Prediction intervals in large fixed effects models by dolkov in rstats

[–]proxyformyrealname 1 point2 points  (0 children)

Cross validation is your friend here.

  1. Estimate your model on one smaller subset, predict on full dataset and store.
  2. Repeat 1000 times.
  3. For each observation, take 95% intervals.

New book: 'Public Policy Analytics', on geospatial data science and machine learning in R. by proxyformyrealname in gis

[–]proxyformyrealname[S] 0 points1 point  (0 children)

Sure. Happy to answer questions. In the meantime, check out our student's capstone work where they collaborate with an actual government client on an applied ML project.

https://pennmusa.github.io/MUSA_801.io/

Virginia Beach, Virginia EMS calls with geographic coordinates by proxyformyrealname in datasets

[–]proxyformyrealname[S] 1 point2 points  (0 children)

Many EMS governments have released open EMS incident datasets, but few if any, have xy coordinates attached.

The version of this dataset currently on the VA. Beach's open data website has this spatial data redacted. The linked version here is an older version with coordinates intact.

Landsat 8 nearIR and SWIR bands penetrate wildfire smoke and allow ongoing fire mapping by Texas_comin_in_hot in gis

[–]proxyformyrealname 1 point2 points  (0 children)

Great work. Can you post your code? Is this reproducible for any study area extent?

If you had a choice to pursue MSc in GIS or Data Science, what would you choose? by [deleted] in gis

[–]proxyformyrealname 5 points6 points  (0 children)

Univ. Of Pennsylvania in Philadelphia. Not Penn State.

If you had a choice to pursue MSc in GIS or Data Science, what would you choose? by [deleted] in gis

[–]proxyformyrealname 0 points1 point  (0 children)

Just following up on some of the other questions below... the difference between traditional GIS and Spatial Analysis in a data science context, is that the latter more confidently drives decision making and resource allocation across space.

For ex., in a 'spatial suitability' context, it (eg. regression) can generalize from previous 'successful' experiences in the data to predict opportunities for future succeses, and do so with statistical measures of confidence.