Export community areas with the most DUIs

Add queries and a management command

README.rst

@@ -256,6 +256,13 @@ Export table of drug convictions
 
     ./manage.py export_drug_stats drug_by_class > export/drug_by_class.csv
 
+Export table of top community areas by DUI
+------------------------------------------
+
+::
+
+    ./manage.py export_dui_convictions_by_geo --model CommunityArea --count 20 > export/top_dui_community_areas.csv
+
 
 Manual Processes
 ================

convictions_data/management/commands/export_dui_convictions_by_geo.py

@@ -0,0 +1,40 @@
+import csv
+from optparse import make_option
+
+from django.core.management.base import BaseCommand
+
+import convictions_data.models
+
+class Command(BaseCommand):
+    help = ("Export CSV table showing most common conviction statute by geography")
+    option_list = BaseCommand.option_list + (
+        make_option('--count',
+            action='store',
+            type='int',
+            default=20,
+            dest='count',
+            help="Show this many statutes"),
+        make_option('--model',
+            action='store',
+            default='CommunityArea',
+            dest='model',
+            help="Get most common statutes for this model"),
+    )
+
+    def handle(self, *args, **options):
+        model_cls = getattr(convictions_data.models, options['model'])
+        qs = model_cls.objects.all().with_dui_annotations().order_by('-num_dui')
+
+        fieldnames = ['name', 'count', 'pct'] 
+        writer = csv.DictWriter(self.stdout,
+            fieldnames=fieldnames)
+
+        writer.writeheader()
+
+        for geo in qs[:options['count']]:
+            row = {
+                'name': geo.name,
+                'count': geo.num_dui,
+                'pct': geo.pct_dui,
+            }
+            writer.writerow(row)

convictions_data/query/__init__.py

@@ -606,7 +606,7 @@ def with_conviction_annotations(self):
             "AND {conviction_table}.iucr_category = 'Homicide'"
         ).format(conviction_table=conviction_table,
             matches_this_id=matches_this_id_where_sql)
-        # BOOKMARK
+
         codes = ["'{}'".format(c) for c in crimes_affecting_women_iucr_codes]
         affecting_women_iucr_codes_str = ", ".join(codes)
         num_affecting_women_sql = ('SELECT COUNT({conviction_table}.id) '
@@ -636,6 +636,45 @@ def with_conviction_annotations(self):
 
         return annotated_qs
 
+    def with_dui_annotations(self):
+        this_table = self.model._meta.db_table
+        conviction_table = self.model.get_conviction_model()._meta.db_table
+        conviction_related_col = self.model.get_conviction_related_column_name()
+        matches_this_id_where_sql = ('{conviction_table}.{related_col} = '
+            '{this_table}.id').format(conviction_table=conviction_table,
+                this_table=this_table, related_col=conviction_related_col)
+        num_dui_sql = ('SELECT COUNT({conviction_table}.id) '
+            'FROM {conviction_table} '
+            'WHERE {matches_this_id} '
+            'AND {conviction_table}.final_statute_formatted ILIKE \'625-5/11-501%%\''
+        ).format(conviction_table=conviction_table,
+            matches_this_id=matches_this_id_where_sql)
+        pct_dui_sql = ('SELECT CAST((SELECT COUNT({conviction_table}.id) '
+            'FROM {conviction_table} '
+            'WHERE {matches_this_id} '
+            'AND {conviction_table}.final_statute_formatted ILIKE '
+            '\'625-5/11-501%%\') AS FLOAT) / '
+            '(SELECT COUNT({conviction_table}.id) '
+            'FROM {conviction_table} '
+            'WHERE {matches_this_id}) '
+        ).format(conviction_table=conviction_table,
+            matches_this_id=matches_this_id_where_sql)
+        dui_per_capita_sql = ('SELECT CAST(COUNT({conviction_table}.id) AS FLOAT) / '
+            '"{this_table}"."total_population" '
+            'FROM {conviction_table} '
+            'WHERE {matches_this_id} '
+            'AND {conviction_table}.final_statute_formatted ILIKE '
+            '\'625-5/11-501%%\''
+        ).format(conviction_table=conviction_table,
+            this_table=this_table,
+            matches_this_id=matches_this_id_where_sql)
+
+        return self.extra(select={
+            'num_dui': num_dui_sql,
+            'pct_dui': pct_dui_sql,
+            'dui_per_capita': dui_per_capita_sql,
+        })
+
     def geojson(self, simplify=0.0):
         """
         Serialize models in this QuerySet as a GeoJSON FeatureCollection.