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Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics



Olivera Kotevska, Aaron G. Kusne, Daniel V. Samarov, Ahmed Lbath, Abdella Battou


Today's cities generate tremendous amounts of data thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context- aware services and systems, ensuring smart city services are optimized to the dynamic city environment. Critical resources in these smart cities will be more rapidly deployed to regions in need, and those regions predicted to have an imminent or prospective need. For example, crime data analytics may be used to optimize the distribution of police, medical, and emergency services. However, as smart city services become dependent on data, they also become susceptible to disruptions in data streams, such as data loss due to signal quality reduction or due to power loss during data collection. This paper presents a dynamic network model for improving service resilience to data loss. The network model identifies statistically significant shared temporal trends across multivariate spatiotemporal data streams and utilizes these trends to improve data prediction performance in the case of data loss. Dynamics also allow the system to respond to changes in the data streams such as the loss or addition of new information flows. The network model is demonstrated on City-based crime rates reported in Montgomery County, Maryland. A resilience network is developed utilizing shared temporal trends between cities to provide improved crime rate prediction and robustness to data loss, compared to the use of single city-based auto-regression, with a maximum improvement of performance of 7.8 % for Silver Spring and an average improvement of 5.6 % among cities with high crime rates. The model also correctly identifies all of the optimal network connections. City-to-city distance is identified as a predictor of shared temporal trends in crime and weather is shown to be a strong predictor of crime in Montgomery County.
IEEE Access Journal


smart city, resilience, adaptive, dynamic, spatiotemporal, prediction, causality, crime
Created October 12, 2017, Updated November 10, 2018