  Many real-world phenomena can be represented by a spatio-temporal signal:
where, when, and how much. Social media is a tantalizing data source for those
who wish to monitor such signals. Unlike most prior work, we assume that the
target phenomenon is known and we are given a method to count its occurrences
in social media. However, counting is plagued by sample bias, incomplete data,
and, paradoxically, data scarcity -- issues inadequately addressed by prior
work. We formulate signal recovery as a Poisson point process estimation
problem. We explicitly incorporate human population bias, time delays and
spatial distortions, and spatio-temporal regularization into the model to
address the noisy count issues. We present an efficient optimization algorithm
and discuss its theoretical properties. We show that our model is more accurate
than commonly-used baselines. Finally, we present a case study on wildlife
roadkill monitoring, where our model produces qualitatively convincing results.
