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Full-Text Articles in Other Economics

Ecological Determinants Of Respiratory Health: Examining Associations Between Asthma Emergency Department Visits, Diesel Particulate Matter, And Public Parks And Open Space In Los Angeles, California, Jason A. Douglas, Reginald S. Archer, Serena E. Alexander Mar 2019

Ecological Determinants Of Respiratory Health: Examining Associations Between Asthma Emergency Department Visits, Diesel Particulate Matter, And Public Parks And Open Space In Los Angeles, California, Jason A. Douglas, Reginald S. Archer, Serena E. Alexander

Health Sciences and Kinesiology Faculty Articles

Los Angeles County (LAC) low-income communities of color experience uneven asthma rates, evidenced by asthma emergency department visits (AEDV). This has partly been attributed to inequitable exposure to diesel particulate matter (DPM). Promisingly, public parks and open space (PPOS) contribute to DPM mitigation. However, low-income communities of color with limited access to PPOS may be deprived of associated public health benefits. Therefore, this novel study investigates the AEDV, DPM, PPOS nexus to address this public health dilemma and inform public policy in at-risk communities. Optimized Hotspot Analysis was used to examine geographic clustering of AEDVs, DPM, and PPOS at the ...


Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz Dec 2017

Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz

Economics Faculty Articles and Research

Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These “poverty maps” are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 m2 and 50 cm2 respectively, covering all 2 million km2 of Mexico. Benchmark poverty estimates come from ...