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| 1 | +spatial_tables = { |
| 2 | + # HUC8 |
| 3 | + "Watershed": dict( |
| 4 | + table_name="wbdhu8", |
| 5 | + id_field="huc8" |
| 6 | + ), |
| 7 | + |
| 8 | + "HUC10 Watersheds": dict( |
| 9 | + table_name="wbdhu10", |
| 10 | + id_field="huc10" |
| 11 | + ), |
| 12 | + |
| 13 | + "HUC12 Watersheds": dict( |
| 14 | + table_name="wbdhu12", |
| 15 | + id_field="huc12" |
| 16 | + ), |
| 17 | + |
| 18 | + #"Ecoregions": dict( |
| 19 | + # table_name="eco_level3", |
| 20 | + # id_field="US_L3NAME" #e.g. Atlantic Coastal Pine Barrens |
| 21 | + # |
| 22 | + #), |
| 23 | + |
| 24 | + "County": dict( |
| 25 | + table_name="tl_2019_us_county", |
| 26 | + id_field="GEOID" # four or five digit code corresponding to two digit state number (e.g. 55) and 2-3 digit county code! |
| 27 | + ), |
| 28 | + |
| 29 | + "Zip Code": dict( |
| 30 | + table_name="tl_2019_us_zcta510", |
| 31 | + id_field="zcta5ce10" |
| 32 | + |
| 33 | + ), |
| 34 | + |
| 35 | + "EPA Region": dict( |
| 36 | + table_name="epa_regions", |
| 37 | + id_field="eparegion" # In the form of "Region 1", "Region 2", up to "Region 10" |
| 38 | + ), |
| 39 | + |
| 40 | + "State": dict( |
| 41 | + table_name = "tl_2019_us_state", |
| 42 | + id_field = "STUSPS" # e.g. MS, IA, AK |
| 43 | + ), |
| 44 | + |
| 45 | + "Congressional District": dict( |
| 46 | + table_name = "tl_2019_us_cd116", |
| 47 | + id_field = "GEOID" # this is the combination of the state id and the CD e.g. AR-2 = 0502 |
| 48 | + ) |
| 49 | +} |
| 50 | + |
1 | 51 | region_field = {
|
2 | 52 | 'State': { "field": 'FAC_STATE' },
|
3 | 53 | 'Congressional District': { "field": 'FAC_DERIVED_CD113' },
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4 | 54 | 'County': { "field": 'FAC_COUNTY' },
|
5 | 55 | 'Zip Code': { "field": 'FAC_ZIP' },
|
| 56 | + 'Watershed': { "field": 'FAC_DERIVED_HUC'}, |
| 57 | + 'EPA Region': { "field": 'FAC_EPA_REGION'} |
6 | 58 | }
|
7 | 59 | # Commenting out these region types until implemented
|
8 | 60 | # 'Watershed': {"field": 'FAC_DERIVED_HUC'},
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