Test Driving a Small-Area Population Forecasting Model: Seeking Additional Horsepower through Updated Engineering and Non-Demographic Fuel Additives Paul R. Voss and Guangqing Chi Applied Population Laboratory Center for Demography and Ecology University of Wisconsin – Madison BSPS Annual Conference 2006 September 2006 The University of Southampton Support provided by the Wisconsin Agricultural Experiment Station (Hatch project no. WIS04536)

Motivating Questions What can be done to improve the abysmally atheoretical nature of small-area population forecasts? In particular, what about a regression approach? Especially, what if we step outside our disciplinary confines and incorporate variables from other fields that, at face value, must be predictors of population growth? nature of the land (ground cover, wetlands, hydrography, slope) accessibility (transportation infrastructure, highways, airports, etc.) developability (high/low growth potential) desirability (natural and built amenities) livibility (potential quality of living) And, surely, should we not begin immediately to adopt some of the spatial econometric approaches long effectively employed by quantitative geographers and regional scientists?

Broaden our thinking regarding the relationships between population change and the host of factors influencing such change – some drawn from demography but many others from disciplines not normally involved in formal population forecasting efforts Categorize and integrate these factors in an effective way (construct indexes) Incorporate spatial process effects into the model Carry out the forecasting at a sufficiently fine geographic level that environmental and geophysical effects on population change can be better captured and modeled Proposed Regression Approach

Strategy Assemble all necessary data for 1990 base year Forecast populations for 2000 Compare 2000 forecasts with 2000 census results

Preview of Findings… It didn’t work

Our Region 1,837 minor civil divisions in state of Wisconsin, U.S. Our Data census data satellite imagery other data from several federal and state statistical agencies

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework Local demographic characteristics Population density Age: the young and the old Minority: black and Hispanic Institutional population (college) Education attainment: HS and Bchl. Geographic mobility Poverty Seasonal housing Sustenance organization: retail and agricultural industrial structure

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework Transportation infrastructure Residential preference Highway infrastructure Accessibility to airports Accessibility to highways Accessibility to workplaces

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework The potential for land conversion & development Water Wetlands Slope Tax-exempt (protected) lands Built-up lands

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework Natural & built amenities desirable for living Forests Water Lakeshore/riverbank/ coastline Golf courses slope

Population Demographics Accessibility Developability Livability Desirability Temporal Spatial Population Change Conceptual framework Urban conditions suitable for living Safety School performance Public transportation Buses Public water New housing County seat Income Real estate value Employment rate

Using Principal Components Analysis, We Developed Indices of Each of These Conceptual Areas Mapping the Indexes Confirmed What We know about the Areas And the Indexes all Revealed Fairly Strong Autocorrelation

Demographics Moran’s I = Moran’s I =

Moran’s I = Moran’s I = Accessibility

Moran’s I = Developability

Moran’s I = Desirability

Moran’s I = Moran’s I = Livability

We Ran Lots of Regressions Whatever the Approach, We Always Ran a Standard Normal Linear Regression and then Corrected this Specification by Incorporating Spatial Effects (spatial lag and spatial error)

OLS: SLM: SEM: Regressions without Any Temporal Consideration

OLS: SLM: SEM: Regressions with Temporal Consideration of Population Change

Regressions with Temporal Considerations of Population Change and Indices OLS: SLM: SEM:

Extrapolation projection Baseline projection Standard regressionPartial spatio-temporal regression Full spatio-temporal regression Dependent variables: population change, population density, population density change Indices generating methods: PCA, coefficients, coefficients and correlations Projections using indices Population forecast adjustments Evaluation and comparison Projection using individual variables Select the best one Select the better one Regression projection Forecasting and Evaluation

Model 1: Extrapolation projection Model 2: Standard regression Model 3: partial spatio-temporal regression (incorporating spatial population effects) Model 4: full spatio-temporal regression (incorporating spatial population effects and other neighbor characteristics) Four Finalized Population Forecasting Models

So… How did it turn out with all this re-engineering and fancy fuel additives? Not well

Population projections to 2000 without adjustments at the MCD level

Population projections to 2000 with adjustments at the MCD level

Summary Things just didn’t turn out as we hypothesized (and hoped) they would Our fancy spatio-temporal model outperformed simple regression in the estimation stage of the analysis (but who cares?) But, to our dismay, in the forecasting stage, the a- theoretical, simple extrapolation model outperformed the regression models in all comparisons but one In only one set of MCDs did the fancy model outperform all others: MCDs of fewer than 250 people. We launched this project in the belief that non-demographic variables might perform best in very small areas, and this finding may suggest that we explore that possibility further

Thanks!