Posted on December 26, 2022 (Updated on July 21, 2025)

Find csv lat and long points in a shapefile polygon with geopandas spatial index

Hiking & Activities

Finding CSV Latitude and Longitude Points Inside a Shapefile Polygon: A GeoPandas Adventure

So, you’ve got a bunch of latitude and longitude points in a CSV file, and you need to figure out which ones are chilling inside a specific geographic area defined by a shapefile? Been there! It’s a classic geospatial head-scratcher, often called a Point-in-Polygon (PIP) operation. Now, you could just loop through each point and check if it’s inside the polygon, but trust me, if you’re dealing with a decent-sized dataset, that’s gonna take forever. We’re talking “go grab a coffee, maybe binge-watch a season of your favorite show” levels of slow.

That’s where GeoPandas comes to the rescue. This nifty Python library has a secret weapon: spatial indexing. Think of it like this: imagine searching for a specific book in a massive library. You could check every single shelf, one by one, or you could use the library’s index to quickly find the right section. Spatial indexing does the same thing for geospatial data.

Instead of comparing each point to the polygon, spatial indexing creates a kind of map that allows GeoPandas to quickly narrow down the possibilities. The most common type of spatial index is based on something called an R-tree. Basically, it builds a bunch of nested boxes around your data, making it super-efficient to find points that might be inside your polygon.

When you ask GeoPandas to find points within a polygon using a spatial index, it first checks which of those boxes intersect with the polygon’s box. This gives you a much smaller subset of points to check, saving you a ton of time. It’s like pre-filtering your data before doing the real work.

So, how do you actually do it? Let’s break it down:

  • Gear Up: First, you’ll need to import the necessary libraries: GeoPandas, Pandas, and Shapely. These are your trusty tools for this adventure.

  • Load the Map: Use GeoPandas’ read_file() to load your shapefile into a GeoDataFrame. This is like loading the map of the area you’re interested in.

  • Gather the Points: Use Pandas’ read_csv() to read your CSV file into a DataFrame. This is where all your latitude and longitude points are stored.

  • Turn Points into Geo-Points: This is where the magic happens. You need to transform your CSV data into a GeoDataFrame. This involves:

    • Using Shapely’s Point() constructor to create Point objects from your latitude and longitude columns. Remember to get the order right: points_from_xy(longitudes, latitudes). I’ve messed that up more times than I care to admit!
    • Setting the GeoDataFrame’s geometry column to these Point objects. This tells GeoPandas that these are your spatial data.
    • Setting the Coordinate Reference System (CRS) of your GeoDataFrame. This is crucial. Make sure it matches the shapefile’s CRS! Otherwise, you’ll be comparing apples and oranges. You can specify the CRS when you create the GeoDataFrame.

  • The Big Reveal: Now, for the grand finale! Use geopandas.sjoin with op=”within” to perform a spatial join. This command cleverly uses the spatial index to find all the points that fall inside your polygon. The sjoin command incorporates a spatial index (rtree), so you don’t have to do that manually.

  • Eureka!: The resulting GeoDataFrame will contain only the points that are inside the polygon, along with all the juicy details from both your CSV and shapefile.

    • Here’s a little code snippet to get you started:

    python

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