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TerraPop Vision An organizational and technical framework to preserve, integrate, disseminate, and analyze global-scale spatiotemporal data describing.

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Presentation on theme: "TerraPop Vision An organizational and technical framework to preserve, integrate, disseminate, and analyze global-scale spatiotemporal data describing."— Presentation transcript:

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2 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

3 TerraPop Goals Lower barriers to conducting interdisciplinary human- environment interactions research by making data with different formats from different scientific domains easily interoperable

4 DOMAINS & FORMATS POPULATION MICRODATA AREA-LEVEL DATA Source Data

5 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

6 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

7 Khartoum, CBS-Sudan

8 1973 Census Tapes arrive at Muller Media (New York) via Barcelona

9 Dhaka, Bangladesh Bureau of Statistics

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11 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

12 Growth of Public-Use Microdata (number of person-records available) We are Here

13 IPUMS-International Microdata Over 100 Collaborating National Statistical Agencies

14 Area Level (Vector) Data Describing characteristics of geographic polygons Median Household Income by Census Tract

15 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

16 Raster Data Represented as pixels in a grid Mean Precipitation

17 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

18 MICRODATA  AREA-LEVEL  RASTER Location-Based Integration

19 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

20 Location-Based Integration Individuals and households with their environmental and social context Microdata Area-level data Rasters

21 Location-Based Integration Summarized environmental and population Microdata Area-level data Rasters County ID G17003100001 G17003100002 G17003100003 G17003100004 G17003100005 G17003100006 G17003100007 County ID Mean Ann. Temp. Max. Ann. Precip. G1700310000121.2768 G1700310000223.4589 G1700310000324.3867 G1700310000421.5943 G1700310000524.1867 G1700310000624.4697 G1700310000725.6701 County ID Mean Ann. Temp. Max. Ann. Precip. Rent, Rural Rent, Urban Own, Rural Own, Urban G1700310000121.276831291063637365 G1700310000223.4589294910751469717 G1700310000324.3867341815891108617 G1700310000421.59431882425202142 G1700310000524.18672416572426197 G1700310000624.46972560934950563 G1700310000725.67012126653321215 characteristics for administrative districts

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

23 Area-Level Summary of Raster Data

24 Rasterization of Area-Level Data

25 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

26 Data Access System

27 General Workflow Browse variables and metadata Select variables and datasets View data cart contents Select output options

28 Prototype Data Access System

29 Variable Groups

30 Variable Browsing

31 Variable Metadata

32 To join the beta test email terrapop@umn.edu

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