# Project configuration code
from geoplay.project import Project
# Data sources
from geoplay.data.precise_zcta import PreciseZCTA
from geoplay.data.ny_farmers_market import NYFarmersMarket
from geoplay.data.ny_trees import NYTrees
project = Project(
name="NY Farmers Market",
output_dir='ny-farmer',
projection='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
zcta = project.use(PreciseZCTA)
markets = project.use(NYFarmersMarket)
wanted_zips = zcta.filterTo(zipCodes=[
# 10128])
10453,10457,10460,10458,10467,10468,10451,10452,10456,10454,10455,10459,10474,10463,10471,10466,10469,10470,10475,10461,10462,10464,10465,10472,10473,11212,11213,11216,11233,11238,11209,11214,11228,11204,11218,11219,11230,11234,11236,11239,11223,11224,11229,11235,11201,11205,11215,11217,11231,11203,11210,11225,11226,11207,11208,11211,11222,11220,11232,11206,11221,11237,10026,10027,10030,10037,10039,10001,10011,10018,10019,10020,10036,10029,10035,10010,10016,10017,10022,10012,10013,10014,10004,10005,10006,10007,10038,10280,10002,10003,10009,10021,10028,10044,10065,10075,10128,10023,10024,10025,10031,10032,10033,10034,10040,11361,11362,11363,11364,11354,11355,11356,11357,11358,11359,11360,11365,11366,11367,11412,11423,11432,11433,11434,11435,11436,11101,11102,11103,11104,11105,11106,11374,11375,11379,11385,11691,11692,11693,11694,11695,11697,11004,11005,11411,11413,11422,11426,11427,11428,11429,11414,11415,11416,11417,11418,11419,11420,11421,11368,11369,11370,11372,11373,11377,11378,10302,10303,10310,10306,10307,10308,10309,10312,10301,10304,10305,10314])
for zip_area in wanted_zips:
markets_in = project.limitToPolygon(bounds=zip_area, collection=markets)
zip_area['properties']['MARKETCOUNT'] = len(list(markets_in))
zip_area['properties']['MARKETS'] = ', '.join([m['properties']['MarketName'] for m in markets_in])
project.save_layer(
name='nearby-farmers',
using_data=wanted_zips,
variables={
'ZCTA5CE10': {'type': 'str', 'from_data': 'ZCTA5CE10'},
'MARKETCOUNT': {'type': 'int', 'from_data': 'MARKETCOUNT'},
# Shapely **HAS** to be imported before anything else.
from shapely.geometry import mapping, shape
from shapely.prepared import prep
# Project configuration code
from geoplay.project import Project
# Data sources
from geoplay.data.zcta import ZCTA
from geoplay.data.stl_parks import StlParks
project = Project(
name="Nearby Parks",
output_dir='stl-parks',
projection='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
zcta = project.use(ZCTA)
parks = project.use(StlParks)
wanted_zips = zcta.filterTo(zipCodes=[63108, 63118, 63103, 63104, 63110])
for zip_area in wanted_zips:
# puff the bounds of the zip codes out 0.001 arcradians
loose_zip_bounds = shape(zip_area['geometry']).buffer(0.001)
# Make a list of parks that touch our new expanded ZIP
touching_parks = list(
filter(lambda p: loose_zip_bounds.intersects(shape(p['geometry'])),
parks.shapefile()))
# Update the geometry in the shapefile
zip_area['geometry'] = mapping(loose_zip_bounds)
# Shapely **HAS** to be imported before anything else.
from shapely.geometry import mapping, shape
# Project configuration code
from geoplay.project import Project
# Data sources
from geoplay.data.precise_zcta import PreciseZCTA
from geoplay.data.stl_parks import StlParks
project = Project(
name="Nearby Parks",
output_dir='stl-parks',
projection='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
zcta = project.use(PreciseZCTA)
parks = project.use(StlParks)
wanted_zips = zcta.filterTo(zipCodes=[63108, 63118, 63103, 63104, 63110])
# Write out an intermediate file so that we can quickly validate it visually
project.save_layer(name='just-stl',
using_data=wanted_zips,
variables={
'ZCTA5CE10': {
'type': 'str',
'from_data': 'ZCTA5CE10'
},
})
for zip_area in wanted_zips:
loose_zip_bounds = shape(zip_area['geometry']).buffer(0.001)
# Data sources
from geoplay.data.ny_zips import NyZips
from geoplay.data.ny_bikes import NyBikes
degrees_to_meter = partial(
pyproj.transform,
pyproj.Proj(init='EPSG:4326'), # degrees
# pyproj.Proj(init='EPSG:6360')) # feet
pyproj.Proj(init='EPSG:32633')) # meter
project = Project(
name="NY Bike Length",
output_dir='ny-bikes',
projection='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
zcta = project.use(NyZips)
streets = project.use(NyBikes)
wanted_zips = zcta.filterTo(zipCodes=[
#63108, 63118])
# 10035])
10055,
10453,
10457,
10460,
10458,
10467,
10468,
10451,
10452,
10456,
# Project configuration code
from geoplay.project import Project
# Data sources
# from geoplay.data.precise_zcta import PreciseZCTA
from geoplay.data.phl_zips import PhlZips
from geoplay.data.phl_canopy import PhlCanopy
project = Project(
name="PHL Canopy",
output_dir='phl-canopy',
projection=
'+proj=lcc +lat_1=40.96666666666667 +lat_2=39.93333333333333 +lat_0=39.33333333333334 +lon_0=-77.75 +x_0=600000 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs '
)
zcta = project.use(PhlZips)
tree_cover = project.use(PhlCanopy)
wanted_zips = list(zcta.shapefile())
# wanted_zips = zcta.filterTo(zipCodes=[
# 10453])
# 10471])
# 11361])
# 19151,19139,19148])
# 10055,10453,10457,10460,10458,10467,10468,10451,10452,10456,10454,10455,10459,10474,10463,10471,10466,10469,10470,10475,10461,10462,10464,10465,10472,10473,11212,11213,11216,11233,11238,11209,11214,11228,11204,11218,11219,11230,11234,11236,11239,11223,11224,11229,11235,11201,11205,11215,11217,11231,11203,11210,11225,11226,11207,11208,11211,11222,11220,11232,11206,11221,11237,10026,10027,10030,10037,10039,10001,10011,10018,10019,10020,10036,10029,10035,10010,10016,10017,10022,10012,10013,10014,10004,10005,10006,10007,10038,10280,10002,10003,10009,10021,10028,10044,10065,10075,10128,10023,10024,10025,10031,10032,10033,10034,10040,11361,11362,11363,11364,11354,11355,11356,11357,11358,11359,11360,11365,11366,11367,11412,11423,11432,11433,11434,11435,11436,11101,11102,11103,11104,11105,11106,11374,11375,11379,11385,11691,11692,11693,11694,11695,11697,11004,11005,11411,11413,11422,11426,11427,11428,11429,11414,11415,11416,11417,11418,11419,11420,11421,11368,11369,11370,11372,11373,11377,11378,10302,10303,10310,10306,10307,10308,10309,10312,.10301,10304,10305,10314])
TREE_COVER_INDEX = 1
for zip_area in wanted_zips:
print("Checking " + str(zip_area['properties']['CODE']))
# TODO: Compact multiple into a single file? Can I?
# raw_data = project.clipRaster(name='cover-for-' + zip_area['properties']['CODE'], raster=tree_cover.image(), bounds=zip_area)
# Data sources
from geoplay.data.precise_zcta import PreciseZCTA
from geoplay.data.stl_streets import StlStreets
degrees_to_meter = partial(
pyproj.transform,
pyproj.Proj(init='EPSG:4326'), # degrees
# pyproj.Proj(init='EPSG:6360')) # feet
pyproj.Proj(init='EPSG:32633')) # meter
project = Project(
name="Street Length",
output_dir='stl-streets',
projection='+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')
zcta = project.use(PreciseZCTA)
streets = project.use(StlStreets)
wanted_zips = zcta.filterTo(zipCodes=[63108, 63118])
for zip_area in wanted_zips:
bounds = prep(shape(zip_area['geometry']).buffer(0.0001))
streets_2 = list(
filter(lambda p: bounds.intersects(shape(p['geometry'])),
streets.shapefile()))
project.save_layer(name=('streets-in-' +
zip_area['properties']['ZCTA5CE10']),
using_data=streets_2,
variables={},
# Project configuration code
from geoplay.project import Project
# Data sources
# from geoplay.data.precise_zcta import PreciseZCTA
from geoplay.data.ny_zips import NyZips
from geoplay.data.ny_canopy import NYCanopy
project = Project(name="NY Canopy",
output_dir='ny-canopy',
projection="+init=esri:102718")
zcta = project.use(NyZips)
tree_cover = project.use(NYCanopy)
wanted_zips = zcta.filterTo(zipCodes=[
# 10453])
# 10471])
11361
])
# 10055,10453,10457,10460,10458,10467,10468,10451,10452,10456,10454,10455,10459,10474,10463,10471,10466,10469,10470,10475,10461,10462,10464,10465,10472,10473,11212,11213,11216,11233,11238,11209,11214,11228,11204,11218,11219,11230,11234,11236,11239,11223,11224,11229,11235,11201,11205,11215,11217,11231,11203,11210,11225,11226,11207,11208,11211,11222,11220,11232,11206,11221,11237,10026,10027,10030,10037,10039,10001,10011,10018,10019,10020,10036,10029,10035,10010,10016,10017,10022,10012,10013,10014,10004,10005,10006,10007,10038,10280,10002,10003,10009,10021,10028,10044,10065,10075,10128,10023,10024,10025,10031,10032,10033,10034,10040,11361,11362,11363,11364,11354,11355,11356,11357,11358,11359,11360,11365,11366,11367,11412,11423,11432,11433,11434,11435,11436,11101,11102,11103,11104,11105,11106,11374,11375,11379,11385,11691,11692,11693,11694,11695,11697,11004,11005,11411,11413,11422,11426,11427,11428,11429,11414,11415,11416,11417,11418,11419,11420,11421,11368,11369,11370,11372,11373,11377,11378,10302,10303,10310,10306,10307,10308,10309,10312,.10301,10304,10305,10314])
TREE_COVER_INDEX = 1
for zip_area in wanted_zips:
print(zip_area['geometry']['coordinates'])
# TODO: Compact multiple into a single file? Can I?
raw_data = project.clipRaster(name='cover-for-' +
zip_area['properties']['ZIPCODE'],
raster=tree_cover.image(),
bounds=zip_area)
pixels_tree_covered = len(raw_data[raw_data == TREE_COVER_INDEX])