{"id":"W2138502087","doi":"10.1111/j.1538-4632.2001.tb00435.x","title":"Power of the Rank Adjacency Statistic to Detect Spatial Clustering in a Small Number of Regions","year":2001,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Statistic; Adjacency list; Cluster analysis; Spatial analysis; Weighting; Autocorrelation; Statistics; Monte Carlo method; Scan statistic; Mathematics; Rank (graph theory); Computer science; Data mining; Algorithm; Combinatorics; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0734489,0.0007669561,0.001971849,0.0037792,0.001024706,0.002800471,0.001920366,0.002143664,0.002469283],"category_scores_gemma":[0.3272409,0.0006338859,0.002135972,0.002729323,0.005029549,0.004102385,0.002461088,0.001587771,0.0004822067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007985336,"about_ca_system_score_gemma":0.001361605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002418384,"about_ca_topic_score_gemma":0.001036745,"domain_scores_codex":[0.9575172,0.03243373,0.001577348,0.004568945,0.003218395,0.0006843487],"domain_scores_gemma":[0.3417894,0.6269581,0.01093278,0.0144665,0.004540367,0.001312835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004128617,0.0002645204,0.3712656,0.000590711,0.004218058,0.001158593,0.001626903,0.3859893,0.003652932,0.08017253,0.004285892,0.1426464],"study_design_scores_gemma":[0.0004812264,0.001298031,0.05028665,0.0001098212,0.0004775466,0.0009030252,0.0007818223,0.8153655,0.003649643,0.1225712,0.003859912,0.0002155834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5690535,0.001055062,0.4163192,0.001592798,0.0001775022,0.0003633621,0.0007828829,0.0004421017,0.01021355],"genre_scores_gemma":[0.9685137,0.0001825367,0.0301617,0.0001847049,0.00008781121,0.0001198908,0.0003705027,0.00005984036,0.0003192194],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0734489,"threshold_uncertainty_score":0.3884396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153834371097068,"score_gpt":0.2277909223865588,"score_spread":0.2062525786755881,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}