attribute “Town” = [Regional capital].
7. Export data and save as “Town_clip.shp”.
8. Label states/regions, capitals, bodies of water.
Map 4: Sri Lanka Districts
1. Download Sri Lanka country “LKA_adm1” shapefile from GADM.
2. Project shapefile to Asia South Albers Equal Area Conic.
3. Join excel table to attribute table with 2001 and 2011 census data.
4. Change symbology to define different districts.
5. Download cities “places” from MapCruzin.com (http://www.mapcruzin.com/freesri-lanka-country-city-place-gis-shapefiles.htm).
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Jacqueline Homann / Myanmar’s Census: Lessons from Sri Lanka / PA 388K-62615 / Fall 2014
6. Select by attribute “Name” = [District capital].
7. Export data and save as “Places_clip.shp”.
8. Label districts, district capitals, bodies of water.
Map 5: Location of Ethnic Groups (Myanmar)
1. Download shapefile from Swiss Federal Institute of Technology Zurich
(http://www.icr.ethz.ch/data/other/greg) and clip for Myanmar.
2. Change symbology to show different ethnic groups.
3. Use “mmr_polbnda_adm1_250k_mimu” shapefile from MIMU to select for
Rakhine, Kayin, Kachin boundaries.
4. Add separate data frames for Rakhine, Kayin, Kachin states using clip to shape
function.
Map 6: Population by State/Region (Myanmar)
1. Use Myanmar GADM shapefile.
2. Change symbology to graduated colors for population by state/region.
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 7: Mapping of Different Census Data by District (Myanmar)
1. Use Myanmar GADM shapefile.
2. For urban/rural map select by attribute “URBAN” > “RURAL” and create new
shapefile out of selected states/regions. Export data and change symbology color
for majority urban and for majority rural.
3. For gender map select by attribute “FEMPOP” > “MALPOP” and create new
shapefile out of selected states/regions. Export data and change symbology color
for majority male and for majority female.
4. For average household size change symbology for different sizes of households.
5. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 8: Rakhine State (Myanmar)
1. Use township, road, rivers, town shapefile for Rakhine state from MIMU and
Bangladesh shape file from GADM.
2. Select townships where IDPs are located, based on information provided by
World Health Organization, export and change symbology.
(http://www.searo.who.int/entity/emergencies/crises/SEARO_ESR_2_2012.pdf).
Map 9: Population, 2011(Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to graduated colors for population field.
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 10: Population Change 2001 to 2011 by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. In the attribute table add field “pop_change” and calculate percentage change
between population by district between 2011 and 2001.
3. Change symbology to graduated colors for “pop_change” field.
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Jacqueline Homann / Myanmar’s Census: Lessons from Sri Lanka / PA 388K-62615 / Fall 2014
4. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 11: Distribution of Ethnic Groups by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to dot density for major ethnic groups, where different dot
colors equal different ethnic groups.
3. Change dot size to 2 and dot value to 500.
4. Add shapefiles for capital city, country outline, district boundaries.
5. Add a second data frame and add dot density symbology for ethnic groups,
excluding Sinhalese, using same dot size and value.
Map 12: Unemployment by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to graduated colors for “Unemployment” field.
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 13: Employment by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to graduated colors for “Employment” field.
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 14: Type of Housing Material (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to pie for all housing material values
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 15: Hotspot Analysis of Housing Materials (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Use Hot Spot Analysis for brick housing material and kadjan
3. Test significance of findings by selecting for the following attribute for each
hotspot anaylsis: "GiZScore" > 2.58 AND "GiPValue" < .05; "GiZScore" < -2.58
AND "GiPValue" < .05
Map 16: Number of Migrants by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. Change symbology to graduated colors for “TotMigPop” field.
3. Add shapefiles for areas not enumerated, capital city, and country outline.
Map 17: Reasons for Migration by District (Sri Lanka)
1. Use “LKA_adm1” shapefile.
2. For each migration reason create separate data frame.
3. Using same intervals, change symbology to graduated colors for each reason for
migration.
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Jacqueline Homann / Myanmar’s Census: Lessons from Sri Lanka / PA 388K-62615 / Fall 2014
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