Week 8B: Raster Data in the Wild

Oct 27, 2021

This week: raster data analysis with the holoviz ecosystem

Two case studies:

  • Last time: Using satellite imagery to detect changes in lake volume
  • Today: Detecting urban heat islands in Philadelphia

Initialize our packages:

In [1]:
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
In [2]:
import intake
import xarray as xr
import cartopy.crs as ccrs
In [3]:
import hvplot.xarray
import hvplot.pandas
import holoviews as hv
import geoviews as gv
from geoviews.tile_sources import EsriImagery

Create the intake data catalog so we can load our datasets:

In [4]:
url = 'https://raw.githubusercontent.com/pyviz-topics/EarthML/master/examples/catalog.yml'
cat = intake.open_catalog(url)

Example #2: Urban heat islands

First load some metadata for Landsat 8

In [5]:
band_info = pd.DataFrame([
        (1,  "Aerosol", " 0.43 - 0.45",    0.440,  "30",         "Coastal aerosol"),
        (2,  "Blue",    " 0.45 - 0.51",    0.480,  "30",         "Blue"),
        (3,  "Green",   " 0.53 - 0.59",    0.560,  "30",         "Green"),
        (4,  "Red",     " 0.64 - 0.67",    0.655,  "30",         "Red"),
        (5,  "NIR",     " 0.85 - 0.88",    0.865,  "30",         "Near Infrared (NIR)"),
        (6,  "SWIR1",   " 1.57 - 1.65",    1.610,  "30",         "Shortwave Infrared (SWIR) 1"),
        (7,  "SWIR2",   " 2.11 - 2.29",    2.200,  "30",         "Shortwave Infrared (SWIR) 2"),
        (8,  "Panc",    " 0.50 - 0.68",    0.590,  "15",         "Panchromatic"),
        (9,  "Cirrus",  " 1.36 - 1.38",    1.370,  "30",         "Cirrus"),
        (10, "TIRS1",   "10.60 - 11.19",   10.895, "100 * (30)", "Thermal Infrared (TIRS) 1"),
        (11, "TIRS2",   "11.50 - 12.51",   12.005, "100 * (30)", "Thermal Infrared (TIRS) 2")],
    columns=['Band', 'Name', 'Wavelength Range (µm)', 'Nominal Wavelength (µm)', 'Resolution (m)', 'Description']).set_index(["Band"])
band_info
Out[5]:
Name Wavelength Range (µm) Nominal Wavelength (µm) Resolution (m) Description
Band
1 Aerosol 0.43 - 0.45 0.440 30 Coastal aerosol
2 Blue 0.45 - 0.51 0.480 30 Blue
3 Green 0.53 - 0.59 0.560 30 Green
4 Red 0.64 - 0.67 0.655 30 Red
5 NIR 0.85 - 0.88 0.865 30 Near Infrared (NIR)
6 SWIR1 1.57 - 1.65 1.610 30 Shortwave Infrared (SWIR) 1
7 SWIR2 2.11 - 2.29 2.200 30 Shortwave Infrared (SWIR) 2
8 Panc 0.50 - 0.68 0.590 15 Panchromatic
9 Cirrus 1.36 - 1.38 1.370 30 Cirrus
10 TIRS1 10.60 - 11.19 10.895 100 * (30) Thermal Infrared (TIRS) 1
11 TIRS2 11.50 - 12.51 12.005 100 * (30) Thermal Infrared (TIRS) 2

Landsat data on Google Cloud Storage

We'll be downloading publicly available Landsat data from Google Cloud Storage

See: https://cloud.google.com/storage/docs/public-datasets/landsat

The relevant information is stored in our intake catalog:

From our catalog.yml file:

google_landsat_band:
    description: Landsat bands from Google Cloud Storage
    driver: rasterio
    parameters:
      path:
        description: landsat path
        type: int
      row:
        description: landsat row
        type: int
      product_id:
        description: landsat file id
        type: str
      band:
        description: band
        type: int
    args:
      urlpath: https://storage.googleapis.com/gcp-public-data-landsat/LC08/01/{{ '%03d' % path }}/{{ '%03d' % row }}/{{ product_id }}/{{ product_id }}_B{{ band }}.TIF
      chunks:
        band: 1
        x: 256
        y: 256

From the "urlpath" above, you can see we need "path", "row", and "product_id" variables to properly identify a Landsat image:

The path and row corresponding to the area over Philadelphia has already been selected using the Earth Explorer. This tool was also used to find the id of the particular date of interest using the same tool.

In [6]:
# Necessary variables
path = 14
row = 32
product_id = 'LC08_L1TP_014032_20160727_20170222_01_T1'

Use a utility function to query the API and request a specific band

This will return a specific Landsat band as a dask array.

In [7]:
from random import random
from time import sleep


def get_band_with_exponential_backoff(path, row, product_id, band, maximum_backoff=32):
    """
    Google Cloud Storage recommends using exponential backoff 
    when accessing the API. 
    
    https://cloud.google.com/storage/docs/exponential-backoff
    """
    n = backoff = 0
    while backoff < maximum_backoff:
        try:
            return cat.google_landsat_band(
                path=path, row=row, product_id=product_id, band=band
            ).to_dask()
        except:
            backoff = min(2 ** n + random(), maximum_backoff)
            sleep(backoff)
            n += 1

Load all of the bands and combine them into a single xarray DataArray

Loop over each band, load that band using the above function, and then concatenate the results together..

In [8]:
bands = [1, 2, 3, 4, 5, 6, 7, 9, 10, 11]

datasets = []
for band in bands:
    da = get_band_with_exponential_backoff(
        path=path, row=row, product_id=product_id, band=band
    )
    da = da.assign_coords(band=[band])
    datasets.append(da)

ds = xr.concat(datasets, "band", compat="identical")

ds
Out[8]:
<xarray.DataArray (band: 10, y: 7871, x: 7741)>
dask.array<concatenate, shape=(10, 7871, 7741), dtype=uint16, chunksize=(1, 256, 256), chunktype=numpy.ndarray>
Coordinates:
  * band     (band) int64 1 2 3 4 5 6 7 9 10 11
  * y        (y) float64 4.582e+06 4.582e+06 4.582e+06 ... 4.346e+06 4.346e+06
  * x        (x) float64 3.954e+05 3.954e+05 3.955e+05 ... 6.276e+05 6.276e+05
Attributes:
    transform:      (30.0, 0.0, 395385.0, 0.0, -30.0, 4582215.0)
    crs:            +init=epsg:32618
    res:            (30.0, 30.0)
    is_tiled:       1
    nodatavals:     (nan,)
    scales:         (1.0,)
    offsets:        (0.0,)
    AREA_OR_POINT:  Point

Important: CRS info

CRS is EPSG=32618

Also grab the metadata from Google Cloud Storage

  • There is an associated metadata file stored on Google Cloud Storage
  • The below function will parse that metadata file for a specific path, row, and product ID
  • The specifics of this are not overly important for our purposes
In [9]:
def load_google_landsat_metadata(path, row, product_id):
    """
    Load Landsat metadata for path, row, product_id from Google Cloud Storage
    """

    def parse_type(x):
        try:
            return eval(x)
        except:
            return x

    baseurl = "https://storage.googleapis.com/gcp-public-data-landsat/LC08/01"
    suffix = f"{path:03d}/{row:03d}/{product_id}/{product_id}_MTL.txt"

    df = intake.open_csv(
        urlpath=f"{baseurl}/{suffix}",
        csv_kwargs={
            "sep": "=",
            "header": None,
            "names": ["index", "value"],
            "skiprows": 2,
            "converters": {"index": (lambda x: x.strip()), "value": parse_type},
        },
    ).read()
    metadata = df.set_index("index")["value"]
    return metadata
In [10]:
metadata = load_google_landsat_metadata(path, row, product_id)
metadata.head(n=10)
Out[10]:
index
ORIGIN                         Image courtesy of the U.S. Geological Survey
REQUEST_ID                                              0501702206266_00020
LANDSAT_SCENE_ID                                      LC80140322016209LGN01
LANDSAT_PRODUCT_ID                 LC08_L1TP_014032_20160727_20170222_01_T1
COLLECTION_NUMBER                                                        01
FILE_DATE                                              2017-02-22T04:00:05Z
STATION_ID                                                              LGN
PROCESSING_SOFTWARE_VERSION                                      LPGS_2.7.0
END_GROUP                                                METADATA_FILE_INFO
GROUP                                                      PRODUCT_METADATA
Name: value, dtype: object

Let's trim our data to the Philadelphia limits

  • The Landsat image covers an area much wider than the Philadelphia limits
  • We'll load the city limits from Open Data Philly
  • Use the slice() function discussed last lecture

1. Load the city limits

In [11]:
# Load the GeoJSON from the URL
url = "http://data.phl.opendata.arcgis.com/datasets/405ec3da942d4e20869d4e1449a2be48_0.geojson"
city_limits = gpd.read_file(url)

# Convert to the right CRS for this data
city_limits = city_limits.to_crs(epsg=32618)

2. Use the xmin/xmax and ymin/ymax of the city limits to trim the Landsat DataArray

  • Use the built-in slice functionality of xarray
  • Remember, the y coordinates are in descending order, so you'll slice will need to be in descending order as well
In [12]:
# Look up the order of xmin/xmax and ymin/ymax
city_limits.total_bounds?
In [13]:
# Get x/y range of city limits from "total_bounds"
xmin, ymin, xmax, ymax = city_limits.total_bounds
In [14]:
# Slice our xarray data
subset = ds.sel(
                x=slice(xmin, xmax), 
                y=slice(ymax, ymin)
               ) # NOTE: y coordinate system is in descending order!

3. Call the compute() function on your sliced data

  • Originally, the Landsat data was stored as dask arrays
  • This should now only load the data into memory that covers Philadelphia
In [15]:
subset = subset.compute()

subset
Out[15]:
<xarray.DataArray (band: 10, y: 1000, x: 924)>
array([[[10702, 10162, 10361, ..., 11681, 11489, 15594],
        [10870, 10376, 10122, ..., 11620, 11477, 12042],
        [10429, 10147, 10063, ..., 11455, 11876, 11790],
        ...,
        [11944, 11696, 11626, ..., 11192, 11404, 10923],
        [12406, 11555, 11155, ..., 11516, 11959, 10766],
        [11701, 11232, 10657, ..., 10515, 11475, 10926]],

       [[ 9809,  9147,  9390, ..., 10848, 10702, 15408],
        [ 9989,  9405,  9139, ..., 10831, 10660, 11361],
        [ 9439,  9083,  8981, ..., 10654, 11141, 11073],
        ...,
        [11220, 10853, 10741, ..., 10318, 10511,  9950],
        [11765, 10743, 10259, ..., 10646, 11378,  9829],
        [10889, 10365,  9630, ...,  9500, 10573, 10008]],

       [[ 9042,  8466,  8889, ..., 10014,  9647, 14981],
        [ 9699,  8714,  8596, ...,  9866,  9783, 11186],
        [ 8623,  8457,  8334, ...,  9688, 10474,  9993],
        ...,
...
        ...,
        [ 5027,  5028,  5038, ...,  5035,  5037,  5029],
        [ 5058,  5021,  5023, ...,  5035,  5041,  5031],
        [ 5036,  5041,  5040, ...,  5036,  5044,  5035]],

       [[29033, 28976, 28913, ..., 32614, 32577, 32501],
        [28940, 28904, 28858, ..., 32516, 32545, 32545],
        [28882, 28879, 28854, ..., 32431, 32527, 32563],
        ...,
        [30094, 29929, 29713, ..., 29521, 29525, 29429],
        [30140, 29972, 29696, ..., 29556, 29516, 29398],
        [30133, 29960, 29614, ..., 29587, 29533, 29424]],

       [[25558, 25519, 25492, ..., 27680, 27650, 27619],
        [25503, 25450, 25402, ..., 27636, 27630, 27639],
        [25473, 25434, 25378, ..., 27609, 27668, 27667],
        ...,
        [26126, 26055, 25934, ..., 25622, 25586, 25520],
        [26149, 26077, 25935, ..., 25651, 25594, 25507],
        [26147, 26050, 25856, ..., 25696, 25644, 25571]]], dtype=uint16)
Coordinates:
  * band     (band) int64 1 2 3 4 5 6 7 9 10 11
  * y        (y) float64 4.443e+06 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
  * x        (x) float64 4.761e+05 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
Attributes:
    transform:      (30.0, 0.0, 395385.0, 0.0, -30.0, 4582215.0)
    crs:            +init=epsg:32618
    res:            (30.0, 30.0)
    is_tiled:       1
    nodatavals:     (nan,)
    scales:         (1.0,)
    offsets:        (0.0,)
    AREA_OR_POINT:  Point
In [16]:
# Original data was about 8000 x 8000 in size
ds.shape
Out[16]:
(10, 7871, 7741)
In [17]:
# Sliced data is only about 1000 x 1000 in size!
subset.shape
Out[17]:
(10, 1000, 924)

Let's plot band 3 of the Landsat data

In [18]:
# City limits plot
limits_plot = city_limits.hvplot.polygons(
    line_color="white", alpha=0, line_alpha=1, crs=32618, geo=True
)

# Landsat plot
landsat_plot = subset.sel(band=3).hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="viridis",
    frame_width=500,
    frame_height=500,
    geo=True,
    crs=32618,
)

# Combined
landsat_plot * limits_plot

OMP: Info #271: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
Out[18]:

Calculate the NDVI

Remember, NDVI = (NIR - Red) / (NIR + Red)

In [19]:
band_info.head()
Out[19]:
Name Wavelength Range (µm) Nominal Wavelength (µm) Resolution (m) Description
Band
1 Aerosol 0.43 - 0.45 0.440 30 Coastal aerosol
2 Blue 0.45 - 0.51 0.480 30 Blue
3 Green 0.53 - 0.59 0.560 30 Green
4 Red 0.64 - 0.67 0.655 30 Red
5 NIR 0.85 - 0.88 0.865 30 Near Infrared (NIR)

NIR is band 5 and Red is band 4

In [20]:
# Selet the bands we need
NIR = subset.sel(band=5)
RED = subset.sel(band=4)

# Calculate the NDVI
NDVI = (NIR - RED) / (NIR + RED)
NDVI = NDVI.where(NDVI < np.inf)

NDVI.head()
Out[20]:
<xarray.DataArray (y: 5, x: 5)>
array([[0.34690712, 0.43934922, 0.45820226, 0.45798014, 0.38205128],
       [0.33747045, 0.40266976, 0.47276229, 0.44722264, 0.4530777 ],
       [0.40783302, 0.44093268, 0.47419128, 0.49448025, 0.49341935],
       [0.40190311, 0.39986498, 0.46889397, 0.48497283, 0.48686142],
       [0.47248631, 0.47255625, 0.46365524, 0.37692424, 0.36055777]])
Coordinates:
  * y        (y) float64 4.443e+06 4.443e+06 4.443e+06 4.443e+06 4.443e+06
  * x        (x) float64 4.761e+05 4.761e+05 4.761e+05 4.762e+05 4.762e+05
In [21]:
# NEW: Use datashader to plot the NDVI
p = NDVI.hvplot.image(
    x="x",
    y="y",
    geo=True,
    crs=32618,
    datashade=True, # NEW: USE DATASHADER
    project=True, # NEW: PROJECT THE DATA BEFORE PLOTTING
    frame_height=500,
    frame_width=500,
    cmap="viridis",
)

# NEW: Use a transparent rasterized version of the plot for hover text
# No hover text available with datashaded images so we can use the rasterized version
p_hover = NDVI.hvplot(
    x="x",
    y="y",
    crs=32618,
    geo=True,
    rasterize=True,
    frame_height=500,
    frame_width=500,
    alpha=0, # SET ALPHA=0 TO MAKE THIS TRANSPARENT
    colorbar=False,
)

# City limits
limits_plot = city_limits.hvplot.polygons(
    geo=True, crs=32618, line_color="white", line_width=2, alpha=0, line_alpha=1
)

p * p_hover * limits_plot
Out[21]:

A couple of notes

  • Notice that we leveraged datashader algorithm to plot the NDVI by specifying the datashade=True keyword
  • Hover tools won't work with datashaded images — instead, we overlaid a transparent rasterized version of the image, which shows the mean pixel values across the image

Now, on to land surface temperature

  • Given the NDVI calculated above, we can determine land surface temperature
  • But, the data must be cleaned first! Several atmospheric corrections and transformations must be applied.
  • We'll use existing an existing package called rio_toa to do this
  • We'll also need to specify one more for transforming satellite temperature (brightness temp) to land surface temperature.
  • For these calculations we'll use both Thermal Infrared bands - 10 and 11.
In [22]:
from rio_toa import brightness_temp, toa_utils
In [23]:
def land_surface_temp(BT, w, NDVI):
    """Calculate land surface temperature of Landsat 8
    
    temp = BT/1 + w * (BT /p) * ln(e)
    
    BT = At Satellite temperature (brightness)
    w = wavelength of emitted radiance (μm)
    
    where p = h * c / s (1.439e-2 mK)
    
    h = Planck's constant (Js)
    s = Boltzmann constant (J/K)
    c = velocity of light (m/s)
    """
    h = 6.626e-34
    s = 1.38e-23
    c = 2.998e8

    p = (h * c / s) * 1e6

    Pv = (NDVI - NDVI.min() / NDVI.max() - NDVI.min()) ** 2
    e = 0.004 * Pv + 0.986

    return BT / 1 + w * (BT / p) * np.log(e)
In [24]:
def land_surface_temp_for_band(band):
    """
    Utility function to get land surface temperature for a specific band
    """
    # params from general Landsat info
    w = band_info.loc[band]["Nominal Wavelength (µm)"]

    # params from specific Landsat data text file for these images
    ML = metadata[f"RADIANCE_MULT_BAND_{band}"]
    AL = metadata[f"RADIANCE_ADD_BAND_{band}"]
    K1 = metadata[f"K1_CONSTANT_BAND_{band}"]
    K2 = metadata[f"K2_CONSTANT_BAND_{band}"]

    at_satellite_temp = brightness_temp.brightness_temp(
        subset.sel(band=band).values, ML, AL, K1, K2
    )

    return land_surface_temp(at_satellite_temp, w, NDVI)

Get land surface temp for band 10

In [25]:
# temperature for band 10
band = 10
temp_10 = land_surface_temp_for_band(band).compute()

# convert to Farenheit
temp_10_f = toa_utils.temp_rescale(temp_10, 'F')

# Save the numpy array as an xarray
band_10 = subset.sel(band=band).copy(data=temp_10_f)

Get the land surface temp for band 11

In [26]:
# Get the land surface temp
band = 11
temp_11 = land_surface_temp_for_band(band).compute()

# Convert to Farenheit
temp_11_f = toa_utils.temp_rescale(temp_11, "F")

# Save as an xarray DataArray
band_11 = subset.sel(band=band).copy(data=temp_11_f)

Plot both temperatures side-by-side

In [27]:
# plot for band 10
plot_10 = band_10.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="fire_r",
    crs=32618,
    geo=True,
    frame_height=400,
    frame_width=400,
    title="Band 10",
)

# plot for band 11
plot_11 = band_11.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    cmap="fire_r",
    crs=32618,
    geo=True,
    frame_height=400,
    frame_width=400,
    title="Band 11",
)

plot_10 + plot_11
Out[27]:

Plot the final product (the average of the bands)

In [28]:
# Let's average the results of these two bands
mean_temp_f = (band_10 + band_11) / 2

# IMPORTANT: copy over the metadata
mean_temp_f.attrs = band_10.attrs
In [29]:
mean_temp_f
Out[29]:
<xarray.DataArray (y: 1000, x: 924)>
array([[79.40579086, 79.18871976, 78.98910218, ..., 91.86524855,
        91.72038357, 91.49660753],
       [79.07306227, 78.86416093, 78.64685031, ..., 91.56712235,
        91.60934815, 91.6311314 ],
       [78.87625297, 78.77157573, 78.57777687, ..., 91.33527213,
        91.66605844, 91.73405961],
       ...,
       [83.01805262, 82.50340008, 81.75768196, ..., 80.579109  ,
        80.49671178, 80.13207064],
       [83.16916028, 82.64632139, 81.7252    , ..., 80.72431108,
        80.4982923 , 80.03521661],
       [83.14992768, 82.55447768, 81.35868618, ..., 80.90148737,
        80.65920451, 80.25022931]])
Coordinates:
  * y        (y) float64 4.443e+06 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
  * x        (x) float64 4.761e+05 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
Attributes:
    transform:      (30.0, 0.0, 395385.0, 0.0, -30.0, 4582215.0)
    crs:            +init=epsg:32618
    res:            (30.0, 30.0)
    is_tiled:       1
    nodatavals:     (nan,)
    scales:         (1.0,)
    offsets:        (0.0,)
    AREA_OR_POINT:  Point
In [30]:
p = mean_temp_f.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    crs=32618,
    geo=True,
    frame_width=600,
    frame_height=600,
    cmap="rainbow",
    alpha=0.5,
    legend=False,
    title="Mean Surface Temp (F)"
)

img = p * EsriImagery

img
Out[30]:

Use the zoom tool to identify hot spots

Key insight: color of the building roof plays a very important role in urban heat islands

Exercise: isolate hot spots on the map

We can use the .where() function in xarray to identify the heat islands across the city

To do:

  • Select only those pixels with mean temperatures about 90 degrees.
  • Remember, the where() function takes a boolean array where each pixel is True/False based on the selection criteria
  • Use hvplot to plot the imagery for Philadelphia, with the hotspots overlaid
In [31]:
hot_spots = mean_temp_f.where(mean_temp_f > 90)
In [32]:
hot_spots_plot = hot_spots.hvplot.image(
    x="x",
    y="y",
    cmap="fire",
    crs=32618,
    geo=True,
    frame_height=500,
    frame_width=500,
    colorbar=False,
    legend=False,
    rasterize=True,
    title="Mean Temp (F) > 90",
    alpha=0.7
)


hot_spots_plot * EsriImagery
Out[32]:

What about cold spots?

Let's select things less than 75 degrees as well...

In [33]:
cold_spots = mean_temp_f.where(mean_temp_f < 75)
In [34]:
cold_spots_plot = cold_spots.hvplot.image(
    x="x",
    y="y",
    cmap="fire",
    geo=True,
    crs=32618,
    frame_height=500,
    frame_width=500,
    colorbar=False,
    legend=False,
    rasterize=True,
    title="Mean Temp (F) < 75",
    alpha=0.5
)


cold_spots_plot * EsriImagery
Out[34]:

Cold spots pick out bodies of water and parks / areas of high vegetation!

Exercise: Exploring the relationship between temperature, trees, and redlining in Philadelphia

In this section, we'll use zonal statistics to calculate the average temperatures for redlined areas and calculate the number of street trees in the city.

Redlining Readings

Saving xarray data to a geoTIFF file

  • Unfortunately, the zonal statistics calculation requires a rasterio file — so, we will need to to save our mean temperature xarray DataArray to a .tif file
  • This is not built in to xarray (yet), but we can use the rioxarray package
In [35]:
import rioxarray
In [36]:
# Save to a raster GeoTIFF file!
mean_temp_f.rio.to_raster("./mean_temp_f.tif")

Warning 1: +init=epsg:XXXX syntax is deprecated. It might return a CRS with a non-EPSG compliant axis order.

Part 1: Load the redlined neighborhoods and make an interactive choropleth

  • The redlining HOLC map for Philadelphia is available in the data/ folder
  • Use hvplot to make the interactive choropleth map
  • Try to match the historical color scheme from the original HOLC categories using the color palette below.

Hint

  • Create a new column called holc_color in the redlining GeoDataFrame
  • You can then specify this column as the color column using the "c=" for hvplot()
  • To create the column, you can use the .apply() function of the holc_grade column.
In [37]:
color_palette = {
    "A": "#388E3C",
    "B": "#1976D2",
    "C": "#FFEE58",
    "D": "#D32F2F",
    "Commercial": "#a1a1a1",
}
In [38]:
redlining = gpd.read_file("./data/redlining.geojson")
In [39]:
redlining.head()
Out[39]:
area_id holc_grade geometry
0 0 A POLYGON ((-75.17329 40.06201, -75.17572 40.060...
1 1 A MULTIPOLYGON (((-75.19831 40.05021, -75.19899 ...
2 2 A POLYGON ((-75.12810 40.04734, -75.12844 40.045...
3 3 A POLYGON ((-75.11494 40.03443, -75.13476 40.036...
4 4 A POLYGON ((-75.18377 40.02725, -75.17894 40.023...
In [40]:
# OPTION 1
colors = redlining.holc_grade.replace(color_palette)

# OPTION 2
# colors = redlining["holc_grade"].apply(lambda grade: color_palette[grade])
In [41]:
colors
Out[41]:
0     #388E3C
1     #388E3C
2     #388E3C
3     #388E3C
4     #388E3C
       ...   
67    #1976D2
68    #a1a1a1
69    #a1a1a1
70    #a1a1a1
71    #a1a1a1
Name: holc_grade, Length: 72, dtype: object
In [42]:
redlining["holc_color"] = colors
In [43]:
holc_map = redlining.hvplot(
    c="holc_color",
    frame_width=400,
    frame_height=400,
    geo=True,
    hover_cols=["holc_grade"],
    alpha=0.5
) 

holc_map * gv.tile_sources.ESRI
Out[43]:

Part 2: Calculate the mean temperature for each redlined area

  • Use rasterio to load the mean temp TIFF file
  • Use the rasterstats package to calculate the zonal stats
  • Look back at week 4's lecture for help!
  • Make sure the CRS matches!
In [44]:
from rasterstats import zonal_stats, gen_zonal_stats
import rasterio as rio
In [45]:
# Open the GeoTIFF file using rasterio
f = rio.open('./mean_temp_f.tif')
f
Out[45]:
<open DatasetReader name='./mean_temp_f.tif' mode='r'>
In [46]:
# Get the CRS
CRS = f.crs.data
print(CRS)

{'init': 'epsg:32618'}
In [47]:
#  Get the transform too
transform = f.transform
In [48]:
# Use zonal_stats to get the mean temperature values within each redlined area
# FIRST ARGUMENT: vector polygons
# SECOND ARGUMENT: mean temperature raster data
redlining_stats = zonal_stats(
    redlining.to_crs(CRS['init']), 
    mean_temp_f.values, 
    affine=transform, 
    stats=["mean"]
)

# Store the mean temp back in the original redlining dataframe
redlining["mean_temp"] = [s["mean"] for s in redlining_stats]

/Users/nhand/opt/miniconda3/envs/musa-550-fall-2021/lib/python3.8/site-packages/rasterstats/io.py:302: UserWarning: Setting nodata to -999; specify nodata explicitly
  warnings.warn("Setting nodata to -999; specify nodata explicitly")
In [66]:
### ALTERNATIVE
### in this case, you don't need to specify the transform, 
### it's loaded from the TIF file

redlining_stats = zonal_stats(
    redlining.to_crs(CRS['init']),
    "./mean_temp_f.tif",
    stats=["mean"]
)

redlining["mean_temp"] = [s["mean"] for s in redlining_stats]
In [65]:
f.s
Out[65]:
<open DatasetReader name='./mean_temp_f.tif' mode='r'>
In [49]:
redlining.head()
Out[49]:
area_id holc_grade geometry holc_color mean_temp
0 0 A POLYGON ((-75.17329 40.06201, -75.17572 40.060... #388E3C 81.403241
1 1 A MULTIPOLYGON (((-75.19831 40.05021, -75.19899 ... #388E3C 77.453336
2 2 A POLYGON ((-75.12810 40.04734, -75.12844 40.045... #388E3C 82.955088
3 3 A POLYGON ((-75.11494 40.03443, -75.13476 40.036... #388E3C 87.345772
4 4 A POLYGON ((-75.18377 40.02725, -75.17894 40.023... #388E3C 79.951871

Part 3: Use hvplot to plot a choropleth of the mean temperature

  • Make a two panel chart with the redlined areas on the right and the mean temperature on the left
  • To quickly let us see which areas are hotter or colder than the citywide average, subtract out the mean temperature of the entire city

Hint

  • You can use the np.nanmean() function to calculate the mean of the temperature raster data, automatically ignoring any NaN pixels
  • Use a diverging color palette to visualize the difference from the citywide average
In [50]:
citywide_mean_temp = np.nanmean(mean_temp_f) # use nanmean() to skip NaN pixels automatically!
redlining['mean_temp_normalized'] = redlining['mean_temp'] - citywide_mean_temp
In [51]:
choro = redlining.hvplot(
    c="mean_temp_normalized",
    frame_width=400,
    frame_height=400,
    geo=True,
    hover_cols=["holc_grade", 'mean_temp'],
    alpha=0.5,
    cmap='coolwarm',
) 


choro * gv.tile_sources.ESRI  + holc_map * gv.tile_sources.ESRI
Out[51]:

Part 4: Calculate the average temp by HOLC grade

Use a groupby / mean operation to calculate the mean tempeature for eah HOLC grade, e.g., A, B, C, etc.

You should find that better HOLC grades correspond to lower mean temperatures

In [52]:
redlining.groupby("holc_grade")["mean_temp"].mean().sort_values()
Out[52]:
holc_grade
A             82.364415
B             84.636333
C             86.298264
D             86.305661
Commercial    86.755547
Name: mean_temp, dtype: float64

Part 5: Calculate the average tree coverage for each HOLC grade

  • We can use the 2015 tree canopy data from OpenDataPhilly
  • I've downloaded the shape file for the dataset and added it to the data/ folder

Part 5.1: Load the data from the data/ folder

Note: these are the outlines of the tree canopy — they are polygon geometry

In [53]:
canopy = gpd.read_file("data/ppr_tree_canopy_outlines_2015/")
In [54]:
canopy.head()
Out[54]:
avg_height geometry
0 6 POLYGON ((-75.27506 39.97745, -75.27505 39.977...
1 17 POLYGON ((-75.26884 39.97958, -75.26884 39.979...
2 32 POLYGON ((-75.26510 39.96519, -75.26512 39.965...
3 15 POLYGON ((-75.27271 39.97838, -75.27271 39.978...
4 32 POLYGON ((-75.26491 39.96518, -75.26497 39.965...

Part 5.2: Calculate the intersection of the tree canopy and redlined areas

  • We want to assign an HOLC grade from the redlining data frame to the tree canopy polygons
  • We can use the geopandas.overlay function (documention) to achieve this
  • We can overlay the redlining data onto the canopy data and calculate the intersection — this should create a new geometry that corresponds to the intersection

Important:

  • Make sure both the redlining and canopy data frames have the same CRS!
  • Both data frames should not be in 4326 (you will get a warning) — otherwise the area calculation in the next step won't work!
  • Project them to EPSG=3857 (or similar) so that the units are meters.
In [55]:
canopy = canopy.to_crs(epsg=3857)
redlining = redlining.to_crs(epsg=3857)
In [56]:
canopy_intersection = gpd.overlay(redlining, canopy, how='intersection')

Part 5.3: Calculate the total area of the canopy and redlined areas

  • Add a new column that corresponds to the area of the geometry (you can the geometry.area attribute)
  • Group by the holc_grade column and sum up the area
  • Do this both for the redlining data frame and the canopy intersection dataframe. This will give you the total area of each HOLC grade and the total area of the tree canopy in each HOLC grade
In [57]:
canopy_intersection['area'] = canopy_intersection.geometry.area
canopy_area = canopy_intersection.groupby("holc_grade").area.sum()
In [58]:
redlining['area'] = redlining.geometry.area
redlining_area = redlining.groupby("holc_grade").area.sum()

Part 5.4: Calculate the average tree coverage for each HOLC grade

Divide the results from the previous step to calculate the percent of the land area covered by trees for each HOLC grade

You should find that better HOLC grades correspond to lower tree coverages

In [59]:
tree_coverage = canopy_area / redlining_area

tree_coverage.sort_values()
Out[59]:
holc_grade
Commercial    0.020991
D             0.029206
C             0.039274
B             0.047298
A             0.094690
Name: area, dtype: float64
In [ ]: