We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark difference-in-differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
"Without divine intervention it is generally difficult to know with certainty which (if either) of the two above cases are true"This is from Fotheringham et al (1998) on the specification of spatial models. I find this quite amusing but I must say this is one of the most well written and accessible articles on spatial econometric models I've come across so far. It's not by chance this paper has become a great reference on the topic with more than 500 citations.
Don't trust summary statistics. Always visualize your data first https://t.co/63RxirsTuY pic.twitter.com/5j94Dw9UAf— Alberto Cairo (@albertocairo) 15 August 2016
As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of spatial flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single-location spatial events or fail to preserve the integrity of spatial flow events. In this research a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K-function, while maintaining the integrity of flow data was introduced. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgraded the classical hot spot detection method to the stage of “hot flow” detection. Spatial proximity of flows was measured by a four-dimensional distance. Several specific aspects of the method were discussed to provide evidence of its robustness and expandability, such as the multiscale issue, relative importance control and adaptive scale detection, using a real dataset of vehicle theft and recovery location pairs in Charlotte, NC.
Hidden biases in data are a problem. Even the largest of datasets have bits of information missing. Quoting Microsoft researcher Kate Crawford, Harford said one might think they have all the data, but there will always be people missing from any dataset.
To illustrate this, Harford pointed to the City of Boston's Street Bump smartphone app - a clever idea to tackle the problem of potholes. Bostonians were encouraged to download the app and set it running when out in their cars so that when their vehicles hit a pothole, the bump would be recorded by the phone's accelerometer and location data sent to the city's public works department. What happened, of course, was that most of the potholes that were identified and fixed were those in young, affluent areas - areas where people owned smartphones and could download the app.
City officials might have thought they had found a way to record every pothole, but that wasn't the case. As Harford concluded: "Some might think we are now able to measure everything; that we can turn everything into numbers. But we need to be wise enough to know that is always an illusion."
