Regression analysis with geographic information needs to take into consideration the inherent spatial autocorrelation and heterogeneity of the data. Due to such spatial effects, it is found that local regression such as the geographically weighted regression (GWR) tends to capture the relationships better. In addition, in panel data analysis, the variable coefficient panel regression can borrow such ideas of spatial autocorrelation and heterogeneity to develop models that would fit the data better and produce more accurate results than the pooled models. Despite the fact that both methods are well developed and utilized, models that take advantage of both methods simultaneously have eluded the research community. Combination of GWR and panel data analysis techniques has an obvious benefit: the added temporal dimension enlarges the sample size hence contains more degrees of freedom, adds more variability, renders less collinearity among the variables, and gives more efficiency for estimation. This research, for the first time, attempts such combination using a short regional development panel data from 1995 – 2001 of the Greater Beijing Area (GBA), China. A geographically weighted panel regression (GWPR) model is developed and compared with both cross-sectional GWR and panel regression. The very promising results from the study reveals that the GWPR indeed produced better and clearer results than both cross-sectional GWR and the panel data model. This indicates that the new method would potentially produce substantial new patterns and new findings that cannot be revealed via pure cross-sectional or time-series analysis.
About Dr. Danlin Yu
Dr. Danlin Yu is an associate professor of GIS and Urban Geography. Dr. Yu has over 10 years of geographic information analysis, cartographical design and presentation, statistical analysis, urban and regional planning and system dynamic modeling experiences, and has published widely in these fields with more than 60 peer-reviewed articles (in both Chinese and English). Dr. Yu has strong statistical analysis skills (especially spatial statistical and geostatsitcal) applied to the fields of spatial data interpolation, urbanization and planning, regional planning, public health, and population prediction, and strong system dynamic modeling skills that have been applied to urban dynamics simulation, sustainability studies and environmental management.