Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.

Purpose: American police departments are beginning to implement social media as a strategy to engage the surrounding communities through various methods, including Facebook and Twitter. The purpose of this paper is to examine the varieties in the use of Facebook and Twitter by local police departments. Design/methodology/approach: This study collected all data between October 1, 2013 and March 31, 2014 from Facebook and Twitter accounts of seven city police departments in the Hampton Roads area of Virginia. These agencies resemble many police departments in the USA, and in total serve a diverse population of approximately 1,435,000. Content analysis and statistical tests are conducted. Findings: Results show that specific types of posts are more engaging for the community. Facebook and Twitter interactions vary depending upon the type of posts, demonstrating that citizens are using Facebook and Twitter to interact in different ways. Research limitations/implications: The findings presented here give police agencies’ insight on how to appropriately adjust their use of social media to fulfill the needs of the citizens and optimize interactions with the community. Originality/value: This is the first study to systematically examine and analyze the varieties in the use of social media by traditional American local police departments and their interactions with citizens.

Particle filters are important techniques to support data assimilation for large-scale spatial temporal simulation systems. Distributed particle filters improve the performance of particle filtering by distributing particles to multiple processing units (PUs). While different resampling algorithms have been developed for distributed particle filters, less research has been conducted to investigate how to route particles among the PUs after resampling in effective and efficient manners. This paper develops particle routing policies in distributed particle filters with both the centralized resampling and the distributed resampling. The developed routing policies are evaluated from the aspects of the communication cost and the data assimilation accuracy based on an application of data assimilation for large-scale wildfire spread simulations.