global mapping resources: insights from spatial analysis & exploration of data
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Global Mapping Resources: Insights from Spatial Analysis & Exploration of Data. Deborah Balk Baruch College, School of Public Affairs & CUNY Institute for Demographic Research 25 March 2008 2 nd Annual Census Workshop Series, Baruch College. Population Distribution. 15 years of progress - PowerPoint PPT PresentationTRANSCRIPT
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Global Mapping Resources: Insights from Spatial Analysis &
Exploration of Data
Deborah BalkBaruch College, School of Public Affairs & CUNY Institute for Demographic Research
25 March 20082nd Annual Census Workshop Series, Baruch College
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Population Distribution
• 15 years of progress• Counts Models• More than just population distribution
– Urbanization– Mortality– Other
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Evolution in global collection of population and poverty data
1920 1930 1940 1950 1960197
0 1980 1990 2000
Population count
Population projection
Population location
Economic Output
Poverty count
Poverty location
Urban Population
Urban locations
More attention to global scope
More attention to comparability
More attention to problem-oriented science
More attention to spatial frameworks
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http://sedac.ciesin.columbia.edu/gpw
Global Population Distribution
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Two views of Population Distribution (2000): Density estimates at the National level vs. 2.5’ grid
Legend
africa_adm0_popdens
PD00SQKM
2 - 10
11 - 25
26 - 100
101 - 250
251 - 633
Spatial data: Drilling down to finer resolution
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GPW v1 (1995) GPW v2 (2000) GPW v3 (2003)
Benchmark 1994 Benchmark 1990, 1995 Benchmark 1990, 1995, 2000
19,000 input units 127,000 input units 400,000+ input units globally
102,000 units in Africa
Population Counts (gridded)
http://sedac.ciesin.columbia.edu/gpw
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http://sedac.ciesin.columbia.edu/gpw
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http://sedac.ciesin.columbia.edu/gpw
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http://sedac.ciesin.columbia.edu/gpw
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Score card on global dataGlobal Extent
Data Quality
Data Availability
Institutional Cooperation
Population
Boundaries
Urban Areas
Roads
Poverty
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The US Census in International Perspective
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MEASURE DHS GPS Data Availability
October 20, 2005Slide courtesy of Livia Montana, Harvard University data available from http://www.measuredhs.com/
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What makes a GIS special?
• Data Visualization• Data Exploration• Data Integration• Data Analysis
– Service provision, public & constituency participation
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Visualization
• The organization of spatial information is different than that of tabular data. – That organization is often intrinsically
visual• Identification of neighbors
– Construction of neighborhoods
• Identification of factors that share characteristics
– Cites that are situated on a coast, along a river, etc
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Brazil 33
Cambodia 97
Cameroon 95
Australia 5
Afghanistan 168
China 30
IMR
…
Zimbabwe 78
In some scholarly traditions, the world is not only flat but also
alphabetized.
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www.ciesin.columbia.edu/povmap
Subnational underweight
database also available (sparser
coverage)
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Data exploration
Example courtesy of Professor Juliana Maantay, Lehman College, CUNY
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“Layers” of GIS InformationMunicipalities
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“Layers” of GIS InformationCensus Tracts
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“Layers” of GIS InformationLakes and Rivers
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“Layers” of GIS InformationPolluting Companies
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“Layers” of GIS InformationSchools
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Exploration Identification
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Human Settlements: Rendered as
Points
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Human Settlements: Render with spatial form or “Polygons”
• Note the variety of shape
• Spatial location of large and small cities
• Form conveys much more than points
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Integration
• Overlay or combine units in a spatial framework to produce estimates or analysis– School buffers (in above example)– Cities and coastal flooding
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Administrative Boundaries
Urban population and coastal flooding
• Calculations based on spatial overlays– All data are gridded+ urban extent boundaries+ low elevation coastal buffer
CambodiaVietnam
Ho Chi Minh City
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Which country has the greatest number of persons living at risk of
coastal flooding? Country Population in LECZ
% of Population in LECZ
China 143,879,600 11%
India 63,188,208 6%
Bangladesh 62,524,048 46%
Vietnam 43,050,593 55%
Indonesia 41,609,754 20%
Japan 30,477,106 24%
Egypt 25,655,481 38%
USA 22,859,359 8%
Thailand 16,478,448 26%
Philippines 13,329,191 18%
But, countries with the highest % of their populations in the zone include the populous deltaic countries and islands.
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Integration Analysis
• Poverty (derived from econometric model for subnational units)
• +• Elevation (derived from satellites,
measured on a contiguous grid)
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Ecuador: Poverty Rate• Urban areas are
centers of population & more affluent
High-poverty parroquias: are numerous more spatially distributed of much lower
population densities
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Ecuador: + Elevation
• Not all of Ecuador's poorest parroquias are found at high elevations, but there is a strong association: – Of the low-poverty parroquias, no non-urban ones are found at
elevations above 2000 meters – In contrast, of the high-poverty parroquias almost half are found at
elevations above 2000 meters, and nearly two-thirds are above 1000 meters.
• In reaching the poor, account for access associated with elevation.www.ciesin.columbia.edu/povmap
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Analysis• Spatial
– Characteristics & patterns associated with • Distance• Spatial relationships (e.g., neighbors)• Spatial correspondence (i.e., to other factors)
• Non-spatial based on spatial integration– Analysis of omitted variables
• May result in maps or tables, or both • May be “descriptive” or “analytic”
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Same pattern in Asia• Largest
cities tend to be near coasts
• Elevation overlay show that they also tend to be in low lying areas
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Final remark: Confidentiality
• Already a concern with information is collected from survey or census respondents
• Investigators and practitioners are ethically obligated to maintain respondent confidentiality– Geocoding may increase the difficulty in
so doing
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Frequency of cluster size(ordered by cluster ID number below)
• Ranges from 2 to 36 persons per cluster
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US Census data is an excellent model
• There are increasingly diverse and high quality data being produced & distributed throughout the world
• In rich and poor countries alike• Though coverage and consistency remain
barriers to global coverage for many variables of interest
• Using international data does not alter responsibility to standards, such as maintaining confidentiality