In this study, we analyze the spatial models to fit the annually recorded Tokyo official land prices data from 1997 to 2018.　For the covariates in the spatial models, we use 13 explanatory variables, which include indexes for access to central Tokyo, walking distances to nearest stations, building coverage ratios, acreage, and so on.　A summary of findings is as follows.　First, the land prices become lower as further away from a central area of Tokyo year by year.　Second, the obtained generalized least squares (GLS) estimates together with its time series variations reveals us that the land price differences become larger between west and east side of Tokyo.　Finally, the nugget effects of the residuals in spatial models are becoming larger and larger for non-central region, whereas those decreasing in central area of Tokyo.　This can be explained by the individual factors, for example, daily life noises of a local area, daylight conditions, and so on, that cause spatial variation in land prices to become large.　As a result, we found that the bipolarization of land prices in Tokyo is getting larger and larger.

For the case when the longitudinal measurement data is observed in an individual with spatial information, we attempt to apply a growth curve model with interaction terms in time and space.　Considering basis functions of time and space, simultaneous confidence intervals for predicted curves on the time axis or predicted surfaces in space are given for each fixed condition.　We also consider the application of a local growth curve model to reduce the number of bases for the interaction terms, especially in space.　The proposed method is applied to the temperature data observed at 35 stations in Canada for six periods from March to August, and the applicability of the model is verified.　The fit of the proposed model is good in terms of multiple correlation coefficients between observation and fitted values, although the observed temperature data show various changes over time such in different stations.