TerraPop Vision

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 terrapop@umn.edu