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TerraPop Vision. An organizational and technical framework to preserve , integrate , disseminate , and analyze global-scale spatiotemporal data describing population and the environment. Develop tools to make data interoperable across data formats and subject areas - PowerPoint PPT Presentation
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TerraPop Vision
An organizational and technical framework to preserve, integrate, disseminate, and analyze global-scale spatiotemporal data describing population and the environment.
Develop tools to make data interoperable across data formats and subject areas
Break down disciplinary silos
TerraPop Goals
Lower barriers to conducting interdisciplinary human-environment interactions research by making data with different formats from different scientific domains easily interoperable
• D O M A I N S & F O R M AT S• P O P U L AT I O N M I C R O D ATA• A R E A - L E V E L D ATA
Source Data
Making disparate data formats interoperable
Microdata: Characteristics of individuals and households
Area-level data: Characteristics of places defined by boundaries
Raster data: Values tied to spatial coordinates
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
Microdata Structure
Household record(shaded) followedby a person recordfor each member of the household
Relationship
AgeSexRace
BirthplaceMother’s birthplace
Occupation
For each type ofrecord, columns correspond tospecific variables
Geographic and housingcharacteristics
Khartoum, CBS-Sudan
1973 Census Tapes arrive at Muller Media (New York) via Barcelona
Dhaka, Bangladesh Bureau of Statistics
The Power of Microdata
Customized measures: Variables based on combined characteristics of family and household members, capitalizing on the hierarchical structure of the data
Multivariate analysis: Analyze many individual, household, and community characteristics simultaneously
Interoperability: Harmonize data across time and space
Age classification for school enrollment in published U.S. Census
1970 19903-4 3-45-6 5-6
7-13 7-914-15 10-1416-17 15-17
0
500,000,000
1,000,000,000
1,500,000,000
2,000,000,000
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Microdata digitized from historicalcensusmanuscripts
Microdata from international statistical agencies
Microdata from U.S. Census Bureau
Growth of Public-Use Microdata (number of person-records available)
We are Here
IPUMS-International Microdata
Over 100 Collaborating National Statistical Agencies
Area Level (Vector) Data
Describing characteristics of geographic polygons
Median Household Income by Census Tract
Area-level Data SourcesCensus tables, especially where microdata is unavailableOther types of surveys, data
Agricultural surveys Economic surveys, data Election data Disease data
Legal/policy information Environmental policy Social policy Human rights
Raster DataRepresented as pixels in a grid
Mean Precipitation
Raster Data
Beta system Global Land Cover 2000 Harvested Area and Yield for 175
crops (Global Landscapes Initiative)
Temperature and precipitation (WorldClim)
Future additions Additional LU/LC and climate
datasets Elevation Vegetation characteristics Bioclimatic & ecologic zones
M I C R O D ATA A R E A - L E V E L R A S T E R
Location-Based Integration
Location-Based IntegrationMicrodata
Area-level dataRasters
Mix and match variables originating in
any of the data structures
Obtain output in the data structure most
useful to you
Location-Based Integration
Individuals and households with their environmental
and social context
Microdata
Area-level dataRasters
Location-Based Integration
Summarized environmental
and population
Microdata
Area-level dataRasters
County IDG17003100001G17003100002G17003100003G17003100004G17003100005G17003100006G17003100007
County IDMean Ann. Temp.
Max. Ann. Precip.
G17003100001 21.2 768G17003100002 23.4 589G17003100003 24.3 867G17003100004 21.5 943G17003100005 24.1 867G17003100006 24.4 697G17003100007 25.6 701
County IDMean Ann. Temp.
Max. Ann. Precip.
Rent, Rural
Rent, Urban
Own, Rural
Own, Urban
G17003100001 21.2 768 3129 1063 637 365G17003100002 23.4 589 2949 1075 1469 717G17003100003 24.3 867 3418 1589 1108 617G17003100004 21.5 943 1882 425 202 142G17003100005 24.1 867 2416 572 426 197G17003100006 24.4 697 2560 934 950 563G17003100007 25.6 701 2126 653 321 215
characteristics for administrative
districts
Location-Based Integration
Rasters of population and environment data
Microdata
Area-level dataRasters
Area-Level Summary of Raster Data
Rasterization of Area-Level Data
RasterizationBeta system – Uniform distribution assumption
Use lowest level units available Rates – same value for all cells in unit Counts – evenly distributed across cells in unit
Future – Distribute based on ancillary data
Requires research on available methods May provide as service – users select:
Ancillary data Weights Spatial distribution parameters
Data Access System
General Workflow
Browse variables and metadataSelect variables and datasetsView data cart contentsSelect output options
Prototype Data Access System
Variable Groups
Variable Browsing
Variable Metadata
To join the beta test email [email protected]