{"id":"W1971794754","doi":"10.1080/13658810802672469","title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","year":2010,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":1440,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Chinese University of Hong Kong","keywords":"Ordinary least squares; Geographically Weighted Regression; Weighting; Statistics; Sample (material); Regression analysis; Spatial variability; Econometrics; Goodness of fit; Geography; Regression; Spatial analysis; Cartography; Computer science; 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":[0.004769875,0.000890231,0.0006717248,0.001706956,0.0003028085,0.0009121169,0.001557694,0.0007947528,0.00215735],"category_scores_gemma":[0.0101861,0.0004826383,0.001572232,0.002987649,0.000505603,0.001243763,0.001032042,0.001328157,0.0004536054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008585406,"about_ca_system_score_gemma":0.000961205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03729881,"about_ca_topic_score_gemma":0.03296743,"domain_scores_codex":[0.9975552,0.001554773,0.00009515096,0.0004339873,0.0002604839,0.0001005029],"domain_scores_gemma":[0.9971809,0.001770179,0.0004446925,0.0001896801,0.0003639275,0.00005065587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005702024,0.00007105243,0.0214073,0.00006504152,0.0004352638,0.0002026133,0.0001032578,0.9103342,0.0006840188,0.0148987,0.001068032,0.05067353],"study_design_scores_gemma":[0.000004075087,0.00001957118,0.002442725,0.000009232288,0.00002567302,0.00002175029,0.00003088104,0.9933014,0.0001004581,0.003336448,0.0006988427,0.000008871139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1043029,0.0006528624,0.8914891,0.0004026943,0.0000922761,0.00007998857,0.0005178037,0.0003949407,0.002067385],"genre_scores_gemma":[0.8553168,0.0007323628,0.1375489,0.0001286434,0.00008808773,0.0001660671,0.0009711422,0.000152258,0.004895775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03729881,"threshold_uncertainty_score":0.07416338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643088557462788,"score_gpt":0.2321562989136509,"score_spread":0.215725413339023,"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."}}