Analyzing satellite data in Stata

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Presentation transcript:

Analyzing satellite data in Stata Austin Nichols & Hiren Nisar Abt Associates Inc. July 27, 2017 Stata Conference Baltimore, MD

Background Recently, there has been an increased interest in remote sensing data for evaluation purposes (especially pictures taken by satellites, but less often drone-based data collection). Kemmerer, Savastano, and Faller (1976) – used Landsat in the 1970’s to find fishing grounds. Henderson, Storeygard, and Weil (2012) – estimated GDP growth from satellite images of lights in settled areas Neal, et al. (2016) – demonstrate a method for estimating poverty from high-resolution satellite imagery. Researchers at Abt used satellite images to quantify the spread of oil from Deepwater Horizon (Abt 2016).

Using Satellite Images in Stata bmp2dta-user-written command converts a Windows 24-bit bitmap image into a Stata dataset; Variables included I and J are rows and columns; R, G, & B are red green and blue content for each pixel; Can do standard image processing before converting images to Stata datasets Further, can also do custom processing afterwards.

Newer Techniques Available in Stata Can use the suite of Spatial autoregressive (SAR) models new to Stata 15. Can also use the full complement of R, Python, & Fortran machine learning tools Or newer tools for Stata (not yet publicly released) As in Jean et al. (2016):

Many Sources for Satellite Images USGS and NASA provide images at high resolution (current and historical)

Example: Baltimore, MD USGS satellite imagery for Baltimore MD: (https://earthexplorer.usgs.gov)

Example: Baltimore, MD Code : bmp2dta using b, pic(balt.bmp) use b, clear scatter i j if r<39&g<39&b<39, m(S) mcol(edkblue) scatter i j if r<39&g<39&b<39, msize(vtiny) m(S) mcol(edkblue) || scatter i j if g>101 & r<101 & b<100, msize(vtiny) m(S) mcol(dkgreen) xsize(5) ysize(5) yla(,nogrid) xsc(off) ysc(off) leg(off) xla(1/500) graphr(fc(white) lw(none) margin(zero)) plotr(margin(zero))

Example: Washington, DC Can use more colors, apply filters, do calculations, estimate classifications (clusters). For example, “cluster kmeans r g b” can be used to detect pixels dominated by water, trees, or built-up areas. Can just plot one class of pixel, to disaggregate layers. Code: bmp2dta using dc276, pic(dc.bmp) use dc276, clear cluster kmeans r g b, k(4) gen(c) forv i=1/4 { su r if c==ì', mean loc cì' "`=round(r(mean))'" su g if c==ì', mean loc cì' "`cì'' `=round(r(mean))'" su b if c==1, mean } sc i j if c==1, msize(small) m(S) mcol("`c1'")||sc i j if c==2, msize(small) m(S) mcol("`c2'%40")||sc i j if c==3, msize(vtiny) m(S) mcol("`c3'%20")||sc i j if c==4, msize(vtiny) m(S) mcol("`c4'%20") xsize(5) ysize(5) yla(1/200 ,nogrid) xsc(off) ysc(off) leg(off) xla(1/200) graphr(fc(white) margin(zero) lw(none)) plotr(margin(zero)) scale(.2)

Example: COPE Similarly, can use the same methods to process any picture COPE Code: bmp2dta using putin, pic(Putin.bmp) use putin, clear loc i 1 foreach c in "29 82 97" "86 151 163" "245 255 201" "161 30 34" "97 10 29" { g dì'=(r-real(word("`c'",1)))^2+(g-real(word("`c'",2)))^2+(g-real(word("`c'",3)))^2 loc i=ì'+1 } g color=1 if d1==min(d1,d2,d3,d4,d5) qui forv i=2/5 { replace color=ì' if dì'==min(d1,d2,d3,d4,d5) keep if inrange(j,250,1600) sc i j if color==2, msize(vtiny) m(S) mcol("86 151 163")||sc i j if color==1, msize(vtiny) m(S) mcol("29 82 97")||sc i j if color==3, msize(vtiny) m(S) mcol("245 255 201")||sc i j if color==4, msize(vtiny) m(S) mcol("161 30 34")||sc i j if color==5, msize(vtiny) m(S) mcol("97 10 29") xsize(5) ysize(4) yla(1/500,nogrid) xsc(off) ysc(off) leg(off) xla(250/500) graphr(fc(white) margin(zero))

References Blumenstock, Joshua E. 2016. “Fighting Poverty with data." Science, 353(6301): 753-54. Henderson, J. Vernon, Adam Storeygard, and David N. Weil. 2012. "Measuring Economic Growth from Outer Space." American Economic Review, 102(2): 994–1028. Jean, Neal, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon. 2016. "Combining satellite imagery and machine learning to predict poverty." Science, 353(6301): 790-4. Kemmerer, Andrew J., Kenneth J. Savastano, and Kenneth Faller. 1976. "LANDSAT Menhaden and Thread Herring Resources Investigation." Slidell, Louisiana: NASA. Wallace BP, Brosnan T, McLamb D, Rowles T and others. 2010. “Effects of the Deepwater Horizon oil spill on protected marine species.” Endangered Species Research, 33:1-7.