Colloquium

Modelling visual impact of power lines; Does visibility influence health risk perception?

Organisator Laboratory of Geo-information Science and Remote Sensing
Datum

wo 9 april 2014 08:30 tot 09:00

Locatie Gaia, building number 101
Droevendaalsesteeg 3
101
6708 PB Wageningen
+31 317 48 16 00
Zaal/kamer 1

by Mihaela Violeta Gheorghe (Romania)

Abstract

In our daily living environment the perception of high voltage power lines in landscapes could have a serious impact on well-being, their presence being associated with certain burdens, like electromagnetic fields, property value estimates or visual intrusion. The present study focuses on the latter burden, visual intrusion, believed to have an influence on how people perceive health-related risk associated to living nearby power lines. Most of the studies that assess visibility use conventional methods, like photography, questionnaire surveys or expert assessments. Since geo-data has become a commodity and offer detailed information in spatial and temporal sense, a geo-data modeling approach could help understanding the relation between high voltage power lines visibility and human health risk perception. This concept is tested by finding appropriate data sets, developing a clear definition for visibility and a transparent calculation procedure to derive visibility, and link it to personal health concerns. The procedure of deriving visibility is applied for a study area where levels of health concern related to power lines have already been derived by means of digital questionnaires, in a previous psychological study. The existence of a study area made possible the validation of the obtained results, through fieldwork measurements. The validation results brought into attention the importance of input data quality. Significant discrepancies between used data and reality raise raise questions regarding even the feasibility of the study. The correlation found between visibility and health risk perception is low. However, the initial hypothesis regarding a possible correlation between the two factors is not entirely dismissed. The possible causes that led to the obtained results are discussed. Considering the possible causes, recommendations are given for future adjustments of the method and correlation analysis.