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relataly.com

  • AI
    • Simple Regression
    • Classification: Two Class
    • Classification: Multi-Class
    • Clustering
    • Time Series Forecasting
    • Anomaly Detection
    • Natural Language
    • Recommender Systems
    • Reinforcement Learning
    • Responsible AI
  • Use Cases
    • Stock Market Forecasting
    • Algorithmic Trading
    • Sentiment Analysis
    • Churn Prediction
    • Fraud Detection
    • Predictive Maintenance
    • Marketing Automation
    • Customer Segmentation
    • Sales Forecasting
    • ChatBots
    • Fighting Crime
    • Risk Management
    • Image Recognition
  • Algorithms
    • CNNs
    • RNNs (LSTM)
    • Decision Trees
    • Random Decision Forests
    • Random Isolation Forest
    • Local Outlier Factor
    • Gradient Boosting
    • Collaborative Filtering
    • Content-based Filtering
    • K-Nearest Neighbors
    • K-Means
    • Affinity Propagation
    • Agglomerative Clustering
    • Logistic Regression
    • Naive Bayes
    • ARIMA
  • Data Science
    • Exploratory Data Analysis
    • Feature Engineering
    • Hyperparameter Tuning
    • Dimensionality Reduction
    • Model Interpretation
    • Data Visualization
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    • Measuring Performance
    • Cross-Validation
    • Vector Databases
    • SQLite
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      • Anaconda
      • Azure Machine Learning
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      • Scikit-Learn
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      • Facebook Prophet
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GeoPandas

GeoPandas is an open-source project that makes it easy to visualize geospatial data in Python. It extends the data types used by Pandas to allow spatial information to be mapped onto a geographic map.

The primary use for GeoPandas is the visualization of statistical data on a geographic map. Furthermore, we can use Geopandas to map information to other geographic objects, such as bodies of water or mountains.

If you want to visualize spatial data, you might consider displaying it on a map. We can use color-overlay on geographic maps to highlight differences between geographical regions. An example is covid-19 cases. We can use a world map and color countries depending on their case count. Assuming there are differences between the countries, it is easy for the viewer to identify regions with a high case count and where it is lower.

Geometric operations are carried out by “Shapely”. Shapely reads the geoinformation in the form of shapefiles. We can find Shapefiles on the web for various geo objects, including as countries, cities, or political regions. Two sources for shapefiles are:

  • statsilk.com
  • igismap.com 

 

worldmap covid-19 data python tutorial api midjourney relataly machine learning-min

Geographic Heat Maps with GeoPandas: Visualizing COVID-19 Data in Python

May 27, 2023April 8, 2020

The spreading of COVID-19 has led to an increased interest in displaying region and country-specific information on geographic heat maps. … Read more

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