Spatial Joins in QGIS


This week,  I visualized broadband availability across Alabama at the census block group level using FCC broad band data. Here’s how I did it.




Step 1: Clean and Prepare the Data

I started with two FCC datasets: cable and fiber broadband availability, and a Census dataset for median income.

  • Merge FCC datasets: Using Python and pandas, I combined the cable and fiber datasets, extracting the first 12 digits of the block_geoid to get the block group level, and calculated the maximum advertised download speed for each block group.

  • Clean Census data: From the Census median income file, using python, I ignored rows with missing values (-666666666) and constructed a 12-digit census block group GEOID by combining state, county, tract, and block group codes.

  • Output: Two clean CSVs — one for max download speed per block group, and one for median income per block group.

Step 2: Load Data into QGIS

  • Loaded the Alabama census block group shapefile (tl_2024_01_bg.shp).

  • Imported the cleaned CSVs as tables (no geometry).

Step 3: Join Data to the Shapefile

  • Joined the broadband CSV and the Census income CSV to the shapefile using the 12-digit GEOID as the key.

  • After the join, each block group polygon had both maximum download speed and median income attributes.

Step 4: Visualize Broadband Speeds

  • Applied Graduated Symbology to the broadband field:

    • Column: max_advertised_download_speed

    • Mode: Natural Breaks (Jenks)

    • Classes: 4–5, with an appropriate color ramp (light → dark blue).

  • Verified numeric data and legend formatting to ensure the map rendered correctly

  • I did similar visualization of median income by census block groups in Alabama.

Result

The final map clearly highlights areas of high and low broadband availability across Alabama’s block groups, providing a spatial view of digital access in the state.

Comments

Post a Comment

Popular posts from this blog

Wrapping Up: A Geospatial Dive into the U.S. Broadband Divide

Image
         For my MSDS692 Data Science Practicum project, I decided to tackle a topic close to my professional background: the U.S. Broadband Divide. My goal was to move beyond simple maps and explore whether socioeconomic factors like income and race contribute to disparities in internet access. I wanted to visualize these patterns and ultimately see if we could predict underserved areas using data science.      Leveraging large datasets from the FCC's Broadband Data Collection (providing provider speeds) and the US Census/ACS (offering income, population demographics, and geographic boundaries), I analyzed broadband availability at a granular Census Block Group level across the nation.      The core data science workflow involved several key steps. First came the significant task of downloading, cleaning, and aggregating hundreds of files to create unified national datasets for speed, income, and minority population percentage at ...
Read more

MSDS Practicum Project - US Broadband divide

Image
  The Term US Broadband divide indicates the unavailability of broadband internet to all americans. According to FCC, 100 mbps speed is the threshold to be called as broadband. The site Ookla.net collects speed tests and shows 60% of the tests > 100 mbps download speeds The data from the FCC- BDC as of December 2024 demonstrates a steady increase in locations with access to high-speed internet. According to the FCC, an additional one million locations gained access to 100/20 Mbps speeds between June and December 2024. Additionally, nearly 7 million additional locations gained access to even faster gigabit speeds (1 Gbps download and 100 Mbps upload) in the same time frame. While the overall trend is positive, the persistent percentage of unserved and underserved locations represents a significant population that is still being left behind The good news is over the years with expanding cable infrastructure and advancement in other technologies - Terrestrial fixed wireless, Cell...
Read more

Downloading data from census.gov's ACS

Image
       The census.gov website has ACS ( American Community survey ) data that has a wealth of information about demographics, median household income etc. It provides API support to download the data. In this tutorial, I will explain how to download the data using API calls, and also creating a python script to automate downloading data with multiple API calls. step1 : get a API key. The API key is needed to make the API calls. sign up and get the key in this URL:  https://api.census.gov/data/key_signup.html step2 : assemble the API call URL. depending upon the information needed, we have to assemble the URL needed to do the API call.  for example: https://api.census.gov/data/2023/acs/acs5?get=NAME,B19013_001E&for=block%20group:*&in=state:36%20county:119&key=xxxxxxxxxxxxxxxxx in the above API Call url,  B19013_001E is the data point for household median income.  in=state:36%20county:119 represents NY state and Westchester county. &...
Read more