PlanetProfit

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  Satellite derived land-surface temperature on a hot July day in 2022

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  PlanetProfit Logo

• 

  PlanetProfit Chatbot Demo

• 

  PlanetProfit Calculator Demo, based on location "Communitech"

Our Presentation:
https://docs.google.com/presentation/d/1pnWV_nW9ZzZwiineRovblO87ItGkQ858-MlDoSi3WNI/edit?usp=sharing

Satellite cover source:
https://storymaps.arcgis.com/stories/34d9ae9ea2c14cbfae03e46159086219

Exhibit A:
https://services1.arcgis.com/qAo1OsXi67t7XgmS/arcgis/rest/services/TreeEquity_WFL1/FeatureServer/13/query?outFields=*&where=1%3D1&f=geojson

Chatbot demo on Streamlit:
https://planetprofit-chatbot-gh24.streamlit.app

Climate Adaptive Cooling Calculator Google Colab (some code preview below): https://colab.research.google.com/drive/1QVu0ed71D9j2E3TGveb4To39Fl7a8tNT?usp=sharing

import requests
import geopandas as gpd
import pandas as pd
from google.colab import drive

zipcode = "N2G 1H6" # Communitech, Kitchener


service_url = "https://services1.arcgis.com/qAo1OsXi67t7XgmS/arcgis/rest/services/TreeEquity_WFL1/FeatureServer/13/query?outFields=*&where=1%3D1&f=geojson"
response = requests.get(service_url)
data = response.json()

# Putting coordinates into CSV file for quick lookup
coordinates_list = []

for feature in data['features']:
    if 'geometry' in feature and 'coordinates' in feature['geometry']:
        coordinates = feature['geometry']['coordinates']
        coordinates_list.append(coordinates)

df = pd.DataFrame(coordinates_list, columns=['Coordinates'])
df.to_csv('coordinates.csv', index=False)


# Get coordinates from zip-code
csv_path = '/content/drive/MyDrive/GOODHack24/CanadianPostalCodes202405Updated.csv'
cc = pd.read_csv(csv_path)
cc = cc[['POSTAL_CODE', 'LATITUDE', 'LONGITUDE']].rename(columns={'POSTAL_CODE': 'postcode', 'LATITUDE': 'lat', 'LONGITUDE': 'lon'})
mydata = pd.DataFrame({'postcode': [zipcode]})
mydata_aug = pd.merge(mydata, cc, on="postcode", how="left")
lon = mydata_aug['lon'].iloc[0]
lat = mydata_aug['lat'].iloc[0]
coordinate_tuple = (lon, lat)

# Find which Kitchener region the zip code is in
df = pd.read_csv('coordinates.csv')
def is_point_inside_polygon(x, y, poly):
    """
    Determine if a point is inside a polygon using the ray casting algorithm.

    Args:
    x (float): x-coordinate of the point
    y (float): y-coordinate of the point
    poly (list of tuples): List of tuples representing the vertices of the polygon

    Returns:
    bool: True if the point is inside the polygon, False otherwise
    """
    n = len(poly)
    inside = False

    p1x, p1y = poly[0]
    for i in range(n + 1):
        p2x, p2y = poly[i % n]
        if y > min(p1y, p2y):
            if y <= max(p1y, p2y):
                if x <= max(p1x, p2x):
                    if p1y != p2y:
                        xinters = (y - p1y) * (p2x - p1x) / (p2y - p1y) + p1x
                    if p1x == p2x or x <= xinters:
                        inside = not inside
        p1x, p1y = p2x, p2y

    return inside

target_id = -1

for index, row in df.iterrows():
    region_coordinates = eval(row['Coordinates'])
    if is_point_inside_polygon(coordinate_tuple[0], coordinate_tuple[1], region_coordinates):
        target_id = index + 1
        break
else:
    print("The zip code is not inside Kitchener.")


# Pulling the desired data
if target_id != -1:
  gdf = gpd.GeoDataFrame.from_features(data['features'])
  filtered_gdf = gdf.loc[gdf['OBJECTID'] == target_id]

  selected_columns = ['mean_surfacetemp', 'heat_disparity']
  filtered_properties = filtered_gdf[['OBJECTID'] + selected_columns]

  print(filtered_properties)

  # Save the selected properties to a CSV file
  filtered_properties.to_csv('filtered_line_data.csv', index=False)

Built With

• geopandas
• langchain
• python
• streamlit