Approaches Towards the Identification of Patterns in Violent Events, Baghdad, Iraq (original) (raw)

Introducing the Spatial Conflict Dynamics Indicator of Political Violence

Terrorism and Political Violence, 2021

While the location of violent events and their propensity to cluster together in space is increasingly well known, a deeper exploration of their spatiality and spatial evolution over time remains an emerging frontier in "Big Data"driven conflict studies. The new Spatial Conflict Dynamics indicator (SCDi) introduced in this article contributes to fill this gap, by measuring both the intensity and spatial concentration of political violence at the subnational level. Articulating between point pattern and areal spatial analyses, the SCDi allows conflict researchers and analysts to not just map which regions experience the most violence but to track how the geography of conflict evolves over time. The SCDi identifies four spatial typologies of violence and can leverage political event data from most datasets with locational information and can be used for analyses across large multi-state regions, within a single state, or in more localized contexts. In this paper, we illustra...

The spatial patterning of county homicide rates: an application of exploratory spatial data analysis

1999

The possibility that homicides can spread from one geographic area to another has been entertained for some time by social scientists, yet systematic efforts to demonstrate the existence, or estimate the strength, of such a diffusion process are just beginning. This paper uses exploratory spatial data analysis (ESDA) to examine the distribution of homicides in 78 counties in, or around, the St. Louis metropolitan area for two time periods: a period of relatively stable homicide (1984)(1985)(1986)(1987)(1988)) and a period of generally increasing homicide (1988)(1989)(1990)(1991)(1992)(1993). The findings reveal that homicides are distributed nonrandomly, suggestive of positive spatial autocorrelation. Moreover, changes over time in the distribution of homicides suggest the possible diffusion of lethal violence out of one county containing a medium-sized city (Macon County) into two nearby counties (Morgan and Sangamon Counties) located to the west. Although traditional correlates of homicide do not account for its nonrandom spatial distribution across counties, we find some evidence that more affluent areas, or those more rural or agricultural areas, serve as barriers against the diffusion of homicides. The patterns of spatial distribution revealed through ESDA provide an empirical foundation for the specification of multivariate models which can provide formal tests for diffusion processes.

Spatial Analysis of U.S. Terrorism Incidents

2017

This research brief represents an overview of basic spatial patterns across a sample of terrorism incidents in the United States. While research concerning characteristics of incidents has received some study, the geospatial patterns of these incidents remains largely unexamined. Logically, different ideological categories of terrorism may lend themselves to different spatial patterns and preferences for target distance. In addition, the distance required to perpetrate an incident may affect the success rate of an attack. Terrorists who must travel further to engage in preparatory activity such as surveillance or transporting weapons may stand an increased chance of failure due to human intervention.

A SPATIAL ANALYSIS OF TERRORIST ATTACKS IN PAKISTAN

2020

The objective of this study is to analyze the temporal and spatial spread of the terrorist attacks in Pakistan. The study uses spatial lag and spatial error models to explain spatial variation in terrorist attacks in the districts of Pakistan for the years 2009 and 2011. The number of attacks in focal districts is associated with the poverty of the neighbouring districts. Another source of variation is the general public's contentment (voter turnout is used as a proxy) with the current regime, which turns out to be negatively correlated with terrorist attacks. A significant spatial variation in terrorism is explained by Federally Administered Tribal Areas (FATA) and Khyber Pakhtunkhwa (KP province). The results also show that clusters of attacks extended to other parts of the country between 2009 and 2011 and terrorism spread through the diffusion of attacks to other districts and provinces. More importantly, the attacks are spatially correlated; hence, hot spots are identifiable.

An analysis of spatial correlates of terrorism using risk terrain modeling

Terrorism and Political Violence, 2016

The purpose of this study is to identify correlates of terrorism in space. It examines whether places with terrorist incidents show similar patterns with respect to the physical features across landscape, and tests the spatial influence of various features of environment on the incidence of terrorism. Drawing on the locations of violent terrorist offenses committed between 2008 and 2012, the study in Istanbul applies the Risk Terrain Modeling framework to terrorism. It uses data on police incidents and infrastructure (e.g., government buildings or parks). The analysis employs GIS techniques and an event count model, and combines all risky layers in a composite map to understand where the risk is higher. The study suggests a concentration of 1153 violent terrorist incidents relative to key physical factors by identifying seventeen potential risk factors, eight of which were significantly correlated in the model. Regardless of terrorists' intent, the significantly associated establishments increase the risk in the surrounding areas where these features are located. The coexistence of leisure places such as bakeries, religious facilities, or eateries results in higher risks. While the environmental backcloth may constitute a risk for terrorism, its components may also help forecast the locations of terrorist incidents in the future.

Spatial Regression Analysis of Violent Crimes in the National Capital Region

Throughout the years, crime analysts continue to regard the incorporation of spatial analytics in crime analyses. This interest, alongside various technological advancements with geographic information systems (GIS), in crime visualization plays a vital role in understanding crime dynamics with the intention of aiding in strategic planning. Utilizing Empirical Bayes smoothed crime rates (per 100,000 population) in 2010, this paper aims to identify the presence of spatial autocorrelation within index crime rates observed in the National Capital Region (NCR) using Moran's I. Spatial units were taken to be the 16 cities and one municipality in NCR. Results revealed that among the seven index crimes, only two, murder and physical injury, were able to exhibit significant spatial autocorrelation. Accounting for the presence of spatial autocorrelation, a regression model which takes spatial dependence into account was used. In particular, spatial lag models were fitted to study the relationships of murder and physical injury with certain demographic variables across cities. It was observed that population density and percentage of male individuals aged 15-24 are found to be positively correlated to the incidence of both of these crimes. On the other hand, factors such as the percentage of married adult population, percentage of foreign immigrants, percentage of the population with a high school diploma, and the crime solution efficiency correlate negatively to the incidence of these crimes. These observed factors could serve as indicators of crime incidences and, thus, monitoring them could aid in crime prevention and security management.

Exploring the spatial configuration of places related to homicide events

Alexandria, VA: Institute of Law and Justice, 2006

This research effort benefited from the input of a variety of individuals who made both substantive and technical contributions. George Rengert, a consultant on the project, provided valuable insights regarding initial patterns of triangles and suggestions regarding the analysis plan that produced them. Deborah Weisel's question about the impact of repeat victimization on triangle type was the impetus for our analysis of the spatial configuration of the three events (dots, lines and triangles section). Also, we are indebted to Jerry Ratcliffe for his initial suggestion to use actual distance rather than social distance to classify the triangles. Two anonymous reviewers contributed comments that improved the final report. In addition, we thank all the individuals who provided interesting and thoughtful feedback on presentations of the data analysis over the last two years.

Spatial Analysis of Crime Occurrence in Various Regions of Iran with an Emphasis on Safety

Journal of Urban and Regional Analysis

Safety is a basic issue in every social system and communities consider safety as one of their main priorities. One of the most important factors that put the safety of various communities at risk is the threats caused by crime occurrence. This paper is aimed to spatially analyze crime occurrence in various regions of Iran with an emphasis on safety. The research method is descriptive-analytical and a documentary and library data collection method is used. In this paper, the Similarity, COPRAS, mean rank method, and cluster analysis method are applied. The final results of the cluster analysis based on the mean rank method indicate a wide gap between the provinces of the country in terms of survey indicators, so that the final coefficient obtained for the provinces in the sixth cluster (the most unsafe group) is about 45 times of the final coefficient of the provinces in the first cluster (the safest group).