Mountainous Terrain and Civil Wars: Geospatial Analysis of Conflict Dynamics in the Post-Soviet Caucasus

Linke, Andrew M.; Witmer, Frank D. W.; Holland, Edward C.; Ο’Loughlin, John

doi:10.1080/24694452.2016.1243038

Cited by 11 publications

(12 citation statements)

References 44 publications

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“…This smoothing parameter is similar to a spatially lagged dependent variable estimate and approximates the influence of location upon the outcome of interest (see Wood 2004, 684). GAM spatial smoothing terms have been used in existing spatial analysis of geographically disaggregated violent events data (e.g., Linke et al 2017).…”

Section: Methodsmentioning

confidence: 99%

Drought, Local Institutional Contexts, and Support for Violence in Kenya

Linke

Witmer

Ο’Loughlin

et al. 2017

Journal of Conflict Resolution

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We address two questions on the effects of climate change for social instability. First, do droughts and their associated environmental impacts affect support for the use of violence? Second, do local-level formal and informal institutions moderate support for violence when and where droughts worsen? To answer these questions, we conducted a national survey of 1,400 Kenyans in 2014. Respondents were asked about patterns of rainfall and the presence of rules regulating natural resource use and access. Survey data are joined to spatially disaggregated observed rainfall trends. The survey uses endorsement experiments to elicit honest responses about support for using violence. There is some evidence of a direct, though limited, link between observed drought and violent attitudes. Certain local-level natural resource use

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Section: Methodsmentioning

confidence: 99%

Drought, Local Institutional Contexts, and Support for Violence in Kenya

Linke

Witmer

Ο’Loughlin

et al. 2017

Journal of Conflict Resolution

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“…Disaggregation of conflicts into their constituent events can occur in a number of ways. Spatial disaggregation, whereby events might be mapped to their specific locations (Donnay, 2014;Linke et al, 2016), or to within areas where those events occur (Braithwaite and Johnson, 2015), enables the investigation of the geographic distribution of conflict events, highlighting areas that are more at risk. Temporal disaggregation, whereby the precise timings of events are analyzed, can be used to identify trends in the intensity of the conflict.…”

Section: Disaggregating Political Violencementioning

confidence: 99%

The exacerbating effect of police presence: A multivariate point process analysis of the Naxal conflict

Baudains

Belur

Braithwaite

et al. 2019

Political Geography

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The effect of police presence is an important consideration when designing counter-insurgency activities. While there has been much empirical research investigating the effects of counter-insurgency in a military setting, the effect of police presence in countering political dissidents has seen more limited attention. We argue that police presence at violent events is likely to exacerbate subsequent levels of violence rather than reduce them. To assess the validity of this claim we analyze a unique police dataset from the Indian state of Andhra Pradesh, which details the timing and location of police-recorded politically motivated violent offenses attributed to Naxals over a ten year period. The data also includes an indicator to determine whether or not police are present at each event, which we use to measure the exacerbating effect of police presence. Our analytical strategy is inspired by recent literatures concerning crime and political violence that examine event interdependence at disaggregated levels of analysis. We calibrate a series of novel multivariate point process models, which are designed to test a series of hypotheses. Our results show that: 1) the Naxal conflict displays patterns of spatio-temporal clustering; 2) police presence at violent events exacerbated the conflict, leading to more events than would have otherwise occurred, at least in the short term; and 3) the space-time regularities can be usefully employed to predict the likely locations of future events.

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“…Each observation corresponds to a grid cell specified by a certain square kilometer. As grid cells are typically created in a smaller size than the entire territory of the average country, grid data allow researchers to focus on the local relationship between a causal factor and an outcome (for examples of applied research, see Buhaug et al 2011;Linke et al 2017;O'Loughlin and Witmer 2012;Ruggeri, Dorussen, and Gizelis 2017;Schutte and Weidmann 2011;Theisen, Holtermann, and Buhaug 2011;Wood and Sullivan 2015).…”

Section: Introductionmentioning

confidence: 99%

“…One is to develop a theory of exactly how each of the three sources of uncertainty specific to grid data affects inference. While the existing literature sometimes explores empirically how changing the size of grid cells affects statistical inference (e.g., Ito and Hinkkainen Elliott 2020;Lee, Rogers, and Soifer 2020;Linke et al 2017;Schutte and Weidmann 2011), this article provides a theoretical foundation on why it is necessary to use different grid cell specifications for robustness checks. A specific choice of grid cell specification affects statistical and causal inference (Soifer 2019, 95).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Grid Data: A Theory and Comprehensive Robustness Test

Suzuki¹

2022

Preprint

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This article makes two novel contributions to spatial political and conflict research using grid data. First, it develops a theory of how uncertainty specific to grid data affects inference. Second, it introduces a comprehensive robustness test on sensitivity to this uncertainty, implemented in R. The uncertainty stems from ( 1) what is the correct size of grid cells, ( 2) what is the correct locations on which to draw dividing lines between these grid cells, and (3) a greater effect of measurement errors due to finer grid cells. My test aggregates grid cells into a larger size of choice as the multiple of the original grid cells. It also enables different starting points of grid cell aggregation (e.g., whether to start the aggregation from the corner of the entire map or one grid cell of the original size away from the corner) to shift the diving lines. I apply my test to Tollefsen, Strand, and Buhaug (2012) to substantiate its use.

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Mountainous Terrain and Civil Wars: Geospatial Analysis of Conflict Dynamics in the Post-Soviet Caucasus

Cited by 11 publications

References 44 publications

Drought, Local Institutional Contexts, and Support for Violence in Kenya

Drought, Local Institutional Contexts, and Support for Violence in Kenya

The exacerbating effect of police presence: A multivariate point process analysis of the Naxal conflict

Uncertainty in Grid Data: A Theory and Comprehensive Robustness Test

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