22 research outputs found
The Influence of Community Areas, Neighborhood Clusters, and Street Segments on the Spatial Variability of Violent Crime in Chicago
Objectives: The influence of three hierarchical units of analysis on the total spatial variability of violent crime incidents in Chicago is assessed. This analysis seeks to replicate a recent study that found street segments, rather than neighborhood units of analysis, accounted for the largest share of the total spatial variability of crime in The Hague, Netherlands (see Steenbeek and Weisburd J Quant Criminol. doi:10.1007/s10940-015- 9276-3, 2015).
Methods: We analyze violent crime incidents reported to the police between 2001 and 2014. 359,786 incidents were geocoded to 41,926 street segments nested within 342 neighborhood clusters, in turn nested within 76 community areas in Chicago. Linear mixed models with random slopes of time were estimated to observe the variance uniquely attributed to each unit of analysis.
Results: Similar to Steenbeek and Weisburd, we find 56–65 % of the total variability in violent crime incidents can be attributed to street segments in Chicago. City-wide reductions in violence over the observation period coincide with increases in the spatial variability attributed to street segments and decreases in the variability attributed to both neighborhood units.
Conclusions: Our results suggest that scholars interested in understanding the spatial variation of crime across urban landscapes should be focused on the small places that comprise larger geographic areas. The next wave of ‘‘neighborhood-effects’’ research should explore the role of hierarchical processes in understanding crime variation within larger areas
What Are the Best Materials To Separate a Xenon/Krypton Mixture?
Accelerating progress in the discovery
and deployment of advanced
nanoporous materials relies on chemical insight and structure–property
relationships for rational design. Because of the complexity of this
problem, trial-and-error is heavily involved in the laboratory today.
A cost-effective route to aid experimental materials discovery is
to construct structure models of nanoporous materials in silico and
use molecular simulations to rapidly test them and elucidate data-driven
guidelines for rational design. For example, highly tunable nanoporous
materials have shown promise as adsorbents for separating an industrially
relevant gaseous mixture of xenon and krypton. In this work, we characterize,
screen, and analyze the Nanoporous Materials Genome, a database of
about 670 000 porous material structures, for candidate adsorbents
for xenon/krypton separations. For over half a million structures,
the computational resources required for a brute-force screening using
grand-canonical Monte Carlo simulations of Xe/Kr adsorption are prohibitive.
To overcome the computational cost, we used a hybrid approach combining
machine learning algorithms (random forests) with molecular simulations.
We compared the results from our large-scale screening with simple
pore models to rationalize the strong link between pore size and selectivity.
With this insight, we then analyzed the anatomy of the binding sites
of the most selective materials. These binding sites can be constructed
from tubes, pockets, rings, or cages and are often composed of nondiscrete
chemical fragments. The complexity of these binding sites emphasizes
the importance of high-throughput computational screenings to identify
optimal materials for a given application. Interestingly, our screening
study predicts that the two most selective materials in the database
are an aluminophosphate zeolite analogue and a calcium based coordination
network, both of which have already been synthesized but not yet tested
for Xe/Kr separations