{"id":"W3012323650","doi":"10.1504/ijbdi.2020.10027766","title":"Combining the richness of GIS techniques with visualisation tools to better understand the spatial distribution of data - a case study of Chicago City crime analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Visualization; Crime analysis; Spatial analysis; Data science; Data visualization; Distribution (mathematics); Computer science; Geography; Cartography; Data mining; Remote sensing; Psychology; Mathematics; Criminology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00168017,0.00008434209,0.0002379091,0.0001208147,0.0001204572,0.0001001728,0.002391737,0.00002951939,0.0001117193],"category_scores_gemma":[0.0007194913,0.0000516697,0.00008001638,0.0006122706,0.0002571594,0.0005800904,0.0005562595,0.0001661839,4.122901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004913767,"about_ca_system_score_gemma":0.0001067071,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01669918,"about_ca_topic_score_gemma":0.008676982,"domain_scores_codex":[0.9977489,0.0002807215,0.0007808963,0.0001881388,0.0009129068,0.00008840006],"domain_scores_gemma":[0.9972579,0.0003859505,0.0009165238,0.0005035221,0.0008828603,0.00005326283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001821969,0.004730646,0.2528364,0.0001014381,0.01076539,0.0004029436,0.1747636,0.00143068,0.002147819,0.004784852,0.005626013,0.5405883],"study_design_scores_gemma":[0.001746629,0.00725597,0.1558268,0.001457492,0.007135645,0.00040466,0.7543414,0.01969173,0.03931456,0.001743988,0.01005763,0.001023479],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.572101,0.00001879059,0.4241584,0.002413654,0.0001028178,0.0001745148,0.0009873392,0.000003255824,0.00004025425],"genre_scores_gemma":[0.9992555,0.0000275584,0.0002719867,0.0001023984,0.0001886684,0.000001862864,0.0001456547,0.000004015373,0.000002333266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5795778,"threshold_uncertainty_score":0.9898487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4316311108539355,"score_gpt":0.4667827832985019,"score_spread":0.03515167244456646,"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."}}