The relationship between environmental amenities and changing human settlement patterns between 1980 and 2000 in the Midwestern USA

Natural resource amenities may be an attractor as people decide where they will live and invest in property. In the American Midwest these amenities range from lakes to forests to pastoral landscapes, depending on the ecological province. We used simple linear regression models to test the hypotheses that physiographic, land cover (composition and spatial pattern), forest characteristics, land use on undeveloped land, public ownership, soil productivity and proximity to urban centers predict changes in population, housing, and seasonal housing densities over a 10-year interval (1980–1990). We then generated multiple-regression models to predict population, total and seasonal housing density change in the most recent decade (1990– 2000) based on ownership and ecological conditions in 1990 and tested them by comparing the predictions to actual change measured by the US Census Bureau. Our results indicate that the independent variables explained between 25 and 40% of the variability in population density change, 42–67% of the variability of total housing density change, and 13–32% of the variability in seasonal housing density change in the 1980s, depending on the province. The strength of the relationships between independent and dependent variables varied by province, and in some cases the sign varied as well. Topographic relief was significantly related to population growth in all provinces, and land cover composition and the presence of water was significantly related to total housing growth in all provinces. There was a surprisingly limited association of any of the independent variables to seasonal housing growth in the northern province, which is commonly perceived to attract seasonal use because of ecological amenities. Proximity to urban centers is related to population and housing density change, but not seasonal housing density change. Our tests indicated that models for population density change showed some utility, but the models for total and seasonal housing density generally performed poorly. Ecologic variables were consistently poor at predicting seasonal housing density change. Our results show that environmental characteristics appear to have some influence on the spatial distribution of population and housing change in the Midwest, although other factors that were not modeled are clearly dominant.