Non-industrial private forest landowners’ choices of timber management strategies.

The factors affecting the non-industrial, private forest landowners' (hereafter referred to using the acronym NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the landowners' choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models. The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, landowner's positive price expectations for the next eight years, landowner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40. In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.34 and the relative root mean square error obtained from the test data was 0.38. In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with the complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.

Onko oppimistulokset valettu betoniin? : Tutkimus Helsingin kaupunkirakenteen ja peruskoulujen oppimistulosten välisestä yhteydestä ja kouluvalintojen vaikutuksista

The aim of this study is to find out how urban segregation is connected to the differentiation in educational outcomes in public schools. The connection between urban structure and educational outcomes is studied on both the primary and secondary school level. The secondary purpose of this study is to find out whether the free school choice policy introduced in the mid-1990´s has an effect on the educational outcomes in secondary schools or on the observed relationship between the urban structure and educational outcomes. The study is quantitative in nature, and the most important method used is statistical regression analysis. The educational outcome data ranging the years from 1999 to 2002 has been provided by the Finnish National Board of Education, and the data containing variables describing the social and physical structure of Helsinki has been provided by Statistics Finland and City of Helsinki Urban Facts. The central observation is that there is a clear connection between urban segregation and differences in educational outcomes in public schools. With variables describing urban structure, it is possible to statistically explain up to 70 % of the variation in educational outcomes in the primary schools and 60 % of the variation in educational oucomes in the secondary schools. The most significant variables in relation to low educational outcomes in Helsinki are abundance of public housing, low educational status of the adult population and high numbers of immigrants in the school's catchment area. The regression model has been constructed using these variables. The lower coefficient of determination in the educational outcomes of secondary schools is mostly due to the effects of secondary school choice. Studying the public school market revealed that students selecting a secondary school outside their local catchment area cause an increase in the variation of the educational outcomes between secondary schools. When the number of students selecting a school outside their local catchment area is taken into account in the regressional model, it is possible to explain up to 80 % of the variation in educational outcomes in the secondary schools in Helsinki.