Inspiration
Visualizing and processing data can be a pretty computer-intensive task depending on its specifications and in many cases when working with spatial data, you just want to visualize the data so you can start your analysis.
What it does
It allows you to easily visualize and analyze your spatial data by creating 3 interactive maps: a point map, a heat map, and a clustered point map.
How I built it
I built this project using Python, Streamlit, pandas, GeoPandas, and Folium.
Challenges I ran into
- Displaying the spatial data into 3 distinct maps
- Identifying what kind of maps could be created as well as data that could be used with Streamlit
Accomplishments that I'm proud of
Learning Streamlit and utilizing it to create a web app that visualizes spatial data for faster analysis.
What I learned
- How to create a data web app using Streamlit
- How to utilize the Streamlit API to display spatial data as well as optimize the web app's performance
- How to use components to extend the capability of Streamlit
What's next for Data Map Viewer
- Accept more common spatial data file formats
- Showcasing a wider range of maps
- Allow the user to customize the maps (editing, filtering, sorting, etc.)
- Allow the user to export an image of a map
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