Detecting urban markets with satellite imagery: An application to India

Author: Ran Goldblatt 

We propose a methodology for defining urban markets based on builtup landcover classified from daytime satellite imagery. Compared to markets defined using minimum thresholds for nighttime light intensity, daytime imagery identify an order of magnitude more markets, capture more of India’s urban population, are more realistically jagged in shape, and reveal more variation in the spatial distribution of economic activity. We conclude that daytime satellite data are a promising source for the study of urban forms.

Satellite Imagery Offers View of Urban Markets In Real-Time

Author: Ran Goldblatt 

In the paper, “Detecting Urban Markets with Satellite Imagery: An Application to India,” Khandelwal and co-authors Kathryn Baragwanath-Vogel, Ran Goldblatt, and Gordon Hanson use nighttime and daytime satellite imagery to define urban markets based on economic activity. While daytime imagery isn’t typically used in economic studies, the researchers find that using nighttime imagery alone has the drawbacks of marking some cities appear larger than they actually are

Standardizing Remote Sensing Data Collection at FEMA

Author: Ran Goldblatt 

During a disastrous event such as a major hurricane, wildfire, catastrophic flooding or a destructive earthquake, first responders must quickly understand the magnitude and the nature of the event and its impacts upon citizens and communities. Remotely sensed data can be crucial for preliminary awareness about the scope of such a disaster. That is why the USA’s Federal Emergency Management Agency (FEMA) has implemented a tool called the Area of Interest (AOI) Tasker to automatically identify and prioritize areas that require collection of satellite and aerial imagery. It was used for the first time during the 2018 hurricane and wildfire seasons.