# Neighborhoods Banding Together Reasoning Globally about Programs Lisa Lippincott The code here is written in a fantasy C++, with extensions supporting local reasoning. ![Image](/uploads/documents/1 felis nisl adipiscing sapien, sed malesuada diam lacus eget erat. ## Local phase Validate small neighborhoods with high-complexity algorithms. Describe neighborhood interactions coarsely in small tables.

nei_topo(nei, topo) SELECT nei, topology.toTopoGeom(geom, 'topo_boston_test', 1) FROM neighborhoods WHERE gid BETWEEN 1 and 15; --use to verify what has happened -- SELECT * FROM topology pg_total_relation_size('public.neighborhoods') As fulltable_size, sum(ST_MemSize( ← the_geom)) As geomsize, sum(ST_MemSize(the_geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As ← pergeom FROM neighborhoods; fulltable_size geomsize pergeom ---------------------------------------- 262144 96238 36.71188354492187500000 ``` ## 5.4.25 ST_NDims ST_NDims — Returns

ic.neighborhoods') As fulltable_size, sum(ST_MemSize(← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM neighborhoods; fulltable_size

ic.neighborhoods') As fulltable_size, sum(ST_MemSize(← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM neighborhoods; fulltable_size

pg_total_relation_size('public.neighborhoods') As fulltable_size, sum(ST_MemSize( ← the_geom)) As geomsize, sum(ST_MemSize(the_geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As ← pergeom FROM neighborhoods; fulltable_size geomsize pergeom ---------------------------------------- 262144 96238 36.71188354492187500000 ``` ## 8.4.25 ST_NDims ST_NDims — Returns

ic.neighborhoods') As fulltable_size, sum(ST_MemSize(← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM neighborhoods; fulltable_size

ic.neighborhoods') As fulltable_size, sum(ST_MemSize(← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM neighborhoods; fulltable_size

ic.neighborhoods') As fulltable_size, sum(ST_MemSize(← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM neighborhoods; fulltable_size

pg_total_relation_size('public.neighborhoods') As fulltable_size, sum(ST_MemSize( ← geom)) As geomsize, sum(ST_MemSize(geom))*1.00/pg_total_relation_size('public.neighborhoods')*100 As pergeom FROM FROM neighborhoods; fulltable_size geomsize pergeom ------------------------------------ 262144 96238 36.71188354492187500000 ``` ## 7.4.28 ST_NDims ST_NDims — Gibt die Dimension der

""" Answers the question: What percentage of U.S. residents live highly walkable neighborhoods? """ "15.26" is the threshold on the index for a highly walkable area 100.0 print(f"{{percentage:.2f}}% of U.S. residents live in highly" "walkable neighborhoods.") if __name__ == __main__: main() Update your main.py file with the code above and should get the following answer: python main.py 10.69% of Americans live in highly walkable neighborhoods ## Conclusion You have just been introduced to creating your own data analysis project by using