# Museumpedia: Comparing differnt museum structures

# Methodology

1.Making the adjacency matrix by numbering the room

We use the floor plan in each museum to make the basic information for creating the network. First, we check the number of rooms, their locations, and the connectivity between rooms. Then, we assign the number for each room. This is the fundamental information for the network.

2.Making the network from the building layout

There are several ways to convert a spatial structure into a graph. We apply the primal representation such that the centroid of the room is the node and the center line of the corridor or any kind of walkable connection between rooms is the link. The undirected graph G consists of two elements: N nodes and E edges (links) between pairs of nodes. The graph G is described by the N*N adjacency matrix A, whose entry *α_ij* (i, j = 1, …, N) is equal to 1 if a link between the node i and j exists and 0 otherwise.

# Network analysis

It presents diagrams of the key properties of the graph and the network analysis. The visitor’s sequential movement can be described as a path composed of the order of visited nodes and the links between them. This acts as the basic data for the network analysis.

# Results

It presents the results of the computation of the centrality indicators in two datasets. We can observe that the centrality scores for all indicators do not change for the single large ring layout. That is, the node’s score is independent of the type of indicator.

The results for the grid layout greatly change depending on the centrality indicators. For example, higher scores for betweenness can be found in the four nodes at the heart of the network. Although visitors are free to explore any rooms in the network, they are required to pass through those rooms to take the shortest paths. Similarly, higher scores for closeness are found in the heart of the network, and those scores gradually decrease toward the periphery. This indicates that the former rooms are close to all the other rooms in the network and, consequently, they are more “integrated” than the ones in the periphery.

The topological representation of the spatial structure of the first floor of the Louvre Museum. The nodes identified by the numbers indicate popular artworks. The size of the node indicates the scores of betweenness centrality (the higher it is, the larger the node becomes).

# Publications

Yoshimura, Y., Sinatra, R., Krebs, A., Ratti, C., 2019, “Analysis of visitors’ mobility patterns through random walk in the Louvre Museum”, Journal of Ambient Intelligence and Humanized Computing, doi.org/10.1007/s12652-019-01428-6

Yoshimura, Y., Krebs, A., Ratti, C., 2017, “Noninvasive Bluetooth Monitoring of Visitors’ Length of Stay at the Louvre”, IEEE Pervasive Computing 16 (2), p.24-34.

Yoshimura, Y., Sobolevsky, S., Ratti, C., Girardin, F., Carrascal, J P., Blat, J., Sinatra, R., 2014, “An analysis of visitors’ behaviour in The Louvre Museum: a study using Bluetooth data”, Environment and Planning B: Planning and Design 41 (6), p.1113-1131.

Yoshimura, Y., Girardin, F., Carrascal, J. P., Ratti, C., Blat, J., 2012, “New Tools for Studing Visitor Behaviours in Museums: A Case Study at the Louvre” in Information and Communication Technologis in Tourism 2012. Proceedings of the International conference in Helsingborg (ENTER 2012) Eds Fucks M, Ricci F, Cantoni L (Springer Wien New York, Mörlenback), p. 391-402.