Cars, Dogs and Mean People: Environmental Fears and Dislikes of Children in Berlin and Paris

Abstract

The new social studies of childhood consider children active and creative users of their immediate environments. To make our cities more child-friendly, we need to understand how children perceive and explore their neighbourhoods, what they find attractive or unattractive about them. The research was carried out in socially and architecturally contrasted areas of two European capitals - Paris and Berlin, and involved, on the whole, 200 schoolchildren aged 10-13. To study children's attitudes towards spaces and places in these areas, I used a questionnaire and the method of subjective maps: children were asked to draw their way home from school, as well as "their territory", and to mark their maps with specially designed "emoticons".

Rather than studying differences in environmental attitudes between the groups of children, the paper draws on the qualitative analysis of those fears and dislikes that were found in common across the groups. Thus, the research resulted in a typology of children's dislikes and fears associated with their city areas. In particular, it was found out that places "occupied" by "mean or bizarre people", as well as by traffic, are strongly disliked or feared. Unpleasant images of the past (memories or "urban myths") or of the future (possible dangers) can also play the role of "the symbolical occupants" of a place and make children avoid it. The concept of a symbolically occupied territory is illustrated in a more detail on the example of a social housing estate on the outskirts of Paris. Examples of children's subjective maps are featured in the article.

The research suggests that the next step in making urban environments more child-friendly would be a consultation process with children - their participatory involvement in the planning and design of city spaces.

Keywords: children, urban segregation, educational segregation, drawings, subjective maps, child-centered research methods, visual methods