Race to the Bottom : Spatial Aggregation and Event Data

Cite This

Files in this item

Files Size Format View

There are no files associated with this item.

COOK, Scott J., Nils B. WEIDMANN, 2022. Race to the Bottom : Spatial Aggregation and Event Data. In: International Interactions. Routledge, Taylor & Francis Group. 48(3), pp. 471-491. ISSN 0305-0629. eISSN 1547-7444. Available under: doi: 10.1080/03050629.2022.2025365

@article{Cook2022Botto-56882, title={Race to the Bottom : Spatial Aggregation and Event Data}, year={2022}, doi={10.1080/03050629.2022.2025365}, number={3}, volume={48}, issn={0305-0629}, journal={International Interactions}, pages={471--491}, author={Cook, Scott J. and Weidmann, Nils B.} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/56882"> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/42"/> <dc:contributor>Cook, Scott J.</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/42"/> <dcterms:title>Race to the Bottom : Spatial Aggregation and Event Data</dcterms:title> <dc:creator>Weidmann, Nils B.</dc:creator> <dcterms:issued>2022</dcterms:issued> <dcterms:abstract xml:lang="eng">Researchers now have greater access to granular georeferenced (i.e., spatial) data on social and political phenomena than ever before. Such data have seen wide use, as they offer the potential for researchers to analyze local phenomena, test mechanisms, and better understand micro-level behavior. With these political event data, it has become increasingly common for researchers to select the smallest spatial scale permitted by the data. We argue that this practice requires greater scrutiny, as smaller spatial or temporal scales do not necessarily improve the quality of inferences. While highly disaggregated data reduce some threats to inference (e.g., aggregation bias), they increase the risk of others (e.g., outcome misclassification). Therefore, we argue that researchers should adopt a more principled approach when selecting the spatial scale for their analysis. To help inform this choice, we characterize the aggregation problem for spatial data, discuss the consequences of too much (or too little) aggregation, and provide some guidance for applied researchers. We demonstrate these issues using both simulated experiments and an analysis of spatial patterns of violence in Afghanistan.</dcterms:abstract> <dc:contributor>Weidmann, Nils B.</dc:contributor> <dc:creator>Cook, Scott J.</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-03-16T09:47:26Z</dcterms:available> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/56882"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-03-16T09:47:26Z</dc:date> <dc:language>eng</dc:language> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> </rdf:Description> </rdf:RDF>

This item appears in the following Collection(s)

Search KOPS


Browse

My Account