Replication Data for: Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Random sampling of graph partitions under constraints has become apopular tool for evaluating legislative redistricting plans.Analysts detect partisan gerrymandering by comparing a proposedredistricting plan with an ensemble of sampled alternative plans.For successful application, sampling methods must scale tomaps with a moderate or large number of districts, incorporate realistic legal constraints,and accurately and efficiently sample from a selected targetdistribution.  Unfortunately, most existing methods struggle in atleast one of these areas.  We present a new Sequential Monte Carlo(SMC) algorithm that generates a sample of redistricting plans convergingto a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevantspace of redistricting plans better t' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Gerrymandering","Graph Partition","Redistricting","Sequential Monte Carlo","Spanning Trees"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Random sampling of graph partitions under constraints has become apopular tool for evaluating legislative redistricting plans.Analysts detect partisan gerrymandering by comparing a proposedredistricting plan with an ensemble of sampled alternative plans.For successful application, sampling methods must scale tomaps with a moderate or large number of districts, incorporate realistic legal constraints,and accurately and efficiently sample from a selected targetdistribution.  Unfortunately, most existing methods struggle in atleast one of these areas.  We present a new Sequential Monte Carlo(SMC) algorithm that generates a sample of redistricting plans convergingto a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevantspace of redistricting plans better t' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Random sampling of graph partitions under constraints has become apopular tool for evaluating legislative redistricting plans.Analysts detect partisan gerrymandering by comparing a proposedredistricting plan with an ensemble of sampled alternative plans.For successful application, sampling methods must scale tomaps with a moderate or large number of districts, incorporate realistic legal constraints,and accurately and efficiently sample from a selected targetdistribution.  Unfortunately, most existing methods struggle in atleast one of these areas.  We present a new Sequential Monte Carlo(SMC) algorithm that generates a sample of redistricting plans convergingto a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevantspace of redistricting plans better t
Gerrymandering; Graph Partition; Redistricting; Sequential Monte Carlo; Spanning Trees