Three Ways to Integrate Social
and Natural-Science Data (and
data analysis)
Larry Hamilton
University of New Hampshire
October 2007
3 Ways to Integrate
• Case study (one place, many times)
• Cross-sectional (many places, ~one time)
• Cross-sectional time series (many places,
many times)
– Most difficult in terms of data and analysis
– Required to approach “Arctic system” scope
• All three modes of integration can be used
within one project, as needed
(1) Case studies: one “place” followed
through time
• Definition of “place” can be fuzzy, e.g. North Iceland.
• Data matrix: each row is one time (e.g., 1990, 1991,
1992 …); columns are many variables.
– Time scale most often annual (for many years)
– But could be on daily, decadal or other scales
• Data analysis
– Informal: time plots, integrated historical narrative
– Formal: multivariate time series models
(1) Case study example: Iceland’s 20th-century “Herring Adventure”:
Changes in salinity, cod and herring catches, and the populations of two
fishing towns over 1900–2003. North Atlantic Arc (NAArc) project, paper
forthcoming in Deep Sea Research II. Informal narrative integration.
(1) Case study example: Observed and predicted daily skier visits, and
snow depth in ski country and in Boston, for individual New Hampshire ski
areas through one season (left) or nine seasons (right). Paper forthcoming
in International Journal of Climatology. Formal modeling integration.
(2) Cross sectional: many places,
mostly at one time
• Definition of “place” requires careful work, e.g. corresponding
grid cells with social units such as counties
• Data matrix: each row is one place (e.g., Barrow, Point Hope,
Kivalina …); columns are many variables
– Spatial scope could be regional (e.g., Alaska) or even pan-Arctic (all
“places” in the Arctic)
– Social units organized hierarchically, e.g. towns within counties within
states within countries
• Data analysis
– Informal: graphical displays, simple correlation-type methods
– Formal: multivariate analysis such as regression-type models
(2) Cross-sectional example: Social units (boroughs, etc.) mapped in
terms of 25 km2 EASE-grid cells for Humans and Hydrology at High Latitudes
(H3L) project.
(2) Cross-sectional example: Percent population 65 and older across
social units of pan-Arctic. Informal mapping integration.
(2) Cross-sectional example: Temperature and population trends of
pan-Arctic social units. Informal graphical integration.
(2) Cross-sectional example: Population change vs. primary or
tertiary-sector employment of pan-Arctic social units. Formal (although
simple) regression modeling.
(3) Cross-sectional time series: many
places, many times
• Definition of “place” requires careful work, as in crosssectional data.
• Data matrix: each row is one place-time (e.g. Barrow 1990,
1991, 1992, …; Point Hope 1990, 1991, 1992, …); columns are
many variables
– Spatial scope could be regional (e.g., Alaska) or even pan-Arctic (all
“places” in the Arctic)
– Data completeness and comparability present major challenges in
pan-Arctic research
• Data analysis
– Informal: “multiple-windows” graphics and parallel historical
narratives
– Formal: multilevel or mixed-effects modeling
(3) Cross-sectional time series example: Time series for multiple places.
Observed and modeled population trends of four Alaska regions for H3L.
(3) Cross-sectional time series example: Step-back view. The four Alaska
regions in previous slide are a subset of 27 “county-equivalent units” in Alaska.
Observed and modeled population trends, 1990–2006 (27 separate time series).
(3) Cross-sectional time series example: Stepping back again to show the
hierarchical nature of cross-sectional time series database (H3L).
Multilevel models
provide tools for
integrated
analysis of
complex data
such as crosssectional time
series. A single
multilevel model
underlies the 3
previous graphs.
There can be
more than one
nested level, e.g.
counties within
states within
nations.
Within one project, we can employ multiple modes of integration. For
example, “Greenland’s demographic transition” applies a case-study
approach, within the cross-sectional case study framework of H3L
This population pyramid for 2006 shows the
number of males (blue) and females (red) at
each age from 0 to 90 years.
Greenland’s population age structure reflects a fast transition
from hunting to postindustrial society, 1901–2005
End