eli knaap

Notepad++

"Chinese-linked cyberespionage group with a long history hijacked the update process for the popular code editing platform Notepad++ to deliver a custom backdoor and other malware."

(That’s real data from the paper :P)

a racial dot density map of the LA regions. Colored lines of varying widths show the level of flow from different communities (before the June ICE raids)

a racial dot density map of the LA regions. Colored lines of varying widths show the level of flow from different communities (after the June ICE raids). There are substantially fewer lines and smaller widths indicating fewer people traveling downtown from a smaller set of origins. Many of the reduced flows come from predominantly nonwhite communities, suggesting a chilling effect across the diverse region

if foot traffic flows into a place like downtown LA looked something like this before the ICE raids, then, say, like this afterward... well, there will be economic consequences, on top of everything else

socialecology.uci.edu/news/oc-busi...

DID NO ONE SEE WATCHMEN????

bsky.app/profile/scot...

it. um. won 11 emmys and explores the inherent racism underlying masked law enforcement

Statement from Michael and Susan Pretti Parents of Alex Jeffrey Pretti “We are heartbroken but also very angry. Alex was a kindhearted soul who cared deeply for his family and friends and also the American veterans whom he cared for as an ICU nurse at the Minneapolis VA hospital. Alex wanted to make a difference in this world. Unfortunately, he will not be with us to see his impact. I do not throw around the ‘hero’ term lightly. However, his last thought and act was to protect a woman. The sickening lies told about our son by the administration are reprehensible and disgusting. Alex is clearly not holding a gun when attacked by Trump’s murdering and cowardly ICE thugs. He had his phone in his right hand and his empty left hand is raised above his head while trying to protect the woman ICE just pushed down, all while being pepper sprayed. Please get the truth out about our son. He was a good man. Thank you.

Kare 11 local news just read, in full, this statement from Michael and Susan Pretti, the parents of Alex Pretti.

"Please get the truth out about our son."

Density timelines – Mountain Doodles Global city density patterns across time.

Global datasets are really cool. Lots of fun to be had, I used this for looking at city evolution over time, sprawl, building heights, … Just really cool when one can do that globally with the click of a button. E.g.:

tobler/notebooks/extract_urban_areas.ipynb at main · pysal/tobler Areal & Dasymetric Interpolation; Change of Support - pysal/tobler

that looks.. awesome? I'd cooked up an example with copernicus data once github.com/pysal/tobler... but im unfamiliar with GHSL. Time to read up. It's a bit like NLCD where they go from remote-sensed imagery into a model of population density?

fwiw, serge has a new paper in the works based on that bipartite idea that's just about the econometrics of two interacting spatial systems, not necessarily schools and neighborhoods (its a tribute to harry kelejian), but its baaaad ass

not sure which one you meant, so heres both

www.dropbox.com/scl/fi/jg7lz...

www.dropbox.com/scl/fi/24lvp...

we're on a roll here... school attendance boundaries are another boondoggle i find extraordinarily interesting :)

link.springer.com/epdf/10.1007...

doi.org/10.1007/s110...

Comparing the effects of an NLCD-derived dasymetric refinement on estimation accuracies for multiple areal interpolation methods Comparability among population data enumerated within different time periods may be complicated by changing enumeration boundaries over time. Areal interpolation methods are commonly used to solve ...

makes me think of this one dx.doi.org/10.1080/1548...

theres another paper that explicitly uses different assumptions about target classes but its escaping me at the moment

hm interesting. If you actually want to *test* for the presence of school aged children, though, you'd definitely need census data center access, because the chidots data is still a noisy estimate? especially since we're talking about a very specific population, not necessarily uniformly distributed

💯 yeah, for sure.

even without access to a census data center, there's a paper in here if i (we?) ever had time, looking at these different methods and the accuracy going from, e.g. tract and downscaling to block using different dasymetric layers and different point process DGPs

ah snap. Thats nice. You still end up with the volumetric problem, but having real parcel data at your disposal is a massive boon

i grew up in urbana... im like 80% sure you can see my childhood home from the hancock observation deck

lololol i meant terrain *over here* 🤣

(aside, as a native illinoisan, one of the things that entertains me about becoming a southern californian is how much quality improvement you get in this kind of approach *just* by considering the natural environment. Because, you know, it has terrain :P)

i like this workflow though because you can choose your dasymetric layer. Maybe I'll play with a building version just for kicks

i can imagine. I considered doing a building version, but then you get the opposite problem where you allocate people to industrial and office buildings. Hard to parse *just* residential. And also you have no height info, so cant densify taller buildings the way you want.

python code to create dot density map using the geosnap and tobler packages import geosnap as gsp from tobler.dasymetric import masked_dot_density raster = "s3://spatial-ucr/nlcd/landcover/nlcd_landcover_2021.tif" cols = [ "n_nonhisp_white_persons", "n_nonhisp_black_persons", "n_hispanic_persons", "n_asian_persons", ] chi = gsp.io.get_acs(gsp.DataStore(), msa_fips="16980", years=2021) chi_race_density = masked_dot_density( chi, raster=raster, categories=cols, scale=0.4, pixel_values=[22, 23, 24] ) chi_race_density.sample(frac=1).lb.explore("category", cmap="rainbow")

racial dot density map of chicagoland

ha, neat! at the moment, this is all the code it takes to do it from scratch (windy city or anywhere else!). All you'd need to do is swap out the NLCD raster for a building layer or similar (though i dont have building footprint/non-building logic as i think you linked)

(you can tell i was thinking a bit about @kylewalker.bsky.social 's LEHD maps this week... :P)

the quality of the allocation is really just a function of the dasymetric mask, so this feels like a pretty useful concept i'll stick in `tobler`.

This also lets you play with DGPs other than uniform (e.g. clustered poisson), which can get interesting)

proportional dot density map showing racial segregation in southern california

proportional dot density map showing racial segregation in the bay area

here im using NLCD to allocate CA's population to only "developed" areas (pixels 22,23,24), which isnt perfect but works pretty well

@kylebarron.dev 's `lonboard` continues to impress. This is 15M points (40% of CA's total pop) and embedding the data in standalone html ends up ~450mb w/ no delay

proportional dot density map showing racial segregation in greater los angeles

dot density maps arent my favorite, but they can be really rich ways of looking at multigroup segregation. If you know the area a bit, you dont even need a legend to recognize what these categories represent.

One thing i havent seen before is a combo of dot density with dasymetric mapping

You Are Reviewer 2 by Matthew Murray 🦇 A solo d6-based

midnitelibrary.itch.io/you-are-revi...

👀

220,000 Fewer Workers: How Trump’s Cuts Affected Every Federal Agency (Gift Article) New data offers the first clear view of the impact of the buyouts and firings.

A long-awaited update of federal employment data shows crippling staff reductions in some federal agencies and offices:
www.nytimes.com/interactive/...

something tells me the lonboard basemap refactor will now move forward quickly :P

Announcing deck.gl-raster: 𝐟𝐮𝐥𝐥𝐲 𝐜𝐥𝐢𝐞𝐧𝐭-𝐬𝐢𝐝𝐞 𝐂𝐎𝐆 𝐫𝐞𝐧𝐝𝐞𝐫𝐢𝐧𝐠. No server required.

1.3 𝐠𝐢𝐠𝐚𝐛𝐲𝐭𝐞 COG, streamed directly into the browser: developmentseed.org/deck.gl-rast...

- GPU-accelerated raster reprojection
- GPU image processing for colormaps, nodata values
- Efficient use of COG overviews

news from PySAL world: we have a new package in the ecosystem(!) As of today, `gwlearn` is available in package managers. It provides a suite of tools for geographically weighted machine learning using a sklearn API.

pysal.org/gwlearn/v0.1...

great work by lead author @martinfleischmann.net