567.1
Network Analysis and Spatial Analysis Combined : The Case of Reform and Philantropy in 1900 Paris

Monday, July 14, 2014: 5:30 PM
Room: 416
Oral Presentation
Christian TOPALOV , Centre Maurice Halbwachs, École Hautes Études Sciences Sociales, Paris, France
Historical monographs on specific spheres of social reform are many when it comes to the late 19th and early 20th c. in the largest industrialised countries of the time – it was a « progressive era » for all of them. Every monograph points out that a good deal of the characters involved were simultaneously present in many other fields of reformist action – e.g. public health, housing reform, work relief, « protection » of women and children, prison reform, social science, etc. This invites scholars to cross the boundaries of specialisation and study reform and philanthropy as a possible unified field of action, interaction and sociability. This can be done through network analysis : voluntary associations used to publish reports and lists of leaders, members and supporters – by making the names of their followers public they increased the legitimacy of the cause.

This circumstance made it possible to collect complete lists of members of 106 reform associations, totaling 17663 people in 1900 Paris. Two directories of Paris charities have been added to the data base, i.e. 1346 charities and 2060 people. Sources provide the adress of the charities and most of the individuals. A GIS was set up that allows it to locate every adress on the street plan of Paris (as of 1888). This material is exceptional by its quantity and systematic character.

Using the data base, we can separately and simultaneously consider both social and spatial links : the affiliation of people to the same institution, the residence of people in the same building or neighbourhood, the co-presence of two institutions at the same adress.

The paper presents the most interesting results and discuss the methodological solutions that had been developed for combining social and spatial analysis, and facing the problems related to the bulky character of the data.