Untitled - Journal of Lighting Engineering

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Although the areas within the neighbourhoods were rather similar, different neighbourhood and lighting components may yield different connections between the factors. Regression analysis examines a group of independent variables at the same time and seeks the independent explanatory effects (controlling for other variables in the model) instead of mere zero-order correlations; it was therefore considered suitable for an exploratory study. Possible connections between the lighting attributes and safety/pleasantness were first explored with linear regression Lighting promoting safety and creating a sense of pleasantness analysis (enter method)/standard multiple regression; the analysis revealed significant relations between the lighting attributes and the dependent variables. Only these attributes were included in the subsequent regression analysis (enter). Finally, the suggested connections were tested using the Spearman correlation as well. As with in some of the statements, the scale ranged from negative to positive and again back to negative (e.g. ‘lighting on the pathway is too strong – totally inadequate’): Nonlinear regression was also checked, but no significant nonlinear connections were detected. Table 3 Mean values (M) (on 0-6 scale) and standard deviations (SD) for all rating items in the five test areas (A-E). 4. Results Descriptive statistics are presented in Table 3. Brightness perceptions corresponded well with the luminance measurements whereas there were considerable differences between the TI values and perceived glare measurements. However, as stated before, TI values describe disability glare in road lighting conditions, whereas perceived glare describes the subjective feeling of discomfort. Data on the significant (p≤0.05) connections and coefficients are presented in Table 4 (perceived safety) and Table 5 (pleasant lighting). The tables present the significant coefficients indicated by the linear regression analysis (enter method). After conducting regression analysis, we used the Spearman correlation test. Data on the significant correlations (Spearman correlation, p≤0.05) between the variables are provided in Table 6 (perceived safety) and Table 7 (pleasant lighting). Ingineria Iluminatului 2012; 14, 2: 33-46 39
Heli NIKUNEN, Marjukka PUOLAKKA, A RANTAKALLIO, K KORPELA, Liisa HALONEN The results indicate that in suburban nightscapes, perceived safety is most strongly connected with the perceived colour quality of light. It also had single connections with even, extensive and bright lighting. Also, a pleasant lighting environment is most strongly connected 40 with the perceived colour quality of light. It had a single positive connection with brightness in area D and a negative connection with glare in area E. In area C, it had a close to significant connection with even lighting. Table 4 Linear regression (enter) between perceived safety and the five lighting attributes, significant (p≤0.05) connections and standardized coefficients Table 5 Linear regression between pleasant lighting and the five lighting attributes, significant (p≤0.05) connections and standardized coefficients Ingineria Iluminatului 2012; 14, 2: 33-46
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