{"id":"W1970830315","doi":"10.1177/0160017607301609","title":"Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?","year":2007,"lang":"en","type":"article","venue":"International Regional Science Review","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Econometrics; Ordinary least squares; Spatial heterogeneity; Context (archaeology); Spatial analysis; Spatial contextual awareness; Geographically Weighted Regression; Regression; Economics; Statistics; Computer science; Geography; Mathematics","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":[0.04722675,0.00235419,0.003790418,0.005430234,0.0008576641,0.00480483,0.003339706,0.002702032,0.009107538],"category_scores_gemma":[0.1824768,0.001114689,0.002417222,0.009900368,0.001897649,0.01166281,0.003294891,0.003313845,0.003354074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175573,"about_ca_system_score_gemma":0.003057724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03460543,"about_ca_topic_score_gemma":0.03205258,"domain_scores_codex":[0.9731393,0.02289089,0.0007640613,0.001933548,0.0007645468,0.0005075834],"domain_scores_gemma":[0.9239946,0.05260191,0.006918891,0.009996234,0.005769799,0.0007185354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000335042,0.000201611,0.05139392,0.001361876,0.004168826,0.0004808674,0.00114729,0.1826659,0.0004503992,0.2803172,0.03419954,0.4432776],"study_design_scores_gemma":[0.0001667423,0.0001724245,0.0146694,0.000825781,0.0009338914,0.0001415176,0.002370677,0.24015,0.0008805827,0.6881086,0.05140889,0.0001715421],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04493208,0.01436155,0.8504444,0.0566068,0.002033568,0.0002806392,0.002385509,0.002216488,0.02673911],"genre_scores_gemma":[0.6044761,0.01467092,0.3620783,0.006135421,0.002005789,0.0003319854,0.001859269,0.001042411,0.007399876],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04722675,"threshold_uncertainty_score":0.2497619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03599320060717616,"score_gpt":0.3216179616685817,"score_spread":0.2856247610614056,"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."}}