Search results

This study investigated the complex relationship between unauthorized immigration and crime at the local level. Through a mix of data fusion, synthetic population modeling, and detailed crime reporting from selected jurisdictions, the study sought to produce nuanced insights to challenge prevailing assumptions about immigration and crime, ultimately aiding in informed policy-making and resource allocation.

This study employed crime and crime reporting data from ten jurisdictions across the United States paired with synthetic data which estimated the unauthorized immigrant population. This research aimed to provide an in-depth analysis at the census tract level. Analyses focused on unauthorized immigration and its correlation with drug, property, and violent crime rates, while accounting for crime reporting in traditional and emerging immigrant destinations along with sites with low foreign populations.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

The study integrated neighborhood-level data on robbery and burglary gathered from local police agencies across the United States, foreclosure data from RealtyTrac (a real estate information company), and a wide variety of social, economic, and demographic control variables from multiple sources. Using census tracts to approximate neighborhoods, the study regressed 2009 neighborhood robbery and burglary rates on foreclosure rates measured for 2007-2008 (a period during which foreclosure spiked dramatically in the nation), while accounting for 2007 robbery and burglary rates and other control variables that captured differences in social, economic, and demographic context across American neighborhoods and cities for this period. The analysis was based on more than 7,200 census tracts in over 60 large cities spread across 29 states. Core research questions were addressed with a series of multivariate multilevel and single-level regression models that accounted for the skewed nature of neighborhood crime patterns and the well-documented spatial dependence of crime.

The study contains one data file with 8,198 cases and 99 variables.

This study collected and combined data from a large number of sources (e.g. crime data, land use data, parolee data, business and employment data, etc.) to study crime and crime trends across two counties in Southern California: Los Angeles and Orange counties. The crime data comes from a number of police agencies for the years 2005-12. Crime data is not available for all cities for all years. The variables from other sources are for the year 2010. All data is aggregated to the common geographic unit of census tracts. The data come from the following sources: 1) crime data from police agencies; 2) socio-demographic data from the American Community Survey (ACS) obtained from ICPSR; 3) business data from Mint data; 4) land use data from the Southern California Association of Governments; 5) voluntary organization data from the National Center for Charitable Statistics.

This wide array of information allows accounting for the multi-dimensional and inter-related sources of crime and crime trends in Southern California in neighborhoods (census tracts). Using these data, the project: 1) built a model to predict crime in small geographic areas; 2) assessed the effect of neighborhood organizations and institutions on crime rates; 3) determined the effect of the spatial distribution of poverty (at both small and large scales) on crime rates; 4) assessed how the clustering of social problems in a neighborhood affects neighborhood crime over time. This project built on prior work done by the Metropolitan Futures Initiative (MFI) team to locate various data sources in Southern California.

This study examined the geospatial contexts of where terrorism incidents occur, where terrorists plan and prepare for their crimes, and where terrorists reside in the United States. The researchers examined data linked to terrorism-related incidents in the United States from the time of the 9/11 terror attacks in 2001 through 2019. Using these data, the researchers applied innovative analytical methodologies of Risk Terrain Modeling (RTM) and Conjunctive Analysis of Case Configurations (CACC) to evaluate their utility in assessing risk of terrorism.

Risk terrain modeling is a method for identifying situational, place-based risk factors most associated with locations where terrorist incidents are likely to be planned or occur. This method looks at specific aspects of the physical landscape, such as locations of buildings or parking lots. The place-based analysis approach to terrorism investigation represents a shift from the conventional research emphasis on targeting suspicious persons by their demographic or other traits. This approach investigates the importance of location in explanations of crime and terrorism.

According to the American Terrorism Study, during this time between 2001 (after 9/11 and 2019) there were 296 terrorism incidents and 617 pre-incident activities occurred where the state was known. In addition, there were 420 known residences tied to terrorism-related incidents in particular states.