{"id":"W1572080216","doi":"10.1007/978-3-540-24709-8_107","title":"An Exploratory Spatial Data Analysis (ESDA) Toolkit for the Analysis of Activity/Travel Data","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Exploratory analysis; Geographic information system; Visualization; Exploratory data analysis; Sample (material); Data science; Data visualization; Data exploration; Spatial analysis; Data mining; Geography; Cartography; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004963523,0.002153122,0.001773799,0.0042547,0.001254763,0.00358341,0.00284475,0.0009029175,0.02903898],"category_scores_gemma":[0.01313432,0.002161555,0.00325022,0.00427049,0.0006660263,0.002642017,0.004826778,0.003254096,0.0162165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006139866,"about_ca_system_score_gemma":0.002749793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006334718,"about_ca_topic_score_gemma":0.01557111,"domain_scores_codex":[0.9981256,0.0004910406,0.0003528407,0.0002796969,0.0006459435,0.0001049289],"domain_scores_gemma":[0.9928253,0.004628479,0.0002937167,0.001010817,0.001014279,0.0002274229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000407706,0.0002877828,0.004998077,0.002934309,0.0008512404,0.0009050021,0.002911653,0.0106729,0.01728693,0.02706762,0.314804,0.6168728],"study_design_scores_gemma":[0.0004977376,0.0001839731,0.01053362,0.0009510765,0.0005501984,0.002252195,0.001243182,0.2333402,0.02548749,0.1172251,0.6072972,0.0004381792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001250214,0.0002149026,0.8702649,0.000167743,0.00007431598,0.0002993834,0.01407583,0.1122065,0.001446104],"genre_scores_gemma":[0.005980599,0.0001959412,0.9729071,0.00007600759,0.00001493162,0.0009940581,0.01041499,0.007629689,0.001786739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02903898,"threshold_uncertainty_score":0.09714514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08359665489999547,"score_gpt":0.3476217796709555,"score_spread":0.26402512477096,"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."}}