Sensor-Driven, Spatially Explicit Agent-Based Models

Abstract

Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting.

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

“…They can use cross‐scale data for calibration and validation, which could be very useful given the increasing availability of non‐numeric data (e.g., images and videos) and individual‐level data. For example, spatial and temporal data received from remote sensors can be used to inform and improve the spatial representation of agent‐based models using deep learning methods (Oloo 2019). ML can also be used to calibrate individual agent behavior, and then, cross‐validate the model with aggregate data.…”