New publication: Jaegal, Y. and Miller, H.J. (2020) “Measuring the structural similarity of network time prisms using temporal signatures with graph indices,” Transactions in GIS, 24, 3-26.
Abstract. The network‐time prism (NTP) is an extension of the space‐time prism that provides a realistic model of the potential pattern of moving objects in transportation networks. Measuring the similarity among NTPs can be useful for clustering, aggregating, and querying potential mobility patterns. Despite its practical importance, however, there has been little attention given to similarity measures for NTPs. In this research, we develop and evaluate a methodology for measuring the structural similarity between NTPs using the temporal signature approach. The approach extracts the one‐dimensional temporal signature of a selected property of NTPs and applies existing path similarity measures to the signatures. Graph‐theoretic indices play an essential role in summarizing the structural properties of NTPs at each moment. Two extensive simulation experiments demonstrate the feasibility of the approach and compare the performance of graph indices for measuring NTP similarity. An empirical application using bike‐share system data shows that the method is useful for detecting different usage patterns of two heterogenous user groups.
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New publication: McHaney-Lindstrom, M., Hebert, C., Miller, H.J., Moffatt-Bruce, S. and Root, E. “Network analysis of intra-hospital transfers and hospital-onset Clostridium Difficile infection,” Health Information and Libraries Journal, https://doi.org/10.1111/hir.12274
Objectives. To explore how SNA can be used to analyse intra‐hospital patient networks of individuals with a HAI for further analysis in a GIS environment.
Methods. A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS.
Results. The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor.
Discussion. This study confirmed the importance that intra‐hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals.
Conclusion. Utilising SNA and GIS analysis in conjunction with one another provided a data‐rich environment in which the risk inherent in intra‐hospital transfer networks was quantified, visualised and interpreted for potential interventions.