An official website of the United States government
Abstract Theoretical units of interest often do not align with the spatial units at which data are available. This problem is pervasive in political science, particularly in subnational empirical research that requires integrating data across incompatible geographic units (e.g., administrative areas, electoral constituencies, and grid cells). Overcoming this challenge requires researchers not only to align the scale of empirical and theoretical units, but also to understand the consequences of this change of support for measurement error and statistical inference. We show how the accuracy of transformed values and the estimation of regression coefficients depend on the degree of nesting (i.e., whether units fall completely and neatly inside each other) and on the relative scale of source and destination units (i.e., aggregation, disaggregation, and hybrid). We introduce simple, nonparametric measures of relative nesting and scale, asex anteindicators of spatial transformation complexity and error susceptibility. Using election data and Monte Carlo simulations, we show that these measures are strongly predictive of transformation quality across multiple change-of-support methods. We propose several validation procedures and provide open-source software to make transformation options more accessible, customizable, and intuitive.
more »
« less
This content will become publicly available on December 11, 2025
Ligand cross-links as a design element in oligo- and polyMOFs
Oligo- and polyMOFs based on DMOF-1 adopt distinct isomeric structures governed by the flexibility, length, and number of repeat units of tethering groups between terephthalate units.
more »
« less
- Award ID(s):
- 2011924
- PAR ID:
- 10589222
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Chemical Science
- Volume:
- 15
- Issue:
- 48
- ISSN:
- 2041-6520
- Page Range / eLocation ID:
- 20448 to 20456
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Transfer entropy is emerging as the statistical approach of choice to support the inference of causal interactions in complex systems from time-series of their individual units. With reference to a simple dyadic system composed of two coupled units, the successful application of net transfer entropy-based inference relies on unidirectional coupling between the units and their homogeneous dynamics. What happens when the units are bidirectionally coupled and have different dynamics? Through analytical and numerical insights, we show that net transfer entropy may lead to erroneous inference of the dominant direction of influence that stems from its dependence on the units’ individual dynamics. To control for these confounding effects, one should incorporate further knowledge about the units’ time-histories through the recent framework offered by momentary information transfer. In this realm, we demonstrate the use of two measures: controlled and fully controlled transfer entropies, which consistently yield the correct direction of dominant coupling irrespective of the sources and targets individual dynamics. Through the study of two real-world examples, we identify critical limitations with respect to the use of net transfer entropy in the inference of causal mechanisms that warrant prudence by the community.more » « less
-
Abstract A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.more » « less
-
ABSTRACT A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user‐friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform's key functions and current progress, as well as long‐range goals for the platform.more » « less
-
Abstract Urban neighborhoods with locations of environmental contamination, known as brownfields, impact entire neighborhoods, but corrective environmental remedial action on brownfields is often tracked on an individual property basis, neglecting the larger neighborhood-level impact. This study addresses this impact by examining spatial differences between brownfields with unmitigated environmental concerns (open site) and sites that are considered fully mitigated or closed in urban neighborhoods (closed site) on the US census tract scale in Wayne County, MI. Michigan’s Department of Environment, Great Lakes, and Energy’s leaking underground storage tank (LUST) database provided brownfield information for Wayne County. Local indicators of spatial association (LISA) produced maps of spatial clustering and outliers. A McNemar’s test demonstrated significant discordances in LISA categories between LUST open and closed sites ( p < 0.001). Geographically weighted regressions (GWR) evaluated the association between open and closed site spatial density (open-closed) with socioeconomic variables (population density, proportion of White or Black residents, proportion of college educated populations, the percentage of owner-occupied units, vacant units, rented units, and median household value). Final multivariate GWR showed that population density, being Black, college education, vacant units, and renter occupied units were significantly associated ( p < 0.05) with open-closed, and that those associations varied across Wayne County. Increases in Black population was associated with increased open-closed. Increases in vacant units, renter-occupied units, and college education were associated with decreased open-closed. These results provide input for environmental justice research to identify inequalities and discover the distribution of environmental hazards among urban neighborhoods.more » « less
