vancouver-datajam / gis-interpolation Goto Github PK

Tasks

Geostatistical Analysis:

Implement Kriging interpolation to estimate climate values at unobserved locations.

• Select Suitable Library: Choose a geostatistical library (e.g., scipy, PyKrige) for Kriging interpolation.
• Load and Prepare Data: Load the climate dataset containing observed values of precipitation, temperature, latitude, and longitude.
• Spatial Data Exploration: Visualize the spatial distribution of observed data points on a map to understand their geographic spread.
• Interpolation Setup: Define the variogram model (e.g., spherical, exponential) and other parameters necessary for Kriging interpolation.
• Split Data: Divide the dataset into training and testing sets, reserving a portion for model validation.
• Perform Kriging: Apply the chosen library's Kriging function to estimate climate values at unobserved locations based on the observed data.

Model Validation:

Evaluate the accuracy and performance of the Kriging model.

• Cross-Validation: Implement cross-validation by comparing the Kriged values to the observed values in the testing set.
• Calculate Prediction Errors: Compute error metrics (e.g., Root Mean Squared Error, Mean Absolute Error) to quantify the accuracy of the Kriging predictions.
• Generate Validation Plots: Create visualizations (e.g., scatter plots, histograms) comparing predicted and observed values to assess model performance.

Generate Kriged Climate Surfaces:

Create continuous climate surfaces for precipitation and temperature across British Columbia.

• Define Grid for Interpolation: Establish a spatial grid covering the entire British Columbia region to generate the continuous surfaces.
• Apply Kriging to Grid: Utilize the trained Kriging model to estimate climate values at each point within the grid.
• Create Visualizations: Generate maps displaying the Kriged climate surfaces for precipitation and temperature.
• Export Results: Save the Kriged climate surfaces as spatial datasets (e.g., raster files) for further analysis and visualization.

Resources

• Article & tutorial: https://towardsdatascience.com/kriging-the-french-temperatures-f0389ca908dd
• What is Kriging: https://gisgeography.com/kriging-interpolation-prediction/
• Article: https://www.publichealth.columbia.edu/research/population-health-methods/kriging-interpolation
• Methods https://pygis.io/docs/e_interpolation.html#kriging
• Example code: https://geostat-framework.readthedocs.io/projects/pykrige/en/latest/examples/04_krige_geometric.html
• ML in Python: https://pygis.io/docs/f_rs_ml_predict.html

Tasks

• Library Installation: Install necessary Python libraries, including plotly, to support the creation of interactive dashboards. You can do this using pip:
  pip install plotly dash pandas geopandas
• Dashboard Layout Design: Plan and design the layout of the dashboard, including the arrangement of maps, charts, and filters.
• Data Integration: Integrate the Kriged climate surfaces and other relevant data into the dashboard for visualization.
• Interactive_ Elements: Implement interactive elements such as dropdown menus, sliders, and checkboxes to allow users to explore the data dynamically.
• Plotly Chart Creation: Use Plotly to create various types of charts (e.g., scatter plots, heatmaps) to visualize the climate data.
• Geospatial Mapping: Incorporate geospatial maps into the dashboard using Plotly's mapping capabilities to display the Kriged results.
• Data Filtering and Selection: Enable users to filter and select specific regions or timeframes within the dashboard to view targeted climate information.
• Dashboard Styling and Aesthetics: Customize the appearance of the dashboard, including colors, fonts, and overall design, for a polished and user-friendly interface.
• Testing and Debugging: Thoroughly test the dashboard functionality to ensure smooth interaction and address any potential issues.
• Documentation and User Guide: Provide clear documentation and user instructions for navigating and utilizing the interactive dashboard effectively.
• Integration with Kriging Results: Link the dashboard to the Kriged climate surfaces, allowing users to visualize both observed and interpolated climate data.

Resources

• Dashboard example: https://viz.pmel.noaa.gov/osmc/?color_by=platform_type
• Article: https://medium.com/plotly/building-a-big-data-geographical-dashboard-with-open-source-tools-c5108d7d5683

Step-By-Step Guide

Guide on creating a geospatial dashboard in Python using Plotly:

# Step 4: Import necessary libraries
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import geopandas as gpd

# Step 5: Create a Dash app
app = dash.Dash(__name__)

# Step 6: Load your geospatial data
gdf = gpd.read_file('path_to_your_shapefile.shp')

# Step 7: Define the layout of your dashboard
app.layout = html.Div([
    html.H1("Geospatial Dashboard"),
    
    # Dropdown for selecting a variable to visualize
    dcc.Dropdown(
        id='variable-dropdown',
        options=[
            {'label': column, 'value': column} for column in gdf.columns
        ],
        value=gdf.columns[0],  # Default selection
        multi=False
    ),
    
    # Geospatial map
    dcc.Graph(
        id='geo-map'
    )
])

# Step 8: Create callback function to update the map
@app.callback(
    Output('geo-map', 'figure'),
    Input('variable-dropdown', 'value')
)
def update_map(selected_variable):
    fig = px.choropleth(
        gdf,
        geojson=gdf.geometry,
        locations=gdf.index,
        color=selected_variable,
        projection="mercator"
    )
    
    fig.update_geos(fitbounds="locations", visible=False)
    fig.update_layout(
        title=f"{selected_variable} Distribution",
        coloraxis_showscale=True
    )
    
    return fig

# Step 9: Run the app
if __name__ == '__main__':
    app.run_server(debug=True)

Here's what each step does:

1. Import the necessary libraries, including Plotly, Dash, Pandas, and Geopandas.
2. Create a Dash app.
3. Load your geospatial data using Geopandas (replace 'path_to_your_shapefile.shp' with your actual file path).
4. Define the layout of your dashboard with HTML and Dash components.
5. Create a callback function to update the geospatial map based on user input (variable selection).
6. Run the app.

Tasks

• Project Relevance Check: Review the project objectives and ensure that the analysis and predictions align with the challenge of visualizing and predicting geospatial BC climates.
• Practicality Assessment: Evaluate the feasibility and practicality of the project's solutions in addressing the climate change challenge specific to British Columbia.
• Impact Evaluation: Assess the potential positive impact of the project's insights and predictions on climate-related decision-making and planning in British Columbia.
• Technical Complexity Review: Analyze the technical intricacy of the implemented Kriging interpolation, considering innovation and scalability in the context of geospatial climate analysis.
• Creativity Check: Evaluate the project's creativity in problem-solving, considering the uniqueness and originality of approaches taken in visualizing and predicting BC climates.
• Presentation Quality Assessment: Review the clarity, organization, and engagement level of the project's presentation, ensuring that information is effectively conveyed with appropriate use of visual aids.
• GitHub Repository Management: Confirm that the project's GitHub repository has a 'main' branch with all code changes merged, and that the README file is complete with comprehensive project documentation.
• Code Review and Documentation: Conduct a thorough review of the codebase to ensure readability, proper comments, and adherence to best practices, while also verifying that the documentation provides clear insights into the methodology.
• Dashboard User Experience (UX) Testing: Test the interactive dashboard's functionality and user interface to ensure a smooth and intuitive experience for users exploring climate data.
• Final Presentation Rehearsal: Conduct a final run-through of the presentation to practice delivery, address any potential hiccups, and ensure a confident and engaging presentation.

Submission

Requirements

• Readme
• In-Person Presentation: To be eligible for judging and prizes, the team must be present in person to present your project.

Judging Criteria

• Relevance & Practicality: Addresses the challenge and offers a relevant solution to tackle the problem.
• Potential Impact: Considers the significance of the climate change challenge addressed and positive change potential.
• Technical Complexity: Evaluates technical skills, innovation, and scalability potential.
• Creativity: Assesses uniqueness, innovative problem-solving, and originality.
• Presentation: Clarity of information conveyed, organization, visual aids usage, and engagement.