eli knaap

@knaaptime.com

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urban planning & public policy prof @ UC Irvine | core dev @ PySAL & QuantEcon | urban social science & spatial data science | open source https://knaaptime.com

connor roy giving lester the molester's eulogy
More abstractly, as discussed in Conley (1999), “locations” might index an economic characteristic and the “economic distance” between locations measures the dissimilarity of the characteristic. We thus follow the geostatistical tradition of positing a continuous parameter model of spatial variation, rather than, say, model dependence by spatial autoregressive (SAR) models of Cliff and Ord (1974) and Anselin (1988).2
Do Spatial effects really matter in regression analysis? By Luc Anselin and Daniel Griffith, Papers in Regional Science 1988
excerpt from the paper's conclusion: Applied researchers are well aware of the pitfalls of conducting inference with persistent time series data. Variables are routinely tested for extreme time series persistence using unit root or related tests, and are often differenced to avoid spurious regression effects. This paper demonstrates that inference with highly persistent spatial data is equally fraught: HAC or clustered standard errors fail in the presence of strong spatial correlation, leading to spurious significance between independent spatial variables. We have provided tools to detect such strong spatial persistence, akin to time series unit root and stationarity tests. We have also suggested ways of restoring valid regression inference by suitably transforming the spatial variables, combined with spatial HAR corrections to accommodate any residual weak correlations. The theory here is much less complete, however.
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)
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")
proportional dot density map showing racial segregation in southern california
proportional dot density map showing racial segregation in greater los angeles
"There's never been anything like these ships,” Trump said. “These have been under design consideration for a long time. And it started with me in my first term, because I said, 'Why aren't we doing battleships like we used to?'” “We used to build a ship a day, during World War 2. And now we don’t really do ships anymore,” the president complained on Monday before praising his first administration on the issue of constructing and revolutionizing the Navy’s submarines. **During his first term, U.S. naval shipbuilding missed targets set by the Obama administration.**
image of lets-plot code that creates a map of africa
<folium.folium.Map at 0x34e1cff80>
occ_rent = occ_rent.set_index('CONTROL') weighted_idx = occ_rent.index.repeat(occ_rent['FW']) weighted_idx weighted_rent = occ_rent.merge(weighted_idx.to_frame(), left_index=True, right_index=True, how='right') weighted_rent.RENT_AMOUNT.hist(bins=20) weighted_rent.RENT_AMOUNT.describe()
import pandas as pd units = pd.read_csv("https://www.nyc.gov/assets/hpd/data/allunits_puf_23.csv") # brooklyn 3br units = units[(units.BORO==2) & (units.BEDROOMS==3)] # "market rate" units = units[units.CSR==80] # occupied table has rents occ = pd.read_csv("https://www.nyc.gov/assets/hpd/data/occupied_puf_23.csv") occ = occ.set_index('CONTROL') occ_rent = occ.merge(units[['CONTROL']], left_index=True, right_on='CONTROL') occ_rent = occ_rent[~occ_rent.RENT_AMOUNT.isin([0,-1,-2])] occ_rent.RENT_AMOUNT.hist(bins=20) occ_rent.RENT_AMOUNT.describe()
post from sean gillies blog https://sgillies.net/ I've been on the Project Steering Committee (PSC) of the GDAL project for a few years now. It's a hugely significant software project, one that I'm very fond of, and I like to think that I've had an impact in my time on the committee. Recently, though, the steering part has become less rewarding. By granting Amazon S3 and its imitators special, featured storage protocols, GDAL is stuck carrying water for big and lucrative cloud service providers and a few hyper-scaling customers that don't adequately support the project. It's a crappy situation, not unique to GDAL. A lot of open source projects find themselves ruthlessly exploited these days. This is my personal view. I don't speak for GDAL. More importantly, I'm doing less geospatial work these days, and thus I'm increasingly uncertain about the rapidly-changing currents in which the PSC is steering the project. Do things like "GeoParquet" and "GeoArrow" really matter to practitioners who aren't working for a handful of hyper-scaling organizations? Is "GeoAI" for real or is it bullshit? Do we steer the project towards these interests or away from them? I truly don't know. I wish I did. Can I fully engage with the problems that GDAL faces over the next year? That's my criteria for participating in the PSC. I can't, and so it's time to go. The project is in good shape, I believe, and I hope that I'm making room for someone with a great vision to join it.
arial photo of armitage ave from google maps; rico fresh is adjacent to funston elementary school
brown boston terrier standing on a side walk with "fuck trump" etched into the concrete
The White House took steps to maximize the pain of the government shutdown, halting funds for democratic-led states and readying mass layoffs
relationship diagram showing different spatial interaction structure(s) underlying alternative concepts of spatial correlation and the Moran's I coefficient, as shown in https://www.tandfonline.com/doi/pdf/10.1080/24694452.2024.2326541
Given the title, there’s little doubt that the provocation was intended; after all, a piece entitled “Why America should carefully extend urban infrastructure to accommodate growth” might work for the Congress for the New Urbanism, but wouldn’t generate much buzz.
The suburban growth in the Sunbelt may be “sprawl,” if one is going to use that term to describe any and all development outside already built-up cities, but as Figure 5 from Princeton, Texas (a city featured in Daugherty’s article) shows, it is very compact sprawl. The homes shown in the image are built at a gross density including streets, playgrounds and such, of roughly five units per acre, and a net density in each block with 6250 square foot lots, of seven units per acre. Not quite downtown apartment densities, to be sure, but if one accepts that there is a continued demand for single family homes, a highly efficient use of land, and comparable to the densities of older single family neighborhoods in cities like Cleveland.
a map of riverside county CA showing four potential harm reduction sites and their service areas, assuming a 5 kilometer service area
Those gains will come at the expense of major Democratic states like New York and California, according to a New York Times analysis of population projections by Esri, a nonpartisan company whose mapping software and demographic data are widely used by businesses and governments across the world.
Columbia University graduate Mahmoud Khalil is seeking $20 million in damages from the Trump administration, according to court records — or an apology and rescission of the government’s policy of targeting pro-Palestine student demonstrators. (Reuters)
charlie murphy laughing hysterically
dave chappelle saying "help me ja rule"
ROOSE The mental model I sometimes have of these chatbots is as a very smart assistant who has a dozen Ph.D.s but is also high on ketamine like 30 percent of the time. But also, the bar of 100 percent reliability is not the right one to aim for here: The base rate that we should be comparing with is not complete factuality but the comparable smart human given the same task.
image from George Galster's *Making our neighborhoods, making our selves" showing partially overlapping polygons representing multiple conceptual 'externality spaces' for two different people
"factorial ecology maps of the chicago metro region, showing maps of dimensions "SES", "segregation, "family structure" and "urbanization"
“He is declaring utterly bogus emergencies for the sake of trying to expand his power, undermine the Constitution and destroy civil liberties,” said Ilya Somin, a libertarian professor at Antonin Scalia Law School who represents a wine importer and other businesses challenging some of Mr. Trump’s tariffs.
As the writing of this book closes and the research infrastructure in the U.S. crumbles, it is important to clarify that part of my work on this collection of materials was funded by NSF POSE Grant #2345820, a project designed to make spatial data science more transparent and accessible for a broader range of users and contributors. This work is made open and freely available, explicitly, so that others can build on top of it to the scientific benefit of us all. It would not be possible without public research funding. More importantly, the vast majority of software packages demonstrated here have been supported directly and indirectly by a string of more than eight NSF grants, as well other funders including the Bill and Melinda Gates Foundation, the National Institutes for Justice, and the National Institute for Health3. geosnap was conceived during my postdoc on spatial distribution dynamics for neighborhoods (NSF #1421935 and #1733705), and tobler, segregation, and spopt were all borne from our RIDIR grant (NSF #1831615) at UCR–and that’s just since I’ve been on the team! Multiple team members before me also received NSF dissertation grants. Today the PySAL stack is cited in everything from genomics (Moses et al., 2023) to astrophysics (Martel et al., 2024)—classic agglomeration from knowledge-spillover, generating a massive return on the original investment—and that only happens because of public funding.
choropleth maps of DC using the same population data; the right hand side shows original units, the left hand shows a regular hexagonal grid
table of contents for Urban Analysis and Spatial Science Introduction Spatial Data 1 Data Sets 2 Geoprocessing 3 Geovisualization Interpolation and Change of Support 4 Areal Interpolation 5 Dasymetric Interpolation 6 Boundary Harmonization Spatial Analysis 7 The Spatial Graph 8 Spatial Autocorrelation 9 Space-Time Event Clustering Accessibility and Interaction 10 Travel Isochrones 11 Accessibility 12 Multimodal and More Urban Spatial Structure 13 Location and Land Use 14 Concentration and Agglomeration 15 Employment Centers Segregation 16 Segregation Measures 17 Space, Scale & Dynamics 18 Single and Comparative Inference Urban Factor Models 19 Latent Spatial Processes 20 Factor Ecology 21 Ecometrics and Composite Indices Neighborhood Delineation 22 Social Areas & Urban Typology 23 Geodemographic Clustering 24 Regionalization Neighborhood Change 25 Spatial Dynamics in Math Achievement 26 Stochastic Cellular Automata 27 Neighborhood Dynamics Spatial Econometrics 28 Applied Spatial Econometrics 29 The Hedonic Housing Price Model 30 Hedonic Models in Space Location Models 31 Location Models 32 Location Choice 33 Spatial Optimization
excerpt from "Stability in Competition" (Hotelling 1929): So general is this tendency that it appears in the most diverse fields of competitive activity, even quite apart from what is called economic life. In politics it is strikingly exemplified. The competition for votes between the Republican and Democratic parties does not lead to a clear drawing of issues, an adoption of two strongly contrasted positions between which the voter may choose. Instead, each party strives.to make its platform as much like the other's as possible. Any radical departure would lose many votes, even though it might lead to stronger commendation of the party by some who would vote for it anyhow. Each candidate " pussyfoots," replies ambiguously to questions, refuses to take a definite stand in any controversy for fear of losing votes. Real differences, if they ever exist, fade gradually with time though the issues may be as important as ever. The Democratic party, once opposed to protective tariffs, moves gradually to a position almost, but not quite, identical with that of the Republicans. It need have no fear of fanatical free-traders, since they will still prefer it to the Republican party, and its advocacy of a continued high tariff will bring it the money and votes of some intermediate groups.
employment centers in the San Diego region with SDSU and UCSD campuses circled
the Robert Weaver bulding, home of the Department of Housing and Urban Development
a choropleth map of the U.S. with a histogram of the mapped variable whose color bars match the classification scheme
four "opportunity" maps of the Baltimore metropolitan region
future trunks is surprised
an interactive map in a jupyter notebook with dropdowns that allow a user to select different classification schemes, with the map updating in real-time
'sex and age' table from the census ACS 5-year sample in 2021
home assistant discovered new skyconnect device
a picture of the first page of "measurement theory with applications to decisionmaking, utility, and the social sciences"
a map of the DC metro region (centered on Alexandria ,VA), with three-dimensional arcing lines drawn between blockgroups representing origins and destinations. The lines are shaded and thickened according to the volume of commuters between the two locations
maps of the Chicago Metropolitan region showing "LISA" clusters of neighborhood "ecological factors"
picture of flyer for the workshop "Spatial Data Analysis and Econmetrics with PySAL"