The racial composition of road users, traffic citations, and police stops.

This paper exploits the potential of Global Positioning System datasets sourced from mobile phones to estimate the racial composition of road users, leveraging data from their respective Census block group. The racial composition data encompasses approximately 46 million trips in the Chicago metropolitan region. The research focuses on the relationship between camera tickets and racial composition of drivers vs. police stops for traffic citations and the racial composition in these locations. Black drivers exhibit a higher likelihood of being ticketed by automated speed cameras and of being stopped for moving violations on roads, irrespective of the proportion of White drivers present. The research observes that this correlation attenuates as the proportion of White drivers on the road increases. The citation rate measured by cameras better matches the racial composition of road users on the links with cameras than do stops by police officers. This study therefore presents an important contribution to understanding racial disparities in moving violation stops, with implications for policy interventions and social justice reforms.

Differential diagnosis of schizophrenia and schizoaffective disorder from normal subjects using virtual reality.

Dysfunction of allocentric and egocentric memories is one of the core features of psychiatric disorders. There are a few navigational studies on these memories in schizophrenia and bipolar disorders, but studies in schizoaffective disorder are lacking. Here, we aim to explore allocentric and egocentric navigation deficits in these subjects using our advanced recently developed virtual reality navigation task (VRNT). Twenty patients with schizophrenia and 20 with schizoaffective disorder were compared with 20 normal volunteer subjects on VRNTs consisting of a virtual neighbourhood (allocentric memory) and a virtual maze (egocentric memory). Compared with schizoaffective disorder and control subjects, patients with schizophrenia had the worst performance on both virtual neighbourhood and virtual maze tasks. The allocentric memory in both patients with schizophrenia and those with schizoaffective disorder was more impaired than the egocentric memory (p Ë‚ 0.001). However, the patients with schizoaffective disorder performed better in egocentric memory than those with schizophrenia, as they had fewer errors in the virtual maze. It was concluded that allocentric memory is more impaired than egocentric in both schizoaffective disorder and schizophrenia patients, whereas patients with schizoaffective disorder performed better in egocentric memory than patients with schizophrenia. It was also concluded that allocentric memory deficits can help differentiate patients with schizophrenia and schizoaffective disorder from healthy participants, whereas egocentric memory deficits can be used to distinguish them from each other.

Exploring spatial autocorrelation of traffic crashes based on severity.

As a developing country, Iran has one of the highest crash-related deaths, with a typical rate of 15.6 cases in every 100 thousand people. This paper is aimed to find the potential temporal and spatial patterns of road crashes aggregated at traffic analysis zonal (TAZ) level in urban environments. Localization pattern and hotspot distribution were examined using geo-information approach to find out the impact of spatial/temporal dimensions on the emergence of such patterns. The spatial clustering of crashes and hotspots were assessed using spatial autocorrelation methods such as the Moran's I and Getis-Ord Gi* index. Comap was used for comparing clusters in three attributes: the time of occurrence, severity, and location. The analysis of the annually crash frequencies aggregated in 156 TAZ in Shiraz; from 2010 to 2014, Iran showed that both Moran's I method and Getis-Ord Gi* statistics produced significant clustering of crash patterns. While crashes emerged a clustered pattern, comparison of the spatio-temporal separations showed an accidental spread in distinct categories. The local governmental agencies can use the outcomes to adopt more effective strategies for traffic safety planning and management.