Using Population Data to Address the Human Dimensions of Population Change D.M. Mageean and J.G. Bartlett Jessica Daniel 10/27/2009

Background Interactions between population and the environment are critical to understand; These interactions are relevant to ecology, economic development and human welfare Population has influences on environment: ◦ global warming, ozone levels, deforestation, biodiversity, pollution

Problem Gaps in the knowledge about how demographic characteristics affect the environment Factors to consider include: population size, growth rate, settlement patterns Many scientific disciplines = difficulty with generating a holistic view of issue

Problem Continued Need for science that focuses on human- environment interactions (Stern) ◦ Human causes of environmental change, effects of change on things humans value, feedback between humanity and environment Need to understand dynamic interaction between population: growth & migration (Hogan) ◦ Driving forces in global environmental change

U.S Case Study Developing countries often studied U.S.= modeling opportunities: ◦ Projected growth rate and predicted environmental consequences ◦ Growth patterns: regional variability ◦ Highly mobile population; fast growth rates in certain regions ◦ Shifts in population- from NE to SW ◦ Growth in areas subject to environmental stress

U.S. Case Study 2 questions: ◦ How to utilize data to increase understanding of human dimensions of land use? ◦ How compatible are data sets with current environmental data sets? Case study integrates various data sets ◦ Utilize methodological & conceptual methods ◦ Integrate remote sensing with environmental & human data

Methods 640 km 2 hexagon –unit of analysis ◦ Derived from EPA’s EMAP ◦ 12,600 hexagons digital overlay onto U.S. ◦ Hexagons used because distance between centroids of 2 adjacent hexagons was 27km AVHRR meteorological satellite images ◦ Landscape and habitat data; land cover classification

Methods: Land Cover Classifications 159 land cover classes > 13 classes (Anderson Level II) + urban class = 14 land cover types ◦ ex: cropland/pasture, rangeland, deciduous forests, water, alpine tundra, urban areas Characteristics calculated for each hexagon; landscape pattern metrics ◦ Ex: Edge frequency & classes, road abundance, shape complexity, etc...

Case Study One goal was to determine extent that population density represented the most human- environment interaction 9 Variables: ◦ From 1990 U.S. Census- county level data ◦ Could be linked to land use/ cover pattern variation Variables were examined with PCA (principle components analysis) Variables were weighted, each hexagon’s variable was calculated from intersected coverage areas

Results PCA: used to create composite indices of human effects 2 indices of interests ◦ Human settlement (PC 1) ◦ Independent growth & settlement (PC 2) Couldn’t use traditional linear regression Used an adaptive statistical technique to identify relationships between land use and climate ◦ CART (classification and regression tree)

CART CART: ◦ Splits focal variables (what want to study) with respect to independent variables ◦ Based on the threshold, data splits off the original variable  Ovals = splitting points at variables  Rectangles = stopping points ◦ Continues splitting until desired criterion is reached

Results Continued 2 general conclusions ◦ Urban centers primarily in East  Wetter, proximity to ocean, historical factors ◦ Interactions between climate variables influenced settlement patterns  Summer temperatures (east), annual temperature variations (west) Study identified interactions rather than correlations between settlement and environment