The Dendritic Cell Algorithm (DCA) is a multi-agent artificial immune system designed for anomaly detection. The algorithm is composed of artificial dendritic cell agents that process timestamped stream data. The lifespan of a cell agent is determined by its migration threshold, which influences the algorithm's dynamics significantly. The migration threshold is fixed during cell initialisation which limits the performance on various problems. This work proposes a dynamic migration threshold adjustment mechanism by mapping the population to a 2D grid and using Von Neumann Neighbourhoods to adapt this parameter at run time. This forms a novel algorithm variant termed 'twoDCA', implemented using the Repast Simphony agent based java API. This new algorithm is applied on synthetic stream data using a sin function generator with two different ways of migration threshold parameter generation. The experimental results show that the introduction of the Von Neumann Neigh-bourhoods has led to a statistically significant impact on certain behaviours of the algorithm. In particular, the great dynamics of twoDCA is realised by carrying forward the updated migration thresholds between cell reincarnations. The twoDCA is readily applicable to 2D data streams, which will diversify the range of applications substantially to which the algorithm can be applied and yields opportunities to add learning components to the core functionality of the algorithm.