Week 14B: Raster Data in the Wild

Section 401
Dec 11, 2023

Last class!

Feedback on proposals via email soon if you haven’t received it yet
Final project due: end of day on Wednesday, December 20

Final project details: https://github.com/MUSA-550-Fall-2023/final-project

Thank you for a great semester!!

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:

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd

import intake
import xarray as xr

import hvplot.xarray
import hvplot.pandas
import holoviews as hv
import geoviews as gv
from geoviews.tile_sources import EsriImagery

# Ignore numpy runtime warnings
np.seterr("ignore");

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

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

Example #2: Urban heat islands

We’ll reproduce an analysis by Urban Spatial on urban heat islands in Philadelphia using Python.
The analysis uses Landsat 8 data (2017)
See: http://urbanspatialanalysis.com/urban-heat-islands-street-trees-in-philadelphia/

First load some metadata for Landsat 8

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

	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.

# 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.

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..

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

<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
  * x            (x) float64 3.954e+05 3.954e+05 ... 6.276e+05 6.276e+05
  * y            (y) float64 4.582e+06 4.582e+06 ... 4.346e+06 4.346e+06
    spatial_ref  int64 0
Attributes:
    AREA_OR_POINT:  Point
    scale_factor:   1.0
    add_offset:     0.0

Important

CRS for Landsat data 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

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

metadata = load_google_landsat_metadata(path, row, product_id)
metadata.head(n=20)

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
DATA_TYPE                                                              L1TP
COLLECTION_CATEGORY                                                      T1
ELEVATION_SOURCE                                                    GLS2000
OUTPUT_FORMAT                                                       GEOTIFF
SPACECRAFT_ID                                                     LANDSAT_8
SENSOR_ID                                                          OLI_TIRS
WRS_PATH                                                                 14
WRS_ROW                                                                  32
NADIR_OFFNADIR                                                        NADIR
TARGET_WRS_PATH                                                          14
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

From OpenDataPhilly, the city limits for Philadelphia are available at: http://data.phl.opendata.arcgis.com/datasets/405ec3da942d4e20869d4e1449a2be48_0.geojson
Be sure to convert to the same CRS as the Landsat data!

# 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

# Look up the order of xmin/xmax and ymin/ymax
city_limits.total_bounds?

Type:        property
String form: <property object at 0x1511c5990>
Docstring:  
Returns a tuple containing ``minx``, ``miny``, ``maxx``, ``maxy``
values for the bounds of the series as a whole.
See ``GeoSeries.bounds`` for the bounds of the geometries contained in
the series.
Examples
--------
>>> from shapely.geometry import Point, Polygon, LineString
>>> d = {'geometry': [Point(3, -1), Polygon([(0, 0), (1, 1), (1, 0)]),
... LineString([(0, 1), (1, 2)])]}
>>> gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")
>>> gdf.total_bounds
array([ 0., -1.,  3.,  2.])

# Get x/y range of city limits from "total_bounds"
xmin, ymin, xmax, ymax = city_limits.total_bounds

# Slice our xarray data
# NOTE: y coordinate system is in descending order!
subset = ds.sel(x=slice(xmin, xmax), y=slice(ymax, ymin))

subset

<xarray.DataArray (band: 10, y: 1000, x: 924)>
dask.array<getitem, shape=(10, 1000, 924), dtype=uint16, chunksize=(1, 256, 256), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int64 1 2 3 4 5 6 7 9 10 11
  * x            (x) float64 4.761e+05 4.761e+05 ... 5.037e+05 5.038e+05
  * y            (y) float64 4.443e+06 4.443e+06 ... 4.413e+06 4.413e+06
    spatial_ref  int64 0
Attributes:
    AREA_OR_POINT:  Point
    scale_factor:   1.0
    add_offset:     0.0

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

subset = subset.compute()

subset

# Original data was about 8000 x 8000 in size
ds.shape

(10, 7871, 7741)

# Sliced data is only about 1000 x 1000 in size!
subset.shape

(10, 1000, 924)

Let’s plot band 3 of the Landsat data

# 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

/Users/nhand/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/osgeo/osr.py:385: FutureWarning: Neither osr.UseExceptions() nor osr.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.
  warnings.warn(
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

Calculate the NDVI

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

band_info.head()

	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

# 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()

# 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

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.

from rio_toa import brightness_temp, toa_utils

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)

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

# 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')

temp_10_f

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

band_10

Get the land surface temp for band 11

# 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

# 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

Plot the final product (the average of the bands)

# 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

mean_temp_f

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

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 cold/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

cond = mean_temp_f > 90

hot_spots = mean_temp_f.where(cond)

hot_spots

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

What about cold spots?

Let’s select things less than 75 degrees as well…

cold_spots = mean_temp_f.where(mean_temp_f < 75)

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

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

import rioxarray

# Save to a raster GeoTIFF file!
mean_temp_f.rio.to_raster("./mean_temp_f.tif")

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

color_palette = {
    "A": "#388E3C",
    "B": "#1976D2",
    "C": "#FFEE58",
    "D": "#D32F2F",
    "Commercial": "#a1a1a1",
}

redlining = gpd.read_file("./data/redlining.geojson")

redlining.head()

	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...

colors = redlining['holc_grade'].replace(color_palette)

colors.head()

0    #388E3C
1    #388E3C
2    #388E3C
3    #388E3C
4    #388E3C
Name: holc_grade, dtype: object

redlining["holc_color"] = colors

holc_map = redlining.hvplot.polygons(
    c="holc_color",
    frame_width=400,
    frame_height=400,
    geo=True,
    hover_cols=["holc_grade"],
    alpha=0.5
) 

holc_map * gv.tile_sources.ESRI

Part 2: Calculate the mean temperature for each redlined area

Use rasterio.open() to load the mean temp TIFF file
Use the rasterstats package to calculate the zonal stats
Look back at week 5’s lecture for help!
Make sure the CRS matches!

from rasterstats import zonal_stats
import rasterio as rio

# Open the GeoTIFF file using rasterio
f = rio.open('./mean_temp_f.tif')
f

<open DatasetReader name='./mean_temp_f.tif' mode='r'>

Load the data from the file:

temp_data = f.read(1)

temp_data

array([[79.40579086, 79.18871976, 78.98910218, ..., 91.86524855,
        91.72038357, 91.49660753],
       [79.07306227, 78.86421586, 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]])

Same as:

mean_temp_f.values

array([[79.40579086, 79.18871976, 78.98910218, ..., 91.86524855,
        91.72038357, 91.49660753],
       [79.07306227, 78.86421586, 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]])

f.crs

CRS.from_epsg(32618)

# Get the CRS
CRS = f.crs.to_epsg()
print(CRS)

#  Get the transform too
transform = f.transform

# 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(epsg=CRS), 
    mean_temp_f.values, 
    affine=transform, 
    nodata=np.nan
)

# Converts stats to a dataframe
redlining_stats = pd.DataFrame(redlining_stats)

redlining_stats.head()

	min	max	mean	count
0	76.009914	85.732779	81.403242	1731
1	73.232288	88.181473	77.453338	7074
2	78.858240	88.117261	82.955090	1639
3	76.950025	95.523473	87.345773	2150
4	74.434519	89.862934	79.951873	1717

### 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(epsg=CRS), "./mean_temp_f.tif")

# Converts stats to a dataframe
redlining_stats = pd.DataFrame(redlining_stats)

/Users/nhand/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/rasterstats/io.py:328: NodataWarning: Setting nodata to -999; specify nodata explicitly
  warnings.warn(

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

redlining.head()

	area_id	holc_grade	geometry	holc_color	mean_temp
0	0	A	POLYGON ((-75.17329 40.06201, -75.17572 40.060...	#388E3C	81.403242
1	1	A	MULTIPOLYGON (((-75.19831 40.05021, -75.19899 ...	#388E3C	77.453338
2	2	A	POLYGON ((-75.12810 40.04734, -75.12844 40.045...	#388E3C	82.955090
3	3	A	POLYGON ((-75.11494 40.03443, -75.13476 40.036...	#388E3C	87.345773
4	4	A	POLYGON ((-75.18377 40.02725, -75.17894 40.023...	#388E3C	79.951873

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

mean_temp_f.values

array([[79.40579086, 79.18871976, 78.98910218, ..., 91.86524855,
        91.72038357, 91.49660753],
       [79.07306227, 78.86421586, 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]])

# Nan values cause this to not be defined!
mean_temp_f.values.mean()

nan

citywide_mean_temp = np.nanmean(mean_temp_f.values) # use nanmean() to skip NaN pixels automatically!
redlining['mean_temp_normalized'] = redlining['mean_temp'] - citywide_mean_temp

citywide_mean_temp

81.70253802309126

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

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

redlining.groupby("holc_grade")["mean_temp"].mean().sort_values()

holc_grade
A             82.364416
B             84.636335
C             86.298266
D             86.305663
Commercial    86.755549
Name: mean_temp, dtype: float64