New publication: Miller, H.J. (2020) “GIScience, fast and slow – Why faster geographic information is not always smarter,” Progress in Human Geography, 44, 129-138.
Abstract. The growing maturity and deployment of low-cost georeferenced sensors, navigation systems, fast wireless communication, cyberinfrastructure and the Internet of Things (IoT) is accelerating the speed of geographic data flowing from the environment and our capabilities for reacting quickly to geographic information, often automatically and in real-time. This is leading to the rise of real-time GIS and smart cities technologies. While reacting quickly to changing circumstances has value, there are potentials for unintended consequences and rebound effects resulting from our inability to build geographic knowledge quickly and the selective acceleration of societal processes. This report discusses why these unintended outcomes may occur, and suggests technical and scientific approaches for understanding and managing the potential impacts of fast geographic data.
New publication: Park, Y., Mount, J., Liu, L., Xiao, N. and Miller, H.J. (2020) “Assessing public transit performance using real-time data: Spatio-temporal patterns of bus operation delays in Columbus, Ohio, USA,” International Journal of Geographical Information Science, 34, 367-392.
ABSTRACT: Public transit vehicles such as buses operate within shared transportation networks subject to dynamic conditions and disruptions such as traffic congestion. The operational delays caused by these conditions can propagate downstream through scheduled transit routes, affecting system performance beyond the initial delay. This paper develops an approach to measuring and assessing vehicle delay propagation in public transit systems. We fuse data on scheduled bus service with real-time vehicle location data to measure the originating, cascading and recovery locations of delay events across space with respect to time. We integrate the resulting patterns to construct stop-specific delay propagation networks. We also analyze the spatiotemporal patterns of propagating delays using parameters such as 1) transit line-based network distance, 2) total propagating delay size, and 3) distance decay. We apply our methodology using publicly available schedule and real-time location data from the Central Ohio Transit Authority (COTA) public bus system in Columbus, Ohio, USA. We find that delay initiation is spatially and temporally uneven, concentrating on specific stops in downtown and specific suburban locations. Core stops play a critical role in propagating delays to a wide range of connected stops, eventually having a disproportional impact on the on-time performance of the bus system.
KEYWORDS: Mobility, urban applications, public bus delay propagation, public transport reliability assessment, spatio-temporal data modelling
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.