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

DOMAINS & FORMATS POPULATION MICRODATA AREA-LEVEL DATA 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

Microdata Structure Household record (shaded) followed by a person record for each member of the household Relationship Age Sex Race Birthplace Mother’s birthplace Occupation For each type of record, columns correspond to specific variables Geographic and housing characteristics

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

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 Sources Census tables, especially where microdata is unavailable Other types of surveys, data  Agricultural surveys  Economic surveys, data  Election data  Disease data Legal/policy information  Environmental policy  Social policy  Human rights

Raster Data Represented 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

MICRODATA  AREA-LEVEL  RASTER Location-Based Integration

Microdata Area-level data Rasters 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 data Rasters

Location-Based Integration Summarized environmental and population Microdata Area-level data Rasters County ID G G G G G G G County ID Mean Ann. Temp. Max. Ann. Precip. G G G G G G G County ID Mean Ann. Temp. Max. Ann. Precip. Rent, Rural Rent, Urban Own, Rural Own, Urban G G G G G G G characteristics for administrative districts

Location-Based Integration Rasters of population and environment data Microdata Area-level data Rasters

Area-Level Summary of Raster Data

Rasterization of Area-Level Data

Rasterization Beta 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 metadata Select variables and datasets View data cart contents Select output options

Prototype Data Access System

Variable Groups

Variable Browsing

Variable Metadata

To join the beta test